diff --git a/docs/notebooks/test_nested.ipynb b/docs/notebooks/test_nested.ipynb
deleted file mode 100644
index 5d61aa4..0000000
--- a/docs/notebooks/test_nested.ipynb
+++ /dev/null
@@ -1,1069 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/wilsonbb/lincc/nested/dropna/nested-pandas/.conda/bin/python\n"
-     ]
-    }
-   ],
-   "source": [
-    "!which python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/wilsonbb/lincc/nested/dropna/nested-pandas/.conda/bin/pip\n"
-     ]
-    }
-   ],
-   "source": [
-    "!which pip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nested_pandas as npd\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.DataFrame({\n",
-    "    'a': [],#1, 2, 3],\n",
-    "    'b': []#,4, float('nan'), 6],\n",
-    "})#, index=pd.Series([0, 1, 2], name='idx'))\n",
-    "\n",
-    "packed = pd.DataFrame({\n",
-    "    'c': [],#7, 8, 9, 10, 11, 12],\n",
-    "    'd': [],#10, float('nan'), 12, float('nan'), 13, 14],\n",
-    "})#, index=pd.Series([0, 0, 1, 1, 2, 2], name='idx'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RangeIndex(start=0, stop=3, step=1)\n",
-      "RangeIndex(start=0, stop=6, step=1)\n",
-      "RangeIndex(start=0, stop=3, step=1)\n",
-      "RangeIndex(start=0, stop=3, step=1)\n",
-      "Index([0, 1, 2], dtype='int64')\n"
-     ]
-    }
-   ],
-   "source": [
-    "nf = npd.NestedFrame(data=data)\n",
-    "nf = nf.add_nested(packed, name='packed')\n",
-    "\n",
-    "print(data.index)\n",
-    "print(packed.index)\n",
-    "print(nf.index)\n",
-    "print(nf.packed.index)\n",
-    "print(nf.packed.nest.to_flat().index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>idx</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      c     d\n",
-       "idx          \n",
-       "0     7  10.0\n",
-       "0     8   NaN\n",
-       "1     9  12.0\n",
-       "1    10   NaN\n",
-       "2    11  13.0\n",
-       "2    12  14.0"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "packed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>idx</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     a    b\n",
-       "idx        \n",
-       "0    1  4.0\n",
-       "1    2  NaN\n",
-       "2    3  6.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nf = npd.NestedFrame(data=data)\n",
-    "nf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nf = nf.add_nested(packed, name='packed')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['packed']\n",
-      "Index(['a', 'b', 'packed'], dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nf.nested_columns)\n",
-    "print(nf.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    c     d\n",
-       "0   7  10.0\n",
-       "0   8   NaN\n",
-       "1   9  12.0\n",
-       "1  10   NaN\n",
-       "2  11  13.0\n",
-       "2  12  14.0"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nf.packed.nest.to_flat()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>packed</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>idx</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>c     d\n",
-       "0  7  10.0\n",
-       "1  8   NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>c     d\n",
-       "0   9  12.0\n",
-       "1  10   NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>c     d\n",
-       "0  11  13.0\n",
-       "1  12  14.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     a    b                               packed\n",
-       "idx                                             \n",
-       "0    1  4.0        c     d\n",
-       "0  7  10.0\n",
-       "1  8   NaN\n",
-       "1    2  NaN      c     d\n",
-       "0   9  12.0\n",
-       "1  10   NaN\n",
-       "2    3  6.0      c     d\n",
-       "0  11  13.0\n",
-       "1  12  14.0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    c     d\n",
-       "0   7  10.0\n",
-       "0   8   NaN\n",
-       "1   9  12.0\n",
-       "1  10   NaN\n",
-       "2  11  13.0\n",
-       "2  12  14.0"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nf.packed.nest.to_flat()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'nested_pandas.nestedframe.core.NestedFrame'>\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a    b   c     d\n",
-       "0  1  4.0   7  10.0\n",
-       "0  1  4.0   8   NaN\n",
-       "1  2  NaN   9  12.0\n",
-       "1  2  NaN  10   NaN\n",
-       "2  3  6.0  11  13.0\n",
-       "2  3  6.0  12  14.0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full = nf.merge(nf.packed.nest.to_flat(), left_index=True, right_index=True).drop(columns=['packed'])\n",
-    "print(type(full))\n",
-    "full"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    c     d\n",
-       "1   9  12.0\n",
-       "1  10   NaN"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func(a, b, c):\n",
-    "    return a + b + c\n",
-    "\n",
-    "\n",
-    "# Figure out that C is a nested column\n",
-    "output_frame = # define meta\n",
-    "result = nf.batch(func, 'a', 'b', 'packed.c')\n",
-    "\n",
-    "nf.join(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>packed</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>idx</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>c     d\n",
-       "0  7  10.0\n",
-       "1  8   NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>c     d\n",
-       "0   9  12.0\n",
-       "1  10   NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>c     d\n",
-       "0  11  13.0\n",
-       "1  12  14.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     a    b                               packed\n",
-       "idx                                             \n",
-       "0    1  4.0        c     d\n",
-       "0  7  10.0\n",
-       "1  8   NaN\n",
-       "1    2  NaN      c     d\n",
-       "0   9  12.0\n",
-       "1  10   NaN\n",
-       "2    3  6.0      c     d\n",
-       "0  11  13.0\n",
-       "1  12  14.0"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nf\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    c     d\n",
-       "1   9  12.0\n",
-       "1  10   NaN\n",
-       "2  11  13.0\n",
-       "2  12  14.0"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_pack = nf.packed.nest.to_flat().query(\"c > 8\")\n",
-    "new_pack"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "Invalid value '<NA>' for dtype nested<c: [int64], d: [double]>",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mArrowTypeError\u001b[0m                            Traceback (most recent call last)",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py:2051\u001b[0m, in \u001b[0;36mArrowExtensionArray._maybe_convert_setitem_value\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   2050\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 2051\u001b[0m     value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_box_pa(value, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_pa_array\u001b[39m.\u001b[39;49mtype)\n\u001b[1;32m   2052\u001b[0m \u001b[39mexcept\u001b[39;00m pa\u001b[39m.\u001b[39mArrowTypeError \u001b[39mas\u001b[39;00m err:\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py:404\u001b[0m, in \u001b[0;36mArrowExtensionArray._box_pa\u001b[0;34m(cls, value, pa_type)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, pa\u001b[39m.\u001b[39mScalar) \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m is_list_like(value):\n\u001b[0;32m--> 404\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39;49m\u001b[39m.\u001b[39;49m_box_pa_scalar(value, pa_type)\n\u001b[1;32m    405\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m_box_pa_array(value, pa_type)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py:440\u001b[0m, in \u001b[0;36mArrowExtensionArray._box_pa_scalar\u001b[0;34m(cls, value, pa_type)\u001b[0m\n\u001b[1;32m    438\u001b[0m         value \u001b[39m=\u001b[39m value\u001b[39m.\u001b[39m_value\n\u001b[0;32m--> 440\u001b[0m     pa_scalar \u001b[39m=\u001b[39m pa\u001b[39m.\u001b[39;49mscalar(value, \u001b[39mtype\u001b[39;49m\u001b[39m=\u001b[39;49mpa_type, from_pandas\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m    442\u001b[0m \u001b[39mif\u001b[39;00m pa_type \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m pa_scalar\u001b[39m.\u001b[39mtype \u001b[39m!=\u001b[39m pa_type:\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pyarrow/scalar.pxi:1150\u001b[0m, in \u001b[0;36mpyarrow.lib.scalar\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pyarrow/error.pxi:154\u001b[0m, in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pyarrow/error.pxi:91\u001b[0m, in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mArrowTypeError\u001b[0m: Could not convert <NA> with type NAType: was not a dict, tuple, or recognized null value for conversion to struct type",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[49], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nf\u001b[39m.\u001b[39;49madd_nested(new_pack, name\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mpacked\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/nested_pandas/nestedframe/core.py:60\u001b[0m, in \u001b[0;36mNestedFrame.add_nested\u001b[0;34m(self, nested, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m packed \u001b[39m=\u001b[39m packer\u001b[39m.\u001b[39mpack_flat(nested, name\u001b[39m=\u001b[39mname)\n\u001b[1;32m     59\u001b[0m label \u001b[39m=\u001b[39m packed\u001b[39m.\u001b[39mname\n\u001b[0;32m---> 60\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49massign(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m{\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m{\u001b[39;49;00mlabel\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m: packed})\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/frame.py:5226\u001b[0m, in \u001b[0;36mDataFrame.assign\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   5223\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcopy(deep\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m)\n\u001b[1;32m   5225\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems():\n\u001b[0;32m-> 5226\u001b[0m     data[k] \u001b[39m=\u001b[39m com\u001b[39m.\u001b[39mapply_if_callable(v, data)\n\u001b[1;32m   5227\u001b[0m \u001b[39mreturn\u001b[39;00m data\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/frame.py:4299\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4296\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setitem_array([key], value)\n\u001b[1;32m   4297\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   4298\u001b[0m     \u001b[39m# set column\u001b[39;00m\n\u001b[0;32m-> 4299\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_set_item(key, value)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/frame.py:4512\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4502\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_set_item\u001b[39m(\u001b[39mself\u001b[39m, key, value) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   4503\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   4504\u001b[0m \u001b[39m    Add series to DataFrame in specified column.\u001b[39;00m\n\u001b[1;32m   4505\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4510\u001b[0m \u001b[39m    ensure homogeneity.\u001b[39;00m\n\u001b[1;32m   4511\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4512\u001b[0m     value, refs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sanitize_column(value)\n\u001b[1;32m   4514\u001b[0m     \u001b[39mif\u001b[39;00m (\n\u001b[1;32m   4515\u001b[0m         key \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\n\u001b[1;32m   4516\u001b[0m         \u001b[39mand\u001b[39;00m value\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m   4517\u001b[0m         \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(value\u001b[39m.\u001b[39mdtype, ExtensionDtype)\n\u001b[1;32m   4518\u001b[0m     ):\n\u001b[1;32m   4519\u001b[0m         \u001b[39m# broadcast across multiple columns if necessary\u001b[39;00m\n\u001b[1;32m   4520\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mis_unique \u001b[39mor\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns, MultiIndex):\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/frame.py:5250\u001b[0m, in \u001b[0;36mDataFrame._sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   5248\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(value, Series):\n\u001b[1;32m   5249\u001b[0m         value \u001b[39m=\u001b[39m Series(value)\n\u001b[0;32m-> 5250\u001b[0m     \u001b[39mreturn\u001b[39;00m _reindex_for_setitem(value, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex)\n\u001b[1;32m   5252\u001b[0m \u001b[39mif\u001b[39;00m is_list_like(value):\n\u001b[1;32m   5253\u001b[0m     com\u001b[39m.\u001b[39mrequire_length_match(value, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/frame.py:12674\u001b[0m, in \u001b[0;36m_reindex_for_setitem\u001b[0;34m(value, index)\u001b[0m\n\u001b[1;32m  12672\u001b[0m \u001b[39m# GH#4107\u001b[39;00m\n\u001b[1;32m  12673\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m> 12674\u001b[0m     reindexed_value \u001b[39m=\u001b[39m value\u001b[39m.\u001b[39;49mreindex(index)\u001b[39m.\u001b[39m_values\n\u001b[1;32m  12675\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m  12676\u001b[0m     \u001b[39m# raised in MultiIndex.from_tuples, see test_insert_error_msmgs\u001b[39;00m\n\u001b[1;32m  12677\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m value\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39mis_unique:\n\u001b[1;32m  12678\u001b[0m         \u001b[39m# duplicate axis\u001b[39;00m\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/series.py:5144\u001b[0m, in \u001b[0;36mSeries.reindex\u001b[0;34m(self, index, axis, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   5127\u001b[0m \u001b[39m@doc\u001b[39m(\n\u001b[1;32m   5128\u001b[0m     NDFrame\u001b[39m.\u001b[39mreindex,  \u001b[39m# type: ignore[has-type]\u001b[39;00m\n\u001b[1;32m   5129\u001b[0m     klass\u001b[39m=\u001b[39m_shared_doc_kwargs[\u001b[39m\"\u001b[39m\u001b[39mklass\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5142\u001b[0m     tolerance\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   5143\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Series:\n\u001b[0;32m-> 5144\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mreindex(\n\u001b[1;32m   5145\u001b[0m         index\u001b[39m=\u001b[39;49mindex,\n\u001b[1;32m   5146\u001b[0m         method\u001b[39m=\u001b[39;49mmethod,\n\u001b[1;32m   5147\u001b[0m         copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[1;32m   5148\u001b[0m         level\u001b[39m=\u001b[39;49mlevel,\n\u001b[1;32m   5149\u001b[0m         fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[1;32m   5150\u001b[0m         limit\u001b[39m=\u001b[39;49mlimit,\n\u001b[1;32m   5151\u001b[0m         tolerance\u001b[39m=\u001b[39;49mtolerance,\n\u001b[1;32m   5152\u001b[0m     )\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/generic.py:5607\u001b[0m, in \u001b[0;36mNDFrame.reindex\u001b[0;34m(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   5604\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reindex_multi(axes, copy, fill_value)\n\u001b[1;32m   5606\u001b[0m \u001b[39m# perform the reindex on the axes\u001b[39;00m\n\u001b[0;32m-> 5607\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_reindex_axes(\n\u001b[1;32m   5608\u001b[0m     axes, level, limit, tolerance, method, fill_value, copy\n\u001b[1;32m   5609\u001b[0m )\u001b[39m.\u001b[39m__finalize__(\u001b[39mself\u001b[39m, method\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mreindex\u001b[39m\u001b[39m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/generic.py:5635\u001b[0m, in \u001b[0;36mNDFrame._reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   5630\u001b[0m new_index, indexer \u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39mreindex(\n\u001b[1;32m   5631\u001b[0m     labels, level\u001b[39m=\u001b[39mlevel, limit\u001b[39m=\u001b[39mlimit, tolerance\u001b[39m=\u001b[39mtolerance, method\u001b[39m=\u001b[39mmethod\n\u001b[1;32m   5632\u001b[0m )\n\u001b[1;32m   5634\u001b[0m axis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_axis_number(a)\n\u001b[0;32m-> 5635\u001b[0m obj \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39;49m_reindex_with_indexers(\n\u001b[1;32m   5636\u001b[0m     {axis: [new_index, indexer]},\n\u001b[1;32m   5637\u001b[0m     fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[1;32m   5638\u001b[0m     copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[1;32m   5639\u001b[0m     allow_dups\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m   5640\u001b[0m )\n\u001b[1;32m   5641\u001b[0m \u001b[39m# If we've made a copy once, no need to make another one\u001b[39;00m\n\u001b[1;32m   5642\u001b[0m copy \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/generic.py:5683\u001b[0m, in \u001b[0;36mNDFrame._reindex_with_indexers\u001b[0;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[1;32m   5680\u001b[0m     indexer \u001b[39m=\u001b[39m ensure_platform_int(indexer)\n\u001b[1;32m   5682\u001b[0m \u001b[39m# TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)\u001b[39;00m\n\u001b[0;32m-> 5683\u001b[0m new_data \u001b[39m=\u001b[39m new_data\u001b[39m.\u001b[39;49mreindex_indexer(\n\u001b[1;32m   5684\u001b[0m     index,\n\u001b[1;32m   5685\u001b[0m     indexer,\n\u001b[1;32m   5686\u001b[0m     axis\u001b[39m=\u001b[39;49mbaxis,\n\u001b[1;32m   5687\u001b[0m     fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[1;32m   5688\u001b[0m     allow_dups\u001b[39m=\u001b[39;49mallow_dups,\n\u001b[1;32m   5689\u001b[0m     copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[1;32m   5690\u001b[0m )\n\u001b[1;32m   5691\u001b[0m \u001b[39m# If we've made a copy once, no need to make another one\u001b[39;00m\n\u001b[1;32m   5692\u001b[0m copy \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/internals/managers.py:680\u001b[0m, in \u001b[0;36mBaseBlockManager.reindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)\u001b[0m\n\u001b[1;32m    677\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mIndexError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mRequested axis not found in manager\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    679\u001b[0m \u001b[39mif\u001b[39;00m axis \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m--> 680\u001b[0m     new_blocks \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_slice_take_blocks_ax0(\n\u001b[1;32m    681\u001b[0m         indexer,\n\u001b[1;32m    682\u001b[0m         fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[1;32m    683\u001b[0m         only_slice\u001b[39m=\u001b[39;49monly_slice,\n\u001b[1;32m    684\u001b[0m         use_na_proxy\u001b[39m=\u001b[39;49muse_na_proxy,\n\u001b[1;32m    685\u001b[0m     )\n\u001b[1;32m    686\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    687\u001b[0m     new_blocks \u001b[39m=\u001b[39m [\n\u001b[1;32m    688\u001b[0m         blk\u001b[39m.\u001b[39mtake_nd(\n\u001b[1;32m    689\u001b[0m             indexer,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    695\u001b[0m         \u001b[39mfor\u001b[39;00m blk \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mblocks\n\u001b[1;32m    696\u001b[0m     ]\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/internals/managers.py:773\u001b[0m, in \u001b[0;36mBaseBlockManager._slice_take_blocks_ax0\u001b[0;34m(self, slice_or_indexer, fill_value, only_slice, use_na_proxy, ref_inplace_op)\u001b[0m\n\u001b[1;32m    770\u001b[0m         \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    771\u001b[0m             bp \u001b[39m=\u001b[39m BlockPlacement(\u001b[39mslice\u001b[39m(\u001b[39m0\u001b[39m, sllen))\n\u001b[1;32m    772\u001b[0m             \u001b[39mreturn\u001b[39;00m [\n\u001b[0;32m--> 773\u001b[0m                 blk\u001b[39m.\u001b[39;49mtake_nd(\n\u001b[1;32m    774\u001b[0m                     slobj,\n\u001b[1;32m    775\u001b[0m                     axis\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m,\n\u001b[1;32m    776\u001b[0m                     new_mgr_locs\u001b[39m=\u001b[39;49mbp,\n\u001b[1;32m    777\u001b[0m                     fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[1;32m    778\u001b[0m                 )\n\u001b[1;32m    779\u001b[0m             ]\n\u001b[1;32m    781\u001b[0m \u001b[39mif\u001b[39;00m sl_type \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mslice\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    782\u001b[0m     blknos \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mblknos[slobj]\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/internals/blocks.py:1307\u001b[0m, in \u001b[0;36mBlock.take_nd\u001b[0;34m(self, indexer, axis, new_mgr_locs, fill_value)\u001b[0m\n\u001b[1;32m   1304\u001b[0m     allow_fill \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m   1306\u001b[0m \u001b[39m# Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype\u001b[39;00m\n\u001b[0;32m-> 1307\u001b[0m new_values \u001b[39m=\u001b[39m algos\u001b[39m.\u001b[39;49mtake_nd(\n\u001b[1;32m   1308\u001b[0m     values, indexer, axis\u001b[39m=\u001b[39;49maxis, allow_fill\u001b[39m=\u001b[39;49mallow_fill, fill_value\u001b[39m=\u001b[39;49mfill_value\n\u001b[1;32m   1309\u001b[0m )\n\u001b[1;32m   1311\u001b[0m \u001b[39m# Called from three places in managers, all of which satisfy\u001b[39;00m\n\u001b[1;32m   1312\u001b[0m \u001b[39m#  these assertions\u001b[39;00m\n\u001b[1;32m   1313\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m, ExtensionBlock):\n\u001b[1;32m   1314\u001b[0m     \u001b[39m# NB: in this case, the 'axis' kwarg will be ignored in the\u001b[39;00m\n\u001b[1;32m   1315\u001b[0m     \u001b[39m#  algos.take_nd call above.\u001b[39;00m\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/array_algos/take.py:114\u001b[0m, in \u001b[0;36mtake_nd\u001b[0;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[1;32m    109\u001b[0m         arr \u001b[39m=\u001b[39m cast(\u001b[39m\"\u001b[39m\u001b[39mNDArrayBackedExtensionArray\u001b[39m\u001b[39m\"\u001b[39m, arr)\n\u001b[1;32m    110\u001b[0m         \u001b[39mreturn\u001b[39;00m arr\u001b[39m.\u001b[39mtake(\n\u001b[1;32m    111\u001b[0m             indexer, fill_value\u001b[39m=\u001b[39mfill_value, allow_fill\u001b[39m=\u001b[39mallow_fill, axis\u001b[39m=\u001b[39maxis\n\u001b[1;32m    112\u001b[0m         )\n\u001b[0;32m--> 114\u001b[0m     \u001b[39mreturn\u001b[39;00m arr\u001b[39m.\u001b[39;49mtake(indexer, fill_value\u001b[39m=\u001b[39;49mfill_value, allow_fill\u001b[39m=\u001b[39;49mallow_fill)\n\u001b[1;32m    116\u001b[0m arr \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray(arr)\n\u001b[1;32m    117\u001b[0m \u001b[39mreturn\u001b[39;00m _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py:1302\u001b[0m, in \u001b[0;36mArrowExtensionArray.take\u001b[0;34m(self, indices, allow_fill, fill_value)\u001b[0m\n\u001b[1;32m   1299\u001b[0m     \u001b[39m# TODO: ArrowNotImplementedError: Function fill_null has no\u001b[39;00m\n\u001b[1;32m   1300\u001b[0m     \u001b[39m# kernel matching input types (array[string], scalar[string])\u001b[39;00m\n\u001b[1;32m   1301\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)(result)\n\u001b[0;32m-> 1302\u001b[0m     result[fill_mask] \u001b[39m=\u001b[39m fill_value\n\u001b[1;32m   1303\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n\u001b[1;32m   1304\u001b[0m     \u001b[39m# return type(self)(pc.fill_null(result, pa.scalar(fill_value)))\u001b[39;00m\n\u001b[1;32m   1305\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1306\u001b[0m     \u001b[39m# Nothing to fill\u001b[39;00m\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/nested_pandas/series/ext_array.py:169\u001b[0m, in \u001b[0;36mNestedExtensionArray.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__setitem__\u001b[39m(\u001b[39mself\u001b[39m, key, value) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    168\u001b[0m     value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_convert_df_value_to_pa(value, \u001b[39mtype\u001b[39m\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dtype\u001b[39m.\u001b[39mpyarrow_dtype)\n\u001b[0;32m--> 169\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__setitem__\u001b[39;49m(key, value)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py:1828\u001b[0m, in \u001b[0;36mArrowExtensionArray.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1825\u001b[0m     key \u001b[39m=\u001b[39m key[\u001b[39m0\u001b[39m]\n\u001b[1;32m   1827\u001b[0m key \u001b[39m=\u001b[39m check_array_indexer(\u001b[39mself\u001b[39m, key)\n\u001b[0;32m-> 1828\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_maybe_convert_setitem_value(value)\n\u001b[1;32m   1830\u001b[0m \u001b[39mif\u001b[39;00m com\u001b[39m.\u001b[39mis_null_slice(key):\n\u001b[1;32m   1831\u001b[0m     \u001b[39m# fast path (GH50248)\u001b[39;00m\n\u001b[1;32m   1832\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_if_else(\u001b[39mTrue\u001b[39;00m, value, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pa_array)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/arrays/arrow/array.py:2054\u001b[0m, in \u001b[0;36mArrowExtensionArray._maybe_convert_setitem_value\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   2052\u001b[0m \u001b[39mexcept\u001b[39;00m pa\u001b[39m.\u001b[39mArrowTypeError \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m   2053\u001b[0m     msg \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mInvalid value \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mstr\u001b[39m(value)\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m for dtype \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdtype\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 2054\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(msg) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   2055\u001b[0m \u001b[39mreturn\u001b[39;00m value\n",
-      "\u001b[0;31mTypeError\u001b[0m: Invalid value '<NA>' for dtype nested<c: [int64], d: [double]>"
-     ]
-    }
-   ],
-   "source": [
-    "nf.add_nested(new_pack, name='packed')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "idx\n",
-       "0          c     d\n",
-       "0  7  10.0\n",
-       "1  8   NaN\n",
-       "1        c     d\n",
-       "0   9  12.0\n",
-       "1  10   NaN\n",
-       "2        c     d\n",
-       "0  11  13.0\n",
-       "1  12  14.0\n",
-       "Name: packed, dtype: nested<c: [int64], d: [double]>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nf.packed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<nested_pandas.series.accessor.NestSeriesAccessor at 0x11e97ca90>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nf.packed.nest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index([0, 0, 1, 1, 2, 2], dtype='int64')"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nf.packed.nest.to_flat()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'idx'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[52], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcalc_median\u001b[39m(x):\n\u001b[1;32m      2\u001b[0m     \u001b[39mreturn\u001b[39;00m x\u001b[39m.\u001b[39mmedian()\n\u001b[0;32m----> 5\u001b[0m result \u001b[39m=\u001b[39m nf\u001b[39m.\u001b[39;49mpacked\u001b[39m.\u001b[39;49mnest\u001b[39m.\u001b[39;49mto_flat()\u001b[39m.\u001b[39;49mgroupby(\u001b[39m'\u001b[39;49m\u001b[39midx\u001b[39;49m\u001b[39m'\u001b[39;49m)\u001b[39m.\u001b[39mapply(calc_median)\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/frame.py:9170\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m   9167\u001b[0m \u001b[39mif\u001b[39;00m level \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m by \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   9168\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mYou have to supply one of \u001b[39m\u001b[39m'\u001b[39m\u001b[39mby\u001b[39m\u001b[39m'\u001b[39m\u001b[39m and \u001b[39m\u001b[39m'\u001b[39m\u001b[39mlevel\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m-> 9170\u001b[0m \u001b[39mreturn\u001b[39;00m DataFrameGroupBy(\n\u001b[1;32m   9171\u001b[0m     obj\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m,\n\u001b[1;32m   9172\u001b[0m     keys\u001b[39m=\u001b[39;49mby,\n\u001b[1;32m   9173\u001b[0m     axis\u001b[39m=\u001b[39;49maxis,\n\u001b[1;32m   9174\u001b[0m     level\u001b[39m=\u001b[39;49mlevel,\n\u001b[1;32m   9175\u001b[0m     as_index\u001b[39m=\u001b[39;49mas_index,\n\u001b[1;32m   9176\u001b[0m     sort\u001b[39m=\u001b[39;49msort,\n\u001b[1;32m   9177\u001b[0m     group_keys\u001b[39m=\u001b[39;49mgroup_keys,\n\u001b[1;32m   9178\u001b[0m     observed\u001b[39m=\u001b[39;49mobserved,\n\u001b[1;32m   9179\u001b[0m     dropna\u001b[39m=\u001b[39;49mdropna,\n\u001b[1;32m   9180\u001b[0m )\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1329\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m   1326\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdropna \u001b[39m=\u001b[39m dropna\n\u001b[1;32m   1328\u001b[0m \u001b[39mif\u001b[39;00m grouper \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1329\u001b[0m     grouper, exclusions, obj \u001b[39m=\u001b[39m get_grouper(\n\u001b[1;32m   1330\u001b[0m         obj,\n\u001b[1;32m   1331\u001b[0m         keys,\n\u001b[1;32m   1332\u001b[0m         axis\u001b[39m=\u001b[39;49maxis,\n\u001b[1;32m   1333\u001b[0m         level\u001b[39m=\u001b[39;49mlevel,\n\u001b[1;32m   1334\u001b[0m         sort\u001b[39m=\u001b[39;49msort,\n\u001b[1;32m   1335\u001b[0m         observed\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m \u001b[39mif\u001b[39;49;00m observed \u001b[39mis\u001b[39;49;00m lib\u001b[39m.\u001b[39;49mno_default \u001b[39melse\u001b[39;49;00m observed,\n\u001b[1;32m   1336\u001b[0m         dropna\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdropna,\n\u001b[1;32m   1337\u001b[0m     )\n\u001b[1;32m   1339\u001b[0m \u001b[39mif\u001b[39;00m observed \u001b[39mis\u001b[39;00m lib\u001b[39m.\u001b[39mno_default:\n\u001b[1;32m   1340\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(ping\u001b[39m.\u001b[39m_passed_categorical \u001b[39mfor\u001b[39;00m ping \u001b[39min\u001b[39;00m grouper\u001b[39m.\u001b[39mgroupings):\n",
-      "File \u001b[0;32m~/lincc/nested/dropna/nested-pandas/.conda/lib/python3.10/site-packages/pandas/core/groupby/grouper.py:1043\u001b[0m, in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[0m\n\u001b[1;32m   1041\u001b[0m         in_axis, level, gpr \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m, gpr, \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1042\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1043\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(gpr)\n\u001b[1;32m   1044\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(gpr, Grouper) \u001b[39mand\u001b[39;00m gpr\u001b[39m.\u001b[39mkey \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1045\u001b[0m     \u001b[39m# Add key to exclusions\u001b[39;00m\n\u001b[1;32m   1046\u001b[0m     exclusions\u001b[39m.\u001b[39madd(gpr\u001b[39m.\u001b[39mkey)\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'idx'"
-     ]
-    }
-   ],
-   "source": [
-    "def calc_median(x):\n",
-    "    return x.median()\n",
-    "\n",
-    "\n",
-    "result = nf.packed.nest.to_flat().groupby('idx').apply(calc_median)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
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