diff --git a/docs/examples/rrlyr-period.ipynb b/docs/examples/rrlyr-period.ipynb
index 5e8f9742..2dfa0089 100644
--- a/docs/examples/rrlyr-period.ipynb
+++ b/docs/examples/rrlyr-period.ipynb
@@ -128,7 +128,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "3.10.11"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "83afbb17b435d9bf8b0d0042367da76f26510da1c5781f0ff6e6c518eab621ec"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/gettingstarted/quickstart.ipynb b/docs/gettingstarted/quickstart.ipynb
index b661ebe8..110442df 100644
--- a/docs/gettingstarted/quickstart.ipynb
+++ b/docs/gettingstarted/quickstart.ipynb
@@ -71,6 +71,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "ens.calc_nobs() # calculates number of observations, produces \"nobs_total\" column \n",
     "ens = ens.query(\"nobs_total >= 95 & nobs_total <= 105\", \"object\")"
    ]
   },
diff --git a/docs/tutorials/binning_slowly_changing_sources.ipynb b/docs/tutorials/binning_slowly_changing_sources.ipynb
index c68fea34..853e62b8 100644
--- a/docs/tutorials/binning_slowly_changing_sources.ipynb
+++ b/docs/tutorials/binning_slowly_changing_sources.ipynb
@@ -60,9 +60,9 @@
    "outputs": [],
    "source": [
     "fig, ax = plt.subplots(1, 1)\n",
-    "_ = ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 500)\n",
-    "_ = ax.set_xlabel(\"Time (MJD)\")\n",
-    "_ = ax.set_ylabel(\"Source Count\")"
+    "ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 500)\n",
+    "ax.set_xlabel(\"Time (MJD)\")\n",
+    "ax.set_ylabel(\"Source Count\")"
    ]
   },
   {
@@ -90,9 +90,9 @@
    "source": [
     "ens.bin_sources(time_window=7.0, offset=0.0)\n",
     "fig, ax = plt.subplots(1, 1)\n",
-    "_ = ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 500)\n",
-    "_ = ax.set_xlabel(\"Time (MJD)\")\n",
-    "_ = ax.set_ylabel(\"Source Count\")"
+    "ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 500)\n",
+    "ax.set_xlabel(\"Time (MJD)\")\n",
+    "ax.set_ylabel(\"Source Count\")"
    ]
   },
   {
@@ -120,9 +120,9 @@
    "source": [
     "ens.bin_sources(time_window=28.0, offset=0.0, custom_aggr={\"midPointTai\": \"min\"})\n",
     "fig, ax = plt.subplots(1, 1)\n",
-    "_ = ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 500)\n",
-    "_ = ax.set_xlabel(\"Time (MJD)\")\n",
-    "_ = ax.set_ylabel(\"Source Count\")"
+    "ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 500)\n",
+    "ax.set_xlabel(\"Time (MJD)\")\n",
+    "ax.set_ylabel(\"Source Count\")"
    ]
   },
   {
@@ -150,9 +150,9 @@
     "ens.from_source_dict(rows, column_mapper=cmap)\n",
     "\n",
     "fig, ax = plt.subplots(1, 1)\n",
-    "_ = ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 60)\n",
-    "_ = ax.set_xlabel(\"Time (MJD)\")\n",
-    "_ = ax.set_ylabel(\"Source Count\")"
+    "ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 60)\n",
+    "ax.set_xlabel(\"Time (MJD)\")\n",
+    "ax.set_ylabel(\"Source Count\")"
    ]
   },
   {
@@ -179,9 +179,9 @@
     "ens.bin_sources(time_window=1.0, offset=0.0)\n",
     "\n",
     "fig, ax = plt.subplots(1, 1)\n",
-    "_ = ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 60)\n",
-    "_ = ax.set_xlabel(\"Time (MJD)\")\n",
-    "_ = ax.set_ylabel(\"Source Count\")"
+    "ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 60)\n",
+    "ax.set_xlabel(\"Time (MJD)\")\n",
+    "ax.set_ylabel(\"Source Count\")"
    ]
   },
   {
@@ -209,9 +209,9 @@
     "ens.bin_sources(time_window=1.0, offset=0.5)\n",
     "\n",
     "fig, ax = plt.subplots(1, 1)\n",
-    "_ = ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 60)\n",
-    "_ = ax.set_xlabel(\"Time (MJD)\")\n",
-    "_ = ax.set_ylabel(\"Source Count\")"
+    "ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 60)\n",
+    "ax.set_xlabel(\"Time (MJD)\")\n",
+    "ax.set_ylabel(\"Source Count\")"
    ]
   },
   {
@@ -259,9 +259,9 @@
     "ens.bin_sources(time_window=1.0, offset=0.5)\n",
     "\n",
     "fig, ax = plt.subplots(1, 1)\n",
-    "_ = ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 60)\n",
-    "_ = ax.set_xlabel(\"Time (MJD)\")\n",
-    "_ = ax.set_ylabel(\"Source Count\")"
+    "ax.hist(ens._source[\"midPointTai\"].compute().tolist(), 60)\n",
+    "ax.set_xlabel(\"Time (MJD)\")\n",
+    "ax.set_ylabel(\"Source Count\")"
    ]
   },
   {
@@ -290,7 +290,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.6"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/tutorials/structure_function_showcase.ipynb b/docs/tutorials/structure_function_showcase.ipynb
index 4090914d..592436fe 100644
--- a/docs/tutorials/structure_function_showcase.ipynb
+++ b/docs/tutorials/structure_function_showcase.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,30 +53,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'magnitude [unit]')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x750 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from eztao.carma import DRW_term\n",
     "from eztao.ts import gpSimRand\n",
@@ -134,20 +113,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We show here structure function for the same 10 lightcurves\n",
     "plt.figure()\n",
@@ -198,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,30 +209,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/astro/users/ncaplar/miniconda3/envs/tiny_lsst/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
-      "Perhaps you already have a cluster running?\n",
-      "Hosting the HTTP server on port 36509 instead\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<tape.ensemble.Ensemble at 0x7fdfe2b1bf40>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# First, we create all the columns that we will want to fill\n",
     "# In addition to time, measurement and errors, this includes \n",
@@ -304,192 +251,30 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The `ensemble` has an `object` table, capturing the information about the global properties of each \n",
-    "lightcurve (such as a number of observations), while the actual observations are stored in the `source` table. \n",
-    "More information is available in the `Working with the TAPE Ensemble object` tutorial."
+    "lightcurve, while the actual observations are stored in the `source` table. In this case, our object table\n",
+    "is empty, as no such information is provided. More information is available in the \n",
+    "`Working with the TAPE Ensemble object` tutorial."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>filter_ens</th>\n",
-       "      <th>nobs_r</th>\n",
-       "      <th>nobs_total</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id_ens</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>200</td>\n",
-       "      <td>200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>200</td>\n",
-       "      <td>200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>200</td>\n",
-       "      <td>200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>200</td>\n",
-       "      <td>200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>200</td>\n",
-       "      <td>200</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "filter_ens  nobs_r  nobs_total\n",
-       "id_ens                        \n",
-       "0              200         200\n",
-       "1              200         200\n",
-       "2              200         200\n",
-       "3              200         200\n",
-       "4              200         200"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.head(\"object\", 5)  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th></th>\n",
-       "      <th>t_ens</th>\n",
-       "      <th>y_ens</th>\n",
-       "      <th>yerr_ens</th>\n",
-       "      <th>filter_ens</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id_ens</th>\n",
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-       "    <tr>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>101.115112</td>\n",
-       "      <td>-0.075878</td>\n",
-       "      <td>0.018284</td>\n",
-       "      <td>r</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>104.400440</td>\n",
-       "      <td>-0.112991</td>\n",
-       "      <td>0.017365</td>\n",
-       "      <td>r</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             t_ens     y_ens  yerr_ens filter_ens\n",
-       "id_ens                                           \n",
-       "0        91.989199 -0.064003  0.018550          r\n",
-       "0        92.354235 -0.063581  0.018565          r\n",
-       "0        98.559856 -0.005713  0.019927          r\n",
-       "0       101.115112 -0.075878  0.018284          r\n",
-       "0       104.400440 -0.112991  0.017365          r"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.head(\"source\", 5)  "
    ]
@@ -514,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -542,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -553,212 +338,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "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>lc_id</th>\n",
-       "      <th>band</th>\n",
-       "      <th>dt</th>\n",
-       "      <th>sf2</th>\n",
-       "      <th>1_sigma</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>combined</td>\n",
-       "      <td>r</td>\n",
-       "      <td>32.831472</td>\n",
-       "      <td>0.019618</td>\n",
-       "      <td>0.000105</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>combined</td>\n",
-       "      <td>r</td>\n",
-       "      <td>102.011323</td>\n",
-       "      <td>0.049699</td>\n",
-       "      <td>0.000318</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>combined</td>\n",
-       "      <td>r</td>\n",
-       "      <td>210.219968</td>\n",
-       "      <td>0.071141</td>\n",
-       "      <td>0.000461</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>combined</td>\n",
-       "      <td>r</td>\n",
-       "      <td>302.687132</td>\n",
-       "      <td>0.072913</td>\n",
-       "      <td>0.000297</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>combined</td>\n",
-       "      <td>r</td>\n",
-       "      <td>385.967629</td>\n",
-       "      <td>0.075691</td>\n",
-       "      <td>0.000317</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      lc_id band          dt       sf2   1_sigma\n",
-       "0  combined    r   32.831472  0.019618  0.000105\n",
-       "1  combined    r  102.011323  0.049699  0.000318\n",
-       "2  combined    r  210.219968  0.071141  0.000461\n",
-       "3  combined    r  302.687132  0.072913  0.000297\n",
-       "4  combined    r  385.967629  0.075691  0.000317"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "res_sf2.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "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>lc_id</th>\n",
-       "      <th>band</th>\n",
-       "      <th>dt</th>\n",
-       "      <th>sf2</th>\n",
-       "      <th>1_sigma</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>33.432537</td>\n",
-       "      <td>0.017711</td>\n",
-       "      <td>0.000863</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>129.368012</td>\n",
-       "      <td>0.064481</td>\n",
-       "      <td>0.003192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>283.996548</td>\n",
-       "      <td>0.086655</td>\n",
-       "      <td>0.002505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>365.770820</td>\n",
-       "      <td>0.083244</td>\n",
-       "      <td>0.003209</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>444.590232</td>\n",
-       "      <td>0.062919</td>\n",
-       "      <td>0.002613</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  lc_id  band          dt       sf2   1_sigma\n",
-       "0     0     0   33.432537  0.017711  0.000863\n",
-       "1     0     0  129.368012  0.064481  0.003192\n",
-       "2     0     0  283.996548  0.086655  0.002505\n",
-       "3     0     0  365.770820  0.083244  0.003209\n",
-       "4     0     0  444.590232  0.062919  0.002613"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "res_one.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.errorbar(res_sf2['dt'], res_sf2['sf2'], yerr=res_sf2['1_sigma'],\n",
@@ -789,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -805,20 +405,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Show all of the 100 results in faint yellow\n",
     "plt.plot(res_one['dt'], res_resample_arr.transpose(), alpha=0.3, color='yellow')\n",
@@ -851,30 +440,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fdfc82ea770>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.hist(res_resample_arr.transpose()[0])\n",
     "err_manual = (np.quantile(res_resample_arr.transpose()[0], 0.84) -\n",
@@ -911,7 +479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -938,30 +506,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fe035e56b00>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plt.plot(res_basic['dt'], res_basic['sf2'], 'b', label='Basic', lw = 3, marker = 'o')\n",
     "plt.plot(res_macleod['dt'], res_macleod['sf2'], 'g',marker='.', label='MacLeod 2012')\n",
@@ -1004,7 +551,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "tape",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1018,7 +565,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.8.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/tape_datasets.ipynb b/docs/tutorials/tape_datasets.ipynb
index 788b8883..1cd3670f 100644
--- a/docs/tutorials/tape_datasets.ipynb
+++ b/docs/tutorials/tape_datasets.ipynb
@@ -85,8 +85,7 @@
     "    flux_col=\"psFlux\",\n",
     "    err_col=\"psFluxErr\",\n",
     "    band_col=\"filterName\",\n",
-    "    nobs_total_col=\"nobs_total\",\n",
-    "    nobs_band_cols=[\"nobs_g\", \"nobs_r\"])\n",
+    ")\n",
     "\n",
     "# Read in data from a parquet file that contains source (timeseries) data\n",
     "ens.from_parquet(source_file=f\"{rel_path}/source/test_source.parquet\",\n",
diff --git a/docs/tutorials/working_with_the_ensemble.ipynb b/docs/tutorials/working_with_the_ensemble.ipynb
index c5098095..10110329 100644
--- a/docs/tutorials/working_with_the_ensemble.ipynb
+++ b/docs/tutorials/working_with_the_ensemble.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:34.203827Z",
@@ -58,23 +58,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.125402Z",
      "start_time": "2023-08-30T14:58:34.190790Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<tape.ensemble.Ensemble at 0x13fa96dd0>"
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tape.ensemble import Ensemble\n",
     "\n",
@@ -109,23 +100,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.209050Z",
      "start_time": "2023-08-30T14:58:36.115521Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<tape.ensemble.Ensemble at 0x13fa96dd0>"
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tape.utils import ColumnMapper\n",
     "\n",
@@ -160,24 +142,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.219081Z",
      "start_time": "2023-08-30T14:58:36.205629Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "Dask DataFrame Structure:\n                  time     flux    error    band\nnpartitions=1                                   \n0              float64  float64  float64  string\n9                  ...      ...      ...     ...\nDask Name: sort_index, 4 graph layers",
-      "text/html": "<div><strong>Dask DataFrame Structure:</strong></div>\n<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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n    </tr>\n    <tr>\n      <th>npartitions=1</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>float64</td>\n      <td>float64</td>\n      <td>float64</td>\n      <td>string</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n<div>Dask Name: sort_index, 4 graph layers</div>"
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens._source  # We have not actually loaded any data into memory"
    ]
@@ -191,24 +163,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.484627Z",
      "start_time": "2023-08-30T14:58:36.213215Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "     time        flux      error band\nid                                   \n0     1.0  120.851100  11.633225    g\n0     2.0  136.016225  12.635291    g\n0     3.0  100.005719  14.429710    g\n0     4.0  115.116629  11.786349    g\n0     5.0  107.337795  14.542676    g\n..    ...         ...        ...  ...\n9    96.0  138.371176  12.237541    r\n9    97.0  104.060829  10.920638    r\n9    98.0  149.920678  14.143664    r\n9    99.0  119.480601  10.154990    r\n9   100.0  145.260138  14.733641    r\n\n[1000 rows x 4 columns]",
-      "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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>120.851100</td>\n      <td>11.633225</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>136.016225</td>\n      <td>12.635291</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>3.0</td>\n      <td>100.005719</td>\n      <td>14.429710</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>4.0</td>\n      <td>115.116629</td>\n      <td>11.786349</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>5.0</td>\n      <td>107.337795</td>\n      <td>14.542676</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>96.0</td>\n      <td>138.371176</td>\n      <td>12.237541</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>97.0</td>\n      <td>104.060829</td>\n      <td>10.920638</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>98.0</td>\n      <td>149.920678</td>\n      <td>14.143664</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>99.0</td>\n      <td>119.480601</td>\n      <td>10.154990</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>100.0</td>\n      <td>145.260138</td>\n      <td>14.733641</td>\n      <td>r</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 4 columns</p>\n</div>"
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.compute(\"source\")  # Compute lets dask know we're ready to bring the data into memory"
    ]
@@ -243,44 +205,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.696142Z",
      "start_time": "2023-08-30T14:58:36.361967Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Object Table\n",
-      "<class 'dask.dataframe.core.DataFrame'>\n",
-      "Index: 10 entries, 0 to 9\n",
-      "Data columns (total 3 columns):\n",
-      " #   Column      Non-Null Count  Dtype\n",
-      "---  ------      --------------  -----\n",
-      " 0   nobs_g      10 non-null      float64\n",
-      " 1   nobs_r      10 non-null      float64\n",
-      " 2   nobs_total  10 non-null      float64\n",
-      "dtypes: float64(3)\n",
-      "memory usage: 320.0 bytes\n",
-      "Source Table\n",
-      "<class 'dask.dataframe.core.DataFrame'>\n",
-      "Index: 1000 entries, 0 to 9\n",
-      "Data columns (total 4 columns):\n",
-      " #   Column  Non-Null Count  Dtype\n",
-      "---  ------  --------------  -----\n",
-      " 0   time    1000 non-null      float64\n",
-      " 1   flux    1000 non-null      float64\n",
-      " 2   error   1000 non-null      float64\n",
-      " 3   band    1000 non-null      string\n",
-      "dtypes: float64(3), string(1)\n",
-      "memory usage: 36.1 KB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Inspection\n",
     "\n",
@@ -296,48 +228,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.696879Z",
      "start_time": "2023-08-30T14:58:36.510953Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "band  nobs_g  nobs_r  nobs_total\nid                              \n0         50      50         100\n1         50      50         100\n2         50      50         100\n3         50      50         100\n4         50      50         100",
-      "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>band</th>\n      <th>nobs_g</th>\n      <th>nobs_r</th>\n      <th>nobs_total</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>50</td>\n      <td>50</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>50</td>\n      <td>50</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>50</td>\n      <td>50</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>50</td>\n      <td>50</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>50</td>\n      <td>50</td>\n      <td>100</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.head(\"object\", 5)  # Grabs the first 5 rows of the object table"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.697259Z",
      "start_time": "2023-08-30T14:58:36.561399Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "     time        flux      error band\nid                                   \n9    96.0  138.371176  12.237541    r\n9    97.0  104.060829  10.920638    r\n9    98.0  149.920678  14.143664    r\n9    99.0  119.480601  10.154990    r\n9   100.0  145.260138  14.733641    r",
-      "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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9</th>\n      <td>96.0</td>\n      <td>138.371176</td>\n      <td>12.237541</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>97.0</td>\n      <td>104.060829</td>\n      <td>10.920638</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>98.0</td>\n      <td>149.920678</td>\n      <td>14.143664</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>99.0</td>\n      <td>119.480601</td>\n      <td>10.154990</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>100.0</td>\n      <td>145.260138</td>\n      <td>14.733641</td>\n      <td>r</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.tail(\"source\", 5)  # Grabs the last 5 rows of the source table"
    ]
@@ -351,24 +263,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.697769Z",
      "start_time": "2023-08-30T14:58:36.592238Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "     time        flux      error band\nid                                   \n0     1.0  120.851100  11.633225    g\n0     2.0  136.016225  12.635291    g\n0     3.0  100.005719  14.429710    g\n0     4.0  115.116629  11.786349    g\n0     5.0  107.337795  14.542676    g\n..    ...         ...        ...  ...\n9    96.0  138.371176  12.237541    r\n9    97.0  104.060829  10.920638    r\n9    98.0  149.920678  14.143664    r\n9    99.0  119.480601  10.154990    r\n9   100.0  145.260138  14.733641    r\n\n[1000 rows x 4 columns]",
-      "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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>120.851100</td>\n      <td>11.633225</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>136.016225</td>\n      <td>12.635291</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>3.0</td>\n      <td>100.005719</td>\n      <td>14.429710</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>4.0</td>\n      <td>115.116629</td>\n      <td>11.786349</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>5.0</td>\n      <td>107.337795</td>\n      <td>14.542676</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>96.0</td>\n      <td>138.371176</td>\n      <td>12.237541</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>97.0</td>\n      <td>104.060829</td>\n      <td>10.920638</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>98.0</td>\n      <td>149.920678</td>\n      <td>14.143664</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>99.0</td>\n      <td>119.480601</td>\n      <td>10.154990</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>100.0</td>\n      <td>145.260138</td>\n      <td>14.733641</td>\n      <td>r</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 4 columns</p>\n</div>"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.compute(\"source\")"
    ]
@@ -386,24 +288,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.698305Z",
      "start_time": "2023-08-30T14:58:36.615492Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "     time        flux      error band\nid                                   \n0     2.0  136.016225  12.635291    g\n0    12.0  134.260975  10.685679    g\n0    14.0  143.905872  13.484091    g\n0    16.0  133.523376  13.777315    g\n0    21.0  140.037228  10.099401    g\n..    ...         ...        ...  ...\n9    91.0  140.368263  14.320720    r\n9    92.0  148.476901  12.239495    r\n9    96.0  138.371176  12.237541    r\n9    98.0  149.920678  14.143664    r\n9   100.0  145.260138  14.733641    r\n\n[422 rows x 4 columns]",
-      "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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>136.016225</td>\n      <td>12.635291</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>12.0</td>\n      <td>134.260975</td>\n      <td>10.685679</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>14.0</td>\n      <td>143.905872</td>\n      <td>13.484091</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>16.0</td>\n      <td>133.523376</td>\n      <td>13.777315</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>21.0</td>\n      <td>140.037228</td>\n      <td>10.099401</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>91.0</td>\n      <td>140.368263</td>\n      <td>14.320720</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>92.0</td>\n      <td>148.476901</td>\n      <td>12.239495</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>96.0</td>\n      <td>138.371176</td>\n      <td>12.237541</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>98.0</td>\n      <td>149.920678</td>\n      <td>14.143664</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>100.0</td>\n      <td>145.260138</td>\n      <td>14.733641</td>\n      <td>r</td>\n    </tr>\n  </tbody>\n</table>\n<p>422 rows × 4 columns</p>\n</div>"
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.query(f\"{ens._flux_col} > 130.0\", table=\"source\")\n",
     "ens.compute(\"source\")"
@@ -418,23 +310,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.754980Z",
      "start_time": "2023-08-30T14:58:36.669055Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "id\n0    False\n0     True\n0    False\n0    False\n0     True\n     ...  \n9    False\n9    False\n9    False\n9    False\n9    False\nName: error, Length: 422, dtype: bool"
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "keep_rows = ens._source[\"error\"] < 12.0\n",
     "keep_rows.compute()"
@@ -449,24 +332,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:36.792088Z",
      "start_time": "2023-08-30T14:58:36.690772Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "    time        flux      error band\nid                                  \n0   12.0  134.260975  10.685679    g\n0   21.0  140.037228  10.099401    g\n0   22.0  148.413079  10.131055    g\n0   24.0  134.616131  11.231055    g\n0   30.0  143.907125  11.395918    g\n..   ...         ...        ...  ...\n9   81.0  149.016644  10.755373    r\n9   85.0  130.071670  11.960329    r\n9   86.0  136.297942  11.419338    r\n9   88.0  134.215481  11.202422    r\n9   89.0  147.302751  11.271162    r\n\n[169 rows x 4 columns]",
-      "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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12.0</td>\n      <td>134.260975</td>\n      <td>10.685679</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>21.0</td>\n      <td>140.037228</td>\n      <td>10.099401</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>22.0</td>\n      <td>148.413079</td>\n      <td>10.131055</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>24.0</td>\n      <td>134.616131</td>\n      <td>11.231055</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>30.0</td>\n      <td>143.907125</td>\n      <td>11.395918</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>81.0</td>\n      <td>149.016644</td>\n      <td>10.755373</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>85.0</td>\n      <td>130.071670</td>\n      <td>11.960329</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>86.0</td>\n      <td>136.297942</td>\n      <td>11.419338</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>88.0</td>\n      <td>134.215481</td>\n      <td>11.202422</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>89.0</td>\n      <td>147.302751</td>\n      <td>11.271162</td>\n      <td>r</td>\n    </tr>\n  </tbody>\n</table>\n<p>169 rows × 4 columns</p>\n</div>"
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.filter_from_series(keep_rows, table=\"source\")\n",
     "ens.compute(\"source\")"
@@ -481,44 +354,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:37.026887Z",
      "start_time": "2023-08-30T14:58:36.715537Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Object Table\n",
-      "<class 'dask.dataframe.core.DataFrame'>\n",
-      "Index: 10 entries, 0 to 9\n",
-      "Data columns (total 3 columns):\n",
-      " #   Column      Non-Null Count  Dtype\n",
-      "---  ------      --------------  -----\n",
-      " 0   nobs_g      10 non-null      float64\n",
-      " 1   nobs_r      10 non-null      float64\n",
-      " 2   nobs_total  10 non-null      float64\n",
-      "dtypes: float64(3)\n",
-      "memory usage: 320.0 bytes\n",
-      "Source Table\n",
-      "<class 'dask.dataframe.core.DataFrame'>\n",
-      "Index: 169 entries, 0 to 9\n",
-      "Data columns (total 4 columns):\n",
-      " #   Column  Non-Null Count  Dtype\n",
-      "---  ------  --------------  -----\n",
-      " 0   time    169 non-null      float64\n",
-      " 1   flux    169 non-null      float64\n",
-      " 2   error   169 non-null      float64\n",
-      " 3   band    169 non-null      string\n",
-      "dtypes: float64(3), string(1)\n",
-      "memory usage: 6.1 KB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Cleaning nans\n",
     "ens.dropna(table=\"source\")  # clean nans from source table\n",
@@ -549,24 +392,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:37.095991Z",
      "start_time": "2023-08-30T14:58:36.917820Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "    time        flux      error band band2\nid                                        \n0   12.0  134.260975  10.685679    g    g2\n0   21.0  140.037228  10.099401    g    g2\n0   22.0  148.413079  10.131055    g    g2\n0   24.0  134.616131  11.231055    g    g2\n0   30.0  143.907125  11.395918    g    g2\n..   ...         ...        ...  ...   ...\n9   81.0  149.016644  10.755373    r    r2\n9   85.0  130.071670  11.960329    r    r2\n9   86.0  136.297942  11.419338    r    r2\n9   88.0  134.215481  11.202422    r    r2\n9   89.0  147.302751  11.271162    r    r2\n\n[169 rows x 5 columns]",
-      "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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n      <th>band2</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12.0</td>\n      <td>134.260975</td>\n      <td>10.685679</td>\n      <td>g</td>\n      <td>g2</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>21.0</td>\n      <td>140.037228</td>\n      <td>10.099401</td>\n      <td>g</td>\n      <td>g2</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>22.0</td>\n      <td>148.413079</td>\n      <td>10.131055</td>\n      <td>g</td>\n      <td>g2</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>24.0</td>\n      <td>134.616131</td>\n      <td>11.231055</td>\n      <td>g</td>\n      <td>g2</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>30.0</td>\n      <td>143.907125</td>\n      <td>11.395918</td>\n      <td>g</td>\n      <td>g2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>81.0</td>\n      <td>149.016644</td>\n      <td>10.755373</td>\n      <td>r</td>\n      <td>r2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>85.0</td>\n      <td>130.071670</td>\n      <td>11.960329</td>\n      <td>r</td>\n      <td>r2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>86.0</td>\n      <td>136.297942</td>\n      <td>11.419338</td>\n      <td>r</td>\n      <td>r2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>88.0</td>\n      <td>134.215481</td>\n      <td>11.202422</td>\n      <td>r</td>\n      <td>r2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>89.0</td>\n      <td>147.302751</td>\n      <td>11.271162</td>\n      <td>r</td>\n      <td>r2</td>\n    </tr>\n  </tbody>\n</table>\n<p>169 rows × 5 columns</p>\n</div>"
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Add a new column so we can filter it out later.\n",
     "ens._source = ens._source.assign(band2=ens._source[\"band\"] + \"2\")\n",
@@ -575,24 +408,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:37.096860Z",
      "start_time": "2023-08-30T14:58:36.937579Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "    time        flux      error band\nid                                  \n0   12.0  134.260975  10.685679    g\n0   21.0  140.037228  10.099401    g\n0   22.0  148.413079  10.131055    g\n0   24.0  134.616131  11.231055    g\n0   30.0  143.907125  11.395918    g\n..   ...         ...        ...  ...\n9   81.0  149.016644  10.755373    r\n9   85.0  130.071670  11.960329    r\n9   86.0  136.297942  11.419338    r\n9   88.0  134.215481  11.202422    r\n9   89.0  147.302751  11.271162    r\n\n[169 rows x 4 columns]",
-      "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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12.0</td>\n      <td>134.260975</td>\n      <td>10.685679</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>21.0</td>\n      <td>140.037228</td>\n      <td>10.099401</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>22.0</td>\n      <td>148.413079</td>\n      <td>10.131055</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>24.0</td>\n      <td>134.616131</td>\n      <td>11.231055</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>30.0</td>\n      <td>143.907125</td>\n      <td>11.395918</td>\n      <td>g</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>81.0</td>\n      <td>149.016644</td>\n      <td>10.755373</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>85.0</td>\n      <td>130.071670</td>\n      <td>11.960329</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>86.0</td>\n      <td>136.297942</td>\n      <td>11.419338</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>88.0</td>\n      <td>134.215481</td>\n      <td>11.202422</td>\n      <td>r</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>89.0</td>\n      <td>147.302751</td>\n      <td>11.271162</td>\n      <td>r</td>\n    </tr>\n  </tbody>\n</table>\n<p>169 rows × 4 columns</p>\n</div>"
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.select([\"time\", \"flux\", \"error\", \"band\"], table=\"source\")\n",
     "ens.compute(\"source\")"
@@ -611,24 +434,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:37.097571Z",
      "start_time": "2023-08-30T14:58:36.958927Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "    time        flux      error band   lower_bnd\nid                                              \n0   12.0  134.260975  10.685679    g  112.889618\n0   21.0  140.037228  10.099401    g  119.838427\n0   22.0  148.413079  10.131055    g  128.150969\n0   24.0  134.616131  11.231055    g  112.154020\n0   30.0  143.907125  11.395918    g  121.115288\n..   ...         ...        ...  ...         ...\n9   81.0  149.016644  10.755373    r  127.505899\n9   85.0  130.071670  11.960329    r  106.151012\n9   86.0  136.297942  11.419338    r  113.459267\n9   88.0  134.215481  11.202422    r  111.810638\n9   89.0  147.302751  11.271162    r  124.760428\n\n[169 rows x 5 columns]",
-      "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>time</th>\n      <th>flux</th>\n      <th>error</th>\n      <th>band</th>\n      <th>lower_bnd</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12.0</td>\n      <td>134.260975</td>\n      <td>10.685679</td>\n      <td>g</td>\n      <td>112.889618</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>21.0</td>\n      <td>140.037228</td>\n      <td>10.099401</td>\n      <td>g</td>\n      <td>119.838427</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>22.0</td>\n      <td>148.413079</td>\n      <td>10.131055</td>\n      <td>g</td>\n      <td>128.150969</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>24.0</td>\n      <td>134.616131</td>\n      <td>11.231055</td>\n      <td>g</td>\n      <td>112.154020</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>30.0</td>\n      <td>143.907125</td>\n      <td>11.395918</td>\n      <td>g</td>\n      <td>121.115288</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>81.0</td>\n      <td>149.016644</td>\n      <td>10.755373</td>\n      <td>r</td>\n      <td>127.505899</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>85.0</td>\n      <td>130.071670</td>\n      <td>11.960329</td>\n      <td>r</td>\n      <td>106.151012</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>86.0</td>\n      <td>136.297942</td>\n      <td>11.419338</td>\n      <td>r</td>\n      <td>113.459267</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>88.0</td>\n      <td>134.215481</td>\n      <td>11.202422</td>\n      <td>r</td>\n      <td>111.810638</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>89.0</td>\n      <td>147.302751</td>\n      <td>11.271162</td>\n      <td>r</td>\n      <td>124.760428</td>\n    </tr>\n  </tbody>\n</table>\n<p>169 rows × 5 columns</p>\n</div>"
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ens.assign(table=\"source\", lower_bnd=lambda x: x[\"flux\"] - 2.0 * x[\"error\"])\n",
     "ens.compute(table=\"source\")"
@@ -646,23 +459,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:37.492980Z",
      "start_time": "2023-08-30T14:58:36.981314Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "id\n0    {'g': -0.8833723170736909, 'r': -0.81291313232...\n1    {'g': -0.7866661902102343, 'r': -0.79927945599...\n2    {'g': -0.8650811883274131, 'r': -0.87939085289...\n3    {'g': -0.9140015912865537, 'r': -0.90284371456...\n4    {'g': -0.8232578922439672, 'r': -0.81922455220...\n5    {'g': -0.668795976899231, 'r': -0.784477243304...\n6    {'g': -0.8115552290707235, 'r': -0.90666227394...\n7    {'g': -0.6217573153267577, 'r': -0.60999974938...\n8    {'g': -0.7001359525394822, 'r': -0.73620435205...\n9    {'g': -0.7266040976469818, 'r': -0.68878460237...\nName: stetsonJ, dtype: object"
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# using tape analysis functions\n",
     "from tape.analysis import calc_stetson_J\n",
@@ -673,43 +477,32 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "## Using light-curve package features\n",
     "\n",
     "`Ensemble.batch` also supports the use of [light-curve](https://pypi.org/project/light-curve/) package feature extractor:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "    amplitude  anderson_darling_normal  stetson_K\nid                                               \n0    7.076052                 0.177751   0.834036\n1    8.591493                 0.513749   0.769344\n2    8.141189                 0.392628   0.856307\n3    5.751674                 0.295631   0.809191\n4    7.871321                 0.555775   0.849305\n5    8.666473                 0.342937   0.823194\n6    8.649326                 0.241117   0.832815\n7    8.856443                 1.141906   0.772267\n8    9.297713                 0.984247   0.968132\n9    8.774109                 0.335798   0.754355",
-      "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>amplitude</th>\n      <th>anderson_darling_normal</th>\n      <th>stetson_K</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7.076052</td>\n      <td>0.177751</td>\n      <td>0.834036</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8.591493</td>\n      <td>0.513749</td>\n      <td>0.769344</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8.141189</td>\n      <td>0.392628</td>\n      <td>0.856307</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5.751674</td>\n      <td>0.295631</td>\n      <td>0.809191</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7.871321</td>\n      <td>0.555775</td>\n      <td>0.849305</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>8.666473</td>\n      <td>0.342937</td>\n      <td>0.823194</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>8.649326</td>\n      <td>0.241117</td>\n      <td>0.832815</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8.856443</td>\n      <td>1.141906</td>\n      <td>0.772267</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9.297713</td>\n      <td>0.984247</td>\n      <td>0.968132</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>8.774109</td>\n      <td>0.335798</td>\n      <td>0.754355</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-30T14:58:37.514514Z",
+     "start_time": "2023-08-30T14:58:37.494001Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "import light_curve as licu\n",
     "\n",
     "extractor = licu.Extractor(licu.Amplitude(), licu.AndersonDarlingNormal(), licu.StetsonK())\n",
     "res = ens.batch(extractor, compute=True, band_to_calc=\"g\")\n",
     "res"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-30T14:58:37.514514Z",
-     "start_time": "2023-08-30T14:58:37.494001Z"
-    }
-   }
+   ]
   },
   {
    "attachments": {},
@@ -724,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:37.519972Z",
@@ -760,23 +553,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:37.583850Z",
      "start_time": "2023-08-30T14:58:37.519056Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "id\n0     {'g': 140.03722843377682, 'r': 138.955084796142}\n1    {'g': 140.91515408243285, 'r': 141.44229039903...\n2    {'g': 139.42093950235392, 'r': 142.21649742828...\n3    {'g': 137.01337116218363, 'r': 139.05032340951...\n4    {'g': 134.61800608117045, 'r': 139.76505837028...\n5    {'g': 135.55144382138587, 'r': 139.41361800167...\n6    {'g': 142.93611137557423, 'r': 137.20679606847...\n7       {'g': 144.52647796976, 'r': 132.2470836256106}\n8     {'g': 144.7469760076462, 'r': 137.5226773361662}\n9    {'g': 136.89977482019205, 'r': 136.29794229244...\nName: id, dtype: object"
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Applying the function to the ensemble\n",
     "res = ens.batch(my_flux_average, \"flux\", \"band\", compute=True, meta=None, method=\"median\")\n",
@@ -792,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-08-30T14:58:37.764841Z",
@@ -821,7 +605,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.6"
   },
   "vscode": {
    "interpreter": {
diff --git a/src/tape/ensemble.py b/src/tape/ensemble.py
index 5d871853..c85b9174 100644
--- a/src/tape/ensemble.py
+++ b/src/tape/ensemble.py
@@ -39,11 +39,13 @@ def __init__(self, client=True, **kwargs):
         self._source_dirty = False  # Source Dirty Flag
         self._object_dirty = False  # Object Dirty Flag
 
+        self._source_temp = []  # List of temporary columns in Source
+        self._object_temp = []  # List of temporary columns in Object
+
         # Default to removing empty objects.
         self.keep_empty_objects = kwargs.get("keep_empty_objects", False)
 
         # Initialize critical column quantities
-        # Source
         self._id_col = None
         self._time_col = None
         self._flux_col = None
@@ -51,10 +53,6 @@ def __init__(self, client=True, **kwargs):
         self._band_col = None
         self._provenance_col = None
 
-        # Object, _id_col is shared
-        self._nobs_tot_col = None
-        self._nobs_band_cols = []
-
         self.client = None
         self.cleanup_client = False
 
@@ -384,7 +382,7 @@ def filter_from_series(self, keep_series, table="object"):
             self._source_dirty = True
         return self
 
-    def assign(self, table="object", **kwargs):
+    def assign(self, table="object", temporary=False, **kwargs):
         """Wrapper for dask.dataframe.DataFrame.assign()
 
         Parameters
@@ -395,6 +393,13 @@ def assign(self, table="object", **kwargs):
         kwargs: dict of {str: callable or Series}
             Each argument is the name of a new column to add and its value specifies
             how to fill it. A callable is called for each row and a series is copied in.
+        temporary: 'bool', optional
+            Dictates whether the resulting columns are flagged as "temporary"
+            columns within the Ensemble. Temporary columns are dropped when
+            table syncs are performed, as their information is often made
+            invalid by future operations. For example, the number of
+            observations information is made invalid by a filter on the source
+            table. Defaults to False.
 
         Returns
         -------
@@ -412,11 +417,23 @@ def assign(self, table="object", **kwargs):
         self._lazy_sync_tables(table)
 
         if table == "object":
+            pre_cols = self._object.columns
             self._object = self._object.assign(**kwargs)
             self._object_dirty = True
+            post_cols = self._object.columns
+
+            if temporary:
+                self._object_temp.extend(col for col in post_cols if col not in pre_cols)
+
         elif table == "source":
+            pre_cols = self._source.columns
             self._source = self._source.assign(**kwargs)
             self._source_dirty = True
+            post_cols = self._source.columns
+
+            if temporary:
+                self._source_temp.extend(col for col in post_cols if col not in pre_cols)
+
         else:
             raise ValueError(f"{table} is not one of 'object' or 'source'")
         return self
@@ -510,7 +527,7 @@ def coalesce_partition(df, input_cols, output_col):
 
         return self
 
-    def calc_nobs(self, by_band=False, label="nobs"):
+    def calc_nobs(self, by_band=False, label="nobs", temporary=True):
         """Calculates the number of observations per lightcurve.
 
         Parameters
@@ -521,6 +538,13 @@ def calc_nobs(self, by_band=False, label="nobs"):
         label: `str`, optional
             The label used to generate output columns. "_total" and the band
             labels (e.g. "_g") are appended.
+        temporary: 'bool', optional
+            Dictates whether the resulting columns are flagged as "temporary"
+            columns within the Ensemble. Temporary columns are dropped when
+            table syncs are performed, as their information is often made
+            invalid by future operations. For example, the number of
+            observations information is made invalid by a filter on the source
+            table. Defaults to True.
 
         Returns
         -------
@@ -528,8 +552,6 @@ def calc_nobs(self, by_band=False, label="nobs"):
             The ensemble object with nobs columns added to the object table.
         """
 
-        obj_npartitions = self._object.npartitions  # to repartition output columns
-
         if by_band:
             band_counts = (
                 self._source.groupby([self._id_col])[self._band_col]  # group by each object
@@ -538,19 +560,38 @@ def calc_nobs(self, by_band=False, label="nobs"):
                 .reset_index()  # break up the multiindex
                 .categorize(columns=[self._band_col])  # retype the band labels as categories
                 .pivot_table(values=self._band_col, index=self._id_col, columns=self._band_col, aggfunc="sum")
-                .repartition(obj_npartitions)  # counts inherits the source partitions
             )  # the pivot_table call makes each band_count a column of the id_col row
 
+            # repartition the result to align with object
+            if self._object.known_divisions:
+                self._object.divisions = tuple([None for i in range(self._object.npartitions + 1)])
+                band_counts = band_counts.repartition(npartitions=self._object.npartitions)
+            else:
+                band_counts = band_counts.repartition(npartitions=self._object.npartitions)
+
             # short-hand for calculating nobs_total
             band_counts["total"] = band_counts[list(band_counts.columns)].sum(axis=1)
 
             bands = band_counts.columns.values
             self._object = self._object.assign(**{label + "_" + band: band_counts[band] for band in bands})
 
+            if temporary:
+                self._object_temp.extend(label + "_" + band for band in bands)
+
         else:
-            counts = self._source.groupby([self._id_col])[self._band_col].aggregate("count")
-            counts = counts.repartition(obj_npartitions)  # counts inherits the source partitions
-            self._object = self._object.assign(**{label + "_total": counts})  # assign new columns
+            counts = self._source.groupby([self._id_col])[[self._band_col]].aggregate("count")
+
+            # repartition the result to align with object
+            if self._object.known_divisions:
+                self._object.divisions = tuple([None for i in range(self._object.npartitions + 1)])
+                counts = counts.repartition(npartitions=self._object.npartitions)
+            else:
+                counts = counts.repartition(npartitions=self._object.npartitions)
+
+            self._object = self._object.assign(**{label + "_total": counts[self._band_col]})
+
+            if temporary:
+                self._object_temp.extend([label + "_total"])
 
         return self
 
@@ -563,19 +604,24 @@ def prune(self, threshold=50, col_name=None):
             The minimum number of observations needed to retain an object.
             Default is 50.
         col_name: `str`, optional
-            The name of the column to assess the threshold
+            The name of the column to assess the threshold if available in
+            the object table. If not specified, the ensemble will calculate
+            the number of observations and filter on the total (sum across
+            bands).
 
         Returns
         -------
         ensemble: `tape.ensemble.Ensemble`
             The ensemble object with pruned rows removed
         """
-        if not col_name:
-            col_name = self._nobs_tot_col
 
         # Sync Required if source is dirty
         self._lazy_sync_tables(table="object")
 
+        if not col_name:
+            self.calc_nobs(label="nobs")
+            col_name = "nobs_total"
+
         # Mask on object table
         mask = self._object[col_name] >= threshold
         self._object = self._object[mask]
@@ -952,21 +998,9 @@ def from_dask_dataframe(
 
         if object_frame is None:  # generate an indexed object table from source
             self._object = self._generate_object_table()
-            self._nobs_bands = [col for col in list(self._object.columns) if col != self._nobs_tot_col]
+
         else:
             self._object = object_frame
-            if self._nobs_band_cols is None:
-                # sets empty nobs cols in object
-                unq_filters = np.unique(self._source[self._band_col])
-                self._nobs_band_cols = [f"nobs_{filt}" for filt in unq_filters]
-                for col in self._nobs_band_cols:
-                    self._object[col] = np.nan
-
-            # Handle nobs_total column
-            if self._nobs_tot_col is None:
-                self._object["nobs_total"] = np.nan
-                self._nobs_tot_col = "nobs_total"
-
             self._object = self._object.set_index(self._id_col)
 
             # Optionally sync the tables, recalculates nobs columns
@@ -1037,8 +1071,6 @@ def make_column_map(self):
             err_col=self._err_col,
             band_col=self._band_col,
             provenance_col=self._provenance_col,
-            nobs_total_col=self._nobs_tot_col,
-            nobs_band_cols=self._nobs_band_cols,
         )
         return result
 
@@ -1100,10 +1132,6 @@ def _load_column_mapper(self, column_mapper, **kwargs):
             # Assign optional columns if provided
             if column_mapper.map["provenance_col"] is not None:
                 self._provenance_col = column_mapper.map["provenance_col"]
-            if column_mapper.map["nobs_total_col"] is not None:
-                self._nobs_tot_col = column_mapper.map["nobs_total_col"]
-            if column_mapper.map["nobs_band_cols"] is not None:
-                self._nobs_band_cols = column_mapper.map["nobs_band_cols"]
 
         else:
             raise ValueError(f"Missing required column mapping information: {needed}")
@@ -1170,11 +1198,6 @@ def from_parquet(
             columns = [self._time_col, self._flux_col, self._err_col, self._band_col]
             if self._provenance_col is not None:
                 columns.append(self._provenance_col)
-            if self._nobs_tot_col is not None:
-                columns.append(self._nobs_tot_col)
-            if self._nobs_band_cols is not None:
-                for col in self._nobs_band_cols:
-                    columns.append(col)
 
         # Read in the source parquet file(s)
         source = dd.read_parquet(source_file, index=self._id_col, columns=columns, split_row_groups=True)
@@ -1360,47 +1383,10 @@ def convert_flux_to_mag(self, zero_point, zp_form="mag", out_col_name=None, flux
         return self
 
     def _generate_object_table(self):
-        """Generate the object table from the source table."""
-        counts = self._source.groupby([self._id_col, self._band_col])[self._time_col].aggregate("count")
-        res = (
-            counts.to_frame()
-            .reset_index()
-            .categorize(columns=[self._band_col])
-            .pivot_table(values=self._time_col, index=self._id_col, columns=self._band_col, aggfunc="sum")
-        )
-
-        # If the ensemble's keep_empty_objects attribute is True and there are previous
-        # objects, then copy them into the res table with counts of zero.
-        if self.keep_empty_objects and self._object is not None:
-            prev_partitions = self._object.npartitions
-
-            # Check that there are existing object ids.
-            object_inds = self._object.index.unique().values.compute()
-            if len(object_inds) > 0:
-                # Determine which object IDs are missing from the source table.
-                source_inds = self._source.index.unique().values.compute()
-                missing_inds = np.setdiff1d(object_inds, source_inds).tolist()
-
-                # Create a dataframe of the missing IDs with zeros for all bands and counts.
-                rows = {self._id_col: missing_inds}
-                for i in res.columns.values:
-                    rows[i] = [0] * len(missing_inds)
-
-                zero_pdf = pd.DataFrame(rows, dtype=int).set_index(self._id_col)
-                zero_ddf = dd.from_pandas(zero_pdf, sort=True, npartitions=1)
-
-                # Concatonate the zero dataframe onto the results.
-                res = dd.concat([res, zero_ddf], interleave_partitions=True).astype(int)
-                res = res.repartition(npartitions=prev_partitions)
-
-        # Rename bands to nobs_[band]
-        band_cols = {col: f"nobs_{col}" for col in list(res.columns)}
-        res = res.rename(columns=band_cols)
-
-        # Add total nobs by summing across each band.
-        if self._nobs_tot_col is None:
-            self._nobs_tot_col = "nobs_total"
-        res[self._nobs_tot_col] = res.sum(axis=1)
+        """Generate an empty object table from the source table."""
+        sor_idx = self._source.index.unique()
+        obj_df = pd.DataFrame(index=sor_idx)
+        res = dd.from_pandas(obj_df, npartitions=int(np.ceil(self._source.npartitions / 100)))
 
         return res
 
@@ -1438,15 +1424,24 @@ def _sync_tables(self):
             self._source = self._source.map_partitions(lambda x: x[x.index.isin(obj_idx)])
             self._source = self._source.persist()  # persist the source frame
 
+            # Drop Temporary Source Columns on Sync
+            if len(self._source_temp):
+                self._source = self._source.drop(columns=self._source_temp)
+                print(f"Temporary columns dropped from Source Table: {self._source_temp}")
+                self._source_temp = []
+
         if self._source_dirty:  # not elif
-            # Generate a new object table; updates n_obs, removes missing ids
-            new_obj = self._generate_object_table()
-
-            # Join old obj to new obj; pulls in other existing obj columns
-            self._object = new_obj.join(self._object, on=self._id_col, how="left", lsuffix="", rsuffix="_old")
-            old_cols = [col for col in list(self._object.columns) if "_old" in col]
-            self._object = self._object.drop(old_cols, axis=1)
-            self._object = self._object.persist()  # persist object
+            if not self.keep_empty_objects:
+                # Sync Source to Object; remove any objects that do not have sources
+                sor_idx = list(self._object.index.unique().compute())
+                self._object = self._object.map_partitions(lambda x: x[x.index.isin(sor_idx)])
+                self._object = self._object.persist()  # persist the object frame
+
+            # Drop Temporary Object Columns on Sync
+            if len(self._object_temp):
+                self._object = self._object.drop(columns=self._object_temp)
+                print(f"Temporary columns dropped from Object Table: {self._object_temp}")
+                self._object_temp = []
 
         # Now synced and clean
         self._source_dirty = False
diff --git a/src/tape/utils/column_mapper/column_mapper.py b/src/tape/utils/column_mapper/column_mapper.py
index 185d7e22..48d3ee6e 100644
--- a/src/tape/utils/column_mapper/column_mapper.py
+++ b/src/tape/utils/column_mapper/column_mapper.py
@@ -12,8 +12,6 @@ def __init__(
         err_col=None,
         band_col=None,
         provenance_col=None,
-        nobs_total_col=None,
-        nobs_band_cols=None,
     ):
         """
 
@@ -32,12 +30,6 @@ def __init__(
         provenance_col: 'str', optional
             Identifies which column contains the provenance information, if
             None the provenance column is generated.
-        nobs_band_cols: list of 'str', optional
-            Identifies which columns contain number of observations for each
-            band, if available in the input object file
-        nobs_total_col: 'str', optional
-            Identifies which column contains the total number of observations,
-            if available in the input object file
 
         Returns
         -------
@@ -53,8 +45,6 @@ def __init__(
             "err_col": err_col,
             "band_col": band_col,
             "provenance_col": provenance_col,
-            "nobs_total_col": nobs_total_col,
-            "nobs_band_cols": nobs_band_cols,
         }
 
         self.required = [
@@ -64,8 +54,6 @@ def __init__(
             Column("err_col", True),
             Column("band_col", True),
             Column("provenance_col", False),
-            Column("nobs_total_col", False),
-            Column("nobs_band_cols", False),
         ]
 
         self.known_maps = {"ZTF": ZTFColumnMapper}
@@ -135,8 +123,6 @@ def assign(
         err_col=None,
         band_col=None,
         provenance_col=None,
-        nobs_total_col=None,
-        nobs_band_cols=None,
     ):
         """Updates a given set of columns
 
@@ -169,8 +155,6 @@ def assign(
             "err_col": err_col,
             "band_col": band_col,
             "provenance_col": provenance_col,
-            "nobs_total_col": nobs_total_col,
-            "nobs_band_cols": nobs_band_cols,
         }
 
         for item in assign_map.items():
@@ -192,8 +176,6 @@ def _set_known_map(self):
             "err_col": "psFluxErr",
             "band_col": "filterName",
             "provenance_col": None,
-            "nobs_total_col": "nobs_total",
-            "nobs_band_cols": None,
         }
         return self
 
diff --git a/tests/tape_tests/conftest.py b/tests/tape_tests/conftest.py
index abd783aa..8e842386 100644
--- a/tests/tape_tests/conftest.py
+++ b/tests/tape_tests/conftest.py
@@ -150,6 +150,7 @@ def dask_dataframe_ensemble(dask_client):
 
     return ens
 
+
 # pylint: disable=redefined-outer-name
 @pytest.fixture
 def dask_dataframe_with_object_ensemble(dask_client):
@@ -188,6 +189,7 @@ def dask_dataframe_with_object_ensemble(dask_client):
 
     return ens
 
+
 # pylint: disable=redefined-outer-name
 @pytest.fixture
 def pandas_ensemble(dask_client):
@@ -215,6 +217,7 @@ def pandas_ensemble(dask_client):
 
     return ens
 
+
 # pylint: disable=redefined-outer-name
 @pytest.fixture
 def pandas_with_object_ensemble(dask_client):
@@ -230,7 +233,7 @@ def pandas_with_object_ensemble(dask_client):
 
     num_points = 1000
     all_bands = np.array(["r", "g", "b", "i"])
-    source_table =pd.DataFrame(
+    source_table = pd.DataFrame(
         {
             "id": 8000 + (np.arange(num_points) % n_obj),
             "time": np.arange(num_points),
diff --git a/tests/tape_tests/test_ensemble.py b/tests/tape_tests/test_ensemble.py
index 30932c09..ac12fef7 100644
--- a/tests/tape_tests/test_ensemble.py
+++ b/tests/tape_tests/test_ensemble.py
@@ -203,9 +203,8 @@ def test_from_source_dict(dask_client):
         assert src_table.iloc[i][ens._err_col] == rows[ens._err_col][i]
 
     # Check that the derived object table is correct.
-    assert obj_table.shape[0] == 2
-    assert obj_table.iloc[0][ens._nobs_tot_col] == 4
-    assert obj_table.iloc[1][ens._nobs_tot_col] == 5
+    assert 8001 in obj_table.index
+    assert 8002 in obj_table.index
 
 
 def test_insert(parquet_ensemble):
@@ -474,6 +473,63 @@ def test_lazy_sync_tables(parquet_ensemble):
     assert not parquet_ensemble._source_dirty
 
 
+def test_temporary_cols(parquet_ensemble):
+    """
+    Test that temporary columns are tracked and dropped as expected.
+    """
+
+    ens = parquet_ensemble
+    ens._object = ens._object.drop(columns=["nobs_r", "nobs_g", "nobs_total"])
+
+    # Make sure temp lists are available but empty
+    assert not len(ens._source_temp)
+    assert not len(ens._object_temp)
+
+    ens.calc_nobs(temporary=True)  # Generates "nobs_total"
+
+    # nobs_total should be a temporary column
+    assert "nobs_total" in ens._object_temp
+    assert "nobs_total" in ens._object.columns
+
+    ens.assign(nobs2=lambda x: x["nobs_total"] * 2, table="object", temporary=True)
+
+    # nobs2 should be a temporary column
+    assert "nobs2" in ens._object_temp
+    assert "nobs2" in ens._object.columns
+
+    # drop NaNs from source, source should be dirty now
+    ens.dropna(how="any", table="source")
+
+    assert ens._source_dirty
+
+    # try a sync
+    ens._sync_tables()
+
+    # nobs_total should be removed from object
+    assert "nobs_total" not in ens._object_temp
+    assert "nobs_total" not in ens._object.columns
+
+    # nobs2 should be removed from object
+    assert "nobs2" not in ens._object_temp
+    assert "nobs2" not in ens._object.columns
+
+    # add a source column that we manually set as dirty, don't have a function
+    # that adds temporary source columns at the moment
+    ens.assign(f2=lambda x: x[ens._flux_col] ** 2, table="source", temporary=True)
+
+    # prune object, object should be dirty
+    ens.prune(threshold=10)
+
+    assert ens._object_dirty
+
+    # try a sync
+    ens._sync_tables()
+
+    # f2 should be removed from source
+    assert "f2" not in ens._source_temp
+    assert "f2" not in ens._source.columns
+
+
 def test_dropna(parquet_ensemble):
     # Try passing in an unrecognized 'table' parameter and verify an exception is thrown
     with pytest.raises(ValueError):
@@ -560,25 +616,22 @@ def test_keep_zeros(parquet_ensemble):
     parquet_ensemble.dropna(table="source")
     parquet_ensemble._sync_tables()
 
+    # Check that objects are preserved after sync
     new_objects_pdf = parquet_ensemble._object.compute()
     assert len(new_objects_pdf.index) == len(old_objects_pdf.index)
     assert parquet_ensemble._object.npartitions == prev_npartitions
 
-    # Check that all counts have stayed the same except the filtered index,
-    # which should now be all zeros.
-    for i in old_objects_pdf.index.values:
-        for c in new_objects_pdf.columns.values:
-            if i == valid_id:
-                assert new_objects_pdf.loc[i, c] == 0
-            else:
-                assert new_objects_pdf.loc[i, c] == old_objects_pdf.loc[i, c]
-
 
 @pytest.mark.parametrize("by_band", [True, False])
-def test_calc_nobs(parquet_ensemble, by_band):
+@pytest.mark.parametrize("know_divisions", [True, False])
+def test_calc_nobs(parquet_ensemble, by_band, know_divisions):
     ens = parquet_ensemble
     ens._object = ens._object.drop(["nobs_g", "nobs_r", "nobs_total"], axis=1)
 
+    if know_divisions:
+        ens._object = ens._object.reset_index().set_index(ens._id_col)
+        assert ens._object.known_divisions
+
     ens.calc_nobs(by_band)
 
     lc = ens._object.loc[88472935274829959].compute()
diff --git a/tests/tape_tests/test_utils.py b/tests/tape_tests/test_utils.py
index 9b882fde..0a75aff8 100644
--- a/tests/tape_tests/test_utils.py
+++ b/tests/tape_tests/test_utils.py
@@ -23,9 +23,7 @@ def test_column_mapper():
     assert col_map.is_ready()  # col_map should now be ready
 
     # Assign the remaining columns
-    col_map.assign(
-        provenance_col="provenance", nobs_total_col="nobs_total", nobs_band_cols=["nobs_g", "nobs_r"]
-    )
+    col_map.assign(provenance_col="provenance")
 
     expected_map = {
         "id_col": "id",
@@ -34,8 +32,6 @@ def test_column_mapper():
         "err_col": "err",
         "band_col": "band",
         "provenance_col": "provenance",
-        "nobs_total_col": "nobs_total",
-        "nobs_band_cols": ["nobs_g", "nobs_r"],
     }
 
     assert col_map.map == expected_map  # The expected mapping
@@ -53,8 +49,6 @@ def test_column_mapper_init():
         err_col="err",
         band_col="band",
         provenance_col="provenance",
-        nobs_total_col="nobs_total",
-        nobs_band_cols=["nobs_g", "nobs_r"],
     )
 
     assert col_map.is_ready()  # col_map should be ready
@@ -66,8 +60,6 @@ def test_column_mapper_init():
         "err_col": "err",
         "band_col": "band",
         "provenance_col": "provenance",
-        "nobs_total_col": "nobs_total",
-        "nobs_band_cols": ["nobs_g", "nobs_r"],
     }
 
     assert col_map.map == expected_map  # The expected mapping