From aea14a40a449739f4e5a64af12c9bd3546abedb6 Mon Sep 17 00:00:00 2001 From: Cameron Kruse <14115927+cameronkruse@users.noreply.github.com> Date: Wed, 13 Mar 2024 10:33:25 -0700 Subject: [PATCH] adding gtag --- data/crop-data-processing.ipynb | 16902 +++++++++++++---------- vacs-map-app/index.html | 8 + vacs-map-app/src/stores/siteContent.js | 2 +- 3 files changed, 9746 insertions(+), 7166 deletions(-) diff --git a/data/crop-data-processing.ipynb b/data/crop-data-processing.ipynb index cc9207d..f4873df 100644 --- a/data/crop-data-processing.ipynb +++ b/data/crop-data-processing.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -739,6 +739,78 @@ "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3505: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n" ] } @@ -753,366 +825,438 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n" ] } @@ -1127,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1152,6 +1296,9 @@ " \n", " \n", " geometry\n", + " yield_plantain_future_ssp370_mri\n", + " biomass_plantain_future_ssp370_mri\n", + " duration_plantain_future_ssp370_mri\n", " yield_taro_future_ssp370_mpi\n", " biomass_taro_future_ssp370_mpi\n", " duration_taro_future_ssp370_mpi\n", @@ -1194,12 +1341,18 @@ " yield_josephscoat_future_ssp126_mpi\n", " biomass_josephscoat_future_ssp126_mpi\n", " duration_josephscoat_future_ssp126_mpi\n", + " yield_pumpkin_future_ssp126_gfdl\n", + " biomass_pumpkin_future_ssp126_gfdl\n", + " duration_pumpkin_future_ssp126_gfdl\n", " yield_grasspea_future_ssp126_gfdl\n", " biomass_grasspea_future_ssp126_gfdl\n", " duration_grasspea_future_ssp126_gfdl\n", " yield_sesame_future_ssp370_ipsl\n", " biomass_sesame_future_ssp370_ipsl\n", " duration_sesame_future_ssp370_ipsl\n", + " yield_plantain_future_ssp126_gfdl\n", + " biomass_plantain_future_ssp126_gfdl\n", + " duration_plantain_future_ssp126_gfdl\n", " yield_yams_future_ssp370_gfdl\n", " biomass_yams_future_ssp370_gfdl\n", " duration_yams_future_ssp370_gfdl\n", @@ -1224,6 +1377,9 @@ " yield_cocoyam_future_ssp126_mri\n", " biomass_cocoyam_future_ssp126_mri\n", " duration_cocoyam_future_ssp126_mri\n", + " yield_pumpkin_future_ssp370_mri\n", + " biomass_pumpkin_future_ssp370_mri\n", + " duration_pumpkin_future_ssp370_mri\n", " yield_lablab_future_ssp126_mpi\n", " biomass_lablab_future_ssp126_mpi\n", " duration_lablab_future_ssp126_mpi\n", @@ -1260,6 +1416,12 @@ " yield_groundnut_future_ssp126_mri\n", " biomass_groundnut_future_ssp126_mri\n", " duration_groundnut_future_ssp126_mri\n", + " yield_plantain_future_ssp126_ipsl\n", + " biomass_plantain_future_ssp126_ipsl\n", + " duration_plantain_future_ssp126_ipsl\n", + " yield_plantain_future_ssp370_mpi\n", + " biomass_plantain_future_ssp370_mpi\n", + " duration_plantain_future_ssp370_mpi\n", " yield_africaneggplant_future_ssp370_ipsl\n", " biomass_africaneggplant_future_ssp370_ipsl\n", " duration_africaneggplant_future_ssp370_ipsl\n", @@ -1290,6 +1452,9 @@ " yield_cowpea_future_ssp126_mpi\n", " biomass_cowpea_future_ssp126_mpi\n", " duration_cowpea_future_ssp126_mpi\n", + " yield_pumpkin_future_ssp126_mpi\n", + " biomass_pumpkin_future_ssp126_mpi\n", + " duration_pumpkin_future_ssp126_mpi\n", " yield_okra_future_ssp126_mpi\n", " biomass_okra_future_ssp126_mpi\n", " duration_okra_future_ssp126_mpi\n", @@ -1311,6 +1476,9 @@ " yield_lablab_future_ssp126_ipsl\n", " biomass_lablab_future_ssp126_ipsl\n", " duration_lablab_future_ssp126_ipsl\n", + " yield_pearlmillet_future_ssp126_ipsl\n", + " biomass_pearlmillet_future_ssp126_ipsl\n", + " duration_pearlmillet_future_ssp126_ipsl\n", " yield_tef_future_ssp126_mri\n", " biomass_tef_future_ssp126_mri\n", " duration_tef_future_ssp126_mri\n", @@ -1323,6 +1491,9 @@ " yield_sweetpotato_future_ssp126_ipsl\n", " biomass_sweetpotato_future_ssp126_ipsl\n", " duration_sweetpotato_future_ssp126_ipsl\n", + " yield_plantain_future_ssp126_mpi\n", + " biomass_plantain_future_ssp126_mpi\n", + " duration_plantain_future_ssp126_mpi\n", " yield_taro_future_ssp370_mri\n", " biomass_taro_future_ssp370_mri\n", " duration_taro_future_ssp370_mri\n", @@ -1335,6 +1506,12 @@ " yield_tomato_future_ssp370_mri\n", " biomass_tomato_future_ssp370_mri\n", " duration_tomato_future_ssp370_mri\n", + " yield_pumpkin_future_ssp126_ipsl\n", + " biomass_pumpkin_future_ssp126_ipsl\n", + " duration_pumpkin_future_ssp126_ipsl\n", + " yield_pumpkin_future_ssp370_mpi\n", + " biomass_pumpkin_future_ssp370_mpi\n", + " duration_pumpkin_future_ssp370_mpi\n", " yield_cowpea_future_ssp370_mpi\n", " biomass_cowpea_future_ssp370_mpi\n", " duration_cowpea_future_ssp370_mpi\n", @@ -1377,6 +1554,9 @@ " yield_yams_future_ssp126_gfdl\n", " biomass_yams_future_ssp126_gfdl\n", " duration_yams_future_ssp126_gfdl\n", + " yield_plantain_future_ssp370_gfdl\n", + " biomass_plantain_future_ssp370_gfdl\n", + " duration_plantain_future_ssp370_gfdl\n", " yield_cassava_future_ssp126_mri\n", " biomass_cassava_future_ssp126_mri\n", " duration_cassava_future_ssp126_mri\n", @@ -1389,6 +1569,9 @@ " yield_tomato_future_ssp370_ipsl\n", " biomass_tomato_future_ssp370_ipsl\n", " duration_tomato_future_ssp370_ipsl\n", + " yield_pumpkin_future_ssp370_gfdl\n", + " biomass_pumpkin_future_ssp370_gfdl\n", + " duration_pumpkin_future_ssp370_gfdl\n", " yield_cowpea_future_ssp370_mri\n", " biomass_cowpea_future_ssp370_mri\n", " duration_cowpea_future_ssp370_mri\n", @@ -1407,6 +1590,9 @@ " yield_africaneggplant_future_ssp370_mri\n", " biomass_africaneggplant_future_ssp370_mri\n", " duration_africaneggplant_future_ssp370_mri\n", + " yield_pearlmillet_future_ssp126_mri\n", + " biomass_pearlmillet_future_ssp126_mri\n", + " duration_pearlmillet_future_ssp126_mri\n", " yield_lablab_future_ssp370_mpi\n", " biomass_lablab_future_ssp370_mpi\n", " duration_lablab_future_ssp370_mpi\n", @@ -1422,6 +1608,9 @@ " yield_tef_future_ssp126_gfdl\n", " biomass_tef_future_ssp126_gfdl\n", " duration_tef_future_ssp126_gfdl\n", + " yield_pearlmillet_future_ssp370_mpi\n", + " biomass_pearlmillet_future_ssp370_mpi\n", + " duration_pearlmillet_future_ssp370_mpi\n", " yield_groundnut_future_ssp370_gfdl\n", " biomass_groundnut_future_ssp370_gfdl\n", " duration_groundnut_future_ssp370_gfdl\n", @@ -1431,6 +1620,9 @@ " yield_lablab_future_ssp370_ipsl\n", " biomass_lablab_future_ssp370_ipsl\n", " duration_lablab_future_ssp370_ipsl\n", + " yield_pearlmillet_future_ssp370_ipsl\n", + " biomass_pearlmillet_future_ssp370_ipsl\n", + " duration_pearlmillet_future_ssp370_ipsl\n", " yield_josephscoat_future_ssp370_ipsl\n", " biomass_josephscoat_future_ssp370_ipsl\n", " duration_josephscoat_future_ssp370_ipsl\n", @@ -1467,12 +1659,21 @@ " yield_africaneggplant_future_ssp126_mri\n", " biomass_africaneggplant_future_ssp126_mri\n", " duration_africaneggplant_future_ssp126_mri\n", + " yield_pearlmillet_future_ssp370_mri\n", + " biomass_pearlmillet_future_ssp370_mri\n", + " duration_pearlmillet_future_ssp370_mri\n", + " yield_pearlmillet_future_ssp370_gfdl\n", + " biomass_pearlmillet_future_ssp370_gfdl\n", + " duration_pearlmillet_future_ssp370_gfdl\n", " yield_lablab_future_ssp370_gfdl\n", " biomass_lablab_future_ssp370_gfdl\n", " duration_lablab_future_ssp370_gfdl\n", " yield_cassava_future_ssp370_mpi\n", " biomass_cassava_future_ssp370_mpi\n", " duration_cassava_future_ssp370_mpi\n", + " yield_pumpkin_future_ssp370_ipsl\n", + " biomass_pumpkin_future_ssp370_ipsl\n", + " duration_pumpkin_future_ssp370_ipsl\n", " yield_cowpea_future_ssp126_mri\n", " biomass_cowpea_future_ssp126_mri\n", " duration_cowpea_future_ssp126_mri\n", @@ -1626,6 +1827,9 @@ " yield_sweetpotato_future_ssp370_mpi\n", " biomass_sweetpotato_future_ssp370_mpi\n", " duration_sweetpotato_future_ssp370_mpi\n", + " yield_pearlmillet_future_ssp126_mpi\n", + " biomass_pearlmillet_future_ssp126_mpi\n", + " duration_pearlmillet_future_ssp126_mpi\n", " yield_fonio_future_ssp126_gfdl\n", " biomass_fonio_future_ssp126_gfdl\n", " duration_fonio_future_ssp126_gfdl\n", @@ -1641,12 +1845,18 @@ " yield_lablab_future_ssp126_gfdl\n", " biomass_lablab_future_ssp126_gfdl\n", " duration_lablab_future_ssp126_gfdl\n", + " yield_pearlmillet_future_ssp126_gfdl\n", + " biomass_pearlmillet_future_ssp126_gfdl\n", + " duration_pearlmillet_future_ssp126_gfdl\n", " yield_sesame_future_ssp370_mpi\n", " biomass_sesame_future_ssp370_mpi\n", " duration_sesame_future_ssp370_mpi\n", " yield_groundnut_future_ssp126_mpi\n", " biomass_groundnut_future_ssp126_mpi\n", " duration_groundnut_future_ssp126_mpi\n", + " yield_plantain_future_ssp126_mri\n", + " biomass_plantain_future_ssp126_mri\n", + " duration_plantain_future_ssp126_mri\n", " yield_africaneggplant_future_ssp126_ipsl\n", " biomass_africaneggplant_future_ssp126_ipsl\n", " duration_africaneggplant_future_ssp126_ipsl\n", @@ -1656,6 +1866,9 @@ " yield_yams_future_ssp370_mri\n", " biomass_yams_future_ssp370_mri\n", " duration_yams_future_ssp370_mri\n", + " yield_plantain_future_ssp370_ipsl\n", + " biomass_plantain_future_ssp370_ipsl\n", + " duration_plantain_future_ssp370_ipsl\n", " yield_tef_future_ssp126_ipsl\n", " biomass_tef_future_ssp126_ipsl\n", " duration_tef_future_ssp126_ipsl\n", @@ -1665,6 +1878,9 @@ " yield_fingermillet_future_ssp126_mpi\n", " biomass_fingermillet_future_ssp126_mpi\n", " duration_fingermillet_future_ssp126_mpi\n", + " yield_pumpkin_future_ssp126_mri\n", + " biomass_pumpkin_future_ssp126_mri\n", + " duration_pumpkin_future_ssp126_mri\n", " yield_sorghum_future_ssp370_gfdl\n", " biomass_sorghum_future_ssp370_gfdl\n", " duration_sorghum_future_ssp370_gfdl\n", @@ -1719,12 +1935,18 @@ " yield_mungbean_historical_gfdl\n", " biomass_mungbean_historical_gfdl\n", " duration_mungbean_historical_gfdl\n", + " yield_pearlmillet_historical_mri\n", + " biomass_pearlmillet_historical_mri\n", + " duration_pearlmillet_historical_mri\n", " yield_cocoyam_historical_mri\n", " biomass_cocoyam_historical_mri\n", " duration_cocoyam_historical_mri\n", " yield_tomato_historical_mri\n", " biomass_tomato_historical_mri\n", " duration_tomato_historical_mri\n", + " yield_plantain_historical_mpi\n", + " biomass_plantain_historical_mpi\n", + " duration_plantain_historical_mpi\n", " yield_maize_historical_gfdl\n", " biomass_maize_historical_gfdl\n", " duration_maize_historical_gfdl\n", @@ -1734,6 +1956,9 @@ " yield_lablab_historical_mpi\n", " biomass_lablab_historical_mpi\n", " duration_lablab_historical_mpi\n", + " yield_pearlmillet_historical_ipsl\n", + " biomass_pearlmillet_historical_ipsl\n", + " duration_pearlmillet_historical_ipsl\n", " yield_sesame_historical_mri\n", " biomass_sesame_historical_mri\n", " duration_sesame_historical_mri\n", @@ -1749,6 +1974,9 @@ " yield_cocoyam_historical_ipsl\n", " biomass_cocoyam_historical_ipsl\n", " duration_cocoyam_historical_ipsl\n", + " yield_pumpkin_historical_mri\n", + " biomass_pumpkin_historical_mri\n", + " duration_pumpkin_historical_mri\n", " yield_yams_historical_mri\n", " biomass_yams_historical_mri\n", " duration_yams_historical_mri\n", @@ -1779,6 +2007,9 @@ " yield_fingermillet_historical_mpi\n", " biomass_fingermillet_historical_mpi\n", " duration_fingermillet_historical_mpi\n", + " yield_pumpkin_historical_ipsl\n", + " biomass_pumpkin_historical_ipsl\n", + " duration_pumpkin_historical_ipsl\n", " yield_fingermillet_historical_gfdl\n", " biomass_fingermillet_historical_gfdl\n", " duration_fingermillet_historical_gfdl\n", @@ -1806,6 +2037,9 @@ " yield_maize_historical_mri\n", " biomass_maize_historical_mri\n", " duration_maize_historical_mri\n", + " yield_pearlmillet_historical_gfdl\n", + " biomass_pearlmillet_historical_gfdl\n", + " duration_pearlmillet_historical_gfdl\n", " yield_groundnut_historical_ipsl\n", " biomass_groundnut_historical_ipsl\n", " duration_groundnut_historical_ipsl\n", @@ -1830,6 +2064,9 @@ " yield_sorghum_historical_mpi\n", " biomass_sorghum_historical_mpi\n", " duration_sorghum_historical_mpi\n", + " yield_plantain_historical_mri\n", + " biomass_plantain_historical_mri\n", + " duration_plantain_historical_mri\n", " yield_tef_historical_ipsl\n", " biomass_tef_historical_ipsl\n", " duration_tef_historical_ipsl\n", @@ -1878,9 +2115,15 @@ " yield_okra_historical_gfdl\n", " biomass_okra_historical_gfdl\n", " duration_okra_historical_gfdl\n", + " yield_pearlmillet_historical_mpi\n", + " biomass_pearlmillet_historical_mpi\n", + " duration_pearlmillet_historical_mpi\n", " yield_cowpea_historical_gfdl\n", " biomass_cowpea_historical_gfdl\n", " duration_cowpea_historical_gfdl\n", + " yield_pumpkin_historical_gfdl\n", + " biomass_pumpkin_historical_gfdl\n", + " duration_pumpkin_historical_gfdl\n", " yield_soybean_historical_ipsl\n", " biomass_soybean_historical_ipsl\n", " duration_soybean_historical_ipsl\n", @@ -1920,6 +2163,9 @@ " yield_africaneggplant_historical_gfdl\n", " biomass_africaneggplant_historical_gfdl\n", " duration_africaneggplant_historical_gfdl\n", + " yield_pumpkin_historical_mpi\n", + " biomass_pumpkin_historical_mpi\n", + " duration_pumpkin_historical_mpi\n", " yield_cassava_historical_mpi\n", " biomass_cassava_historical_mpi\n", " duration_cassava_historical_mpi\n", @@ -1974,6 +2220,12 @@ " yield_grasspea_historical_mpi\n", " biomass_grasspea_historical_mpi\n", " duration_grasspea_historical_mpi\n", + " yield_plantain_historical_ipsl\n", + " biomass_plantain_historical_ipsl\n", + " duration_plantain_historical_ipsl\n", + " yield_plantain_historical_gfdl\n", + " biomass_plantain_historical_gfdl\n", + " duration_plantain_historical_gfdl\n", " yield_mungbean_historical_mri\n", " biomass_mungbean_historical_mri\n", " duration_mungbean_historical_mri\n", @@ -2010,6 +2262,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 88.069\n", " 258.862\n", " 79.966\n", @@ -2028,6 +2283,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 514.400\n", + " 3429.367\n", + " 212.567\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -2052,6 +2319,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 465.000\n", + " 3100.100\n", + " 197.867\n", " NaN\n", " NaN\n", " NaN\n", @@ -2109,9 +2379,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 1028.867\n", - " 3810.667\n", - " 91.0\n", + " 1295.759\n", + " 4798.931\n", + " 78.448\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -2121,15 +2394,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 500.500\n", + " 3336.500\n", + " 211.700\n", " NaN\n", " NaN\n", " NaN\n", + " 1265.172\n", + " 4686.069\n", + " 80.793\n", " NaN\n", " NaN\n", " NaN\n", - " 988.567\n", - " 3661.133\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -2145,9 +2421,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 827.933\n", - " 3066.467\n", - " 91.0\n", + " 1243.621\n", + " 4605.931\n", + " 80.276\n", " NaN\n", " NaN\n", " NaN\n", @@ -2172,12 +2448,21 @@ " NaN\n", " NaN\n", " NaN\n", + " 497.533\n", + " 3317.200\n", + " 206.100\n", + " 544.567\n", + " 3630.433\n", + " 205.967\n", " NaN\n", " NaN\n", " NaN\n", - " 869.500\n", - " 3220.267\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1208.517\n", + " 4475.690\n", + " 82.241\n", " NaN\n", " NaN\n", " NaN\n", @@ -2226,6 +2511,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 518.233\n", + " 3454.733\n", + " 208.300\n", + " NaN\n", + " NaN\n", + " NaN\n", " 172.621\n", " 507.690\n", " 79.207\n", @@ -2253,9 +2544,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 816.233\n", - " 3023.367\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1236.828\n", + " 4580.724\n", + " 83.103\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -2313,6 +2613,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 532.100\n", + " 3547.700\n", + " 199.333\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -2325,9 +2634,9 @@ " 122.393\n", " 359.821\n", " 57.536\n", - " 910.067\n", - " 3370.700\n", - " 91.0\n", + " 1291.276\n", + " 4782.345\n", + " 79.172\n", " NaN\n", " NaN\n", " NaN\n", @@ -2490,15 +2799,30 @@ " NaN\n", " NaN\n", " NaN\n", - " 898.733\n", - " 3329.033\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1285.655\n", + " 4761.862\n", + " 80.483\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 505.867\n", + " 3371.967\n", + " 211.967\n", " 127.571\n", " 375.250\n", " 57.821\n", @@ -2535,9 +2859,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 964.967\n", - " 3573.633\n", - " 91.0\n", + " 1282.552\n", + " 4750.069\n", + " 80.069\n", " NaN\n", " NaN\n", " NaN\n", @@ -2559,6 +2883,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 121.724\n", " 357.966\n", " 82.517\n", @@ -2586,6 +2916,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 499.033\n", + " 3326.367\n", + " 213.833\n", + " NaN\n", + " NaN\n", + " NaN\n", " 774.000\n", " 1290.103\n", " 85.483\n", @@ -2613,6 +2949,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 499.233\n", + " 3328.167\n", + " 211.933\n", " NaN\n", " NaN\n", " NaN\n", @@ -2661,12 +3000,18 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 98.778\n", " 290.519\n", " 58.889\n", - " 744.833\n", - " 2758.767\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1248.724\n", + " 4625.138\n", + " 84.414\n", " NaN\n", " NaN\n", " NaN\n", @@ -2718,6 +3063,11 @@ " NaN\n", " NaN\n", " NaN\n", + " 502.100\n", + " 3347.333\n", + " 213.033\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -2739,10 +3089,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 692.467\n", - " 2564.400\n", - " 91.0\n", " NaN\n", + " 1169.552\n", + " 4331.517\n", + " 85.897\n", " NaN\n", " NaN\n", " NaN\n", @@ -2755,15 +3105,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 508.600\n", + " 3390.667\n", + " 213.033\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 671.567\n", - " 2487.400\n", - " 91.0\n", " NaN\n", + " 1169.517\n", + " 4331.655\n", + " 85.931\n", " NaN\n", " NaN\n", " NaN\n", @@ -2781,9 +3134,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 702.567\n", - " 2602.500\n", - " 91.0\n", + " NaN\n", + " 1179.034\n", + " 4366.655\n", + " 85.414\n", " NaN\n", " NaN\n", " NaN\n", @@ -2814,6 +3168,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 1\n", @@ -2842,6 +3202,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 68.517\n", " 201.483\n", " 78.483\n", @@ -2860,6 +3223,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 430.000\n", + " 2866.000\n", + " 210.633\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -2887,6 +3259,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 383.800\n", + " 2558.600\n", + " 187.800\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -2941,9 +3319,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 882.633\n", - " 3269.000\n", - " 91.0\n", + " 1235.207\n", + " 4574.793\n", + " 76.379\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -2955,13 +3334,16 @@ " NaN\n", " NaN\n", " NaN\n", + " 409.067\n", + " 2726.200\n", + " 206.533\n", " NaN\n", " NaN\n", " NaN\n", + " 1219.862\n", + " 4518.448\n", + " 78.862\n", " NaN\n", - " 842.667\n", - " 3121.167\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -2977,9 +3359,11 @@ " NaN\n", " NaN\n", " NaN\n", - " 730.367\n", - " 2705.100\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " 1180.172\n", + " 4371.172\n", + " 78.276\n", " NaN\n", " NaN\n", " NaN\n", @@ -3004,12 +3388,21 @@ " NaN\n", " NaN\n", " NaN\n", + " 415.533\n", + " 2770.567\n", + " 201.000\n", + " 462.667\n", + " 3083.900\n", + " 201.067\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 752.767\n", - " 2788.367\n", - " 91.0\n", + " NaN\n", + " 1174.483\n", + " 4349.897\n", + " 80.345\n", " NaN\n", " NaN\n", " NaN\n", @@ -3058,6 +3451,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 429.767\n", + " 2865.067\n", + " 200.800\n", + " NaN\n", + " NaN\n", + " NaN\n", " 153.931\n", " 452.621\n", " 78.414\n", @@ -3085,9 +3484,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 701.967\n", - " 2600.200\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1174.276\n", + " 4349.345\n", + " 80.448\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -3145,6 +3553,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 455.733\n", + " 3037.967\n", + " 192.467\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -3157,9 +3574,9 @@ " 117.000\n", " 344.071\n", " 56.679\n", - " 775.500\n", - " 2872.533\n", - " 91.0\n", + " 1218.172\n", + " 4511.724\n", + " 77.103\n", " NaN\n", " NaN\n", " NaN\n", @@ -3322,15 +3739,30 @@ " NaN\n", " NaN\n", " NaN\n", - " 804.833\n", - " 2981.033\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1205.034\n", + " 4463.000\n", + " 78.828\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 426.400\n", + " 2842.667\n", + " 208.600\n", " 122.750\n", " 360.929\n", " 56.964\n", @@ -3367,9 +3799,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 816.867\n", - " 3025.267\n", - " 91.0\n", + " 1216.793\n", + " 4506.862\n", + " 78.069\n", " NaN\n", " NaN\n", " NaN\n", @@ -3391,6 +3823,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 100.621\n", " 296.000\n", " 80.931\n", @@ -3418,6 +3856,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 428.533\n", + " 2856.967\n", + " 212.233\n", + " NaN\n", + " NaN\n", + " NaN\n", " 775.966\n", " 1293.483\n", " 83.069\n", @@ -3445,6 +3889,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 445.133\n", + " 2967.833\n", + " 209.600\n", " NaN\n", " NaN\n", " NaN\n", @@ -3493,12 +3940,18 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 93.407\n", " 274.741\n", " 57.556\n", - " 666.700\n", - " 2469.333\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1199.379\n", + " 4441.828\n", + " 82.345\n", " NaN\n", " NaN\n", " NaN\n", @@ -3550,6 +4003,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 412.800\n", + " 2752.367\n", + " 210.767\n", " NaN\n", " NaN\n", " NaN\n", @@ -3571,12 +4027,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 609.167\n", - " 2256.300\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", + " 1116.448\n", + " 4135.103\n", + " 83.655\n", " NaN\n", " NaN\n", " NaN\n", @@ -3589,15 +4045,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 429.500\n", + " 2863.067\n", + " 210.700\n", " NaN\n", " NaN\n", " NaN\n", - " 591.633\n", - " 2191.033\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", + " 1119.621\n", + " 4146.828\n", + " 83.586\n", " NaN\n", " NaN\n", " NaN\n", @@ -3613,9 +4072,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 614.767\n", - " 2277.067\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1130.379\n", + " 4186.552\n", + " 82.862\n", " NaN\n", " NaN\n", " NaN\n", @@ -3646,6 +4108,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 2\n", @@ -3674,6 +4142,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 125.000\n", " 367.483\n", " 74.000\n", @@ -3692,6 +4163,13 @@ " NaN\n", " NaN\n", " NaN\n", + " 383.433\n", + " 2555.900\n", + " 175.167\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -3721,6 +4199,14 @@ " NaN\n", " NaN\n", " NaN\n", + " 350.667\n", + " 2337.367\n", + " 142.467\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -3773,9 +4259,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 1057.767\n", - " 3917.500\n", - " 91.0\n", + " 1281.931\n", + " 4747.586\n", + " 70.966\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -3785,15 +4274,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 408.633\n", + " 2724.067\n", + " 172.767\n", " NaN\n", " NaN\n", " NaN\n", + " 1300.276\n", + " 4816.069\n", + " 73.690\n", " NaN\n", " NaN\n", " NaN\n", - " 979.100\n", - " 3626.067\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -3809,9 +4301,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 903.967\n", - " 3348.133\n", - " 91.0\n", + " 1260.517\n", + " 4668.345\n", + " 72.897\n", " NaN\n", " NaN\n", " NaN\n", @@ -3836,12 +4328,21 @@ " NaN\n", " NaN\n", " NaN\n", + " 365.833\n", + " 2438.567\n", + " 148.033\n", + " 464.900\n", + " 3099.500\n", + " 166.933\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 903.867\n", - " 3347.633\n", - " 91.0\n", + " 1234.759\n", + " 4573.448\n", + " 75.000\n", " NaN\n", " NaN\n", " NaN\n", @@ -3890,6 +4391,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 412.500\n", + " 2749.767\n", + " 162.233\n", + " NaN\n", + " NaN\n", + " NaN\n", " 272.103\n", " 800.345\n", " 75.448\n", @@ -3917,9 +4424,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 894.500\n", - " 3313.033\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1216.517\n", + " 4505.414\n", + " 75.345\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -3977,6 +4493,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 407.100\n", + " 2713.667\n", + " 145.333\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -3989,9 +4514,9 @@ " 174.893\n", " 514.464\n", " 56.250\n", - " 940.133\n", - " 3481.700\n", - " 91.0\n", + " 1305.828\n", + " 4836.241\n", + " 72.103\n", " NaN\n", " NaN\n", " NaN\n", @@ -4154,15 +4679,30 @@ " NaN\n", " NaN\n", " NaN\n", - " 1002.167\n", - " 3711.633\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1269.483\n", + " 4701.759\n", + " 73.000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 409.533\n", + " 2730.133\n", + " 171.367\n", " 144.000\n", " 423.643\n", " 55.107\n", @@ -4199,9 +4739,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 935.533\n", - " 3465.200\n", - " 91.0\n", + " 1273.862\n", + " 4717.931\n", + " 72.759\n", " NaN\n", " NaN\n", " NaN\n", @@ -4223,6 +4763,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 172.069\n", " 506.172\n", " 77.897\n", @@ -4250,6 +4796,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 421.300\n", + " 2808.200\n", + " 195.800\n", + " NaN\n", + " NaN\n", + " NaN\n", " 1059.931\n", " 1766.655\n", " 79.448\n", @@ -4277,6 +4829,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 407.533\n", + " 2716.533\n", + " 189.233\n", " NaN\n", " NaN\n", " NaN\n", @@ -4325,12 +4880,18 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 118.556\n", " 348.667\n", " 55.556\n", - " 840.033\n", - " 3111.033\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1268.379\n", + " 4697.517\n", + " 76.552\n", " NaN\n", " NaN\n", " NaN\n", @@ -4382,6 +4943,11 @@ " NaN\n", " NaN\n", " NaN\n", + " 405.333\n", + " 2702.533\n", + " 190.000\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -4403,10 +4969,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 777.300\n", - " 2878.867\n", - " 91.0\n", " NaN\n", + " 1212.586\n", + " 4490.931\n", + " 78.207\n", " NaN\n", " NaN\n", " NaN\n", @@ -4419,15 +4985,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 420.933\n", + " 2805.733\n", + " 197.267\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 782.067\n", - " 2896.433\n", - " 91.0\n", " NaN\n", + " 1206.828\n", + " 4469.552\n", + " 78.379\n", " NaN\n", " NaN\n", " NaN\n", @@ -4445,9 +5014,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 802.833\n", - " 2973.033\n", - " 91.0\n", + " NaN\n", + " 1225.207\n", + " 4537.724\n", + " 78.069\n", " NaN\n", " NaN\n", " NaN\n", @@ -4478,6 +5048,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 3\n", @@ -4506,6 +5082,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 112.690\n", " 331.483\n", " 73.966\n", @@ -4524,6 +5103,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 420.267\n", + " 2801.700\n", + " 173.967\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -4548,6 +5139,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 384.067\n", + " 2560.433\n", + " 142.233\n", " NaN\n", " NaN\n", " NaN\n", @@ -4605,9 +5199,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 1041.733\n", - " 3858.333\n", - " 91.0\n", + " 1280.897\n", + " 4743.931\n", + " 71.241\n", " NaN\n", " NaN\n", " NaN\n", @@ -4620,12 +5214,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 441.900\n", + " 2945.833\n", + " 171.533\n", " NaN\n", " NaN\n", " NaN\n", - " 968.800\n", - " 3588.133\n", - " 91.0\n", + " 1300.690\n", + " 4817.379\n", + " 73.862\n", " NaN\n", " NaN\n", " NaN\n", @@ -4641,9 +5238,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 907.633\n", - " 3361.667\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1244.241\n", + " 4607.828\n", + " 73.069\n", " NaN\n", " NaN\n", " NaN\n", @@ -4668,12 +5268,21 @@ " NaN\n", " NaN\n", " NaN\n", + " 396.567\n", + " 2644.300\n", + " 147.400\n", + " 485.467\n", + " 3236.300\n", + " 162.867\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 903.367\n", - " 3346.067\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " 1226.207\n", + " 4541.586\n", + " 75.207\n", " NaN\n", " NaN\n", " NaN\n", @@ -4722,6 +5331,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 438.233\n", + " 2922.100\n", + " 158.867\n", + " NaN\n", + " NaN\n", + " NaN\n", " 283.552\n", " 833.966\n", " 76.034\n", @@ -4749,9 +5364,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 899.967\n", - " 3333.700\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1221.276\n", + " 4523.103\n", + " 75.345\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -4809,6 +5433,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 448.367\n", + " 2988.800\n", + " 145.400\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -4821,9 +5454,9 @@ " 171.214\n", " 503.571\n", " 55.857\n", - " 934.467\n", - " 3461.133\n", - " 91.0\n", + " 1290.931\n", + " 4780.966\n", + " 72.172\n", " NaN\n", " NaN\n", " NaN\n", @@ -4986,15 +5619,30 @@ " NaN\n", " NaN\n", " NaN\n", - " 999.633\n", - " 3701.933\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1284.414\n", + " 4757.310\n", + " 73.207\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 433.600\n", + " 2891.400\n", + " 167.200\n", " 135.679\n", " 399.286\n", " 54.571\n", @@ -5031,9 +5679,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 932.400\n", - " 3453.200\n", - " 91.0\n", + " 1263.690\n", + " 4680.000\n", + " 73.000\n", " NaN\n", " NaN\n", " NaN\n", @@ -5055,6 +5703,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 199.379\n", " 586.552\n", " 78.069\n", @@ -5082,6 +5736,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 448.467\n", + " 2989.667\n", + " 193.933\n", + " NaN\n", + " NaN\n", + " NaN\n", " 1084.828\n", " 1808.000\n", " 80.207\n", @@ -5109,6 +5769,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 425.533\n", + " 2836.233\n", + " 184.700\n", " NaN\n", " NaN\n", " NaN\n", @@ -5157,12 +5820,18 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 115.852\n", " 340.852\n", " 55.259\n", - " 857.733\n", - " 3176.800\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1282.448\n", + " 4749.517\n", + " 76.828\n", " NaN\n", " NaN\n", " NaN\n", @@ -5214,6 +5883,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 422.867\n", + " 2820.000\n", + " 187.667\n", " NaN\n", " NaN\n", " NaN\n", @@ -5235,9 +5907,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 791.800\n", - " 2932.667\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1187.828\n", + " 4399.241\n", + " 78.000\n", " NaN\n", " NaN\n", " NaN\n", @@ -5250,15 +5925,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 440.967\n", + " 2940.433\n", + " 193.067\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 797.533\n", - " 2953.767\n", - " 91.0\n", + " 1222.759\n", + " 4528.552\n", + " 78.379\n", " NaN\n", " NaN\n", " NaN\n", @@ -5277,9 +5955,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 816.167\n", - " 3022.667\n", - " 91.0\n", + " 1207.103\n", + " 4470.828\n", + " 77.931\n", " NaN\n", " NaN\n", " NaN\n", @@ -5310,6 +5988,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 4\n", @@ -5338,6 +6022,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 96.931\n", " 285.138\n", " 73.966\n", @@ -5356,6 +6043,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 410.133\n", + " 2734.500\n", + " 171.167\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -5383,6 +6079,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 388.367\n", + " 2589.100\n", + " 143.767\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -5437,9 +6139,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 973.400\n", - " 3605.100\n", - " 91.0\n", + " 1195.897\n", + " 4429.241\n", + " 73.276\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -5451,13 +6154,17 @@ " NaN\n", " NaN\n", " NaN\n", + " 430.400\n", + " 2869.133\n", + " 169.267\n", " NaN\n", " NaN\n", " NaN\n", + " 1196.000\n", + " 4429.655\n", + " 75.828\n", + " NaN\n", " NaN\n", - " 878.267\n", - " 3253.133\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -5473,9 +6180,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 844.133\n", - " 3126.367\n", - " 91.0\n", + " NaN\n", + " 1172.793\n", + " 4343.966\n", + " 74.828\n", " NaN\n", " NaN\n", " NaN\n", @@ -5500,12 +6208,21 @@ " NaN\n", " NaN\n", " NaN\n", + " 408.067\n", + " 2720.400\n", + " 150.133\n", + " 472.667\n", + " 3150.800\n", + " 158.100\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 823.100\n", - " 3048.567\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " 1128.517\n", + " 4179.862\n", + " 77.034\n", " NaN\n", " NaN\n", " NaN\n", @@ -5554,6 +6271,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 429.733\n", + " 2865.200\n", + " 157.467\n", + " NaN\n", + " NaN\n", + " NaN\n", " 277.172\n", " 814.966\n", " 76.103\n", @@ -5581,9 +6304,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 839.800\n", - " 3110.400\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1127.138\n", + " 4174.655\n", + " 76.379\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -5641,6 +6373,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 448.533\n", + " 2990.567\n", + " 146.733\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -5653,9 +6394,9 @@ " 119.893\n", " 352.571\n", " 53.929\n", - " 874.733\n", - " 3239.767\n", - " 91.0\n", + " 1191.724\n", + " 4414.069\n", + " 74.172\n", " NaN\n", " NaN\n", " NaN\n", @@ -5818,15 +6559,30 @@ " NaN\n", " NaN\n", " NaN\n", - " 927.433\n", - " 3434.700\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1184.862\n", + " 4388.448\n", + " 75.276\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 430.833\n", + " 2872.367\n", + " 164.367\n", " 113.250\n", " 333.107\n", " 53.750\n", @@ -5863,9 +6619,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 859.600\n", - " 3183.767\n", - " 91.0\n", + " 1157.759\n", + " 4288.034\n", + " 74.759\n", " NaN\n", " NaN\n", " NaN\n", @@ -5887,6 +6643,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 160.931\n", " 473.172\n", " 77.931\n", @@ -5914,6 +6676,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 428.567\n", + " 2856.833\n", + " 190.700\n", + " NaN\n", + " NaN\n", + " NaN\n", " 880.897\n", " 1468.103\n", " 79.931\n", @@ -5941,6 +6709,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 423.733\n", + " 2824.767\n", + " 183.233\n", " NaN\n", " NaN\n", " NaN\n", @@ -5989,12 +6760,18 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 89.000\n", " 261.630\n", " 54.259\n", - " 810.200\n", - " 3000.967\n", - " 91.0\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1196.172\n", + " 4430.414\n", + " 78.793\n", " NaN\n", " NaN\n", " NaN\n", @@ -6046,6 +6823,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 408.600\n", + " 2723.867\n", + " 184.667\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -6067,9 +6850,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 733.567\n", - " 2716.767\n", - " 91.0\n", + " 1116.172\n", + " 4133.966\n", + " 79.690\n", " NaN\n", " NaN\n", " NaN\n", @@ -6082,15 +6865,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 420.933\n", + " 2806.367\n", + " 188.933\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 752.167\n", - " 2785.933\n", - " 91.0\n", + " 1149.034\n", + " 4256.000\n", + " 80.000\n", " NaN\n", " NaN\n", " NaN\n", @@ -6109,9 +6895,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 771.233\n", - " 2856.700\n", - " 91.0\n", + " 1123.517\n", + " 4161.241\n", + " 79.517\n", " NaN\n", " NaN\n", " NaN\n", @@ -6142,6 +6928,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " ...\n", @@ -6974,28 +7766,139 @@ " ...\n", " ...\n", " ...\n", - " \n", - " \n", - " 10230\n", - " POINT (9.25000 36.75000)\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 10230\n", + " POINT (9.25000 36.75000)\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -7020,6 +7923,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 428.800\n", + " 2858.100\n", + " 115.200\n", " 3811.793\n", " 8470.828\n", " 109.448\n", @@ -7044,9 +7950,21 @@ " NaN\n", " NaN\n", " NaN\n", - " 140.167\n", - " 274.767\n", - " 87.867\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1417.793\n", + " 2779.897\n", + " 153.828\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 453.033\n", + " 3020.367\n", + " 115.100\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -7116,6 +8034,11 @@ " NaN\n", " NaN\n", " NaN\n", + " 377.833\n", + " 2518.667\n", + " 114.600\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -7158,13 +8081,19 @@ " NaN\n", " NaN\n", " NaN\n", - " 159.500\n", - " 312.533\n", - " 80.733\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 637.276\n", + " 1249.517\n", + " 104.552\n", + " 379.800\n", + " 2531.633\n", + " 114.633\n", + " 393.933\n", + " 2626.633\n", + " 112.233\n", " NaN\n", " NaN\n", " NaN\n", @@ -7185,9 +8114,13 @@ " NaN\n", " NaN\n", " NaN\n", - " 140.467\n", - " 275.367\n", - " 83.500\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 847.241\n", + " 1661.207\n", + " 134.172\n", " 4037.793\n", " 8972.897\n", " 111.379\n", @@ -7206,15 +8139,27 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 3757.000\n", " 8349.034\n", " 104.345\n", " NaN\n", " NaN\n", " NaN\n", - " 161.000\n", - " 315.667\n", - " 82.533\n", + " 579.897\n", + " 1136.931\n", + " 115.897\n", + " 421.867\n", + " 2812.367\n", + " 108.500\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -7275,6 +8220,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 228.800\n", " 673.033\n", " 103.400\n", @@ -7305,6 +8253,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 460.533\n", + " 3070.767\n", + " 116.500\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -7320,9 +8277,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 103.967\n", - " 204.0\n", - " 83.800\n", + " 640.034\n", + " 1254.966\n", + " 131.207\n", " NaN\n", " NaN\n", " NaN\n", @@ -7338,12 +8295,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 157.667\n", - " 309.133\n", - " 83.400\n", - " 132.800\n", - " 260.600\n", - " 83.933\n", + " 536.724\n", + " 1052.586\n", + " 112.793\n", + " 747.897\n", + " 1466.310\n", + " 128.103\n", " NaN\n", " NaN\n", " NaN\n", @@ -7437,9 +8394,16 @@ " NaN\n", " NaN\n", " NaN\n", - " 124.033\n", - " 243.167\n", - " 93.167\n", + " 1204.897\n", + " 2362.517\n", + " 154.103\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -7491,6 +8455,14 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 442.033\n", + " 2946.400\n", + " 117.833\n", " 215.600\n", " 634.167\n", " 98.000\n", @@ -7548,9 +8520,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 156.733\n", - " 307.300\n", - " 99.933\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1598.759\n", + " 3134.828\n", + " 157.379\n", " NaN\n", " NaN\n", " NaN\n", @@ -7581,6 +8556,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 401.900\n", + " 2679.300\n", + " 125.200\n", " NaN\n", " NaN\n", " NaN\n", @@ -7611,6 +8589,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 396.933\n", + " 2646.167\n", + " 123.433\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -7650,9 +8637,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 116.233\n", - " 227.967\n", - " 103.200\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1346.690\n", + " 2640.345\n", + " 158.448\n", " 126.000\n", " 370.633\n", " 106.000\n", @@ -7662,6 +8652,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 164.567\n", " 483.833\n", " 107.400\n", @@ -7710,6 +8703,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 387.700\n", + " 2584.967\n", + " 122.033\n", " NaN\n", " NaN\n", " NaN\n", @@ -7740,12 +8736,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 157.267\n", - " 308.467\n", - " 101.933\n", " NaN\n", " NaN\n", " NaN\n", + " 1449.793\n", + " 2842.690\n", + " 158.966\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 362.733\n", + " 2418.267\n", + " 122.167\n", " NaN\n", " NaN\n", " NaN\n", @@ -7782,9 +8784,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 119.433\n", - " 234.267\n", - " 102.933\n", + " 1376.379\n", + " 2698.828\n", + " 161.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -7806,6 +8808,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 10231\n", @@ -7840,6 +8848,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 63.533\n", " 186.933\n", " 91.133\n", @@ -7852,6 +8863,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 427.967\n", + " 2854.100\n", + " 115.233\n", " 3015.586\n", " 6701.655\n", " 103.448\n", @@ -7876,9 +8890,21 @@ " NaN\n", " NaN\n", " NaN\n", - " 32.133\n", - " 63.100\n", - " 91.500\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1364.069\n", + " 2674.655\n", + " 152.069\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 449.233\n", + " 2994.600\n", + " 114.767\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -7948,6 +8974,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 381.800\n", + " 2545.200\n", + " 114.667\n", " NaN\n", " NaN\n", " NaN\n", @@ -7990,9 +9019,21 @@ " NaN\n", " NaN\n", " NaN\n", - " 40.533\n", - " 79.500\n", - " 83.433\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 639.793\n", + " 1254.310\n", + " 103.069\n", + " 394.167\n", + " 2628.433\n", + " 114.733\n", + " 387.900\n", + " 2585.867\n", + " 112.133\n", " NaN\n", " NaN\n", " NaN\n", @@ -8017,9 +9058,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 27.333\n", - " 53.633\n", - " 87.000\n", + " 734.448\n", + " 1440.103\n", + " 131.448\n", " 3203.414\n", " 7118.931\n", " 106.069\n", @@ -8038,15 +9079,27 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 2970.759\n", " 6601.517\n", " 99.207\n", " NaN\n", " NaN\n", " NaN\n", - " 26.067\n", - " 51.300\n", - " 85.167\n", + " 569.759\n", + " 1117.103\n", + " 110.690\n", + " 426.133\n", + " 2840.300\n", + " 108.333\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -8107,6 +9160,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 106.233\n", " 312.267\n", " 91.233\n", @@ -8137,6 +9193,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 457.400\n", + " 3048.933\n", + " 116.167\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -8152,9 +9217,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 25.767\n", - " 50.7\n", - " 87.500\n", + " 598.655\n", + " 1173.931\n", + " 128.103\n", " NaN\n", " NaN\n", " NaN\n", @@ -8170,12 +9235,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 30.800\n", - " 60.200\n", - " 85.967\n", - " 41.300\n", - " 80.833\n", - " 87.333\n", + " 562.207\n", + " 1102.379\n", + " 109.655\n", + " 768.069\n", + " 1505.931\n", + " 123.586\n", " NaN\n", " NaN\n", " NaN\n", @@ -8269,9 +9334,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 35.067\n", - " 68.700\n", - " 97.200\n", + " 1175.138\n", + " 2304.207\n", + " 152.724\n", " NaN\n", " NaN\n", " NaN\n", @@ -8323,9 +9388,6 @@ " NaN\n", " NaN\n", " NaN\n", - " 105.767\n", - " 311.200\n", - " 86.567\n", " NaN\n", " NaN\n", " NaN\n", @@ -8338,6 +9400,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 442.900\n", + " 2952.733\n", + " 117.833\n", + " 105.767\n", + " 311.200\n", + " 86.567\n", " NaN\n", " NaN\n", " NaN\n", @@ -8380,9 +9448,6 @@ " NaN\n", " NaN\n", " NaN\n", - " 27.033\n", - " 52.867\n", - " 105.900\n", " NaN\n", " NaN\n", " NaN\n", @@ -8398,6 +9463,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 1585.897\n", + " 3109.621\n", + " 158.207\n", " NaN\n", " NaN\n", " NaN\n", @@ -8428,6 +9496,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 405.700\n", + " 2705.300\n", + " 125.667\n", " NaN\n", " NaN\n", " NaN\n", @@ -8446,9 +9517,6 @@ " NaN\n", " NaN\n", " NaN\n", - " 74.333\n", - " 218.600\n", - " 95.900\n", " NaN\n", " NaN\n", " NaN\n", @@ -8461,6 +9529,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 406.433\n", + " 2709.333\n", + " 123.700\n", " NaN\n", " NaN\n", " NaN\n", @@ -8470,6 +9541,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 74.333\n", + " 218.600\n", + " 95.900\n", " NaN\n", " NaN\n", " NaN\n", @@ -8482,9 +9556,33 @@ " NaN\n", " NaN\n", " NaN\n", - " 26.000\n", - " 51.133\n", - " 108.633\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1251.172\n", + " 2453.586\n", + " 158.448\n", " 62.667\n", " 184.467\n", " 94.033\n", @@ -8494,6 +9592,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 71.333\n", " 209.533\n", " 95.033\n", @@ -8542,6 +9643,10 @@ " NaN\n", " NaN\n", " NaN\n", + " 396.133\n", + " 2640.800\n", + " 122.033\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -8572,14 +9677,19 @@ " NaN\n", " NaN\n", " NaN\n", - " 31.600\n", - " 62.033\n", - " 106.467\n", " NaN\n", " NaN\n", + " 1413.448\n", + " 2771.379\n", + " 158.172\n", " NaN\n", " NaN\n", " NaN\n", + " 374.833\n", + " 2498.367\n", + " 122.367\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -8614,9 +9724,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 33.300\n", - " 65.133\n", - " 108.133\n", + " 1275.034\n", + " 2500.172\n", + " 161.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -8638,6 +9748,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 10232\n", @@ -8672,6 +9788,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 59.967\n", " 176.200\n", " 86.100\n", @@ -8684,6 +9803,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 489.300\n", + " 3262.067\n", + " 119.500\n", " 2691.276\n", " 5980.517\n", " 100.966\n", @@ -8708,9 +9830,21 @@ " NaN\n", " NaN\n", " NaN\n", - " 18.300\n", - " 35.900\n", - " 101.200\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1458.000\n", + " 2859.069\n", + " 155.379\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 522.600\n", + " 3484.067\n", + " 118.133\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -8780,6 +9914,12 @@ " NaN\n", " NaN\n", " NaN\n", + " 481.600\n", + " 3210.800\n", + " 120.700\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -8822,12 +9962,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 21.600\n", - " 42.333\n", - " 91.000\n", " NaN\n", " NaN\n", " NaN\n", + " 893.897\n", + " 1752.897\n", + " 119.034\n", + " 466.700\n", + " 3111.433\n", + " 117.600\n", + " 492.833\n", + " 3285.933\n", + " 118.600\n", " NaN\n", " NaN\n", " NaN\n", @@ -8849,9 +9995,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 14.467\n", - " 28.400\n", - " 98.600\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 889.034\n", + " 1743.034\n", + " 141.103\n", " 2988.655\n", " 6641.310\n", " 103.448\n", @@ -8870,15 +10019,27 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 2773.862\n", " 6164.207\n", " 98.310\n", " NaN\n", " NaN\n", " NaN\n", - " 22.200\n", - " 43.733\n", - " 92.100\n", + " 673.621\n", + " 1320.897\n", + " 118.759\n", + " 504.967\n", + " 3366.733\n", + " 114.733\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -8939,6 +10100,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 68.067\n", " 200.000\n", " 84.967\n", @@ -8969,6 +10133,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 535.367\n", + " 3568.667\n", + " 118.467\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -8984,9 +10157,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 19.500\n", - " 38.3\n", - " 96.533\n", + " 583.034\n", + " 1143.276\n", + " 137.379\n", " NaN\n", " NaN\n", " NaN\n", @@ -9002,12 +10175,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 14.467\n", - " 28.300\n", - " 93.133\n", - " 21.933\n", - " 43.167\n", - " 96.000\n", + " 846.310\n", + " 1659.586\n", + " 125.000\n", + " 1212.724\n", + " 2377.862\n", + " 137.655\n", " NaN\n", " NaN\n", " NaN\n", @@ -9101,9 +10274,14 @@ " NaN\n", " NaN\n", " NaN\n", - " 13.933\n", - " 27.433\n", - " 105.967\n", + " 1587.552\n", + " 3112.828\n", + " 155.724\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9155,6 +10333,16 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 509.300\n", + " 3395.467\n", + " 120.500\n", " 80.667\n", " 237.400\n", " 82.200\n", @@ -9212,9 +10400,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 44.733\n", - " 87.667\n", - " 119.900\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1807.034\n", + " 3543.138\n", + " 160.414\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9239,6 +10436,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 466.733\n", + " 3111.467\n", + " 127.867\n", " NaN\n", " NaN\n", " NaN\n", @@ -9269,6 +10469,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 480.000\n", + " 3199.967\n", + " 125.933\n", " NaN\n", " NaN\n", " NaN\n", @@ -9314,9 +10517,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 44.433\n", - " 87.133\n", - " 122.300\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1397.828\n", + " 2740.931\n", + " 160.690\n", " 55.367\n", " 162.800\n", " 89.100\n", @@ -9326,6 +10532,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 53.000\n", " 155.833\n", " 89.333\n", @@ -9374,6 +10583,11 @@ " NaN\n", " NaN\n", " NaN\n", + " 455.167\n", + " 3034.967\n", + " 126.633\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9404,13 +10618,17 @@ " NaN\n", " NaN\n", " NaN\n", - " 31.633\n", - " 61.900\n", - " 116.600\n", " NaN\n", + " 1421.483\n", + " 2787.034\n", + " 159.552\n", " NaN\n", " NaN\n", " NaN\n", + " 458.067\n", + " 3053.833\n", + " 126.733\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9446,9 +10664,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 111.967\n", - " 219.633\n", - " 122.533\n", + " 1557.172\n", + " 3053.241\n", + " 161.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -9470,6 +10688,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 10233\n", @@ -9504,6 +10728,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 71.067\n", " 208.900\n", " 90.800\n", @@ -9516,6 +10743,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 521.100\n", + " 3473.700\n", + " 125.233\n", " 2972.414\n", " 6605.034\n", " 104.172\n", @@ -9540,9 +10770,21 @@ " NaN\n", " NaN\n", " NaN\n", - " 94.633\n", - " 185.467\n", - " 118.200\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1936.138\n", + " 3796.345\n", + " 160.586\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 571.000\n", + " 3806.367\n", + " 123.833\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9612,6 +10854,14 @@ " NaN\n", " NaN\n", " NaN\n", + " 533.667\n", + " 3557.733\n", + " 127.367\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9654,10 +10904,16 @@ " NaN\n", " NaN\n", " NaN\n", - " 53.833\n", - " 105.800\n", - " 101.133\n", " NaN\n", + " 1499.897\n", + " 2940.862\n", + " 149.000\n", + " 508.200\n", + " 3388.100\n", + " 123.433\n", + " 544.267\n", + " 3628.667\n", + " 124.800\n", " NaN\n", " NaN\n", " NaN\n", @@ -9681,9 +10937,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 85.533\n", - " 167.700\n", - " 113.533\n", + " NaN\n", + " 1395.414\n", + " 2736.241\n", + " 156.448\n", " 3279.069\n", " 7286.759\n", " 106.517\n", @@ -9702,15 +10959,27 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 3064.069\n", " 6809.138\n", " 101.724\n", " NaN\n", " NaN\n", " NaN\n", - " 52.500\n", - " 102.867\n", - " 103.100\n", + " 1220.034\n", + " 2392.172\n", + " 146.207\n", + " 546.700\n", + " 3645.100\n", + " 121.333\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9771,6 +11040,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 83.467\n", " 245.267\n", " 89.800\n", @@ -9801,6 +11073,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 569.967\n", + " 3800.433\n", + " 123.400\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9816,9 +11097,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 44.367\n", - " 87.1\n", - " 110.167\n", + " 1157.517\n", + " 2269.483\n", + " 157.862\n", " NaN\n", " NaN\n", " NaN\n", @@ -9834,12 +11115,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 46.067\n", - " 90.300\n", - " 103.900\n", - " 50.933\n", - " 99.833\n", - " 109.867\n", + " 1393.724\n", + " 2732.862\n", + " 152.034\n", + " 1866.862\n", + " 3660.517\n", + " 156.586\n", " NaN\n", " NaN\n", " NaN\n", @@ -9933,9 +11214,21 @@ " NaN\n", " NaN\n", " NaN\n", - " 49.567\n", - " 97.333\n", - " 124.900\n", + " 2037.103\n", + " 3994.379\n", + " 161.000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -9987,6 +11280,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 554.600\n", + " 3697.600\n", + " 125.900\n", " 99.467\n", " 292.933\n", " 87.100\n", @@ -10044,9 +11340,17 @@ " NaN\n", " NaN\n", " NaN\n", - " 220.767\n", - " 432.800\n", - " 142.100\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1973.172\n", + " 3869.103\n", + " 161.000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -10072,6 +11376,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 512.333\n", + " 3415.733\n", + " 134.300\n", " NaN\n", " NaN\n", " NaN\n", @@ -10102,6 +11409,10 @@ " NaN\n", " NaN\n", " NaN\n", + " 517.033\n", + " 3446.533\n", + " 132.533\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -10146,9 +11457,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 222.100\n", - " 435.600\n", - " 145.800\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1759.448\n", + " 3449.724\n", + " 161.000\n", " 59.267\n", " 174.267\n", " 94.167\n", @@ -10158,6 +11472,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 65.800\n", " 193.367\n", " 94.033\n", @@ -10206,6 +11523,11 @@ " NaN\n", " NaN\n", " NaN\n", + " 502.667\n", + " 3350.800\n", + " 133.567\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -10236,12 +11558,16 @@ " NaN\n", " NaN\n", " NaN\n", - " 145.367\n", - " 285.067\n", - " 141.700\n", + " NaN\n", + " 1813.931\n", + " 3556.483\n", + " 161.000\n", " NaN\n", " NaN\n", " NaN\n", + " 495.667\n", + " 3304.500\n", + " 133.433\n", " NaN\n", " NaN\n", " NaN\n", @@ -10278,9 +11604,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 211.733\n", - " 415.200\n", - " 144.167\n", + " 1651.172\n", + " 3237.690\n", + " 161.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -10302,6 +11628,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 10234\n", @@ -10336,6 +11668,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 111.267\n", " 327.500\n", " 93.100\n", @@ -10348,6 +11683,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 524.933\n", + " 3499.500\n", + " 120.267\n", " 3421.483\n", " 7603.448\n", " 105.931\n", @@ -10372,9 +11710,21 @@ " NaN\n", " NaN\n", " NaN\n", - " 57.300\n", - " 112.367\n", - " 103.433\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1795.448\n", + " 3520.690\n", + " 157.034\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 552.300\n", + " 3681.800\n", + " 119.067\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -10444,6 +11794,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 498.133\n", + " 3320.433\n", + " 120.200\n", " NaN\n", " NaN\n", " NaN\n", @@ -10486,15 +11839,21 @@ " NaN\n", " NaN\n", " NaN\n", - " 55.300\n", - " 108.500\n", - " 94.933\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1264.069\n", + " 2478.414\n", + " 130.759\n", + " 480.833\n", + " 3204.967\n", + " 117.467\n", + " 520.467\n", + " 3469.900\n", + " 119.033\n", " NaN\n", " NaN\n", " NaN\n", @@ -10513,9 +11872,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 47.700\n", - " 93.633\n", - " 101.700\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1426.276\n", + " 2796.828\n", + " 150.345\n", " 3704.690\n", " 8232.552\n", " 107.655\n", @@ -10534,15 +11899,27 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 3406.138\n", " 7569.310\n", " 102.379\n", " NaN\n", " NaN\n", " NaN\n", - " 56.233\n", - " 110.400\n", - " 92.467\n", + " 792.586\n", + " 1554.138\n", + " 121.793\n", + " 527.967\n", + " 3519.500\n", + " 115.267\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -10603,6 +11980,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 157.600\n", " 463.567\n", " 94.367\n", @@ -10633,6 +12013,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 563.000\n", + " 3753.067\n", + " 118.867\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -10648,9 +12037,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 37.667\n", - " 73.9\n", - " 96.800\n", + " 886.103\n", + " 1737.207\n", + " 140.207\n", " NaN\n", " NaN\n", " NaN\n", @@ -10666,12 +12055,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 44.667\n", - " 87.500\n", - " 96.233\n", - " 56.500\n", - " 110.833\n", - " 98.133\n", + " 905.241\n", + " 1775.034\n", + " 135.552\n", + " 1478.897\n", + " 2899.862\n", + " 142.931\n", " NaN\n", " NaN\n", " NaN\n", @@ -10765,9 +12154,14 @@ " NaN\n", " NaN\n", " NaN\n", - " 41.167\n", - " 80.833\n", - " 108.267\n", + " 1824.172\n", + " 3576.793\n", + " 158.759\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -10819,6 +12213,16 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 540.033\n", + " 3600.633\n", + " 120.967\n", " 160.000\n", " 470.567\n", " 90.400\n", @@ -10876,12 +12280,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 75.033\n", - " 147.033\n", - " 124.167\n", " NaN\n", " NaN\n", " NaN\n", + " 1979.276\n", + " 3880.897\n", + " 160.759\n", " NaN\n", " NaN\n", " NaN\n", @@ -10912,6 +12316,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 505.433\n", + " 3370.367\n", + " 128.967\n", " NaN\n", " NaN\n", " NaN\n", @@ -10942,6 +12349,18 @@ " NaN\n", " NaN\n", " NaN\n", + " 498.633\n", + " 3324.433\n", + " 126.900\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 129.500\n", " 380.967\n", " 98.333\n", @@ -10978,9 +12397,12 @@ " NaN\n", " NaN\n", " NaN\n", - " 75.700\n", - " 148.567\n", - " 124.167\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1702.345\n", + " 3337.793\n", + " 161.000\n", " 106.800\n", " 314.033\n", " 97.067\n", @@ -10990,6 +12412,9 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 118.133\n", " 347.567\n", " 97.900\n", @@ -11038,6 +12463,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 480.000\n", + " 3199.467\n", + " 127.333\n", " NaN\n", " NaN\n", " NaN\n", @@ -11068,12 +12496,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 80.633\n", - " 158.200\n", - " 120.833\n", " NaN\n", " NaN\n", " NaN\n", + " 1824.724\n", + " 3577.897\n", + " 160.517\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 476.200\n", + " 3174.400\n", + " 126.500\n", " NaN\n", " NaN\n", " NaN\n", @@ -11110,9 +12544,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 112.167\n", - " 219.800\n", - " 124.000\n", + " 1820.759\n", + " 3570.103\n", + " 161.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -11134,25 +12568,57 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", "\n", - "

10235 rows × 829 columns

\n", + "

10235 rows × 937 columns

\n", "" ], "text/plain": [ - " geometry yield_taro_future_ssp370_mpi \n", - "0 POINT (19.25000 -34.25000) NaN \\\n", - "1 POINT (19.75000 -34.25000) NaN \n", - "2 POINT (20.25000 -34.25000) NaN \n", - "3 POINT (20.75000 -34.25000) NaN \n", - "4 POINT (21.25000 -34.25000) NaN \n", - "... ... ... \n", - "10230 POINT (9.25000 36.75000) NaN \n", - "10231 POINT (9.75000 36.75000) NaN \n", - "10232 POINT (10.25000 36.75000) NaN \n", - "10233 POINT (10.75000 36.75000) NaN \n", - "10234 POINT (9.75000 37.25000) NaN \n", + " geometry yield_plantain_future_ssp370_mri \n", + "0 POINT (19.25000 -34.25000) NaN \\\n", + "1 POINT (19.75000 -34.25000) NaN \n", + "2 POINT (20.25000 -34.25000) NaN \n", + "3 POINT (20.75000 -34.25000) NaN \n", + "4 POINT (21.25000 -34.25000) NaN \n", + "... ... ... \n", + "10230 POINT (9.25000 36.75000) NaN \n", + "10231 POINT (9.75000 36.75000) NaN \n", + "10232 POINT (10.25000 36.75000) NaN \n", + "10233 POINT (10.75000 36.75000) NaN \n", + "10234 POINT (9.75000 37.25000) NaN \n", + "\n", + " biomass_plantain_future_ssp370_mri \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_plantain_future_ssp370_mri yield_taro_future_ssp370_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " biomass_taro_future_ssp370_mpi duration_taro_future_ssp370_mpi \n", "0 NaN NaN \\\n", @@ -11518,18 +12984,44 @@ "10233 NaN \n", "10234 NaN \n", "\n", - " yield_grasspea_future_ssp126_gfdl biomass_grasspea_future_ssp126_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 3811.793 8470.828 \n", - "10231 3015.586 6701.655 \n", - "10232 2691.276 5980.517 \n", - "10233 2972.414 6605.034 \n", - "10234 3421.483 7603.448 \n", + " yield_pumpkin_future_ssp126_gfdl biomass_pumpkin_future_ssp126_gfdl \n", + "0 514.400 3429.367 \\\n", + "1 430.000 2866.000 \n", + "2 383.433 2555.900 \n", + "3 420.267 2801.700 \n", + "4 410.133 2734.500 \n", + "... ... ... \n", + "10230 428.800 2858.100 \n", + "10231 427.967 2854.100 \n", + "10232 489.300 3262.067 \n", + "10233 521.100 3473.700 \n", + "10234 524.933 3499.500 \n", + "\n", + " duration_pumpkin_future_ssp126_gfdl yield_grasspea_future_ssp126_gfdl \n", + "0 212.567 NaN \\\n", + "1 210.633 NaN \n", + "2 175.167 NaN \n", + "3 173.967 NaN \n", + "4 171.167 NaN \n", + "... ... ... \n", + "10230 115.200 3811.793 \n", + "10231 115.233 3015.586 \n", + "10232 119.500 2691.276 \n", + "10233 125.233 2972.414 \n", + "10234 120.267 3421.483 \n", + "\n", + " biomass_grasspea_future_ssp126_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 8470.828 \n", + "10231 6701.655 \n", + "10232 5980.517 \n", + "10233 6605.034 \n", + "10234 7603.448 \n", "\n", " duration_grasspea_future_ssp126_gfdl yield_sesame_future_ssp370_ipsl \n", "0 NaN NaN \\\n", @@ -11557,31 +13049,57 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_yams_future_ssp370_gfdl biomass_yams_future_ssp370_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_plantain_future_ssp126_gfdl biomass_plantain_future_ssp126_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_plantain_future_ssp126_gfdl yield_yams_future_ssp370_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_yams_future_ssp370_gfdl duration_yams_future_ssp370_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " duration_yams_future_ssp370_gfdl yield_josephscoat_future_ssp126_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_josephscoat_future_ssp126_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", "\n", " biomass_josephscoat_future_ssp126_gfdl \n", "0 NaN \\\n", @@ -11733,11 +13251,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 140.167 274.767 \n", - "10231 32.133 63.100 \n", - "10232 18.300 35.900 \n", - "10233 94.633 185.467 \n", - "10234 57.300 112.367 \n", + "10230 1417.793 2779.897 \n", + "10231 1364.069 2674.655 \n", + "10232 1458.000 2859.069 \n", + "10233 1936.138 3796.345 \n", + "10234 1795.448 3520.690 \n", "\n", " duration_tomato_future_ssp126_gfdl yield_cocoyam_future_ssp126_mri \n", "0 NaN NaN \\\n", @@ -11746,11 +13264,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 87.867 NaN \n", - "10231 91.500 NaN \n", - "10232 101.200 NaN \n", - "10233 118.200 NaN \n", - "10234 103.433 NaN \n", + "10230 153.828 NaN \n", + "10231 152.069 NaN \n", + "10232 155.379 NaN \n", + "10233 160.586 NaN \n", + "10234 157.034 NaN \n", "\n", " biomass_cocoyam_future_ssp126_mri duration_cocoyam_future_ssp126_mri \n", "0 NaN NaN \\\n", @@ -11765,44 +13283,57 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_lablab_future_ssp126_mpi biomass_lablab_future_ssp126_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_pumpkin_future_ssp370_mri biomass_pumpkin_future_ssp370_mri \n", + "0 465.000 3100.100 \\\n", + "1 383.800 2558.600 \n", + "2 350.667 2337.367 \n", + "3 384.067 2560.433 \n", + "4 388.367 2589.100 \n", + "... ... ... \n", + "10230 453.033 3020.367 \n", + "10231 449.233 2994.600 \n", + "10232 522.600 3484.067 \n", + "10233 571.000 3806.367 \n", + "10234 552.300 3681.800 \n", + "\n", + " duration_pumpkin_future_ssp370_mri yield_lablab_future_ssp126_mpi \n", + "0 197.867 NaN \\\n", + "1 187.800 NaN \n", + "2 142.467 NaN \n", + "3 142.233 NaN \n", + "4 143.767 NaN \n", + "... ... ... \n", + "10230 115.100 NaN \n", + "10231 114.767 NaN \n", + "10232 118.133 NaN \n", + "10233 123.833 NaN \n", + "10234 119.067 NaN \n", "\n", - " duration_lablab_future_ssp126_mpi yield_cocoyam_future_ssp126_ipsl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " biomass_lablab_future_ssp126_mpi duration_lablab_future_ssp126_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_cocoyam_future_ssp126_ipsl \n", - "0 NaN \\\n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " yield_cocoyam_future_ssp126_ipsl biomass_cocoyam_future_ssp126_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " duration_cocoyam_future_ssp126_ipsl \n", "0 NaN \\\n", @@ -12103,6 +13634,58 @@ "10233 NaN \n", "10234 NaN \n", "\n", + " yield_plantain_future_ssp126_ipsl biomass_plantain_future_ssp126_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_plantain_future_ssp126_ipsl yield_plantain_future_ssp370_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_plantain_future_ssp370_mpi \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_plantain_future_ssp370_mpi \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", " yield_africaneggplant_future_ssp370_ipsl \n", "0 NaN \\\n", "1 NaN \n", @@ -12221,11 +13804,11 @@ "10234 NaN \n", "\n", " duration_cassava_future_ssp126_ipsl yield_tef_future_ssp370_ipsl \n", - "0 NaN 1028.867 \\\n", - "1 NaN 882.633 \n", - "2 NaN 1057.767 \n", - "3 NaN 1041.733 \n", - "4 NaN 973.400 \n", + "0 NaN 1295.759 \\\n", + "1 NaN 1235.207 \n", + "2 NaN 1281.931 \n", + "3 NaN 1280.897 \n", + "4 NaN 1195.897 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -12234,11 +13817,11 @@ "10234 NaN NaN \n", "\n", " biomass_tef_future_ssp370_ipsl duration_tef_future_ssp370_ipsl \n", - "0 3810.667 91.0 \\\n", - "1 3269.000 91.0 \n", - "2 3917.500 91.0 \n", - "3 3858.333 91.0 \n", - "4 3605.100 91.0 \n", + "0 4798.931 78.448 \\\n", + "1 4574.793 76.379 \n", + "2 4747.586 70.966 \n", + "3 4743.931 71.241 \n", + "4 4429.241 73.276 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -12324,44 +13907,70 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_okra_future_ssp126_mpi biomass_okra_future_ssp126_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_pumpkin_future_ssp126_mpi biomass_pumpkin_future_ssp126_mpi \n", + "0 500.500 3336.500 \\\n", + "1 409.067 2726.200 \n", + "2 408.633 2724.067 \n", + "3 441.900 2945.833 \n", + "4 430.400 2869.133 \n", + "... ... ... \n", + "10230 377.833 2518.667 \n", + "10231 381.800 2545.200 \n", + "10232 481.600 3210.800 \n", + "10233 533.667 3557.733 \n", + "10234 498.133 3320.433 \n", + "\n", + " duration_pumpkin_future_ssp126_mpi yield_okra_future_ssp126_mpi \n", + "0 211.700 NaN \\\n", + "1 206.533 NaN \n", + "2 172.767 NaN \n", + "3 171.533 NaN \n", + "4 169.267 NaN \n", + "... ... ... \n", + "10230 114.600 NaN \n", + "10231 114.667 NaN \n", + "10232 120.700 NaN \n", + "10233 127.367 NaN \n", + "10234 120.200 NaN \n", "\n", - " duration_okra_future_ssp126_mpi yield_tef_future_ssp370_mpi \n", - "0 NaN 988.567 \\\n", - "1 NaN 842.667 \n", - "2 NaN 979.100 \n", - "3 NaN 968.800 \n", - "4 NaN 878.267 \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " biomass_okra_future_ssp126_mpi duration_okra_future_ssp126_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_tef_future_ssp370_mpi duration_tef_future_ssp370_mpi \n", - "0 3661.133 91.0 \\\n", - "1 3121.167 91.0 \n", - "2 3626.067 91.0 \n", - "3 3588.133 91.0 \n", - "4 3253.133 91.0 \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_tef_future_ssp370_mpi biomass_tef_future_ssp370_mpi \n", + "0 1265.172 4686.069 \\\n", + "1 1219.862 4518.448 \n", + "2 1300.276 4816.069 \n", + "3 1300.690 4817.379 \n", + "4 1196.000 4429.655 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_tef_future_ssp370_mpi \n", + "0 80.793 \\\n", + "1 78.862 \n", + "2 73.690 \n", + "3 73.862 \n", + "4 75.828 \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", "\n", " yield_bambaragroundnut_future_ssp370_mri \n", "0 NaN \\\n", @@ -12519,31 +14128,57 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_tef_future_ssp126_mri biomass_tef_future_ssp126_mri \n", - "0 827.933 3066.467 \\\n", - "1 730.367 2705.100 \n", - "2 903.967 3348.133 \n", - "3 907.633 3361.667 \n", - "4 844.133 3126.367 \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_pearlmillet_future_ssp126_ipsl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", "\n", - " duration_tef_future_ssp126_mri \n", - "0 91.0 \\\n", - "1 91.0 \n", - "2 91.0 \n", - "3 91.0 \n", - "4 91.0 \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " biomass_pearlmillet_future_ssp126_ipsl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_pearlmillet_future_ssp126_ipsl yield_tef_future_ssp126_mri \n", + "0 NaN 1243.621 \\\n", + "1 NaN 1180.172 \n", + "2 NaN 1260.517 \n", + "3 NaN 1244.241 \n", + "4 NaN 1172.793 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_tef_future_ssp126_mri duration_tef_future_ssp126_mri \n", + "0 4605.931 80.276 \\\n", + "1 4371.172 78.276 \n", + "2 4668.345 72.897 \n", + "3 4607.828 73.069 \n", + "4 4343.966 74.828 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " yield_bambaragroundnut_future_ssp126_ipsl \n", "0 NaN \\\n", @@ -12649,18 +14284,44 @@ "10233 NaN \n", "10234 NaN \n", "\n", - " duration_sweetpotato_future_ssp126_ipsl yield_taro_future_ssp370_mri \n", - "0 82.000 NaN \\\n", - "1 78.966 NaN \n", - "2 75.931 NaN \n", - "3 76.552 NaN \n", - "4 75.655 NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_sweetpotato_future_ssp126_ipsl \n", + "0 82.000 \\\n", + "1 78.966 \n", + "2 75.931 \n", + "3 76.552 \n", + "4 75.655 \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " yield_plantain_future_ssp126_mpi biomass_plantain_future_ssp126_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_plantain_future_ssp126_mpi yield_taro_future_ssp370_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " biomass_taro_future_ssp370_mri duration_taro_future_ssp370_mri \n", "0 NaN NaN \\\n", @@ -12721,37 +14382,89 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 159.500 312.533 \n", - "10231 40.533 79.500 \n", - "10232 21.600 42.333 \n", - "10233 53.833 105.800 \n", - "10234 55.300 108.500 \n", - "\n", - " duration_tomato_future_ssp370_mri yield_cowpea_future_ssp370_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 80.733 NaN \n", - "10231 83.433 NaN \n", - "10232 91.000 NaN \n", - "10233 101.133 NaN \n", - "10234 94.933 NaN \n", - "\n", - " biomass_cowpea_future_ssp370_mpi duration_cowpea_future_ssp370_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + "10230 637.276 1249.517 \n", + "10231 639.793 1254.310 \n", + "10232 893.897 1752.897 \n", + "10233 1499.897 2940.862 \n", + "10234 1264.069 2478.414 \n", + "\n", + " duration_tomato_future_ssp370_mri yield_pumpkin_future_ssp126_ipsl \n", + "0 NaN 497.533 \\\n", + "1 NaN 415.533 \n", + "2 NaN 365.833 \n", + "3 NaN 396.567 \n", + "4 NaN 408.067 \n", + "... ... ... \n", + "10230 104.552 379.800 \n", + "10231 103.069 394.167 \n", + "10232 119.034 466.700 \n", + "10233 149.000 508.200 \n", + "10234 130.759 480.833 \n", + "\n", + " biomass_pumpkin_future_ssp126_ipsl \n", + "0 3317.200 \\\n", + "1 2770.567 \n", + "2 2438.567 \n", + "3 2644.300 \n", + "4 2720.400 \n", + "... ... \n", + "10230 2531.633 \n", + "10231 2628.433 \n", + "10232 3111.433 \n", + "10233 3388.100 \n", + "10234 3204.967 \n", + "\n", + " duration_pumpkin_future_ssp126_ipsl yield_pumpkin_future_ssp370_mpi \n", + "0 206.100 544.567 \\\n", + "1 201.000 462.667 \n", + "2 148.033 464.900 \n", + "3 147.400 485.467 \n", + "4 150.133 472.667 \n", + "... ... ... \n", + "10230 114.633 393.933 \n", + "10231 114.733 387.900 \n", + "10232 117.600 492.833 \n", + "10233 123.433 544.267 \n", + "10234 117.467 520.467 \n", + "\n", + " biomass_pumpkin_future_ssp370_mpi duration_pumpkin_future_ssp370_mpi \n", + "0 3630.433 205.967 \\\n", + "1 3083.900 201.067 \n", + "2 3099.500 166.933 \n", + "3 3236.300 162.867 \n", + "4 3150.800 158.100 \n", + "... ... ... \n", + "10230 2626.633 112.233 \n", + "10231 2585.867 112.133 \n", + "10232 3285.933 118.600 \n", + "10233 3628.667 124.800 \n", + "10234 3469.900 119.033 \n", + "\n", + " yield_cowpea_future_ssp370_mpi biomass_cowpea_future_ssp370_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_cowpea_future_ssp370_mpi \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", "\n", " yield_africaneggplant_future_ssp370_gfdl \n", "0 NaN \\\n", @@ -12793,11 +14506,11 @@ "10234 NaN \n", "\n", " yield_tef_future_ssp126_mpi biomass_tef_future_ssp126_mpi \n", - "0 869.500 3220.267 \\\n", - "1 752.767 2788.367 \n", - "2 903.867 3347.633 \n", - "3 903.367 3346.067 \n", - "4 823.100 3048.567 \n", + "0 1208.517 4475.690 \\\n", + "1 1174.483 4349.897 \n", + "2 1234.759 4573.448 \n", + "3 1226.207 4541.586 \n", + "4 1128.517 4179.862 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -12806,11 +14519,11 @@ "10234 NaN NaN \n", "\n", " duration_tef_future_ssp126_mpi yield_okra_future_ssp370_ipsl \n", - "0 91.0 NaN \\\n", - "1 91.0 NaN \n", - "2 91.0 NaN \n", - "3 91.0 NaN \n", - "4 91.0 NaN \n", + "0 82.241 NaN \\\n", + "1 80.345 NaN \n", + "2 75.000 NaN \n", + "3 75.207 NaN \n", + "4 77.034 NaN \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -12916,11 +14629,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 NaN 140.467 \n", - "10231 NaN 27.333 \n", - "10232 NaN 14.467 \n", - "10233 NaN 85.533 \n", - "10234 NaN 47.700 \n", + "10230 NaN 847.241 \n", + "10231 NaN 734.448 \n", + "10232 NaN 889.034 \n", + "10233 NaN 1395.414 \n", + "10234 NaN 1426.276 \n", "\n", " biomass_tomato_future_ssp370_gfdl duration_tomato_future_ssp370_gfdl \n", "0 NaN NaN \\\n", @@ -12929,11 +14642,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 275.367 83.500 \n", - "10231 53.633 87.000 \n", - "10232 28.400 98.600 \n", - "10233 167.700 113.533 \n", - "10234 93.633 101.700 \n", + "10230 1661.207 134.172 \n", + "10231 1440.103 131.448 \n", + "10232 1743.034 141.103 \n", + "10233 2736.241 156.448 \n", + "10234 2796.828 150.345 \n", "\n", " yield_grasspea_future_ssp126_mri biomass_grasspea_future_ssp126_mri \n", "0 NaN NaN \\\n", @@ -13065,18 +14778,44 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " duration_yams_future_ssp126_gfdl yield_cassava_future_ssp126_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_yams_future_ssp126_gfdl yield_plantain_future_ssp370_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_plantain_future_ssp370_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_plantain_future_ssp370_gfdl yield_cassava_future_ssp126_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " biomass_cassava_future_ssp126_mri duration_cassava_future_ssp126_mri \n", "0 NaN NaN \\\n", @@ -13163,24 +14902,50 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 161.000 315.667 \n", - "10231 26.067 51.300 \n", - "10232 22.200 43.733 \n", - "10233 52.500 102.867 \n", - "10234 56.233 110.400 \n", - "\n", - " duration_tomato_future_ssp370_ipsl yield_cowpea_future_ssp370_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 82.533 NaN \n", - "10231 85.167 NaN \n", - "10232 92.100 NaN \n", - "10233 103.100 NaN \n", - "10234 92.467 NaN \n", + "10230 579.897 1136.931 \n", + "10231 569.759 1117.103 \n", + "10232 673.621 1320.897 \n", + "10233 1220.034 2392.172 \n", + "10234 792.586 1554.138 \n", + "\n", + " duration_tomato_future_ssp370_ipsl yield_pumpkin_future_ssp370_gfdl \n", + "0 NaN 518.233 \\\n", + "1 NaN 429.767 \n", + "2 NaN 412.500 \n", + "3 NaN 438.233 \n", + "4 NaN 429.733 \n", + "... ... ... \n", + "10230 115.897 421.867 \n", + "10231 110.690 426.133 \n", + "10232 118.759 504.967 \n", + "10233 146.207 546.700 \n", + "10234 121.793 527.967 \n", + "\n", + " biomass_pumpkin_future_ssp370_gfdl \n", + "0 3454.733 \\\n", + "1 2865.067 \n", + "2 2749.767 \n", + "3 2922.100 \n", + "4 2865.200 \n", + "... ... \n", + "10230 2812.367 \n", + "10231 2840.300 \n", + "10232 3366.733 \n", + "10233 3645.100 \n", + "10234 3519.500 \n", + "\n", + " duration_pumpkin_future_ssp370_gfdl yield_cowpea_future_ssp370_mri \n", + "0 208.300 NaN \\\n", + "1 200.800 NaN \n", + "2 162.233 NaN \n", + "3 158.867 NaN \n", + "4 157.467 NaN \n", + "... ... ... \n", + "10230 108.500 NaN \n", + "10231 108.333 NaN \n", + "10232 114.733 NaN \n", + "10233 121.333 NaN \n", + "10234 115.267 NaN \n", "\n", " biomass_cowpea_future_ssp370_mri duration_cowpea_future_ssp370_mri \n", "0 NaN NaN \\\n", @@ -13364,57 +15129,83 @@ "10233 NaN \n", "10234 NaN \n", "\n", - " yield_lablab_future_ssp370_mpi biomass_lablab_future_ssp370_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_pearlmillet_future_ssp126_mri \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", "\n", - " duration_lablab_future_ssp370_mpi yield_okra_future_ssp126_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " biomass_pearlmillet_future_ssp126_mri \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", "\n", - " biomass_okra_future_ssp126_gfdl duration_okra_future_ssp126_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_pearlmillet_future_ssp126_mri yield_lablab_future_ssp370_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " yield_pigeonpea_future_ssp126_ipsl \n", - "0 NaN \\\n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " biomass_lablab_future_ssp370_mpi duration_lablab_future_ssp370_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_okra_future_ssp126_gfdl biomass_okra_future_ssp126_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_okra_future_ssp126_gfdl yield_pigeonpea_future_ssp126_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " biomass_pigeonpea_future_ssp126_ipsl \n", "0 NaN \\\n", @@ -13456,11 +15247,11 @@ "10234 NaN NaN \n", "\n", " yield_tef_future_ssp126_gfdl biomass_tef_future_ssp126_gfdl \n", - "0 816.233 3023.367 \\\n", - "1 701.967 2600.200 \n", - "2 894.500 3313.033 \n", - "3 899.967 3333.700 \n", - "4 839.800 3110.400 \n", + "0 1236.828 4580.724 \\\n", + "1 1174.276 4349.345 \n", + "2 1216.517 4505.414 \n", + "3 1221.276 4523.103 \n", + "4 1127.138 4174.655 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -13468,18 +15259,57 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " duration_tef_future_ssp126_gfdl yield_groundnut_future_ssp370_gfdl \n", - "0 91.0 NaN \\\n", - "1 91.0 NaN \n", - "2 91.0 NaN \n", - "3 91.0 NaN \n", - "4 91.0 NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_tef_future_ssp126_gfdl yield_pearlmillet_future_ssp370_mpi \n", + "0 83.103 NaN \\\n", + "1 80.448 NaN \n", + "2 75.345 NaN \n", + "3 75.345 NaN \n", + "4 76.379 NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_pearlmillet_future_ssp370_mpi \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_pearlmillet_future_ssp370_mpi \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " yield_groundnut_future_ssp370_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", "\n", " biomass_groundnut_future_ssp370_gfdl \n", "0 NaN \\\n", @@ -13546,6 +15376,45 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", + " yield_pearlmillet_future_ssp370_ipsl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " biomass_pearlmillet_future_ssp370_ipsl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_pearlmillet_future_ssp370_ipsl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", " yield_josephscoat_future_ssp370_ipsl \n", "0 NaN \\\n", "1 NaN \n", @@ -13936,6 +15805,84 @@ "10233 NaN \n", "10234 NaN \n", "\n", + " yield_pearlmillet_future_ssp370_mri \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " biomass_pearlmillet_future_ssp370_mri \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_pearlmillet_future_ssp370_mri \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " yield_pearlmillet_future_ssp370_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " biomass_pearlmillet_future_ssp370_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_pearlmillet_future_ssp370_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", " yield_lablab_future_ssp370_gfdl biomass_lablab_future_ssp370_gfdl \n", "0 NaN NaN \\\n", "1 NaN NaN \n", @@ -13975,103 +15922,116 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_cowpea_future_ssp126_mri biomass_cowpea_future_ssp126_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_pumpkin_future_ssp370_ipsl biomass_pumpkin_future_ssp370_ipsl \n", + "0 532.100 3547.700 \\\n", + "1 455.733 3037.967 \n", + "2 407.100 2713.667 \n", + "3 448.367 2988.800 \n", + "4 448.533 2990.567 \n", + "... ... ... \n", + "10230 460.533 3070.767 \n", + "10231 457.400 3048.933 \n", + "10232 535.367 3568.667 \n", + "10233 569.967 3800.433 \n", + "10234 563.000 3753.067 \n", + "\n", + " duration_pumpkin_future_ssp370_ipsl yield_cowpea_future_ssp126_mri \n", + "0 199.333 NaN \\\n", + "1 192.467 NaN \n", + "2 145.333 NaN \n", + "3 145.400 NaN \n", + "4 146.733 NaN \n", + "... ... ... \n", + "10230 116.500 NaN \n", + "10231 116.167 NaN \n", + "10232 118.467 NaN \n", + "10233 123.400 NaN \n", + "10234 118.867 NaN \n", "\n", - " duration_cowpea_future_ssp126_mri yield_okra_future_ssp126_ipsl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " biomass_cowpea_future_ssp126_mri duration_cowpea_future_ssp126_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_okra_future_ssp126_ipsl duration_okra_future_ssp126_ipsl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " yield_fonio_future_ssp370_gfdl biomass_fonio_future_ssp370_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " duration_fonio_future_ssp370_gfdl yield_grasspea_future_ssp370_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN 3636.276 \n", - "10231 NaN 2836.241 \n", - "10232 NaN 2709.724 \n", - "10233 NaN 2940.621 \n", - "10234 NaN 3407.345 \n", - "\n", - " biomass_grasspea_future_ssp370_mpi \n", - "0 NaN \\\n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "10230 8080.483 \n", - "10231 6302.862 \n", - "10232 6021.655 \n", - "10233 6534.552 \n", - "10234 7572.103 \n", - "\n", - " duration_grasspea_future_ssp370_mpi yield_maize_future_ssp126_mpi \n", - "0 NaN 192.103 \\\n", - "1 NaN 190.690 \n", - "2 NaN 295.862 \n", - "3 NaN 321.828 \n", - "4 NaN 263.345 \n", - "... ... ... \n", - "10230 106.483 NaN \n", - "10231 100.690 NaN \n", - "10232 100.069 NaN \n", - "10233 103.069 NaN \n", - "10234 104.483 NaN \n", - "\n", - " biomass_maize_future_ssp126_mpi duration_maize_future_ssp126_mpi \n", - "0 565.000 80.690 \\\n", - "1 560.655 79.379 \n", - "2 870.276 76.000 \n", - "3 946.517 76.241 \n", - "4 774.621 75.931 \n", + " yield_okra_future_ssp126_ipsl biomass_okra_future_ssp126_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_okra_future_ssp126_ipsl yield_fonio_future_ssp370_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_fonio_future_ssp370_gfdl duration_fonio_future_ssp370_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_grasspea_future_ssp370_mpi biomass_grasspea_future_ssp370_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 3636.276 8080.483 \n", + "10231 2836.241 6302.862 \n", + "10232 2709.724 6021.655 \n", + "10233 2940.621 6534.552 \n", + "10234 3407.345 7572.103 \n", + "\n", + " duration_grasspea_future_ssp370_mpi yield_maize_future_ssp126_mpi \n", + "0 NaN 192.103 \\\n", + "1 NaN 190.690 \n", + "2 NaN 295.862 \n", + "3 NaN 321.828 \n", + "4 NaN 263.345 \n", + "... ... ... \n", + "10230 106.483 NaN \n", + "10231 100.690 NaN \n", + "10232 100.069 NaN \n", + "10233 103.069 NaN \n", + "10234 104.483 NaN \n", + "\n", + " biomass_maize_future_ssp126_mpi duration_maize_future_ssp126_mpi \n", + "0 565.000 80.690 \\\n", + "1 560.655 79.379 \n", + "2 870.276 76.000 \n", + "3 946.517 76.241 \n", + "4 774.621 75.931 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -14093,11 +16053,11 @@ "10234 133.233 391.767 \n", "\n", " duration_sorghum_future_ssp370_mpi yield_tef_future_ssp370_mri \n", - "0 57.536 910.067 \\\n", - "1 56.679 775.500 \n", - "2 56.250 940.133 \n", - "3 55.857 934.467 \n", - "4 53.929 874.733 \n", + "0 57.536 1291.276 \\\n", + "1 56.679 1218.172 \n", + "2 56.250 1305.828 \n", + "3 55.857 1290.931 \n", + "4 53.929 1191.724 \n", "... ... ... \n", "10230 99.467 NaN \n", "10231 87.733 NaN \n", @@ -14106,11 +16066,11 @@ "10234 92.100 NaN \n", "\n", " biomass_tef_future_ssp370_mri duration_tef_future_ssp370_mri \n", - "0 3370.700 91.0 \\\n", - "1 2872.533 91.0 \n", - "2 3481.700 91.0 \n", - "3 3461.133 91.0 \n", - "4 3239.767 91.0 \n", + "0 4782.345 79.172 \\\n", + "1 4511.724 77.103 \n", + "2 4836.241 72.103 \n", + "3 4780.966 72.172 \n", + "4 4414.069 74.172 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -14125,11 +16085,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 103.967 204.0 \n", - "10231 25.767 50.7 \n", - "10232 19.500 38.3 \n", - "10233 44.367 87.1 \n", - "10234 37.667 73.9 \n", + "10230 640.034 1254.966 \n", + "10231 598.655 1173.931 \n", + "10232 583.034 1143.276 \n", + "10233 1157.517 2269.483 \n", + "10234 886.103 1737.207 \n", "\n", " duration_tomato_future_ssp126_ipsl \n", "0 NaN \\\n", @@ -14138,11 +16098,11 @@ "3 NaN \n", "4 NaN \n", "... ... \n", - "10230 83.800 \n", - "10231 87.500 \n", - "10232 96.533 \n", - "10233 110.167 \n", - "10234 96.800 \n", + "10230 131.207 \n", + "10231 128.103 \n", + "10232 137.379 \n", + "10233 157.862 \n", + "10234 140.207 \n", "\n", " yield_bambaragroundnut_future_ssp126_gfdl \n", "0 NaN \\\n", @@ -14268,11 +16228,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 NaN 157.667 \n", - "10231 NaN 30.800 \n", - "10232 NaN 14.467 \n", - "10233 NaN 46.067 \n", - "10234 NaN 44.667 \n", + "10230 NaN 536.724 \n", + "10231 NaN 562.207 \n", + "10232 NaN 846.310 \n", + "10233 NaN 1393.724 \n", + "10234 NaN 905.241 \n", "\n", " biomass_tomato_future_ssp126_mri duration_tomato_future_ssp126_mri \n", "0 NaN NaN \\\n", @@ -14281,11 +16241,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 309.133 83.400 \n", - "10231 60.200 85.967 \n", - "10232 28.300 93.133 \n", - "10233 90.300 103.900 \n", - "10234 87.500 96.233 \n", + "10230 1052.586 112.793 \n", + "10231 1102.379 109.655 \n", + "10232 1659.586 125.000 \n", + "10233 2732.862 152.034 \n", + "10234 1775.034 135.552 \n", "\n", " yield_tomato_future_ssp370_mpi biomass_tomato_future_ssp370_mpi \n", "0 NaN NaN \\\n", @@ -14294,11 +16254,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 132.800 260.600 \n", - "10231 41.300 80.833 \n", - "10232 21.933 43.167 \n", - "10233 50.933 99.833 \n", - "10234 56.500 110.833 \n", + "10230 747.897 1466.310 \n", + "10231 768.069 1505.931 \n", + "10232 1212.724 2377.862 \n", + "10233 1866.862 3660.517 \n", + "10234 1478.897 2899.862 \n", "\n", " duration_tomato_future_ssp370_mpi yield_soybean_future_ssp370_ipsl \n", "0 NaN NaN \\\n", @@ -14307,11 +16267,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 83.933 NaN \n", - "10231 87.333 NaN \n", - "10232 96.000 NaN \n", - "10233 109.867 NaN \n", - "10234 98.133 NaN \n", + "10230 128.103 NaN \n", + "10231 123.586 NaN \n", + "10232 137.655 NaN \n", + "10233 156.586 NaN \n", + "10234 142.931 NaN \n", "\n", " biomass_soybean_future_ssp370_ipsl \n", "0 NaN \\\n", @@ -15087,11 +17047,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 124.033 243.167 \n", - "10231 35.067 68.700 \n", - "10232 13.933 27.433 \n", - "10233 49.567 97.333 \n", - "10234 41.167 80.833 \n", + "10230 1204.897 2362.517 \n", + "10231 1175.138 2304.207 \n", + "10232 1587.552 3112.828 \n", + "10233 2037.103 3994.379 \n", + "10234 1824.172 3576.793 \n", "\n", " duration_tomato_future_ssp126_mpi yield_cocoyam_future_ssp370_ipsl \n", "0 NaN NaN \\\n", @@ -15100,11 +17060,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 93.167 NaN \n", - "10231 97.200 NaN \n", - "10232 105.967 NaN \n", - "10233 124.900 NaN \n", - "10234 108.267 NaN \n", + "10230 154.103 NaN \n", + "10231 152.724 NaN \n", + "10232 155.724 NaN \n", + "10233 161.000 NaN \n", + "10234 158.759 NaN \n", "\n", " biomass_cocoyam_future_ssp370_ipsl \n", "0 NaN \\\n", @@ -15197,12 +17157,51 @@ "10233 NaN \n", "10234 NaN \n", "\n", - " duration_sweetpotato_future_ssp370_mpi yield_fonio_future_ssp126_gfdl \n", - "0 81.379 NaN \\\n", - "1 79.379 NaN \n", - "2 78.241 NaN \n", - "3 79.138 NaN \n", - "4 78.000 NaN \n", + " duration_sweetpotato_future_ssp370_mpi \n", + "0 81.379 \\\n", + "1 79.379 \n", + "2 78.241 \n", + "3 79.138 \n", + "4 78.000 \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " yield_pearlmillet_future_ssp126_mpi \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " biomass_pearlmillet_future_ssp126_mpi \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_pearlmillet_future_ssp126_mpi yield_fonio_future_ssp126_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -15314,6 +17313,45 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", + " yield_pearlmillet_future_ssp126_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " biomass_pearlmillet_future_ssp126_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_pearlmillet_future_ssp126_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", " yield_sesame_future_ssp370_mpi biomass_sesame_future_ssp370_mpi \n", "0 NaN NaN \\\n", "1 NaN NaN \n", @@ -15353,18 +17391,44 @@ "10233 NaN \n", "10234 NaN \n", "\n", - " duration_groundnut_future_ssp126_mpi \n", - "0 NaN \\\n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " duration_groundnut_future_ssp126_mpi yield_plantain_future_ssp126_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_plantain_future_ssp126_mri \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_plantain_future_ssp126_mri \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", "\n", " yield_africaneggplant_future_ssp126_ipsl \n", "0 NaN \\\n", @@ -15444,44 +17508,57 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_tef_future_ssp126_ipsl biomass_tef_future_ssp126_ipsl \n", - "0 898.733 3329.033 \\\n", - "1 804.833 2981.033 \n", - "2 1002.167 3711.633 \n", - "3 999.633 3701.933 \n", - "4 927.433 3434.700 \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_plantain_future_ssp370_ipsl biomass_plantain_future_ssp370_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " duration_tef_future_ssp126_ipsl yield_cassava_future_ssp370_ipsl \n", - "0 91.0 NaN \\\n", - "1 91.0 NaN \n", - "2 91.0 NaN \n", - "3 91.0 NaN \n", - "4 91.0 NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_plantain_future_ssp370_ipsl yield_tef_future_ssp126_ipsl \n", + "0 NaN 1285.655 \\\n", + "1 NaN 1205.034 \n", + "2 NaN 1269.483 \n", + "3 NaN 1284.414 \n", + "4 NaN 1184.862 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_cassava_future_ssp370_ipsl \n", - "0 NaN \\\n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " biomass_tef_future_ssp126_ipsl duration_tef_future_ssp126_ipsl \n", + "0 4761.862 80.483 \\\n", + "1 4463.000 78.828 \n", + "2 4701.759 73.000 \n", + "3 4757.310 73.207 \n", + "4 4388.448 75.276 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_cassava_future_ssp370_ipsl biomass_cassava_future_ssp370_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " duration_cassava_future_ssp370_ipsl \n", "0 NaN \\\n", @@ -15535,18 +17612,44 @@ "10233 NaN \n", "10234 NaN \n", "\n", - " yield_sorghum_future_ssp370_gfdl biomass_sorghum_future_ssp370_gfdl \n", - "0 127.571 375.250 \\\n", - "1 122.750 360.929 \n", - "2 144.000 423.643 \n", - "3 135.679 399.286 \n", - "4 113.250 333.107 \n", - "... ... ... \n", - "10230 215.600 634.167 \n", - "10231 105.767 311.200 \n", - "10232 80.667 237.400 \n", - "10233 99.467 292.933 \n", - "10234 160.000 470.567 \n", + " yield_pumpkin_future_ssp126_mri biomass_pumpkin_future_ssp126_mri \n", + "0 505.867 3371.967 \\\n", + "1 426.400 2842.667 \n", + "2 409.533 2730.133 \n", + "3 433.600 2891.400 \n", + "4 430.833 2872.367 \n", + "... ... ... \n", + "10230 442.033 2946.400 \n", + "10231 442.900 2952.733 \n", + "10232 509.300 3395.467 \n", + "10233 554.600 3697.600 \n", + "10234 540.033 3600.633 \n", + "\n", + " duration_pumpkin_future_ssp126_mri yield_sorghum_future_ssp370_gfdl \n", + "0 211.967 127.571 \\\n", + "1 208.600 122.750 \n", + "2 171.367 144.000 \n", + "3 167.200 135.679 \n", + "4 164.367 113.250 \n", + "... ... ... \n", + "10230 117.833 215.600 \n", + "10231 117.833 105.767 \n", + "10232 120.500 80.667 \n", + "10233 125.900 99.467 \n", + "10234 120.967 160.000 \n", + "\n", + " biomass_sorghum_future_ssp370_gfdl \n", + "0 375.250 \\\n", + "1 360.929 \n", + "2 423.643 \n", + "3 399.286 \n", + "4 333.107 \n", + "... ... \n", + "10230 634.167 \n", + "10231 311.200 \n", + "10232 237.400 \n", + "10233 292.933 \n", + "10234 470.567 \n", "\n", " duration_sorghum_future_ssp370_gfdl \n", "0 57.821 \\\n", @@ -15848,11 +17951,11 @@ "10234 NaN NaN \n", "\n", " yield_tef_future_ssp370_gfdl biomass_tef_future_ssp370_gfdl \n", - "0 964.967 3573.633 \\\n", - "1 816.867 3025.267 \n", - "2 935.533 3465.200 \n", - "3 932.400 3453.200 \n", - "4 859.600 3183.767 \n", + "0 1282.552 4750.069 \\\n", + "1 1216.793 4506.862 \n", + "2 1273.862 4717.931 \n", + "3 1263.690 4680.000 \n", + "4 1157.759 4288.034 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -15861,11 +17964,11 @@ "10234 NaN NaN \n", "\n", " duration_tef_future_ssp370_gfdl yield_okra_historical_mri \n", - "0 91.0 NaN \\\n", - "1 91.0 NaN \n", - "2 91.0 NaN \n", - "3 91.0 NaN \n", - "4 91.0 NaN \n", + "0 80.069 NaN \\\n", + "1 78.069 NaN \n", + "2 72.759 NaN \n", + "3 73.000 NaN \n", + "4 74.759 NaN \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -15964,44 +18067,83 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_cocoyam_historical_mri biomass_cocoyam_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " duration_cocoyam_historical_mri yield_tomato_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN 156.733 \n", - "10231 NaN 27.033 \n", - "10232 NaN 44.733 \n", - "10233 NaN 220.767 \n", - "10234 NaN 75.033 \n", + " yield_pearlmillet_historical_mri biomass_pearlmillet_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_tomato_historical_mri duration_tomato_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 307.300 99.933 \n", - "10231 52.867 105.900 \n", - "10232 87.667 119.900 \n", - "10233 432.800 142.100 \n", - "10234 147.033 124.167 \n", + " duration_pearlmillet_historical_mri yield_cocoyam_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_cocoyam_historical_mri duration_cocoyam_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_tomato_historical_mri biomass_tomato_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 1598.759 3134.828 \n", + "10231 1585.897 3109.621 \n", + "10232 1807.034 3543.138 \n", + "10233 1973.172 3869.103 \n", + "10234 1979.276 3880.897 \n", + "\n", + " duration_tomato_historical_mri yield_plantain_historical_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 157.379 NaN \n", + "10231 158.207 NaN \n", + "10232 160.414 NaN \n", + "10233 161.000 NaN \n", + "10234 160.759 NaN \n", + "\n", + " biomass_plantain_historical_mpi duration_plantain_historical_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " yield_maize_historical_gfdl biomass_maize_historical_gfdl \n", "0 121.724 357.966 \\\n", @@ -16055,18 +18197,44 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " duration_lablab_historical_mpi yield_sesame_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_lablab_historical_mpi yield_pearlmillet_historical_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_pearlmillet_historical_ipsl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 NaN \n", + "10231 NaN \n", + "10232 NaN \n", + "10233 NaN \n", + "10234 NaN \n", + "\n", + " duration_pearlmillet_historical_ipsl yield_sesame_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " biomass_sesame_historical_mri duration_sesame_historical_mri \n", "0 NaN NaN \\\n", @@ -16159,44 +18327,57 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_yams_historical_mri biomass_yams_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_pumpkin_historical_mri biomass_pumpkin_historical_mri \n", + "0 499.033 3326.367 \\\n", + "1 428.533 2856.967 \n", + "2 421.300 2808.200 \n", + "3 448.467 2989.667 \n", + "4 428.567 2856.833 \n", + "... ... ... \n", + "10230 401.900 2679.300 \n", + "10231 405.700 2705.300 \n", + "10232 466.733 3111.467 \n", + "10233 512.333 3415.733 \n", + "10234 505.433 3370.367 \n", + "\n", + " duration_pumpkin_historical_mri yield_yams_historical_mri \n", + "0 213.833 NaN \\\n", + "1 212.233 NaN \n", + "2 195.800 NaN \n", + "3 193.933 NaN \n", + "4 190.700 NaN \n", + "... ... ... \n", + "10230 125.200 NaN \n", + "10231 125.667 NaN \n", + "10232 127.867 NaN \n", + "10233 134.300 NaN \n", + "10234 128.967 NaN \n", "\n", - " duration_yams_historical_mri yield_sweetpotato_historical_ipsl \n", - "0 NaN 774.000 \\\n", - "1 NaN 775.966 \n", - "2 NaN 1059.931 \n", - "3 NaN 1084.828 \n", - "4 NaN 880.897 \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " biomass_yams_historical_mri duration_yams_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_sweetpotato_historical_ipsl \n", - "0 1290.103 \\\n", - "1 1293.483 \n", - "2 1766.655 \n", - "3 1808.000 \n", - "4 1468.103 \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " yield_sweetpotato_historical_ipsl biomass_sweetpotato_historical_ipsl \n", + "0 774.000 1290.103 \\\n", + "1 775.966 1293.483 \n", + "2 1059.931 1766.655 \n", + "3 1084.828 1808.000 \n", + "4 880.897 1468.103 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " duration_sweetpotato_historical_ipsl yield_maize_historical_ipsl \n", "0 85.483 119.379 \\\n", @@ -16380,18 +18561,31 @@ "10233 NaN \n", "10234 NaN \n", "\n", - " duration_fingermillet_historical_mpi \n", - "0 NaN \\\n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " duration_fingermillet_historical_mpi yield_pumpkin_historical_ipsl \n", + "0 NaN 499.233 \\\n", + "1 NaN 445.133 \n", + "2 NaN 407.533 \n", + "3 NaN 425.533 \n", + "4 NaN 423.733 \n", + "... ... ... \n", + "10230 NaN 396.933 \n", + "10231 NaN 406.433 \n", + "10232 NaN 480.000 \n", + "10233 NaN 517.033 \n", + "10234 NaN 498.633 \n", + "\n", + " biomass_pumpkin_historical_ipsl duration_pumpkin_historical_ipsl \n", + "0 3328.167 211.933 \\\n", + "1 2967.833 209.600 \n", + "2 2716.533 189.233 \n", + "3 2836.233 184.700 \n", + "4 2824.767 183.233 \n", + "... ... ... \n", + "10230 2646.167 123.433 \n", + "10231 2709.333 123.700 \n", + "10232 3199.967 125.933 \n", + "10233 3446.533 132.533 \n", + "10234 3324.433 126.900 \n", "\n", " yield_fingermillet_historical_gfdl \n", "0 NaN \\\n", @@ -16588,44 +18782,57 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_groundnut_historical_ipsl biomass_groundnut_historical_ipsl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_pearlmillet_historical_gfdl biomass_pearlmillet_historical_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " duration_groundnut_historical_ipsl yield_josephscoat_historical_ipsl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_pearlmillet_historical_gfdl yield_groundnut_historical_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_josephscoat_historical_ipsl \n", - "0 NaN \\\n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " biomass_groundnut_historical_ipsl duration_groundnut_historical_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_josephscoat_historical_ipsl biomass_josephscoat_historical_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " duration_josephscoat_historical_ipsl yield_groundnut_historical_gfdl \n", "0 NaN NaN \\\n", @@ -16725,11 +18932,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 NaN 116.233 \n", - "10231 NaN 26.000 \n", - "10232 NaN 44.433 \n", - "10233 NaN 222.100 \n", - "10234 NaN 75.700 \n", + "10230 NaN 1346.690 \n", + "10231 NaN 1251.172 \n", + "10232 NaN 1397.828 \n", + "10233 NaN 1759.448 \n", + "10234 NaN 1702.345 \n", "\n", " biomass_tomato_historical_gfdl duration_tomato_historical_gfdl \n", "0 NaN NaN \\\n", @@ -16738,11 +18945,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 227.967 103.200 \n", - "10231 51.133 108.633 \n", - "10232 87.133 122.300 \n", - "10233 435.600 145.800 \n", - "10234 148.567 124.167 \n", + "10230 2640.345 158.448 \n", + "10231 2453.586 158.448 \n", + "10232 2740.931 160.690 \n", + "10233 3449.724 161.000 \n", + "10234 3337.793 161.000 \n", "\n", " yield_sorghum_historical_mpi biomass_sorghum_historical_mpi \n", "0 98.778 290.519 \\\n", @@ -16757,109 +18964,135 @@ "10233 59.267 174.267 \n", "10234 106.800 314.033 \n", "\n", - " duration_sorghum_historical_mpi yield_tef_historical_ipsl \n", - "0 58.889 744.833 \\\n", - "1 57.556 666.700 \n", - "2 55.556 840.033 \n", - "3 55.259 857.733 \n", - "4 54.259 810.200 \n", - "... ... ... \n", - "10230 106.000 NaN \n", - "10231 94.033 NaN \n", - "10232 89.100 NaN \n", - "10233 94.167 NaN \n", - "10234 97.067 NaN \n", - "\n", - " biomass_tef_historical_ipsl duration_tef_historical_ipsl \n", - "0 2758.767 91.0 \\\n", - "1 2469.333 91.0 \n", - "2 3111.033 91.0 \n", - "3 3176.800 91.0 \n", - "4 3000.967 91.0 \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " yield_soybean_historical_mpi biomass_soybean_historical_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " duration_soybean_historical_mpi yield_sorghum_historical_gfdl \n", - "0 NaN 93.963 \\\n", - "1 NaN 94.222 \n", - "2 NaN 123.704 \n", - "3 NaN 116.519 \n", - "4 NaN 89.259 \n", + " duration_sorghum_historical_mpi yield_plantain_historical_mri \n", + "0 58.889 NaN \\\n", + "1 57.556 NaN \n", + "2 55.556 NaN \n", + "3 55.259 NaN \n", + "4 54.259 NaN \n", "... ... ... \n", - "10230 NaN 164.567 \n", - "10231 NaN 71.333 \n", - "10232 NaN 53.000 \n", - "10233 NaN 65.800 \n", - "10234 NaN 118.133 \n", - "\n", - " biomass_sorghum_historical_gfdl duration_sorghum_historical_gfdl \n", - "0 276.519 58.407 \\\n", - "1 277.111 57.333 \n", - "2 363.741 55.852 \n", - "3 342.704 55.296 \n", - "4 262.630 54.185 \n", + "10230 106.000 NaN \n", + "10231 94.033 NaN \n", + "10232 89.100 NaN \n", + "10233 94.167 NaN \n", + "10234 97.067 NaN \n", + "\n", + " biomass_plantain_historical_mri duration_plantain_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", "... ... ... \n", - "10230 483.833 107.400 \n", - "10231 209.533 95.033 \n", - "10232 155.833 89.333 \n", - "10233 193.367 94.033 \n", - "10234 347.567 97.900 \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " yield_cowpea_historical_ipsl biomass_cowpea_historical_ipsl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_tef_historical_ipsl biomass_tef_historical_ipsl \n", + "0 1248.724 4625.138 \\\n", + "1 1199.379 4441.828 \n", + "2 1268.379 4697.517 \n", + "3 1282.448 4749.517 \n", + "4 1196.172 4430.414 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " duration_cowpea_historical_ipsl yield_grasspea_historical_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN 3453.276 \n", - "10231 NaN 2683.172 \n", - "10232 NaN 2470.207 \n", - "10233 NaN 2740.621 \n", - "10234 NaN 3084.897 \n", + " duration_tef_historical_ipsl yield_soybean_historical_mpi \n", + "0 84.414 NaN \\\n", + "1 82.345 NaN \n", + "2 76.552 NaN \n", + "3 76.828 NaN \n", + "4 78.793 NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_grasspea_historical_gfdl duration_grasspea_historical_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 7673.931 113.724 \n", - "10231 5962.586 107.862 \n", - "10232 5489.310 105.138 \n", - "10233 6090.310 108.517 \n", - "10234 6855.172 109.621 \n", + " biomass_soybean_historical_mpi duration_soybean_historical_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_sorghum_historical_gfdl biomass_sorghum_historical_gfdl \n", + "0 93.963 276.519 \\\n", + "1 94.222 277.111 \n", + "2 123.704 363.741 \n", + "3 116.519 342.704 \n", + "4 89.259 262.630 \n", + "... ... ... \n", + "10230 164.567 483.833 \n", + "10231 71.333 209.533 \n", + "10232 53.000 155.833 \n", + "10233 65.800 193.367 \n", + "10234 118.133 347.567 \n", + "\n", + " duration_sorghum_historical_gfdl yield_cowpea_historical_ipsl \n", + "0 58.407 NaN \\\n", + "1 57.333 NaN \n", + "2 55.852 NaN \n", + "3 55.296 NaN \n", + "4 54.185 NaN \n", + "... ... ... \n", + "10230 107.400 NaN \n", + "10231 95.033 NaN \n", + "10232 89.333 NaN \n", + "10233 94.033 NaN \n", + "10234 97.900 NaN \n", + "\n", + " biomass_cowpea_historical_ipsl duration_cowpea_historical_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_grasspea_historical_gfdl biomass_grasspea_historical_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 3453.276 7673.931 \n", + "10231 2683.172 5962.586 \n", + "10232 2470.207 5489.310 \n", + "10233 2740.621 6090.310 \n", + "10234 3084.897 6855.172 \n", + "\n", + " duration_grasspea_historical_gfdl \n", + "0 NaN \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "10230 113.724 \n", + "10231 107.862 \n", + "10232 105.138 \n", + "10233 108.517 \n", + "10234 109.621 \n", "\n", " yield_bambaragroundnut_historical_mri \n", "0 NaN \\\n", @@ -17108,31 +19341,70 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " yield_cowpea_historical_gfdl biomass_cowpea_historical_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_pearlmillet_historical_mpi biomass_pearlmillet_historical_mpi \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " duration_cowpea_historical_gfdl yield_soybean_historical_ipsl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_pearlmillet_historical_mpi yield_cowpea_historical_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_cowpea_historical_gfdl duration_cowpea_historical_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_pumpkin_historical_gfdl biomass_pumpkin_historical_gfdl \n", + "0 502.100 3347.333 \\\n", + "1 412.800 2752.367 \n", + "2 405.333 2702.533 \n", + "3 422.867 2820.000 \n", + "4 408.600 2723.867 \n", + "... ... ... \n", + "10230 387.700 2584.967 \n", + "10231 396.133 2640.800 \n", + "10232 455.167 3034.967 \n", + "10233 502.667 3350.800 \n", + "10234 480.000 3199.467 \n", + "\n", + " duration_pumpkin_historical_gfdl yield_soybean_historical_ipsl \n", + "0 213.033 NaN \\\n", + "1 210.767 NaN \n", + "2 190.000 NaN \n", + "3 187.667 NaN \n", + "4 184.667 NaN \n", + "... ... ... \n", + "10230 122.033 NaN \n", + "10231 122.033 NaN \n", + "10232 126.633 NaN \n", + "10233 133.567 NaN \n", + "10234 127.333 NaN \n", "\n", " biomass_soybean_historical_ipsl duration_soybean_historical_ipsl \n", "0 NaN NaN \\\n", @@ -17304,11 +19576,11 @@ "10234 NaN NaN \n", "\n", " duration_lablab_historical_gfdl yield_tef_historical_mpi \n", - "0 NaN 692.467 \\\n", - "1 NaN 609.167 \n", - "2 NaN 777.300 \n", - "3 NaN 791.800 \n", - "4 NaN 733.567 \n", + "0 NaN 1169.552 \\\n", + "1 NaN 1116.448 \n", + "2 NaN 1212.586 \n", + "3 NaN 1187.828 \n", + "4 NaN 1116.172 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -17317,11 +19589,11 @@ "10234 NaN NaN \n", "\n", " biomass_tef_historical_mpi duration_tef_historical_mpi \n", - "0 2564.400 91.0 \\\n", - "1 2256.300 91.0 \n", - "2 2878.867 91.0 \n", - "3 2932.667 91.0 \n", - "4 2716.767 91.0 \n", + "0 4331.517 85.897 \\\n", + "1 4135.103 83.655 \n", + "2 4490.931 78.207 \n", + "3 4399.241 78.000 \n", + "4 4133.966 79.690 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -17375,11 +19647,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 157.267 308.467 \n", - "10231 31.600 62.033 \n", - "10232 31.633 61.900 \n", - "10233 145.367 285.067 \n", - "10234 80.633 158.200 \n", + "10230 1449.793 2842.690 \n", + "10231 1413.448 2771.379 \n", + "10232 1421.483 2787.034 \n", + "10233 1813.931 3556.483 \n", + "10234 1824.724 3577.897 \n", "\n", " duration_tomato_historical_ipsl yield_africaneggplant_historical_gfdl \n", "0 NaN NaN \\\n", @@ -17388,11 +19660,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 101.933 NaN \n", - "10231 106.467 NaN \n", - "10232 116.600 NaN \n", - "10233 141.700 NaN \n", - "10234 120.833 NaN \n", + "10230 158.966 NaN \n", + "10231 158.172 NaN \n", + "10232 159.552 NaN \n", + "10233 161.000 NaN \n", + "10234 160.517 NaN \n", "\n", " biomass_africaneggplant_historical_gfdl \n", "0 NaN \\\n", @@ -17407,72 +19679,33 @@ "10233 NaN \n", "10234 NaN \n", "\n", - " duration_africaneggplant_historical_gfdl yield_cassava_historical_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", + " duration_africaneggplant_historical_gfdl yield_pumpkin_historical_mpi \n", + "0 NaN 508.600 \\\n", + "1 NaN 429.500 \n", + "2 NaN 420.933 \n", + "3 NaN 440.967 \n", + "4 NaN 420.933 \n", "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " biomass_cassava_historical_mpi duration_cassava_historical_mpi \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", + "10230 NaN 362.733 \n", + "10231 NaN 374.833 \n", + "10232 NaN 458.067 \n", + "10233 NaN 495.667 \n", + "10234 NaN 476.200 \n", + "\n", + " biomass_pumpkin_historical_mpi duration_pumpkin_historical_mpi \n", + "0 3390.667 213.033 \\\n", + "1 2863.067 210.700 \n", + "2 2805.733 197.267 \n", + "3 2940.433 193.067 \n", + "4 2806.367 188.933 \n", "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " yield_fonio_historical_mri biomass_fonio_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " duration_fonio_historical_mri yield_tef_historical_mri \n", - "0 NaN 671.567 \\\n", - "1 NaN 591.633 \n", - "2 NaN 782.067 \n", - "3 NaN 797.533 \n", - "4 NaN 752.167 \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " biomass_tef_historical_mri duration_tef_historical_mri \n", - "0 2487.400 91.0 \\\n", - "1 2191.033 91.0 \n", - "2 2896.433 91.0 \n", - "3 2953.767 91.0 \n", - "4 2785.933 91.0 \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + "10230 2418.267 122.167 \n", + "10231 2498.367 122.367 \n", + "10232 3053.833 126.733 \n", + "10233 3304.500 133.433 \n", + "10234 3174.400 126.500 \n", "\n", - " yield_soybean_historical_mri biomass_soybean_historical_mri \n", + " yield_cassava_historical_mpi biomass_cassava_historical_mpi \n", "0 NaN NaN \\\n", "1 NaN NaN \n", "2 NaN NaN \n", @@ -17485,85 +19718,137 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " duration_soybean_historical_mri yield_grasspea_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN 3460.345 \n", - "10231 NaN 2667.793 \n", - "10232 NaN 2518.931 \n", - "10233 NaN 2807.310 \n", - "10234 NaN 3212.759 \n", - "\n", - " biomass_grasspea_historical_mri duration_grasspea_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 7689.448 115.310 \n", - "10231 5928.552 108.483 \n", - "10232 5597.655 105.517 \n", - "10233 6238.448 109.138 \n", - "10234 7139.414 110.276 \n", + " duration_cassava_historical_mpi yield_fonio_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " yield_taro_historical_gfdl biomass_taro_historical_gfdl \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " biomass_fonio_historical_mri duration_fonio_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " duration_taro_historical_gfdl yield_lablab_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_tef_historical_mri biomass_tef_historical_mri \n", + "0 1169.517 4331.655 \\\n", + "1 1119.621 4146.828 \n", + "2 1206.828 4469.552 \n", + "3 1222.759 4528.552 \n", + "4 1149.034 4256.000 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_lablab_historical_mri duration_lablab_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " duration_tef_historical_mri yield_soybean_historical_mri \n", + "0 85.931 NaN \\\n", + "1 83.586 NaN \n", + "2 78.379 NaN \n", + "3 78.379 NaN \n", + "4 80.000 NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " yield_africaneggplant_historical_mri \n", - "0 NaN \\\n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "... ... \n", - "10230 NaN \n", - "10231 NaN \n", - "10232 NaN \n", - "10233 NaN \n", - "10234 NaN \n", + " biomass_soybean_historical_mri duration_soybean_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_africaneggplant_historical_mri \n", + " yield_grasspea_historical_mri biomass_grasspea_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 3460.345 7689.448 \n", + "10231 2667.793 5928.552 \n", + "10232 2518.931 5597.655 \n", + "10233 2807.310 6238.448 \n", + "10234 3212.759 7139.414 \n", + "\n", + " duration_grasspea_historical_mri yield_taro_historical_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 115.310 NaN \n", + "10231 108.483 NaN \n", + "10232 105.517 NaN \n", + "10233 109.138 NaN \n", + "10234 110.276 NaN \n", + "\n", + " biomass_taro_historical_gfdl duration_taro_historical_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_lablab_historical_mri biomass_lablab_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_lablab_historical_mri yield_africaneggplant_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " biomass_africaneggplant_historical_mri \n", "0 NaN \\\n", "1 NaN \n", "2 NaN \n", @@ -17603,11 +19888,11 @@ "10234 NaN NaN \n", "\n", " duration_fingermillet_historical_mri yield_tef_historical_gfdl \n", - "0 NaN 702.567 \\\n", - "1 NaN 614.767 \n", - "2 NaN 802.833 \n", - "3 NaN 816.167 \n", - "4 NaN 771.233 \n", + "0 NaN 1179.034 \\\n", + "1 NaN 1130.379 \n", + "2 NaN 1225.207 \n", + "3 NaN 1207.103 \n", + "4 NaN 1123.517 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -17616,11 +19901,11 @@ "10234 NaN NaN \n", "\n", " biomass_tef_historical_gfdl duration_tef_historical_gfdl \n", - "0 2602.500 91.0 \\\n", - "1 2277.067 91.0 \n", - "2 2973.033 91.0 \n", - "3 3022.667 91.0 \n", - "4 2856.700 91.0 \n", + "0 4366.655 85.414 \\\n", + "1 4186.552 82.862 \n", + "2 4537.724 78.069 \n", + "3 4470.828 77.931 \n", + "4 4161.241 79.517 \n", "... ... ... \n", "10230 NaN NaN \n", "10231 NaN NaN \n", @@ -17687,11 +19972,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 NaN 119.433 \n", - "10231 NaN 33.300 \n", - "10232 NaN 111.967 \n", - "10233 NaN 211.733 \n", - "10234 NaN 112.167 \n", + "10230 NaN 1376.379 \n", + "10231 NaN 1275.034 \n", + "10232 NaN 1557.172 \n", + "10233 NaN 1651.172 \n", + "10234 NaN 1820.759 \n", "\n", " biomass_tomato_historical_mpi duration_tomato_historical_mpi \n", "0 NaN NaN \\\n", @@ -17700,11 +19985,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "10230 234.267 102.933 \n", - "10231 65.133 108.133 \n", - "10232 219.633 122.533 \n", - "10233 415.200 144.167 \n", - "10234 219.800 124.000 \n", + "10230 2698.828 161.0 \n", + "10231 2500.172 161.0 \n", + "10232 3053.241 161.0 \n", + "10233 3237.690 161.0 \n", + "10234 3570.103 161.0 \n", "\n", " yield_lablab_historical_ipsl biomass_lablab_historical_ipsl \n", "0 NaN NaN \\\n", @@ -17797,18 +20082,57 @@ "10233 2695.862 5990.931 \n", "10234 3032.621 6739.034 \n", "\n", - " duration_grasspea_historical_mpi yield_mungbean_historical_mri \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 113.690 NaN \n", - "10231 107.862 NaN \n", - "10232 105.517 NaN \n", - "10233 108.759 NaN \n", - "10234 109.966 NaN \n", + " duration_grasspea_historical_mpi yield_plantain_historical_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 113.690 NaN \n", + "10231 107.862 NaN \n", + "10232 105.517 NaN \n", + "10233 108.759 NaN \n", + "10234 109.966 NaN \n", + "\n", + " biomass_plantain_historical_ipsl duration_plantain_historical_ipsl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " yield_plantain_historical_gfdl biomass_plantain_historical_gfdl \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_plantain_historical_gfdl yield_mungbean_historical_mri \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", " biomass_mungbean_historical_mri duration_mungbean_historical_mri \n", "0 NaN NaN \\\n", @@ -17849,10 +20173,10 @@ "10233 NaN \n", "10234 NaN \n", "\n", - "[10235 rows x 829 columns]" + "[10235 rows x 937 columns]" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -17863,7 +20187,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -17873,426 +20197,480 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " super().__setitem__(key, value)\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1543: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " super().__setitem__(key, value)\n" ] } @@ -18303,7 +20681,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -18328,213 +20706,240 @@ " \n", " \n", " geometry\n", - " yield_yams_historical\n", - " biomass_soybean_future_ssp126\n", - " duration_sesame_future_ssp370\n", - " yield_cocoyam_future_ssp370\n", - " yield_mungbean_future_ssp126\n", - " duration_okra_future_ssp126\n", - " yield_pigeonpea_historical\n", + " yield_africaneggplant_future_ssp126\n", + " duration_pearlmillet_historical\n", + " biomass_grasspea_historical\n", + " duration_plantain_future_ssp370\n", + " biomass_tef_future_ssp126\n", + " yield_fingermillet_historical\n", " biomass_pigeonpea_historical\n", - " duration_pigeonpea_future_ssp370\n", - " duration_mungbean_historical\n", - " biomass_pigeonpea_future_ssp126\n", - " yield_cocoyam_future_ssp126\n", - " duration_mungbean_future_ssp370\n", - " yield_sweetpotato_historical\n", " yield_yams_future_ssp370\n", - " yield_soybean_future_ssp126\n", - " biomass_maize_future_ssp126\n", - " duration_tomato_future_ssp126\n", - " duration_fingermillet_historical\n", - " duration_maize_historical\n", - " duration_groundnut_future_ssp126\n", - " duration_maize_future_ssp370\n", + " duration_groundnut_historical\n", + " duration_taro_future_ssp370\n", + " yield_tomato_future_ssp126\n", + " duration_josephscoat_future_ssp126\n", + " biomass_yams_historical\n", " duration_cassava_future_ssp126\n", - " duration_sorghum_future_ssp370\n", - " biomass_pigeonpea_future_ssp370\n", - " biomass_sesame_future_ssp370\n", - " biomass_sweetpotato_future_ssp126\n", - " duration_okra_historical\n", - " biomass_cocoyam_future_ssp370\n", - " duration_cassava_future_ssp370\n", - " yield_fingermillet_future_ssp126\n", - " biomass_tef_future_ssp370\n", - " biomass_cassava_historical\n", - " biomass_soybean_future_ssp370\n", - " yield_sorghum_future_ssp370\n", - " biomass_cocoyam_historical\n", - " yield_groundnut_historical\n", - " biomass_josephscoat_future_ssp126\n", - " biomass_cocoyam_future_ssp126\n", - " yield_africaneggplant_historical\n", + " yield_maize_future_ssp126\n", + " yield_fonio_future_ssp126\n", + " duration_sesame_future_ssp126\n", + " duration_sesame_historical\n", + " yield_pigeonpea_future_ssp126\n", + " yield_fonio_historical\n", + " duration_plantain_future_ssp126\n", + " duration_fonio_historical\n", + " biomass_maize_future_ssp126\n", + " biomass_africaneggplant_future_ssp126\n", + " biomass_mungbean_future_ssp126\n", + " yield_pumpkin_future_ssp370\n", + " duration_fonio_future_ssp126\n", + " duration_sorghum_future_ssp126\n", " biomass_josephscoat_future_ssp370\n", - " duration_africaneggplant_future_ssp370\n", - " duration_africaneggplant_historical\n", - " biomass_cassava_future_ssp126\n", - " duration_yams_future_ssp370\n", - " biomass_fonio_future_ssp370\n", - " duration_fingermillet_future_ssp126\n", - " yield_bambaragroundnut_future_ssp370\n", - " biomass_yams_future_ssp126\n", - " biomass_tef_future_ssp126\n", - " yield_tef_historical\n", - " duration_cocoyam_future_ssp126\n", - " yield_tef_future_ssp370\n", + " biomass_maize_future_ssp370\n", + " yield_pearlmillet_future_ssp370\n", + " biomass_groundnut_future_ssp126\n", " duration_cowpea_future_ssp126\n", - " duration_sorghum_historical\n", - " biomass_sweetpotato_future_ssp370\n", - " yield_josephscoat_future_ssp370\n", - " duration_josephscoat_historical\n", - " yield_sesame_future_ssp126\n", - " biomass_okra_future_ssp370\n", - " duration_soybean_historical\n", " biomass_taro_future_ssp370\n", - " yield_sesame_future_ssp370\n", + " duration_lablab_future_ssp126\n", + " biomass_pigeonpea_future_ssp370\n", + " duration_mungbean_historical\n", + " biomass_mungbean_historical\n", + " biomass_sorghum_future_ssp126\n", + " biomass_pearlmillet_historical\n", + " yield_soybean_future_ssp126\n", + " biomass_plantain_historical\n", + " duration_pumpkin_historical\n", + " yield_grasspea_historical\n", + " biomass_pigeonpea_future_ssp126\n", + " duration_pigeonpea_future_ssp370\n", + " yield_cowpea_future_ssp370\n", + " duration_grasspea_future_ssp370\n", + " biomass_tomato_historical\n", + " biomass_soybean_future_ssp370\n", + " yield_pearlmillet_future_ssp126\n", + " biomass_groundnut_historical\n", + " biomass_sweetpotato_future_ssp126\n", + " yield_cowpea_historical\n", + " duration_cocoyam_historical\n", + " duration_cassava_historical\n", " biomass_sorghum_historical\n", - " duration_tef_future_ssp126\n", - " duration_tef_historical\n", - " biomass_cassava_future_ssp370\n", + " duration_josephscoat_historical\n", + " biomass_tomato_future_ssp126\n", + " duration_pigeonpea_future_ssp126\n", + " duration_sorghum_future_ssp370\n", + " yield_fingermillet_future_ssp370\n", + " duration_groundnut_future_ssp126\n", + " duration_fingermillet_future_ssp126\n", " yield_soybean_future_ssp370\n", - " yield_okra_historical\n", - " duration_cocoyam_future_ssp370\n", - " biomass_soybean_historical\n", - " yield_cowpea_historical\n", - " biomass_africaneggplant_future_ssp126\n", - " biomass_sesame_historical\n", - " duration_lablab_future_ssp370\n", - " biomass_grasspea_historical\n", - " duration_grasspea_historical\n", - " yield_pigeonpea_future_ssp126\n", - " duration_fonio_future_ssp370\n", - " yield_grasspea_future_ssp370\n", - " yield_pigeonpea_future_ssp370\n", - " duration_grasspea_future_ssp126\n", - " duration_fingermillet_future_ssp370\n", - " yield_cassava_future_ssp126\n", - " biomass_africaneggplant_historical\n", + " duration_tomato_future_ssp126\n", + " yield_okra_future_ssp370\n", + " yield_plantain_historical\n", + " duration_maize_historical\n", + " biomass_groundnut_future_ssp370\n", + " biomass_bambaragroundnut_historical\n", + " duration_mungbean_future_ssp126\n", + " biomass_tef_future_ssp370\n", " duration_tef_future_ssp370\n", - " biomass_grasspea_future_ssp370\n", - " duration_groundnut_historical\n", - " duration_bambaragroundnut_historical\n", + " biomass_yams_future_ssp370\n", + " biomass_bambaragroundnut_future_ssp370\n", + " yield_cocoyam_future_ssp370\n", + " biomass_sweetpotato_historical\n", + " yield_sorghum_historical\n", + " yield_fingermillet_future_ssp126\n", + " duration_fonio_future_ssp370\n", + " biomass_sweetpotato_future_ssp370\n", + " biomass_pumpkin_future_ssp370\n", + " yield_bambaragroundnut_future_ssp370\n", + " duration_sesame_future_ssp370\n", + " yield_cassava_future_ssp370\n", " yield_grasspea_future_ssp126\n", - " duration_africaneggplant_future_ssp126\n", - " yield_cassava_future_ssp370\n", - " biomass_yams_historical\n", + " yield_tef_future_ssp370\n", + " yield_taro_future_ssp126\n", + " biomass_africaneggplant_future_ssp370\n", + " duration_bambaragroundnut_historical\n", + " yield_yams_future_ssp126\n", + " yield_lablab_future_ssp126\n", + " duration_lablab_future_ssp370\n", + " duration_sweetpotato_future_ssp126\n", + " yield_pumpkin_historical\n", + " biomass_plantain_future_ssp370\n", + " biomass_cassava_historical\n", + " yield_lablab_future_ssp370\n", + " duration_cassava_future_ssp370\n", + " yield_pigeonpea_historical\n", + " biomass_soybean_future_ssp126\n", + " biomass_pearlmillet_future_ssp126\n", + " biomass_okra_historical\n", + " yield_sesame_historical\n", + " biomass_yams_future_ssp126\n", + " biomass_cocoyam_future_ssp126\n", + " yield_josephscoat_future_ssp126\n", + " biomass_bambaragroundnut_future_ssp126\n", + " duration_yams_historical\n", " yield_taro_future_ssp370\n", - " biomass_fonio_future_ssp126\n", - " biomass_mungbean_historical\n", - " yield_cowpea_future_ssp370\n", + " biomass_fingermillet_future_ssp126\n", + " yield_sweetpotato_historical\n", + " biomass_tomato_future_ssp370\n", + " biomass_lablab_future_ssp370\n", + " duration_africaneggplant_historical\n", " duration_sweetpotato_future_ssp370\n", - " yield_bambaragroundnut_historical\n", - " yield_maize_future_ssp126\n", - " yield_africaneggplant_future_ssp370\n", + " duration_pearlmillet_future_ssp126\n", + " yield_yams_historical\n", + " biomass_fingermillet_future_ssp370\n", + " yield_groundnut_future_ssp126\n", + " biomass_josephscoat_historical\n", " yield_maize_historical\n", - " yield_soybean_historical\n", - " duration_mungbean_future_ssp126\n", - " biomass_fingermillet_future_ssp126\n", - " duration_sesame_future_ssp126\n", - " biomass_fonio_historical\n", - " biomass_okra_future_ssp126\n", - " duration_cocoyam_historical\n", + " duration_bambaragroundnut_future_ssp370\n", + " yield_groundnut_historical\n", + " duration_cowpea_historical\n", + " biomass_taro_historical\n", + " yield_okra_historical\n", " yield_tomato_historical\n", + " biomass_soybean_historical\n", + " biomass_cowpea_historical\n", + " duration_cocoyam_future_ssp126\n", + " yield_groundnut_future_ssp370\n", + " yield_mungbean_future_ssp370\n", + " duration_okra_historical\n", + " yield_tef_future_ssp126\n", + " yield_africaneggplant_future_ssp370\n", " duration_taro_historical\n", + " biomass_plantain_future_ssp126\n", + " yield_cassava_future_ssp126\n", + " yield_cassava_historical\n", + " duration_pearlmillet_future_ssp370\n", + " biomass_grasspea_future_ssp126\n", + " biomass_pearlmillet_future_ssp370\n", + " duration_pumpkin_future_ssp126\n", + " yield_tomato_future_ssp370\n", + " yield_sweetpotato_future_ssp370\n", + " biomass_cassava_future_ssp126\n", + " duration_yams_future_ssp126\n", + " biomass_fonio_future_ssp370\n", " biomass_maize_historical\n", - " duration_lablab_historical\n", - " biomass_yams_future_ssp370\n", - " biomass_bambaragroundnut_future_ssp370\n", " yield_mungbean_historical\n", - " biomass_mungbean_future_ssp370\n", - " yield_tef_future_ssp126\n", - " biomass_lablab_future_ssp126\n", - " biomass_josephscoat_historical\n", + " duration_plantain_historical\n", " duration_tomato_future_ssp370\n", - " biomass_fingermillet_future_ssp370\n", - " yield_cassava_historical\n", - " duration_fonio_future_ssp126\n", - " biomass_tomato_historical\n", - " yield_africaneggplant_future_ssp126\n", - " biomass_lablab_historical\n", - " biomass_groundnut_historical\n", - " biomass_bambaragroundnut_future_ssp126\n", - " yield_cocoyam_historical\n", + " duration_soybean_future_ssp370\n", + " biomass_cocoyam_historical\n", + " biomass_grasspea_future_ssp370\n", + " yield_cowpea_future_ssp126\n", + " biomass_okra_future_ssp370\n", + " duration_bambaragroundnut_future_ssp126\n", + " yield_pearlmillet_historical\n", + " duration_tef_historical\n", + " yield_mungbean_future_ssp126\n", + " biomass_sesame_future_ssp370\n", + " biomass_cassava_future_ssp370\n", + " duration_africaneggplant_future_ssp370\n", " biomass_tef_historical\n", - " yield_fingermillet_historical\n", - " yield_fingermillet_future_ssp370\n", - " yield_taro_future_ssp126\n", - " duration_josephscoat_future_ssp370\n", - " biomass_tomato_future_ssp126\n", - " duration_cowpea_historical\n", - " yield_groundnut_future_ssp370\n", - " duration_lablab_future_ssp126\n", - " duration_bambaragroundnut_future_ssp370\n", - " biomass_tomato_future_ssp370\n", - " yield_sweetpotato_future_ssp370\n", - " yield_josephscoat_historical\n", - " duration_fonio_historical\n", - " yield_tomato_future_ssp370\n", - " duration_tomato_historical\n", - " yield_lablab_future_ssp370\n", - " duration_sweetpotato_future_ssp126\n", - " yield_okra_future_ssp370\n", - " yield_fonio_future_ssp126\n", - " biomass_cowpea_future_ssp126\n", - " duration_taro_future_ssp126\n", - " yield_okra_future_ssp126\n", - " yield_fonio_historical\n", - " duration_groundnut_future_ssp370\n", - " biomass_fingermillet_historical\n", - " yield_yams_future_ssp126\n", - " yield_grasspea_historical\n", - " biomass_sesame_future_ssp126\n", - " biomass_grasspea_future_ssp126\n", - " biomass_groundnut_future_ssp370\n", - " duration_josephscoat_future_ssp126\n", - " duration_pigeonpea_future_ssp126\n", - " yield_sorghum_future_ssp126\n", - " biomass_cowpea_future_ssp370\n", - " biomass_taro_historical\n", - " yield_mungbean_future_ssp370\n", - " biomass_sweetpotato_historical\n", - " yield_tomato_future_ssp126\n", + " biomass_lablab_future_ssp126\n", + " biomass_fonio_future_ssp126\n", + " yield_tef_historical\n", + " duration_cocoyam_future_ssp370\n", + " yield_cocoyam_historical\n", + " yield_pigeonpea_future_ssp370\n", + " yield_sesame_future_ssp370\n", + " duration_pumpkin_future_ssp370\n", + " duration_yams_future_ssp370\n", " yield_fonio_future_ssp370\n", + " yield_plantain_future_ssp126\n", + " yield_africaneggplant_historical\n", + " biomass_africaneggplant_historical\n", + " yield_okra_future_ssp126\n", + " duration_tef_future_ssp126\n", + " yield_pumpkin_future_ssp126\n", + " yield_sweetpotato_future_ssp126\n", + " duration_grasspea_future_ssp126\n", + " duration_pigeonpea_historical\n", " duration_maize_future_ssp126\n", + " duration_okra_future_ssp126\n", + " yield_bambaragroundnut_historical\n", + " duration_taro_future_ssp126\n", + " biomass_cocoyam_future_ssp370\n", + " biomass_cowpea_future_ssp126\n", " yield_bambaragroundnut_future_ssp126\n", - " biomass_okra_historical\n", - " duration_pigeonpea_historical\n", - " biomass_sorghum_future_ssp370\n", - " yield_sorghum_historical\n", - " biomass_taro_future_ssp126\n", - " yield_maize_future_ssp370\n", - " duration_yams_future_ssp126\n", - " duration_grasspea_future_ssp370\n", - " duration_soybean_future_ssp370\n", - " yield_cowpea_future_ssp126\n", - " yield_lablab_future_ssp126\n", - " duration_cowpea_future_ssp370\n", - " duration_yams_historical\n", - " duration_taro_future_ssp370\n", - " biomass_mungbean_future_ssp126\n", - " yield_sweetpotato_future_ssp126\n", - " biomass_groundnut_future_ssp126\n", - " duration_cassava_historical\n", - " duration_sesame_historical\n", - " biomass_lablab_future_ssp370\n", - " duration_soybean_future_ssp126\n", - " duration_bambaragroundnut_future_ssp126\n", - " biomass_sorghum_future_ssp126\n", - " biomass_africaneggplant_future_ssp370\n", - " duration_sorghum_future_ssp126\n", - " yield_groundnut_future_ssp126\n", + " duration_sorghum_historical\n", + " yield_cocoyam_future_ssp126\n", + " biomass_cowpea_future_ssp370\n", + " biomass_sesame_historical\n", " duration_sweetpotato_historical\n", - " yield_sesame_historical\n", - " biomass_cowpea_historical\n", + " yield_sorghum_future_ssp126\n", + " duration_africaneggplant_future_ssp126\n", + " yield_lablab_historical\n", + " biomass_lablab_historical\n", + " yield_sesame_future_ssp126\n", " yield_taro_historical\n", - " biomass_bambaragroundnut_historical\n", + " yield_sorghum_future_ssp370\n", + " biomass_taro_future_ssp126\n", + " yield_josephscoat_historical\n", + " biomass_pumpkin_historical\n", + " duration_lablab_historical\n", + " biomass_fingermillet_historical\n", + " biomass_okra_future_ssp126\n", + " duration_mungbean_future_ssp370\n", + " duration_tomato_historical\n", + " yield_maize_future_ssp370\n", + " duration_fingermillet_future_ssp370\n", " duration_okra_future_ssp370\n", - " yield_lablab_historical\n", - " biomass_maize_future_ssp370\n", - " yield_josephscoat_future_ssp126\n", + " yield_soybean_historical\n", + " duration_soybean_historical\n", + " biomass_mungbean_future_ssp370\n", + " yield_plantain_future_ssp370\n", + " biomass_sorghum_future_ssp370\n", + " biomass_pumpkin_future_ssp126\n", + " yield_grasspea_future_ssp370\n", + " duration_fingermillet_historical\n", + " biomass_fonio_historical\n", + " yield_josephscoat_future_ssp370\n", + " duration_groundnut_future_ssp370\n", + " biomass_josephscoat_future_ssp126\n", + " duration_josephscoat_future_ssp370\n", + " duration_soybean_future_ssp126\n", + " biomass_sesame_future_ssp126\n", + " duration_grasspea_historical\n", + " duration_cowpea_future_ssp370\n", + " duration_maize_future_ssp370\n", " \n", " \n", " \n", @@ -18545,6 +20950,7 @@ " NaN\n", " NaN\n", " NaN\n", + " 4606.052\n", " NaN\n", " NaN\n", " NaN\n", @@ -18554,59 +20960,53 @@ " NaN\n", " NaN\n", " NaN\n", - " 671.448\n", + " 143.724\n", " NaN\n", " NaN\n", - " 422.716\n", " NaN\n", " NaN\n", - " 82.690\n", " NaN\n", - " 78.414\n", " NaN\n", - " 56.938\n", " NaN\n", + " 422.716\n", " NaN\n", - " 1404.819\n", " NaN\n", + " 514.975\n", " NaN\n", + " 56.956\n", " NaN\n", + " 496.052\n", " NaN\n", - " 3604.033\n", " NaN\n", " NaN\n", - " 130.116\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 320.402\n", " NaN\n", " NaN\n", " NaN\n", + " 212.958\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 3159.784\n", - " 702.858\n", " NaN\n", - " 973.117\n", " NaN\n", - " 58.694\n", - " 1637.362\n", " NaN\n", + " 1404.819\n", " NaN\n", " NaN\n", " NaN\n", + " 290.806\n", " NaN\n", " NaN\n", " NaN\n", - " 290.806\n", - " 91.0\n", - " 91.0\n", + " 56.938\n", " NaN\n", " NaN\n", " NaN\n", @@ -18614,23 +21014,34 @@ " NaN\n", " NaN\n", " NaN\n", + " 82.690\n", + " NaN\n", " NaN\n", " NaN\n", + " 4754.354\n", + " 79.620\n", " NaN\n", " NaN\n", " NaN\n", + " 1119.138\n", + " 98.852\n", + " NaN\n", " NaN\n", + " 1637.362\n", + " 3433.242\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1283.690\n", " NaN\n", " NaN\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 82.078\n", + " 502.242\n", " NaN\n", " NaN\n", " NaN\n", @@ -18638,11 +21049,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 80.707\n", " NaN\n", - " 143.724\n", " NaN\n", - " 135.060\n", " NaN\n", " NaN\n", " NaN\n", @@ -18650,16 +21058,17 @@ " NaN\n", " NaN\n", " NaN\n", + " 671.448\n", " NaN\n", " NaN\n", - " 397.146\n", " NaN\n", + " 80.707\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 853.100\n", " NaN\n", + " 135.060\n", " NaN\n", " NaN\n", " NaN\n", @@ -18671,8 +21080,8 @@ " NaN\n", " NaN\n", " NaN\n", - " 2603.267\n", " NaN\n", + " 1243.655\n", " NaN\n", " NaN\n", " NaN\n", @@ -18681,14 +21090,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 210.584\n", " NaN\n", " 982.414\n", " NaN\n", " NaN\n", " NaN\n", + " 397.146\n", " NaN\n", " NaN\n", - " 82.078\n", " NaN\n", " NaN\n", " NaN\n", @@ -18697,66 +21107,67 @@ " NaN\n", " NaN\n", " NaN\n", + " 85.414\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 4413.741\n", " NaN\n", " NaN\n", + " 1191.707\n", " NaN\n", - " 108.938\n", " NaN\n", " NaN\n", " NaN\n", - " 1119.138\n", + " 202.867\n", " NaN\n", " NaN\n", - " 79.259\n", " NaN\n", " NaN\n", " NaN\n", - " 382.660\n", - " 98.852\n", " NaN\n", - " 168.612\n", + " 81.526\n", + " 504.575\n", + " 842.922\n", " NaN\n", " NaN\n", + " 79.259\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 58.694\n", " NaN\n", - " 842.922\n", " NaN\n", " NaN\n", + " 85.258\n", + " 108.938\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 320.402\n", " NaN\n", - " 56.956\n", + " 130.116\n", " NaN\n", - " 85.258\n", " NaN\n", + " 3348.134\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 496.052\n", + " 168.612\n", " NaN\n", - " \n", - " \n", - " 1\n", - " POINT (19.75000 -34.25000)\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 382.660\n", + " 3363.758\n", " NaN\n", " NaN\n", " NaN\n", @@ -18765,30 +21176,29 @@ " NaN\n", " NaN\n", " NaN\n", - " 671.328\n", " NaN\n", " NaN\n", - " 360.466\n", " NaN\n", + " 78.414\n", + " \n", + " \n", + " 1\n", + " POINT (19.75000 -34.25000)\n", " NaN\n", - " 80.948\n", " NaN\n", - " 77.060\n", " NaN\n", - " 55.902\n", " NaN\n", + " 4383.354\n", " NaN\n", - " 1333.086\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 3071.992\n", " NaN\n", " NaN\n", - " 120.080\n", " NaN\n", " NaN\n", + " 122.595\n", " NaN\n", " NaN\n", " NaN\n", @@ -18796,29 +21206,27 @@ " NaN\n", " NaN\n", " NaN\n", + " 360.466\n", " NaN\n", " NaN\n", + " 432.992\n", " NaN\n", + " 55.768\n", " NaN\n", + " 421.474\n", " NaN\n", - " 2768.675\n", - " 620.567\n", " NaN\n", - " 829.417\n", " NaN\n", - " 57.315\n", - " 1547.845\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 287.928\n", " NaN\n", " NaN\n", - " 271.889\n", - " 91.0\n", - " 91.0\n", " NaN\n", + " 210.825\n", " NaN\n", " NaN\n", " NaN\n", @@ -18828,48 +21236,55 @@ " NaN\n", " NaN\n", " NaN\n", + " 1333.086\n", " NaN\n", " NaN\n", " NaN\n", + " 271.889\n", " NaN\n", " NaN\n", " NaN\n", + " 55.902\n", " NaN\n", " NaN\n", " NaN\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 80.948\n", " NaN\n", " NaN\n", " NaN\n", + " 4527.957\n", + " 77.603\n", " NaN\n", " NaN\n", " NaN\n", + " 1118.897\n", + " 92.454\n", " NaN\n", - " 78.000\n", " NaN\n", - " 122.595\n", + " 1547.845\n", + " 2886.384\n", " NaN\n", - " 104.276\n", " NaN\n", " NaN\n", " NaN\n", + " 1222.509\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 306.716\n", + " 79.181\n", + " 428.992\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 747.484\n", " NaN\n", " NaN\n", " NaN\n", @@ -18881,29 +21296,30 @@ " NaN\n", " NaN\n", " NaN\n", + " 671.328\n", " NaN\n", - " 2298.433\n", " NaN\n", " NaN\n", + " 78.000\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 104.276\n", " NaN\n", " NaN\n", " NaN\n", - " 928.716\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 79.181\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1183.491\n", " NaN\n", " NaN\n", " NaN\n", @@ -18912,92 +21328,88 @@ " NaN\n", " NaN\n", " NaN\n", + " 206.692\n", " NaN\n", + " 928.716\n", " NaN\n", " NaN\n", - " 97.884\n", " NaN\n", + " 306.716\n", " NaN\n", " NaN\n", - " 1118.897\n", " NaN\n", " NaN\n", - " 77.750\n", " NaN\n", " NaN\n", " NaN\n", - " 353.125\n", - " 92.454\n", " NaN\n", - " 143.319\n", " NaN\n", " NaN\n", + " 83.112\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 4227.578\n", " NaN\n", " NaN\n", + " 1141.457\n", " NaN\n", - " 799.836\n", " NaN\n", " NaN\n", " NaN\n", + " 195.534\n", " NaN\n", " NaN\n", " NaN\n", - " 287.928\n", " NaN\n", - " 55.768\n", " NaN\n", - " 82.422\n", " NaN\n", + " 79.474\n", + " 420.250\n", + " 799.836\n", " NaN\n", " NaN\n", + " 77.750\n", " NaN\n", " NaN\n", " NaN\n", - " 421.474\n", " NaN\n", - " \n", - " \n", - " 2\n", - " POINT (20.25000 -34.25000)\n", " NaN\n", " NaN\n", + " 57.315\n", " NaN\n", " NaN\n", " NaN\n", + " 82.422\n", + " 97.884\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 120.080\n", " NaN\n", " NaN\n", + " 2860.058\n", " NaN\n", - " 943.285\n", " NaN\n", " NaN\n", - " 659.776\n", " NaN\n", " NaN\n", - " 77.543\n", + " 143.319\n", " NaN\n", - " 74.035\n", " NaN\n", - " 54.589\n", " NaN\n", " NaN\n", - " 1699.759\n", " NaN\n", " NaN\n", + " 353.125\n", + " 2801.358\n", " NaN\n", " NaN\n", - " 3622.617\n", " NaN\n", " NaN\n", - " 156.938\n", " NaN\n", " NaN\n", " NaN\n", @@ -19005,38 +21417,41 @@ " NaN\n", " NaN\n", " NaN\n", + " 77.060\n", + " \n", + " \n", + " 2\n", + " POINT (20.25000 -34.25000)\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 4612.242\n", " NaN\n", " NaN\n", " NaN\n", - " 3430.108\n", - " 800.558\n", " NaN\n", - " 978.133\n", " NaN\n", - " 55.528\n", - " 1969.440\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 224.319\n", " NaN\n", " NaN\n", " NaN\n", - " 361.556\n", - " 91.0\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 659.776\n", " NaN\n", " NaN\n", + " 408.792\n", " NaN\n", + " 53.866\n", " NaN\n", + " 823.664\n", " NaN\n", " NaN\n", " NaN\n", @@ -19045,10 +21460,11 @@ " NaN\n", " NaN\n", " NaN\n", + " 367.250\n", " NaN\n", " NaN\n", " NaN\n", - " 91.0\n", + " 193.075\n", " NaN\n", " NaN\n", " NaN\n", @@ -19058,15 +21474,15 @@ " NaN\n", " NaN\n", " NaN\n", + " 1699.759\n", " NaN\n", " NaN\n", - " 75.500\n", " NaN\n", - " 224.319\n", + " 361.556\n", " NaN\n", - " 198.819\n", " NaN\n", " NaN\n", + " 54.589\n", " NaN\n", " NaN\n", " NaN\n", @@ -19074,27 +21490,34 @@ " NaN\n", " NaN\n", " NaN\n", - " 584.733\n", + " 77.543\n", " NaN\n", " NaN\n", " NaN\n", + " 4779.457\n", + " 72.380\n", " NaN\n", " NaN\n", - " 926.125\n", " NaN\n", + " 1572.250\n", + " 122.954\n", " NaN\n", " NaN\n", + " 1969.440\n", + " 2725.075\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1290.474\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 2964.841\n", " NaN\n", + " 75.957\n", + " 413.775\n", " NaN\n", " NaN\n", " NaN\n", @@ -19104,132 +21527,125 @@ " NaN\n", " NaN\n", " NaN\n", - " 1181.672\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 75.957\n", " NaN\n", " NaN\n", + " 943.285\n", " NaN\n", " NaN\n", " NaN\n", + " 75.500\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 198.819\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 124.893\n", " NaN\n", " NaN\n", " NaN\n", - " 1572.250\n", " NaN\n", " NaN\n", - " 73.974\n", " NaN\n", " NaN\n", + " 1245.319\n", " NaN\n", - " 461.589\n", - " 122.954\n", " NaN\n", - " 280.008\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 166.834\n", " NaN\n", + " 1181.672\n", " NaN\n", " NaN\n", - " 1019.862\n", " NaN\n", + " 584.733\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 367.250\n", " NaN\n", - " 53.866\n", " NaN\n", - " 79.819\n", " NaN\n", " NaN\n", " NaN\n", + " 77.802\n", " NaN\n", " NaN\n", " NaN\n", - " 823.664\n", " NaN\n", - " \n", - " \n", - " 3\n", - " POINT (20.75000 -34.25000)\n", + " 4548.931\n", " NaN\n", " NaN\n", + " 1228.250\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 154.242\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 74.060\n", + " 391.858\n", + " 1019.862\n", " NaN\n", - " 968.336\n", " NaN\n", + " 73.974\n", " NaN\n", - " 744.845\n", " NaN\n", " NaN\n", - " 77.767\n", " NaN\n", - " 74.440\n", " NaN\n", - " 54.205\n", " NaN\n", + " 55.528\n", " NaN\n", - " 1700.164\n", " NaN\n", " NaN\n", + " 79.819\n", + " 124.893\n", " NaN\n", " NaN\n", - " 3590.200\n", " NaN\n", " NaN\n", - " 153.072\n", " NaN\n", + " 156.938\n", " NaN\n", " NaN\n", + " 2758.250\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 280.008\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 3435.842\n", - " 815.808\n", + " 461.589\n", + " 2612.167\n", " NaN\n", - " 969.350\n", " NaN\n", - " 55.139\n", - " 2051.991\n", " NaN\n", " NaN\n", " NaN\n", @@ -19237,15 +21653,18 @@ " NaN\n", " NaN\n", " NaN\n", - " 348.463\n", - " 91.0\n", - " 91.0\n", " NaN\n", " NaN\n", + " 74.035\n", + " \n", + " \n", + " 3\n", + " POINT (20.75000 -34.25000)\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 4607.457\n", " NaN\n", " NaN\n", " NaN\n", @@ -19255,77 +21674,88 @@ " NaN\n", " NaN\n", " NaN\n", + " 253.259\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", + " 744.845\n", " NaN\n", " NaN\n", + " 439.034\n", " NaN\n", + " 53.482\n", " NaN\n", + " 881.681\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 76.267\n", " NaN\n", - " 253.259\n", " NaN\n", - " 236.966\n", " NaN\n", " NaN\n", + " 352.000\n", " NaN\n", " NaN\n", " NaN\n", + " 189.842\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 697.164\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 927.650\n", + " 1700.164\n", " NaN\n", " NaN\n", " NaN\n", + " 348.463\n", " NaN\n", " NaN\n", " NaN\n", + " 54.205\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 3021.475\n", " NaN\n", + " 77.767\n", " NaN\n", " NaN\n", " NaN\n", + " 4755.569\n", + " 72.569\n", " NaN\n", " NaN\n", " NaN\n", + " 1613.888\n", + " 118.454\n", " NaN\n", " NaN\n", + " 2051.991\n", + " 2926.908\n", " NaN\n", - " 1231.198\n", " NaN\n", " NaN\n", " NaN\n", + " 1284.052\n", " NaN\n", " NaN\n", - " 76.250\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 76.250\n", + " 434.458\n", " NaN\n", " NaN\n", " NaN\n", @@ -19337,58 +21767,50 @@ " NaN\n", " NaN\n", " NaN\n", - " 119.732\n", " NaN\n", " NaN\n", " NaN\n", - " 1613.888\n", " NaN\n", " NaN\n", - " 74.233\n", + " 968.336\n", " NaN\n", " NaN\n", " NaN\n", - " 450.357\n", - " 118.454\n", + " 76.267\n", " NaN\n", - " 299.767\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 236.966\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 1020.095\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 352.000\n", " NaN\n", - " 53.482\n", + " 1244.034\n", " NaN\n", - " 80.224\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 881.681\n", " NaN\n", - " \n", - " \n", - " 4\n", - " POINT (21.25000 -34.25000)\n", + " 165.025\n", " NaN\n", + " 1231.198\n", " NaN\n", " NaN\n", " NaN\n", + " 697.164\n", " NaN\n", " NaN\n", " NaN\n", @@ -19398,66 +21820,68 @@ " NaN\n", " NaN\n", " NaN\n", - " 794.121\n", " NaN\n", + " 77.784\n", " NaN\n", - " 580.931\n", " NaN\n", " NaN\n", - " 77.362\n", " NaN\n", - " 74.690\n", + " 4537.034\n", " NaN\n", - " 53.357\n", " NaN\n", + " 1225.034\n", " NaN\n", - " 1410.232\n", " NaN\n", " NaN\n", " NaN\n", + " 152.342\n", " NaN\n", - " 3320.442\n", " NaN\n", " NaN\n", - " 120.170\n", " NaN\n", " NaN\n", " NaN\n", + " 74.207\n", + " 423.083\n", + " 1020.095\n", + " NaN\n", + " NaN\n", + " 74.233\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 55.139\n", " NaN\n", " NaN\n", " NaN\n", + " 80.224\n", + " 119.732\n", " NaN\n", " NaN\n", - " 3180.008\n", - " 766.792\n", " NaN\n", - " 896.500\n", " NaN\n", - " 53.861\n", - " 1725.077\n", " NaN\n", + " 153.072\n", " NaN\n", " NaN\n", + " 2896.583\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 261.824\n", - " 91.0\n", - " 91.0\n", " NaN\n", + " 299.767\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 450.357\n", + " 2820.808\n", " NaN\n", " NaN\n", " NaN\n", @@ -19469,11 +21893,16 @@ " NaN\n", " NaN\n", " NaN\n", + " 74.440\n", + " \n", + " \n", + " 4\n", + " POINT (21.25000 -34.25000)\n", " NaN\n", - " 91.0\n", " NaN\n", " NaN\n", " NaN\n", + " 4271.733\n", " NaN\n", " NaN\n", " NaN\n", @@ -19482,87 +21911,93 @@ " NaN\n", " NaN\n", " NaN\n", - " 76.172\n", " NaN\n", " 197.492\n", " NaN\n", - " 172.638\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 580.931\n", " NaN\n", " NaN\n", + " 434.825\n", " NaN\n", - " 507.776\n", + " 52.312\n", " NaN\n", + " 753.854\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 858.617\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 266.750\n", " NaN\n", " NaN\n", " NaN\n", + " 186.883\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 2840.092\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1410.232\n", " NaN\n", " NaN\n", " NaN\n", + " 261.824\n", " NaN\n", " NaN\n", " NaN\n", - " 1035.034\n", + " 53.357\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 75.793\n", " NaN\n", " NaN\n", + " 77.362\n", " NaN\n", " NaN\n", " NaN\n", + " 4390.250\n", + " 74.509\n", " NaN\n", " NaN\n", " NaN\n", + " 1323.448\n", + " 89.009\n", " NaN\n", " NaN\n", + " 1725.077\n", + " 2898.917\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1185.345\n", " NaN\n", - " 90.768\n", " NaN\n", " NaN\n", " NaN\n", - " 1323.448\n", " NaN\n", " NaN\n", - " 74.259\n", + " 75.793\n", + " 420.458\n", " NaN\n", " NaN\n", " NaN\n", - " 353.393\n", - " 89.009\n", " NaN\n", - " 256.310\n", " NaN\n", " NaN\n", " NaN\n", @@ -19572,29 +22007,161 @@ " NaN\n", " NaN\n", " NaN\n", - " 846.078\n", " NaN\n", " NaN\n", " NaN\n", + " 794.121\n", " NaN\n", " NaN\n", " NaN\n", - " 266.750\n", + " 76.172\n", " NaN\n", - " 52.312\n", " NaN\n", - " 79.810\n", " NaN\n", " NaN\n", " NaN\n", + " 172.638\n", " NaN\n", " NaN\n", " NaN\n", - " 753.854\n", " NaN\n", - " \n", - " \n", - " ...\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1153.327\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 163.734\n", + " NaN\n", + " 1035.034\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 507.776\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 79.500\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 4245.405\n", + " NaN\n", + " NaN\n", + " 1146.224\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 151.517\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 75.879\n", + " 419.858\n", + " 846.078\n", + " NaN\n", + " NaN\n", + " 74.259\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 53.861\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 79.810\n", + " 90.768\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 120.170\n", + " NaN\n", + " NaN\n", + " 2802.958\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 256.310\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 353.393\n", + " 2799.100\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 74.690\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " ...\n", " ...\n", " ...\n", @@ -19809,6 +22376,7 @@ " POINT (9.25000 36.75000)\n", " NaN\n", " NaN\n", + " 7535.638\n", " NaN\n", " NaN\n", " NaN\n", @@ -19816,6 +22384,7 @@ " NaN\n", " NaN\n", " NaN\n", + " 949.862\n", " NaN\n", " NaN\n", " NaN\n", @@ -19824,15 +22393,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 87.059\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 100.642\n", " NaN\n", + " 432.342\n", " NaN\n", + " 103.834\n", " NaN\n", " NaN\n", " NaN\n", @@ -19841,15 +22410,19 @@ " NaN\n", " NaN\n", " NaN\n", - " 199.408\n", " NaN\n", " NaN\n", + " 594.017\n", " NaN\n", " NaN\n", " NaN\n", + " 123.208\n", + " 3391.043\n", " NaN\n", " NaN\n", " NaN\n", + " 107.233\n", + " 2829.173\n", " NaN\n", " NaN\n", " NaN\n", @@ -19857,46 +22430,46 @@ " NaN\n", " NaN\n", " NaN\n", + " 448.542\n", " NaN\n", + " 1862.491\n", " NaN\n", + " 100.642\n", " NaN\n", " NaN\n", - " 107.283\n", " NaN\n", " NaN\n", + " 137.983\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 448.542\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 152.542\n", " NaN\n", " NaN\n", " NaN\n", + " 2882.534\n", " NaN\n", " NaN\n", - " 7535.638\n", - " 114.198\n", " NaN\n", + " 3747.198\n", " NaN\n", - " 3767.216\n", " NaN\n", - " 109.603\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 8371.603\n", " NaN\n", " NaN\n", - " 3747.198\n", + " 387.316\n", " NaN\n", " NaN\n", " NaN\n", @@ -19914,9 +22487,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 1378.491\n", " NaN\n", " NaN\n", - " 137.416\n", " NaN\n", " NaN\n", " NaN\n", @@ -19927,11 +22500,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 82.675\n", " NaN\n", " NaN\n", + " 1442.905\n", " NaN\n", - " 269.500\n", " NaN\n", " NaN\n", " NaN\n", @@ -19942,54 +22514,51 @@ " NaN\n", " NaN\n", " NaN\n", - " 257.767\n", " NaN\n", " NaN\n", + " 8327.198\n", " NaN\n", + " 115.566\n", + " 703.078\n", " NaN\n", - " 291.042\n", " NaN\n", " NaN\n", " NaN\n", - " 148.442\n", - " 102.000\n", " NaN\n", " NaN\n", " NaN\n", + " 120.681\n", " NaN\n", " NaN\n", + " 8371.603\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 3391.043\n", " NaN\n", - " 8327.198\n", " NaN\n", " NaN\n", " NaN\n", - " 201.942\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 131.459\n", " NaN\n", " NaN\n", " NaN\n", + " 113.083\n", " NaN\n", " NaN\n", - " 586.408\n", - " 152.542\n", " NaN\n", " NaN\n", " NaN\n", - " 107.233\n", " NaN\n", " NaN\n", + " 407.116\n", " NaN\n", + " 109.603\n", " NaN\n", " NaN\n", " NaN\n", @@ -19998,27 +22567,26 @@ " NaN\n", " NaN\n", " NaN\n", + " 107.283\n", " NaN\n", " NaN\n", " NaN\n", - " 594.017\n", " NaN\n", - " 103.834\n", + " 201.942\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 199.408\n", " NaN\n", " NaN\n", + " 2582.175\n", " NaN\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 10231\n", - " POINT (9.75000 36.75000)\n", " NaN\n", + " 158.948\n", " NaN\n", " NaN\n", " NaN\n", @@ -20026,6 +22594,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 586.408\n", + " 2713.700\n", + " 3767.216\n", " NaN\n", " NaN\n", " NaN\n", @@ -20034,25 +22605,29 @@ " NaN\n", " NaN\n", " NaN\n", + " 114.198\n", " NaN\n", - " 90.542\n", " NaN\n", + " \n", + " \n", + " 10231\n", + " POINT (9.75000 36.75000)\n", " NaN\n", " NaN\n", + " 5904.319\n", " NaN\n", " NaN\n", - " 88.542\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 925.017\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 91.175\n", " NaN\n", " NaN\n", " NaN\n", @@ -20062,7 +22637,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 430.167\n", " NaN\n", + " 92.366\n", " NaN\n", " NaN\n", " NaN\n", @@ -20072,52 +22649,56 @@ " NaN\n", " NaN\n", " NaN\n", - " 95.133\n", " NaN\n", + " 270.442\n", " NaN\n", " NaN\n", " NaN\n", + " 123.442\n", + " 2656.966\n", " NaN\n", " NaN\n", " NaN\n", + " 101.552\n", + " 2708.690\n", " NaN\n", - " 203.375\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 203.375\n", " NaN\n", + " 1813.793\n", " NaN\n", + " 88.542\n", " NaN\n", " NaN\n", " NaN\n", - " 5904.319\n", - " 108.112\n", " NaN\n", + " 135.638\n", " NaN\n", - " 2949.052\n", " NaN\n", - " 104.302\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 6553.457\n", " NaN\n", " NaN\n", - " 2968.853\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 69.150\n", " NaN\n", " NaN\n", " NaN\n", + " 2867.425\n", " NaN\n", " NaN\n", " NaN\n", + " 2968.853\n", " NaN\n", " NaN\n", " NaN\n", @@ -20126,8 +22707,8 @@ " NaN\n", " NaN\n", " NaN\n", + " 395.775\n", " NaN\n", - " 29.483\n", " NaN\n", " NaN\n", " NaN\n", @@ -20138,14 +22719,13 @@ " NaN\n", " NaN\n", " NaN\n", - " 85.733\n", " NaN\n", " NaN\n", " NaN\n", - " 57.792\n", " NaN\n", " NaN\n", " NaN\n", + " 1329.362\n", " NaN\n", " NaN\n", " NaN\n", @@ -20153,17 +22733,14 @@ " NaN\n", " NaN\n", " NaN\n", - " 60.675\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 66.316\n", " NaN\n", " NaN\n", " NaN\n", - " 33.808\n", - " 107.283\n", + " 1381.388\n", " NaN\n", " NaN\n", " NaN\n", @@ -20175,29 +22752,27 @@ " NaN\n", " NaN\n", " NaN\n", - " 2656.966\n", " NaN\n", - " 6597.578\n", " NaN\n", + " 6597.578\n", " NaN\n", + " 115.616\n", + " 678.017\n", " NaN\n", - " 91.941\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 30.942\n", " NaN\n", " NaN\n", + " 117.198\n", " NaN\n", " NaN\n", + " 6553.457\n", " NaN\n", - " 268.183\n", - " 69.150\n", " NaN\n", " NaN\n", " NaN\n", - " 101.552\n", " NaN\n", " NaN\n", " NaN\n", @@ -20211,65 +22786,73 @@ " NaN\n", " NaN\n", " NaN\n", + " 112.850\n", " NaN\n", - " 270.442\n", " NaN\n", - " 92.366\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 411.708\n", " NaN\n", + " 104.302\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 10232\n", - " POINT (10.25000 36.75000)\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 95.133\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 91.941\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 91.175\n", " NaN\n", " NaN\n", + " 2638.450\n", " NaN\n", " NaN\n", - " 99.208\n", " NaN\n", " NaN\n", + " 158.957\n", " NaN\n", " NaN\n", " NaN\n", - " 83.633\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 268.183\n", + " 2745.116\n", + " 2949.052\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 75.175\n", " NaN\n", " NaN\n", + " 108.112\n", " NaN\n", " NaN\n", + " \n", + " \n", + " 10232\n", + " POINT (10.25000 36.75000)\n", " NaN\n", " NaN\n", + " 5578.017\n", " NaN\n", " NaN\n", " NaN\n", @@ -20277,13 +22860,13 @@ " NaN\n", " NaN\n", " NaN\n", + " 1118.724\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 89.525\n", " NaN\n", " NaN\n", " NaN\n", @@ -20292,8 +22875,9 @@ " NaN\n", " NaN\n", " NaN\n", - " 174.075\n", + " 513.942\n", " NaN\n", + " 86.792\n", " NaN\n", " NaN\n", " NaN\n", @@ -20304,31 +22888,34 @@ " NaN\n", " NaN\n", " NaN\n", - " 5578.017\n", - " 105.422\n", + " 214.408\n", " NaN\n", " NaN\n", - " 2801.966\n", " NaN\n", - " 102.112\n", + " 126.792\n", + " 2510.069\n", " NaN\n", " NaN\n", " NaN\n", + " 99.983\n", + " 3031.086\n", " NaN\n", - " 6226.655\n", " NaN\n", " NaN\n", - " 2765.672\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 174.075\n", " NaN\n", + " 2193.690\n", " NaN\n", + " 83.633\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 143.370\n", " NaN\n", " NaN\n", " NaN\n", @@ -20338,47 +22925,45 @@ " NaN\n", " NaN\n", " NaN\n", - " 58.192\n", " NaN\n", " NaN\n", " NaN\n", + " 59.192\n", " NaN\n", " NaN\n", " NaN\n", + " 3426.350\n", " NaN\n", " NaN\n", " NaN\n", + " 2765.672\n", " NaN\n", - " 94.425\n", " NaN\n", " NaN\n", " NaN\n", - " 114.083\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 464.992\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 32.483\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 39.408\n", " NaN\n", " NaN\n", " NaN\n", - " 20.050\n", - " 120.333\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 1798.672\n", " NaN\n", " NaN\n", " NaN\n", @@ -20386,34 +22971,31 @@ " NaN\n", " NaN\n", " NaN\n", - " 2510.069\n", " NaN\n", - " 6145.785\n", " NaN\n", " NaN\n", " NaN\n", - " 72.942\n", " NaN\n", " NaN\n", " NaN\n", + " 1545.879\n", " NaN\n", - " 16.550\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 221.134\n", - " 59.192\n", " NaN\n", " NaN\n", " NaN\n", - " 99.983\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 6145.785\n", " NaN\n", + " 119.575\n", + " 917.319\n", " NaN\n", " NaN\n", " NaN\n", @@ -20421,11 +23003,11 @@ " NaN\n", " NaN\n", " NaN\n", + " 129.138\n", " NaN\n", " NaN\n", - " 214.408\n", + " 6226.655\n", " NaN\n", - " 86.792\n", " NaN\n", " NaN\n", " NaN\n", @@ -20436,16 +23018,13 @@ " NaN\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 10233\n", - " POINT (10.75000 36.75000)\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 117.483\n", " NaN\n", " NaN\n", " NaN\n", @@ -20453,33 +23032,37 @@ " NaN\n", " NaN\n", " NaN\n", + " 486.725\n", " NaN\n", + " 102.112\n", " NaN\n", " NaN\n", " NaN\n", - " 114.292\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 88.525\n", + " 89.525\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 72.942\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 75.175\n", " NaN\n", - " 90.017\n", " NaN\n", + " 3100.058\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 160.414\n", " NaN\n", " NaN\n", " NaN\n", @@ -20487,6 +23070,39 @@ " NaN\n", " NaN\n", " NaN\n", + " 221.134\n", + " 3244.942\n", + " 2801.966\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 105.422\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 10233\n", + " POINT (10.75000 36.75000)\n", + " NaN\n", + " NaN\n", + " 6151.215\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1631.120\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -20494,10 +23110,35 @@ " NaN\n", " NaN\n", " NaN\n", - " 94.575\n", " NaN\n", " NaN\n", " NaN\n", + " 557.984\n", + " NaN\n", + " 91.442\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 257.258\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 133.458\n", + " 2768.026\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 103.259\n", + " 3528.250\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -20505,28 +23146,32 @@ " NaN\n", " 200.708\n", " NaN\n", + " 3198.267\n", + " NaN\n", + " 88.525\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 157.870\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 6151.215\n", - " 108.750\n", " NaN\n", " NaN\n", - " 3083.810\n", " NaN\n", - " 105.327\n", " NaN\n", " NaN\n", + " 68.242\n", " NaN\n", " NaN\n", - " 6852.879\n", + " NaN\n", + " 3720.142\n", + " NaN\n", " NaN\n", " NaN\n", " 3043.948\n", @@ -20538,6 +23183,7 @@ " NaN\n", " NaN\n", " NaN\n", + " 506.925\n", " NaN\n", " NaN\n", " NaN\n", @@ -20549,22 +23195,20 @@ " NaN\n", " NaN\n", " NaN\n", - " 199.992\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 2932.448\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 106.908\n", " NaN\n", " NaN\n", " NaN\n", - " 392.167\n", " NaN\n", " NaN\n", " NaN\n", @@ -20572,24 +23216,24 @@ " NaN\n", " NaN\n", " NaN\n", + " 1799.431\n", " NaN\n", " NaN\n", " NaN\n", - " 115.050\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 119.050\n", " NaN\n", " NaN\n", " NaN\n", - " 60.700\n", - " 143.442\n", " NaN\n", " NaN\n", " NaN\n", + " 6764.242\n", " NaN\n", + " 125.483\n", + " 1495.552\n", " NaN\n", " NaN\n", " NaN\n", @@ -20597,31 +23241,28 @@ " NaN\n", " NaN\n", " NaN\n", - " 2768.026\n", + " 152.060\n", " NaN\n", - " 6764.242\n", + " NaN\n", + " 6852.879\n", " NaN\n", " NaN\n", " NaN\n", - " 87.483\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 58.658\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 264.800\n", - " 68.242\n", " NaN\n", " NaN\n", " NaN\n", - " 103.259\n", " NaN\n", " NaN\n", + " 123.342\n", " NaN\n", " NaN\n", " NaN\n", @@ -20629,30 +23270,65 @@ " NaN\n", " NaN\n", " NaN\n", + " 529.392\n", " NaN\n", + " 105.327\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 257.258\n", " NaN\n", - " 91.442\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 94.575\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 87.483\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 90.017\n", + " NaN\n", + " NaN\n", + " 3379.391\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 161.000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 264.800\n", + " 3529.283\n", + " 3083.810\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 108.750\n", + " NaN\n", + " NaN\n", " \n", " \n", " 10234\n", " POINT (9.75000 37.25000)\n", " NaN\n", " NaN\n", + " 6929.465\n", " NaN\n", " NaN\n", " NaN\n", @@ -20660,6 +23336,7 @@ " NaN\n", " NaN\n", " NaN\n", + " 1352.741\n", " NaN\n", " NaN\n", " NaN\n", @@ -20668,15 +23345,15 @@ " NaN\n", " NaN\n", " NaN\n", - " 101.183\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 92.216\n", " NaN\n", + " 540.934\n", " NaN\n", + " 95.034\n", " NaN\n", " NaN\n", " NaN\n", @@ -20685,15 +23362,19 @@ " NaN\n", " NaN\n", " NaN\n", - " 153.216\n", " NaN\n", " NaN\n", + " 444.225\n", " NaN\n", " NaN\n", " NaN\n", + " 127.425\n", + " 3118.285\n", " NaN\n", " NaN\n", " NaN\n", + " 104.207\n", + " 3591.672\n", " NaN\n", " NaN\n", " NaN\n", @@ -20701,46 +23382,46 @@ " NaN\n", " NaN\n", " NaN\n", + " 353.034\n", " NaN\n", + " 2652.431\n", " NaN\n", + " 92.216\n", " NaN\n", " NaN\n", - " 97.992\n", " NaN\n", " NaN\n", + " 147.888\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 353.034\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " 120.033\n", " NaN\n", " NaN\n", " NaN\n", + " 3606.067\n", " NaN\n", " NaN\n", - " 6929.465\n", - " 109.880\n", " NaN\n", + " 3426.845\n", " NaN\n", - " 3463.534\n", " NaN\n", - " 106.284\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 7696.853\n", " NaN\n", " NaN\n", - " 3426.845\n", + " 490.066\n", " NaN\n", " NaN\n", " NaN\n", @@ -20758,9 +23439,9 @@ " NaN\n", " NaN\n", " NaN\n", + " 2432.310\n", " NaN\n", " NaN\n", - " 85.883\n", " NaN\n", " NaN\n", " NaN\n", @@ -20771,11 +23452,10 @@ " NaN\n", " NaN\n", " NaN\n", - " 96.808\n", " NaN\n", " NaN\n", + " 1831.776\n", " NaN\n", - " 168.400\n", " NaN\n", " NaN\n", " NaN\n", @@ -20786,53 +23466,51 @@ " NaN\n", " NaN\n", " NaN\n", - " 88.650\n", " NaN\n", " NaN\n", + " 7615.190\n", " NaN\n", + " 119.725\n", + " 1240.457\n", " NaN\n", - " 105.842\n", " NaN\n", " NaN\n", " NaN\n", - " 53.933\n", - " 123.292\n", " NaN\n", " NaN\n", " NaN\n", + " 136.457\n", " NaN\n", " NaN\n", + " 7696.853\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 3118.285\n", " NaN\n", - " 7615.190\n", " NaN\n", " NaN\n", " NaN\n", - " 151.000\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 45.200\n", " NaN\n", " NaN\n", " NaN\n", + " 118.058\n", " NaN\n", " NaN\n", - " 450.633\n", - " 120.033\n", " NaN\n", " NaN\n", " NaN\n", - " 104.207\n", " NaN\n", " NaN\n", + " 510.983\n", + " NaN\n", + " 106.284\n", " NaN\n", " NaN\n", " NaN\n", @@ -20841,13 +23519,36 @@ " NaN\n", " NaN\n", " NaN\n", + " 97.992\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 151.000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 153.216\n", + " NaN\n", + " NaN\n", + " 3267.167\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 160.819\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 444.225\n", " NaN\n", - " 95.034\n", + " NaN\n", + " NaN\n", + " 450.633\n", + " 3406.383\n", + " 3463.534\n", " NaN\n", " NaN\n", " NaN\n", @@ -20856,185 +23557,355 @@ " NaN\n", " NaN\n", " NaN\n", + " 109.880\n", " NaN\n", " NaN\n", " \n", " \n", "\n", - "

10235 rows × 208 columns

\n", + "

10235 rows × 235 columns

\n", "" ], "text/plain": [ - " geometry yield_yams_historical \n", - "0 POINT (19.25000 -34.25000) NaN \\\n", - "1 POINT (19.75000 -34.25000) NaN \n", - "2 POINT (20.25000 -34.25000) NaN \n", - "3 POINT (20.75000 -34.25000) NaN \n", - "4 POINT (21.25000 -34.25000) NaN \n", - "... ... ... \n", - "10230 POINT (9.25000 36.75000) NaN \n", - "10231 POINT (9.75000 36.75000) NaN \n", - "10232 POINT (10.25000 36.75000) NaN \n", - "10233 POINT (10.75000 36.75000) NaN \n", - "10234 POINT (9.75000 37.25000) NaN \n", + " geometry yield_africaneggplant_future_ssp126 \n", + "0 POINT (19.25000 -34.25000) NaN \\\n", + "1 POINT (19.75000 -34.25000) NaN \n", + "2 POINT (20.25000 -34.25000) NaN \n", + "3 POINT (20.75000 -34.25000) NaN \n", + "4 POINT (21.25000 -34.25000) NaN \n", + "... ... ... \n", + "10230 POINT (9.25000 36.75000) NaN \n", + "10231 POINT (9.75000 36.75000) NaN \n", + "10232 POINT (10.25000 36.75000) NaN \n", + "10233 POINT (10.75000 36.75000) NaN \n", + "10234 POINT (9.75000 37.25000) NaN \n", + "\n", + " duration_pearlmillet_historical biomass_grasspea_historical \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN 7535.638 \n", + "10231 NaN 5904.319 \n", + "10232 NaN 5578.017 \n", + "10233 NaN 6151.215 \n", + "10234 NaN 6929.465 \n", + "\n", + " duration_plantain_future_ssp370 biomass_tef_future_ssp126 \n", + "0 NaN 4606.052 \\\n", + "1 NaN 4383.354 \n", + "2 NaN 4612.242 \n", + "3 NaN 4607.457 \n", + "4 NaN 4271.733 \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_soybean_future_ssp126 duration_sesame_future_ssp370 \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_fingermillet_historical biomass_pigeonpea_historical \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " yield_cocoyam_future_ssp370 yield_mungbean_future_ssp126 \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_yams_future_ssp370 duration_groundnut_historical \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " duration_okra_future_ssp126 yield_pigeonpea_historical \n", + " duration_taro_future_ssp370 yield_tomato_future_ssp126 \n", "0 NaN NaN \\\n", "1 NaN NaN 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+ "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_pigeonpea_historical duration_pigeonpea_future_ssp370 \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " duration_mungbean_historical biomass_pigeonpea_future_ssp126 \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", - "\n", - " yield_cocoyam_future_ssp126 duration_mungbean_future_ssp370 \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... 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+ "10232 NaN 126.792 \n", + "10233 NaN 133.458 \n", + "10234 NaN 127.425 \n", + "\n", + " yield_grasspea_historical biomass_pigeonpea_future_ssp126 \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 3391.043 NaN \n", + "10231 2656.966 NaN \n", + "10232 2510.069 NaN \n", + "10233 2768.026 NaN \n", + "10234 3118.285 NaN \n", + "\n", + " duration_pigeonpea_future_ssp370 yield_cowpea_future_ssp370 \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_grasspea_future_ssp370 biomass_tomato_historical \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 107.233 2829.173 \n", + "10231 101.552 2708.690 \n", + "10232 99.983 3031.086 \n", + "10233 103.259 3528.250 \n", + "10234 104.207 3591.672 \n", + "\n", + " biomass_soybean_future_ssp370 yield_pearlmillet_future_ssp126 \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", "\n", - " biomass_sesame_future_ssp370 biomass_sweetpotato_future_ssp126 \n", + " biomass_groundnut_historical biomass_sweetpotato_future_ssp126 \n", "0 NaN 1404.819 \\\n", "1 NaN 1333.086 \n", "2 NaN 1699.759 \n", @@ -21047,64 +23918,142 @@ "10233 NaN NaN \n", "10234 NaN NaN \n", "\n", - " duration_okra_historical biomass_cocoyam_future_ssp370 \n", - "0 NaN NaN \\\n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "10230 NaN NaN \n", - "10231 NaN NaN \n", - "10232 NaN NaN \n", - "10233 NaN NaN \n", - "10234 NaN NaN \n", + " yield_cowpea_historical duration_cocoyam_historical \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN NaN \n", + "10231 NaN NaN \n", + "10232 NaN NaN \n", + "10233 NaN NaN \n", + "10234 NaN NaN \n", + "\n", + " duration_cassava_historical biomass_sorghum_historical \n", + "0 NaN 290.806 \\\n", + "1 NaN 271.889 \n", + "2 NaN 361.556 \n", + "3 NaN 348.463 \n", + "4 NaN 261.824 \n", + "... ... ... \n", + "10230 NaN 448.542 \n", + "10231 NaN 203.375 \n", + "10232 NaN 174.075 \n", + "10233 NaN 200.708 \n", + "10234 NaN 353.034 \n", + "\n", + " duration_josephscoat_historical biomass_tomato_future_ssp126 \n", + "0 NaN NaN \\\n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "10230 NaN 1862.491 \n", + "10231 NaN 1813.793 \n", + "10232 NaN 2193.690 \n", + "10233 NaN 3198.267 \n", + "10234 NaN 2652.431 \n", + "\n", + " duration_pigeonpea_future_ssp126 duration_sorghum_future_ssp370 \n", + "0 NaN 56.938 \\\n", + "1 NaN 55.902 \n", + "2 NaN 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{'min': 305.543,\n", + " 'quantile_1': 532.95712,\n", + " 'quantile_2': 737.74288,\n", + " 'quantile_10': 1277.2022,\n", + " 'quantile_20': 1796.2724,\n", + " 'quantile_30': 1966.2392,\n", + " 'quantile_40': 2190.284,\n", + " 'quantile_50': 2403.595,\n", + " 'quantile_60': 2672.114,\n", + " 'quantile_70': 2903.8967999999995,\n", + " 'quantile_80': 3234.8946000000005,\n", + " 'quantile_90': 3842.986,\n", + " 'quantile_98': 4717.20208,\n", + " 'quantile_99': 4788.10844,\n", + " 'max': 5630.052},\n", + " 'duration_fingermillet_historical': {'min': 2.0,\n", + " 'quantile_1': 2.0,\n", + " 'quantile_2': 2.0,\n", + " 'quantile_10': 2.0,\n", + " 'quantile_20': 5.378400000000001,\n", + " 'quantile_30': 18.8084,\n", + " 'quantile_40': 36.03600000000001,\n", + " 'quantile_50': 51.5185,\n", + " 'quantile_60': 68.8428,\n", + " 'quantile_70': 85.65769999999998,\n", + " 'quantile_80': 96.24260000000002,\n", + " 'quantile_90': 108.33760000000001,\n", + " 'quantile_98': 123.60458,\n", + " 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12177.9432,\n", + " 'quantile_90': 12842.6052,\n", + " 'quantile_98': 13433.57892,\n", + " 'quantile_99': 13573.78276,\n", + " 'max': 14139.017},\n", + " 'duration_groundnut_future_ssp370': {'min': 34.783,\n", + " 'quantile_1': 53.27194,\n", + " 'quantile_2': 65.35000000000001,\n", + " 'quantile_10': 106.5468,\n", + " 'quantile_20': 126.9936,\n", + " 'quantile_30': 137.0,\n", + " 'quantile_40': 145.9488,\n", + " 'quantile_50': 150.0,\n", + " 'quantile_60': 151.241,\n", + " 'quantile_70': 151.241,\n", + " 'quantile_80': 152.7918,\n", + " 'quantile_90': 153.0,\n", + " 'quantile_98': 154.0,\n", + " 'quantile_99': 154.0,\n", + " 'max': 154.0},\n", + " 'biomass_josephscoat_future_ssp126': {'min': 313.666,\n", + " 'quantile_1': 419.24824,\n", + " 'quantile_2': 588.94448,\n", + " 'quantile_10': 2195.6714,\n", + " 'quantile_20': 3193.3532,\n", + " 'quantile_30': 4134.3656,\n", + " 'quantile_40': 4626.6144,\n", + " 'quantile_50': 7049.992,\n", + " 'quantile_60': 11339.1234,\n", + " 'quantile_70': 12941.6648,\n", + " 'quantile_80': 13574.094000000001,\n", + " 'quantile_90': 14231.952,\n", + " 'quantile_98': 14835.61796,\n", + " 'quantile_99': 14889.56,\n", + " 'max': 15710.208},\n", + " 'duration_josephscoat_future_ssp370': {'min': 18.158,\n", + " 'quantile_1': 18.81624,\n", + " 'quantile_2': 19.851,\n", + " 'quantile_10': 30.7316,\n", + " 'quantile_20': 39.0128,\n", + " 'quantile_30': 50.0204,\n", + " 'quantile_40': 65.85,\n", + " 'quantile_50': 71.408,\n", + " 'quantile_60': 78.047,\n", + " 'quantile_70': 81.2064,\n", + " 'quantile_80': 83.1412,\n", + " 'quantile_90': 86.5834,\n", + " 'quantile_98': 104.49948,\n", + " 'quantile_99': 106.098,\n", + " 'max': 107.717},\n", " 'duration_soybean_future_ssp126': {'min': 22.147,\n", " 'quantile_1': 33.29215,\n", " 'quantile_2': 40.3576,\n", @@ -25165,219 +28591,69 @@ " 'quantile_98': 152.612,\n", " 'quantile_99': 162.13199999999998,\n", " 'max': 286.526},\n", - " 'duration_bambaragroundnut_future_ssp126': {'min': 51.4,\n", - " 'quantile_1': 56.656569999999995,\n", - " 'quantile_2': 58.58986,\n", - " 'quantile_10': 82.93280000000001,\n", - " 'quantile_20': 109.6254,\n", - " 'quantile_30': 116.4136,\n", - " 'quantile_40': 122.8102,\n", - " 'quantile_50': 126.98750000000001,\n", - " 'quantile_60': 130.9348,\n", - " 'quantile_70': 136.1969,\n", - " 'quantile_80': 137.0,\n", - " 'quantile_90': 151.53889999999998,\n", - " 'quantile_98': 153.0,\n", - " 'quantile_99': 153.77616000000015,\n", - " 'max': 190.938},\n", - " 'biomass_sorghum_future_ssp126': {'min': 27.784,\n", - " 'quantile_1': 80.01960000000001,\n", - " 'quantile_2': 110.32600000000001,\n", - " 'quantile_10': 412.2099,\n", - " 'quantile_20': 934.2886,\n", - " 'quantile_30': 1384.3861,\n", - " 'quantile_40': 1847.6466,\n", - " 'quantile_50': 2218.8990000000003,\n", - " 'quantile_60': 2464.3084,\n", - " 'quantile_70': 2683.4047,\n", - " 'quantile_80': 2874.5078,\n", - " 'quantile_90': 3040.4201000000003,\n", - " 'quantile_98': 3242.98252,\n", - " 'quantile_99': 3310.822660000002,\n", - " 'max': 3690.595},\n", - " 'biomass_africaneggplant_future_ssp370': {'min': 284.275,\n", - " 'quantile_1': 406.41472,\n", - " 'quantile_2': 553.66756,\n", - " 'quantile_10': 1362.8952,\n", - " 'quantile_20': 1976.7002,\n", - " 'quantile_30': 3467.1365999999994,\n", - " 'quantile_40': 6194.6016,\n", - " 'quantile_50': 9294.334,\n", - " 'quantile_60': 16871.349999999995,\n", - " 'quantile_70': 19942.413,\n", - " 'quantile_80': 20750.045000000002,\n", - " 'quantile_90': 22313.0168,\n", - " 'quantile_98': 26535.201439999997,\n", - " 'quantile_99': 26925.435280000005,\n", - " 'max': 27485.392},\n", - " 'duration_sorghum_future_ssp126': {'min': 19.092,\n", - " 'quantile_1': 25.61625,\n", - " 'quantile_2': 32.77798000000001,\n", - " 'quantile_10': 45.3801,\n", - " 'quantile_20': 53.5218,\n", - " 'quantile_30': 59.1305,\n", - " 'quantile_40': 63.5108,\n", - " 'quantile_50': 67.48750000000001,\n", - " 'quantile_60': 71.2734,\n", - " 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'quantile_2': 123.584,\n", - " 'quantile_10': 512.417,\n", - " 'quantile_20': 1310.525,\n", - " 'quantile_30': 1991.655,\n", - " 'quantile_40': 2507.258,\n", - " 'quantile_50': 2789.05,\n", - " 'quantile_60': 3079.825,\n", - " 'quantile_70': 3327.0,\n", - " 'quantile_80': 3528.642,\n", - " 'quantile_90': 3685.94,\n", - " 'quantile_98': 3921.181,\n", - " 'quantile_99': 3981.533,\n", - " 'max': 4175.167},\n", - " 'yield_taro_historical': {'min': 5.45,\n", - " 'quantile_1': 48.24502,\n", - " 'quantile_2': 85.25112,\n", - " 'quantile_10': 324.4536,\n", - " 'quantile_20': 667.1860000000011,\n", - " 'quantile_30': 1793.1295999999993,\n", - " 'quantile_40': 3476.746600000001,\n", - " 'quantile_50': 5591.108,\n", - " 'quantile_60': 8017.6232,\n", - " 'quantile_70': 9032.2904,\n", - " 'quantile_80': 9638.3836,\n", - " 'quantile_90': 10699.851400000001,\n", - " 'quantile_98': 11931.625999999997,\n", - " 'quantile_99': 12105.8865,\n", - " 'max': 12633.7},\n", - " 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'quantile_90': 121.0,\n", - " 'quantile_98': 121.0,\n", - " 'quantile_99': 121.0,\n", - " 'max': 121.0},\n", - " 'yield_lablab_historical': {'min': 147.44,\n", - " 'quantile_1': 399.43666,\n", - " 'quantile_2': 539.3234000000001,\n", - " 'quantile_10': 1118.7383,\n", - " 'quantile_20': 1516.6724000000002,\n", - " 'quantile_30': 1805.4141,\n", - " 'quantile_40': 2073.0516000000002,\n", - " 'quantile_50': 2324.362,\n", - " 'quantile_60': 2691.693,\n", - " 'quantile_70': 3063.0334,\n", - " 'quantile_80': 3493.0966,\n", - " 'quantile_90': 4015.2705,\n", - " 'quantile_98': 4581.151580000001,\n", - " 'quantile_99': 4703.370440000001,\n", - " 'max': 5092.732},\n", - " 'biomass_maize_future_ssp370': {'min': 1.0,\n", - " 'quantile_1': 21.36928,\n", - " 'quantile_2': 39.1433,\n", - " 'quantile_10': 527.3235000000001,\n", - " 'quantile_20': 1541.2364000000005,\n", - " 'quantile_30': 2742.9815,\n", - " 'quantile_40': 4154.6738,\n", - " 'quantile_50': 5871.004,\n", - " 'quantile_60': 7598.945,\n", - " 'quantile_70': 9034.345899999998,\n", - " 'quantile_80': 10285.0064,\n", - " 'quantile_90': 11710.161100000001,\n", - " 'quantile_98': 13193.100699999999,\n", - " 'quantile_99': 13563.792930000003,\n", - " 'max': 14918.5},\n", - " 'yield_josephscoat_future_ssp126': {'min': 655.75,\n", - " 'quantile_1': 705.13624,\n", - " 'quantile_2': 773.7814400000001,\n", - " 'quantile_10': 1222.727,\n", - " 'quantile_20': 1713.9912,\n", - " 'quantile_30': 2979.77,\n", - " 'quantile_40': 4212.807,\n", - " 'quantile_50': 4586.442,\n", - " 'quantile_60': 6630.367,\n", - " 'quantile_70': 10122.6866,\n", - " 'quantile_80': 11450.4234,\n", - " 'quantile_90': 12442.363200000002,\n", - " 'quantile_98': 13036.511,\n", - " 'quantile_99': 13279.62852,\n", - " 'max': 13873.858}}" + " 'biomass_sesame_future_ssp126': {'min': 5.867,\n", + " 'quantile_1': 111.80456,\n", + " 'quantile_2': 149.18672,\n", + " 'quantile_10': 472.45000000000005,\n", + " 'quantile_20': 1047.0852,\n", + " 'quantile_30': 1553.6067999999998,\n", + " 'quantile_40': 2087.0296000000008,\n", + " 'quantile_50': 2536.267,\n", + " 'quantile_60': 2804.1968,\n", + " 'quantile_70': 3011.0197999999996,\n", + " 'quantile_80': 3232.8554,\n", + " 'quantile_90': 3428.7636,\n", + " 'quantile_98': 3607.1357199999998,\n", + " 'quantile_99': 3674.9839999999995,\n", + " 'max': 3934.183},\n", + " 'duration_grasspea_historical': {'min': 34.052,\n", + " 'quantile_1': 43.27932,\n", + " 'quantile_2': 47.754799999999996,\n", + " 'quantile_10': 70.6464,\n", + " 'quantile_20': 83.8636,\n", + " 'quantile_30': 91.6018,\n", + " 'quantile_40': 98.4846,\n", + " 'quantile_50': 104.198,\n", + " 'quantile_60': 114.0946,\n", + " 'quantile_70': 135.56199999999995,\n", + " 'quantile_80': 150.2192,\n", + " 'quantile_90': 151.1308,\n", + " 'quantile_98': 151.241,\n", + " 'quantile_99': 151.241,\n", + " 'max': 151.241},\n", + " 'duration_cowpea_future_ssp370': {'min': 16.433,\n", + " 'quantile_1': 18.017,\n", + " 'quantile_2': 19.575,\n", + " 'quantile_10': 30.167,\n", + " 'quantile_20': 47.517,\n", + " 'quantile_30': 57.1,\n", + " 'quantile_40': 62.95,\n", + " 'quantile_50': 66.625,\n", + " 'quantile_60': 69.466,\n", + " 'quantile_70': 71.425,\n", + " 'quantile_80': 74.383,\n", + " 'quantile_90': 79.225,\n", + " 'quantile_98': 86.664,\n", + " 'quantile_99': 89.842,\n", + " 'max': 107.292},\n", + " 'duration_maize_future_ssp370': {'min': 16.241,\n", + " 'quantile_1': 32.19457,\n", + " 'quantile_2': 35.0965,\n", + " 'quantile_10': 46.7879,\n", + " 'quantile_20': 55.0848,\n", + " 'quantile_30': 62.3037,\n", + " 'quantile_40': 68.3262,\n", + " 'quantile_50': 73.082,\n", + " 'quantile_60': 76.5878,\n", + " 'quantile_70': 80.25,\n", + " 'quantile_80': 86.17240000000001,\n", + " 'quantile_90': 96.32100000000003,\n", + " 'quantile_98': 119.27386000000001,\n", + " 'quantile_99': 128.24714000000003,\n", + " 'max': 257.681}}" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -25391,723 +28667,19 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", - " super().__setitem__(key, value)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/numpy/lib/function_base.py:4527: RuntimeWarning: invalid value encountered in subtract\n", - " diff_b_a = subtract(b, a)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/numpy/lib/function_base.py:4527: RuntimeWarning: invalid value encountered in subtract\n", - " diff_b_a = subtract(b, a)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/numpy/lib/function_base.py:4527: RuntimeWarning: invalid value encountered in subtract\n", - " diff_b_a = subtract(b, a)\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/geo/lib/python3.10/site-packages/numpy/lib/function_base.py:4527: RuntimeWarning: invalid value encountered in subtract\n", - " diff_b_a = subtract(b, a)\n" + "ename": "NameError", + "evalue": "name 'generate_ratio_quantiles' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ratio_quantiles \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_ratio_quantiles\u001b[49m(grid_reduced)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./synced-data/crop-yield-data-ratio-quantiles.json\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m fp:\n\u001b[1;32m 3\u001b[0m json\u001b[38;5;241m.\u001b[39mdump(ratio_quantiles, fp)\n", + "\u001b[0;31mNameError\u001b[0m: name 'generate_ratio_quantiles' is not defined" ] - }, - { - "data": { - "text/plain": [ - "{'yield_cocoyam_future_ssp370': {'min': 0.5821398547717628,\n", - " 'quantile_1': 0.6118012502623061,\n", - " 'quantile_2': 0.6315330382957874,\n", - " 'quantile_10': 0.8071655376344714,\n", - " 'quantile_20': 0.8936833908773617,\n", - " 'quantile_30': 0.9800790822905625,\n", - " 'quantile_40': 1.0138104369702485,\n", - " 'quantile_50': 1.0544867689627788,\n", - " 'quantile_60': 1.1207695238147428,\n", - " 'quantile_70': 1.1589981538531238,\n", - " 'quantile_80': 1.18398461915349,\n", - " 'quantile_90': 1.2139062653198593,\n", - " 'quantile_98': 1.3531588846102216,\n", - " 'quantile_99': 1.5835294178521846,\n", - " 'max': 2.7633743097637176},\n", - " 'yield_mungbean_future_ssp126': {'min': 0.5357827074044694,\n", - " 'quantile_1': 0.5442988471866819,\n", - " 'quantile_2': 0.5498699184211211,\n", - " 'quantile_10': 0.5913067925834001,\n", - " 'quantile_20': 0.652499499178016,\n", - " 'quantile_30': 0.7841425381060181,\n", - " 'quantile_40': 0.8551068883610451,\n", - 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diff --git a/vacs-map-app/src/stores/siteContent.js b/vacs-map-app/src/stores/siteContent.js index 98da660..2b2f921 100644 --- a/vacs-map-app/src/stores/siteContent.js +++ b/vacs-map-app/src/stores/siteContent.js @@ -9,7 +9,7 @@ export const useContentStore = defineStore('siteContent', () => { vacs_mini: 'The Vision for Adapted Crops and Soils (VACS) is a global movement that aims to foster more resilient food systems by focusing on the fundamentals of food security: climate-adapted crops and healthy soils.', vacs_short: - 'The Vision for Adapted Crops and Soils (VACS) is a global movement that aims to foster more resilient food systems by focusing on the fundamentals of food security: climate-adapted crops and healthy soils. Initially launched by the U.S. Department of State, the African Union, and the Food and Agriculture Organization of the UN, VACS seeks to boost agricultural productivity and nutrition by developing diverse, climate-resilient crop varieties and building healthy soils. VACS provides a unified investment framework for stakeholders to advance these goals. VACS is working with AgMIP to develop a models of future crop productivity that will help identify the most promising crops for a changing climate. This application visualizes iniial results of these models.', + 'The Vision for Adapted Crops and Soils (VACS) is a global movement that aims to foster more resilient food systems by focusing on the fundamentals of food security: climate-adapted crops and healthy soils. Initially launched by the U.S. Department of State, the African Union, and the Food and Agriculture Organization of the UN, VACS seeks to boost agricultural productivity and nutrition by developing diverse, climate-resilient crop varieties and building healthy soils. VACS provides a unified investment framework for stakeholders to advance these goals. VACS is working with AgMIP to develop a models of future crop productivity that will help identify the most promising crops for a changing climate. This application visualizes initial results of these models.', future_ssp126_label: 'Low emissions', future_ssp370_label: 'High emissions', future_ssp126_short: