diff --git a/SCoPe_classification_demo.ipynb b/SCoPe_classification_demo.ipynb
new file mode 100644
index 0000000..0b4bc63
--- /dev/null
+++ b/SCoPe_classification_demo.ipynb
@@ -0,0 +1,1240 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e4c8a783-200a-4762-8b66-cf13cf8b3aa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
+    "colors = prop_cycle.by_key()['color']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06ba2568-f53e-44c9-9cba-4b99bad53ded",
+   "metadata": {},
+   "source": [
+    "#### Read field_296_100rows.csv (downsampled from field_296.csv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e8a073fd-3f33-4671-97d5-48f670b97433",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "field_296_100rows = pd.read_csv(f'field_296_100rows.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e137ddf7-bff4-4afb-a00e-4ec2e1581cc4",
+   "metadata": {},
+   "source": [
+    "#### Display dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1386d78d-745c-4483-8a53-5dddffee1842",
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>Gaia_EDR3___id</th>\n",
+       "      <th>AllWISE___id</th>\n",
+       "      <th>PS1_DR1___id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>period</th>\n",
+       "      <th>field</th>\n",
+       "      <th>ccd</th>\n",
+       "      <th>quad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>eb_xgb</th>\n",
+       "      <th>hp_xgb</th>\n",
+       "      <th>blyr_xgb</th>\n",
+       "      <th>rscvn_xgb</th>\n",
+       "      <th>rrd_xgb</th>\n",
+       "      <th>mir_xgb</th>\n",
+       "      <th>rrc_xgb</th>\n",
+       "      <th>bogus_xgb</th>\n",
+       "      <th>rrab_xgb</th>\n",
+       "      <th>agn_xgb</th>\n",
+       "    </tr>\n",
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+       "    </tr>\n",
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+       "    </tr>\n",
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+       "      <td>2373028431006093312</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
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+       "      <td>0</td>\n",
+       "      <td>88190168016806816</td>\n",
+       "      <td>16.801518</td>\n",
+       "      <td>-16.503011</td>\n",
+       "      <td>0.366488</td>\n",
+       "      <td>296</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
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+       "      <td>0.20</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 99 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               _id       Gaia_EDR3___id        AllWISE___id  \\\n",
+       "0   10296582009946                    0  140015201351056624   \n",
+       "1   10296531000371  2468292420539961088  170013701351041248   \n",
+       "2   10296332007046  2358756082883576832  188016701351063040   \n",
+       "3   10296352004611                    0                   0   \n",
+       "4   10296193002860  2355129515577873280  190018201351021696   \n",
+       "..             ...                  ...                 ...   \n",
+       "95  10296311002011                    0  142018201351013568   \n",
+       "96  10296162004575  2355334535841260160  190018201351060032   \n",
+       "97  10296332000029                    0  172015201351026912   \n",
+       "98  10296561018331  2373028431006093312  155013701351040992   \n",
+       "99  10296392037084                    0                   0   \n",
+       "\n",
+       "         PS1_DR1___id         ra        dec      period  field  ccd  quad  \\\n",
+       "0   90610143756592496  14.375673 -14.489669    5.751999    296   15     3   \n",
+       "1   91920166559039984  16.655579 -13.392433    0.253672    296   14     2   \n",
+       "2   88900182352849744  18.235299 -15.908833    0.025950    296    9     2   \n",
+       "3   88150190046276512  19.004638 -16.536509    0.033279    296    9     4   \n",
+       "4   85910189467390912  18.946842 -18.407839    0.025762    296    5     4   \n",
+       "..                ...        ...        ...         ...    ...  ...   ...   \n",
+       "95  85930135304772064  13.530474 -18.390206    0.099423    296    8     4   \n",
+       "96  86850190585978048  19.058643 -17.618552  637.168335    296    5     1   \n",
+       "97  89710179705849520  17.970427 -15.234044    0.079058    296    9     2   \n",
+       "98  92000149780744640  14.978093 -13.329795    0.034946    296   15     1   \n",
+       "99  88190168016806816  16.801518 -16.503011    0.366488    296   10     4   \n",
+       "\n",
+       "    ...  eb_xgb  hp_xgb  blyr_xgb  rscvn_xgb  rrd_xgb  mir_xgb  rrc_xgb  \\\n",
+       "0   ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "1   ...    0.00    0.00       0.0        0.0     0.77      0.0     0.34   \n",
+       "2   ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "3   ...    0.00    0.01       0.0        0.0     0.00      0.0     0.00   \n",
+       "4   ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "..  ...     ...     ...       ...        ...      ...      ...      ...   \n",
+       "95  ...    0.00    0.01       0.0        0.0     0.00      0.0     0.00   \n",
+       "96  ...    0.01    0.01       0.0        0.0     0.00      0.0     0.00   \n",
+       "97  ...    0.05    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "98  ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "99  ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "\n",
+       "    bogus_xgb  rrab_xgb  agn_xgb  \n",
+       "0        0.01      0.19     0.09  \n",
+       "1        0.00      0.19     0.00  \n",
+       "2        0.00      0.19     0.00  \n",
+       "3        0.01      0.20     0.00  \n",
+       "4        0.00      0.19     0.00  \n",
+       "..        ...       ...      ...  \n",
+       "95       0.01      0.19     0.00  \n",
+       "96       0.03      0.20     0.02  \n",
+       "97       0.03      0.32     0.00  \n",
+       "98       0.02      0.24     0.00  \n",
+       "99       0.00      0.20     0.00  \n",
+       "\n",
+       "[100 rows x 99 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "field_296_100rows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e42f2fc-2818-40bf-804c-16c1caac3a9b",
+   "metadata": {},
+   "source": [
+    "#### Describe dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f4b3f66b-3f64-43f4-b376-46768be7d164",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "\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>_id</th>\n",
+       "      <th>Gaia_EDR3___id</th>\n",
+       "      <th>AllWISE___id</th>\n",
+       "      <th>PS1_DR1___id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>period</th>\n",
+       "      <th>field</th>\n",
+       "      <th>ccd</th>\n",
+       "      <th>quad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>eb_xgb</th>\n",
+       "      <th>hp_xgb</th>\n",
+       "      <th>blyr_xgb</th>\n",
+       "      <th>rscvn_xgb</th>\n",
+       "      <th>rrd_xgb</th>\n",
+       "      <th>mir_xgb</th>\n",
+       "      <th>rrc_xgb</th>\n",
+       "      <th>bogus_xgb</th>\n",
+       "      <th>rrab_xgb</th>\n",
+       "      <th>agn_xgb</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.0000</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.029634e+13</td>\n",
+       "      <td>1.757088e+18</td>\n",
+       "      <td>1.259936e+17</td>\n",
+       "      <td>8.795916e+16</td>\n",
+       "      <td>15.559047</td>\n",
+       "      <td>-16.696696</td>\n",
+       "      <td>20.546433</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>9.160000</td>\n",
+       "      <td>2.550000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.001100</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000800</td>\n",
+       "      <td>0.0077</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.004100</td>\n",
+       "      <td>0.010700</td>\n",
+       "      <td>0.197500</td>\n",
+       "      <td>0.017300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.774576e+08</td>\n",
+       "      <td>1.047139e+18</td>\n",
+       "      <td>6.459028e+16</td>\n",
+       "      <td>2.553461e+15</td>\n",
+       "      <td>2.168615</td>\n",
+       "      <td>2.128283</td>\n",
+       "      <td>104.364753</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.416955</td>\n",
+       "      <td>1.085953</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.005412</td>\n",
+       "      <td>0.003145</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.004422</td>\n",
+       "      <td>0.0770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.034468</td>\n",
+       "      <td>0.014788</td>\n",
+       "      <td>0.021945</td>\n",
+       "      <td>0.081239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.029601e+13</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>8.321013e+16</td>\n",
+       "      <td>12.136068</td>\n",
+       "      <td>-20.651207</td>\n",
+       "      <td>0.021168</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.029620e+13</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.250167e+17</td>\n",
+       "      <td>8.600515e+16</td>\n",
+       "      <td>13.763547</td>\n",
+       "      <td>-18.323814</td>\n",
+       "      <td>0.026841</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>5.750000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.029636e+13</td>\n",
+       "      <td>2.357130e+18</td>\n",
+       "      <td>1.415175e+17</td>\n",
+       "      <td>8.844513e+16</td>\n",
+       "      <td>15.020253</td>\n",
+       "      <td>-16.293143</td>\n",
+       "      <td>0.039651</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>9.500000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.029647e+13</td>\n",
+       "      <td>2.371505e+18</td>\n",
+       "      <td>1.720156e+17</td>\n",
+       "      <td>8.981017e+16</td>\n",
+       "      <td>17.482961</td>\n",
+       "      <td>-15.151479</td>\n",
+       "      <td>0.096073</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>12.250000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.029663e+13</td>\n",
+       "      <td>2.468292e+18</td>\n",
+       "      <td>1.930213e+17</td>\n",
+       "      <td>9.200015e+16</td>\n",
+       "      <td>19.534744</td>\n",
+       "      <td>-13.329795</td>\n",
+       "      <td>637.168335</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.040000</td>\n",
+       "      <td>0.7700</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.340000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.580000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 99 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                _id  Gaia_EDR3___id  AllWISE___id  PS1_DR1___id          ra  \\\n",
+       "count  1.000000e+02    1.000000e+02  1.000000e+02  1.000000e+02  100.000000   \n",
+       "mean   1.029634e+13    1.757088e+18  1.259936e+17  8.795916e+16   15.559047   \n",
+       "std    1.774576e+08    1.047139e+18  6.459028e+16  2.553461e+15    2.168615   \n",
+       "min    1.029601e+13    0.000000e+00  0.000000e+00  8.321013e+16   12.136068   \n",
+       "25%    1.029620e+13    0.000000e+00  1.250167e+17  8.600515e+16   13.763547   \n",
+       "50%    1.029636e+13    2.357130e+18  1.415175e+17  8.844513e+16   15.020253   \n",
+       "75%    1.029647e+13    2.371505e+18  1.720156e+17  8.981017e+16   17.482961   \n",
+       "max    1.029663e+13    2.468292e+18  1.930213e+17  9.200015e+16   19.534744   \n",
+       "\n",
+       "              dec      period  field         ccd        quad  ...      eb_xgb  \\\n",
+       "count  100.000000  100.000000  100.0  100.000000  100.000000  ...  100.000000   \n",
+       "mean   -16.696696   20.546433  296.0    9.160000    2.550000  ...    0.001000   \n",
+       "std      2.128283  104.364753    0.0    4.416955    1.085953  ...    0.005412   \n",
+       "min    -20.651207    0.021168  296.0    1.000000    1.000000  ...    0.000000   \n",
+       "25%    -18.323814    0.026841  296.0    5.750000    2.000000  ...    0.000000   \n",
+       "50%    -16.293143    0.039651  296.0    9.500000    2.000000  ...    0.000000   \n",
+       "75%    -15.151479    0.096073  296.0   12.250000    4.000000  ...    0.000000   \n",
+       "max    -13.329795  637.168335  296.0   16.000000    4.000000  ...    0.050000   \n",
+       "\n",
+       "           hp_xgb  blyr_xgb   rscvn_xgb   rrd_xgb  mir_xgb     rrc_xgb  \\\n",
+       "count  100.000000     100.0  100.000000  100.0000    100.0  100.000000   \n",
+       "mean     0.001100       0.0    0.000800    0.0077      0.0    0.004100   \n",
+       "std      0.003145       0.0    0.004422    0.0770      0.0    0.034468   \n",
+       "min      0.000000       0.0    0.000000    0.0000      0.0    0.000000   \n",
+       "25%      0.000000       0.0    0.000000    0.0000      0.0    0.000000   \n",
+       "50%      0.000000       0.0    0.000000    0.0000      0.0    0.000000   \n",
+       "75%      0.000000       0.0    0.000000    0.0000      0.0    0.000000   \n",
+       "max      0.010000       0.0    0.040000    0.7700      0.0    0.340000   \n",
+       "\n",
+       "        bogus_xgb    rrab_xgb     agn_xgb  \n",
+       "count  100.000000  100.000000  100.000000  \n",
+       "mean     0.010700    0.197500    0.017300  \n",
+       "std      0.014788    0.021945    0.081239  \n",
+       "min      0.000000    0.190000    0.000000  \n",
+       "25%      0.000000    0.190000    0.000000  \n",
+       "50%      0.010000    0.190000    0.000000  \n",
+       "75%      0.010000    0.200000    0.000000  \n",
+       "max      0.100000    0.320000    0.580000  \n",
+       "\n",
+       "[8 rows x 99 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "field_296_100rows.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32116af4-c2c9-418e-899d-f66292966588",
+   "metadata": {},
+   "source": [
+    "#### List all columns found in prediction files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5e9b6e37-a30a-44cb-9c88-4c989ad8e2b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['_id',\n",
+       " 'Gaia_EDR3___id',\n",
+       " 'AllWISE___id',\n",
+       " 'PS1_DR1___id',\n",
+       " 'ra',\n",
+       " 'dec',\n",
+       " 'period',\n",
+       " 'field',\n",
+       " 'ccd',\n",
+       " 'quad',\n",
+       " 'filter',\n",
+       " 'e_dnn',\n",
+       " 'cv_dnn',\n",
+       " 'el_dnn',\n",
+       " 'osarg_dnn',\n",
+       " 'ea_dnn',\n",
+       " 'rrblz_dnn',\n",
+       " 'ceph2_dnn',\n",
+       " 'bis_dnn',\n",
+       " 'puls_dnn',\n",
+       " 'agn_dnn',\n",
+       " 'longt_dnn',\n",
+       " 'mir_dnn',\n",
+       " 'dscu_dnn',\n",
+       " 'rrc_dnn',\n",
+       " 'eb_dnn',\n",
+       " 'wp_dnn',\n",
+       " 'rrd_dnn',\n",
+       " 'hp_dnn',\n",
+       " 'blend_dnn',\n",
+       " 'wvir_dnn',\n",
+       " 'lpv_dnn',\n",
+       " 'emsms_dnn',\n",
+       " 'i_dnn',\n",
+       " 'rrlyr_dnn',\n",
+       " 'rrab_dnn',\n",
+       " 'sin_dnn',\n",
+       " 'rscvn_dnn',\n",
+       " 'bright_dnn',\n",
+       " 'bogus_dnn',\n",
+       " 'yso_dnn',\n",
+       " 'blyr_dnn',\n",
+       " 'dp_dnn',\n",
+       " 'saw_dnn',\n",
+       " 'ceph_dnn',\n",
+       " 'blher_dnn',\n",
+       " 'ext_dnn',\n",
+       " 'wuma_dnn',\n",
+       " 'ew_dnn',\n",
+       " 'srv_dnn',\n",
+       " 'fla_dnn',\n",
+       " 'vnv_dnn',\n",
+       " 'pnp_dnn',\n",
+       " 'dip_dnn',\n",
+       " 'mp_dnn',\n",
+       " 'saw_xgb',\n",
+       " 'dp_xgb',\n",
+       " 'i_xgb',\n",
+       " 'longt_xgb',\n",
+       " 'yso_xgb',\n",
+       " 'wvir_xgb',\n",
+       " 'puls_xgb',\n",
+       " 'sin_xgb',\n",
+       " 'dscu_xgb',\n",
+       " 'blend_xgb',\n",
+       " 'dip_xgb',\n",
+       " 'mp_xgb',\n",
+       " 'pnp_xgb',\n",
+       " 'vnv_xgb',\n",
+       " 'rrblz_xgb',\n",
+       " 'ew_xgb',\n",
+       " 'osarg_xgb',\n",
+       " 'fla_xgb',\n",
+       " 'srv_xgb',\n",
+       " 'ext_xgb',\n",
+       " 'ceph2_xgb',\n",
+       " 'bis_xgb',\n",
+       " 'blher_xgb',\n",
+       " 'e_xgb',\n",
+       " 'ea_xgb',\n",
+       " 'wuma_xgb',\n",
+       " 'ceph_xgb',\n",
+       " 'el_xgb',\n",
+       " 'bright_xgb',\n",
+       " 'cv_xgb',\n",
+       " 'lpv_xgb',\n",
+       " 'emsms_xgb',\n",
+       " 'rrlyr_xgb',\n",
+       " 'wp_xgb',\n",
+       " 'eb_xgb',\n",
+       " 'hp_xgb',\n",
+       " 'blyr_xgb',\n",
+       " 'rscvn_xgb',\n",
+       " 'rrd_xgb',\n",
+       " 'mir_xgb',\n",
+       " 'rrc_xgb',\n",
+       " 'bogus_xgb',\n",
+       " 'rrab_xgb',\n",
+       " 'agn_xgb']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_columns = [x for x in field_296_100rows.columns]\n",
+    "df_columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69df69-f902-4a35-b1f3-1427c8d885ab",
+   "metadata": {},
+   "source": [
+    "#### 99 total columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "43383b84-6b46-4e6b-98c9-2ba9b5384403",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df_columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b6bd50f-4b4a-4f4b-a139-5293574c8309",
+   "metadata": {},
+   "source": [
+    "#### Dataframe columns begin with ZTF ID and Gaia/AllWISE/Pan-STARRS IDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "05c0d38b-7cc0-4887-b06b-e0e1b5f78567",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['_id', 'Gaia_EDR3___id', 'AllWISE___id', 'PS1_DR1___id']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_columns[0:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac65805-769e-4183-abb7-5bb10bdf9dfd",
+   "metadata": {},
+   "source": [
+    "#### Next columns include ra/dec, computed period, ZTF field/CCD/quadrant/filter of observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4056636d-032e-44e4-ab08-d7d31e9b76c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ra', 'dec', 'period', 'field', 'ccd', 'quad', 'filter']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_columns[4:11]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18906ea8-0be4-45a8-ab9d-442bcf728d73",
+   "metadata": {},
+   "source": [
+    "#### Next are DNN classification columns, one per class, each containing probabilities from 0-1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e789cfc0-a7a4-4950-9c4e-976db80c2bd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['e_dnn',\n",
+       " 'cv_dnn',\n",
+       " 'el_dnn',\n",
+       " 'osarg_dnn',\n",
+       " 'ea_dnn',\n",
+       " 'rrblz_dnn',\n",
+       " 'ceph2_dnn',\n",
+       " 'bis_dnn',\n",
+       " 'puls_dnn',\n",
+       " 'agn_dnn',\n",
+       " 'longt_dnn',\n",
+       " 'mir_dnn',\n",
+       " 'dscu_dnn',\n",
+       " 'rrc_dnn',\n",
+       " 'eb_dnn',\n",
+       " 'wp_dnn',\n",
+       " 'rrd_dnn',\n",
+       " 'hp_dnn',\n",
+       " 'blend_dnn',\n",
+       " 'wvir_dnn',\n",
+       " 'lpv_dnn',\n",
+       " 'emsms_dnn',\n",
+       " 'i_dnn',\n",
+       " 'rrlyr_dnn',\n",
+       " 'rrab_dnn',\n",
+       " 'sin_dnn',\n",
+       " 'rscvn_dnn',\n",
+       " 'bright_dnn',\n",
+       " 'bogus_dnn',\n",
+       " 'yso_dnn',\n",
+       " 'blyr_dnn',\n",
+       " 'dp_dnn',\n",
+       " 'saw_dnn',\n",
+       " 'ceph_dnn',\n",
+       " 'blher_dnn',\n",
+       " 'ext_dnn',\n",
+       " 'wuma_dnn',\n",
+       " 'ew_dnn',\n",
+       " 'srv_dnn',\n",
+       " 'fla_dnn',\n",
+       " 'vnv_dnn',\n",
+       " 'pnp_dnn',\n",
+       " 'dip_dnn',\n",
+       " 'mp_dnn']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_columns[11:55]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9586adb7-c7e0-451e-85bf-cdf2a1118771",
+   "metadata": {},
+   "source": [
+    "#### Columns conclude with XGB classifications "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f4738d54-032e-43e4-89a8-6b77c593a23d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['saw_xgb',\n",
+       " 'dp_xgb',\n",
+       " 'i_xgb',\n",
+       " 'longt_xgb',\n",
+       " 'yso_xgb',\n",
+       " 'wvir_xgb',\n",
+       " 'puls_xgb',\n",
+       " 'sin_xgb',\n",
+       " 'dscu_xgb',\n",
+       " 'blend_xgb',\n",
+       " 'dip_xgb',\n",
+       " 'mp_xgb',\n",
+       " 'pnp_xgb',\n",
+       " 'vnv_xgb',\n",
+       " 'rrblz_xgb',\n",
+       " 'ew_xgb',\n",
+       " 'osarg_xgb',\n",
+       " 'fla_xgb',\n",
+       " 'srv_xgb',\n",
+       " 'ext_xgb',\n",
+       " 'ceph2_xgb',\n",
+       " 'bis_xgb',\n",
+       " 'blher_xgb',\n",
+       " 'e_xgb',\n",
+       " 'ea_xgb',\n",
+       " 'wuma_xgb',\n",
+       " 'ceph_xgb',\n",
+       " 'el_xgb',\n",
+       " 'bright_xgb',\n",
+       " 'cv_xgb',\n",
+       " 'lpv_xgb',\n",
+       " 'emsms_xgb',\n",
+       " 'rrlyr_xgb',\n",
+       " 'wp_xgb',\n",
+       " 'eb_xgb',\n",
+       " 'hp_xgb',\n",
+       " 'blyr_xgb',\n",
+       " 'rscvn_xgb',\n",
+       " 'rrd_xgb',\n",
+       " 'mir_xgb',\n",
+       " 'rrc_xgb',\n",
+       " 'bogus_xgb',\n",
+       " 'rrab_xgb',\n",
+       " 'agn_xgb']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_columns[55:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24440618-baa8-473a-bbb6-17d16d39436f",
+   "metadata": {},
+   "source": [
+    "#### This code down-sampled the full field_296.csv data from 380,571 rows to 100 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8271f4d7-e726-4f35-a874-ff4aa8d5ff75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nimport random\\nfield_296 = pd.read_csv(\"field_296.csv\")\\nrandom = field_296.sample(n=100, frac=None, replace=False, weights=None, random_state=1, axis=None, ignore_index=False)\\nrandom.to_csv(\\'field_296_100rows.csv\\', index=False)\\n'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "import random\n",
+    "field_296 = pd.read_csv(\"field_296.csv\")\n",
+    "random = field_296.sample(n=100, frac=None, replace=False, weights=None, random_state=1, axis=None, ignore_index=False)\n",
+    "random.to_csv('field_296_100rows.csv', index=False)\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba978685-59ba-4550-95bf-c67002910d70",
+   "metadata": {},
+   "source": [
+    "#### Scatter plot shows the ra/dec values of the 100 randomized rows above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9e6cc6d7-2153-4567-8f5e-46426748c9cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ra = field_296_100rows.iloc[0:,4]\n",
+    "dec = field_296_100rows.iloc[0:,5]\n",
+    "plt.scatter(ra,dec)\n",
+    "plt.xlabel('RA [deg]',fontsize=14)\n",
+    "plt.ylabel('Dec [deg]',fontsize=14)\n",
+    "plt.xticks(fontsize=14)\n",
+    "plt.yticks(fontsize=14)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "262e4e19-5e0d-4de2-a3ff-bd805b4c0391",
+   "metadata": {},
+   "source": [
+    "#### Histogram shows the classification probability distributions of specific DNN and XGB classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5673afe3-a97f-4685-a0e0-14c460877b00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Number of Light Curves')"
+      ]
+     },
+     "execution_count": 13,
+     "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"
+    }
+   ],
+   "source": [
+    "#plots - histogram (class probs) - variable or periodic - xaxis 0-1\n",
+    "plt.hist(field_296_100rows['vnv_dnn'], bins = 10, range = (0, 1), label = 'vnv_dnn')\n",
+    "plt.hist(field_296_100rows['vnv_xgb'], bins = 10, range = (0, 1), alpha = 0.7, label = 'vnv_xgb')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Classification Probability')\n",
+    "plt.ylabel('Number of Light Curves')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b2aa06e-7ea7-457c-b7d9-a54155d8695a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/field_296_100rows.csv b/field_296_100rows.csv
new file mode 100644
index 0000000..7421a92
--- /dev/null
+++ b/field_296_100rows.csv
@@ -0,0 +1,101 @@
+_id,Gaia_EDR3___id,AllWISE___id,PS1_DR1___id,ra,dec,period,field,ccd,quad,filter,e_dnn,cv_dnn,el_dnn,osarg_dnn,ea_dnn,rrblz_dnn,ceph2_dnn,bis_dnn,puls_dnn,agn_dnn,longt_dnn,mir_dnn,dscu_dnn,rrc_dnn,eb_dnn,wp_dnn,rrd_dnn,hp_dnn,blend_dnn,wvir_dnn,lpv_dnn,emsms_dnn,i_dnn,rrlyr_dnn,rrab_dnn,sin_dnn,rscvn_dnn,bright_dnn,bogus_dnn,yso_dnn,blyr_dnn,dp_dnn,saw_dnn,ceph_dnn,blher_dnn,ext_dnn,wuma_dnn,ew_dnn,srv_dnn,fla_dnn,vnv_dnn,pnp_dnn,dip_dnn,mp_dnn,saw_xgb,dp_xgb,i_xgb,longt_xgb,yso_xgb,wvir_xgb,puls_xgb,sin_xgb,dscu_xgb,blend_xgb,dip_xgb,mp_xgb,pnp_xgb,vnv_xgb,rrblz_xgb,ew_xgb,osarg_xgb,fla_xgb,srv_xgb,ext_xgb,ceph2_xgb,bis_xgb,blher_xgb,e_xgb,ea_xgb,wuma_xgb,ceph_xgb,el_xgb,bright_xgb,cv_xgb,lpv_xgb,emsms_xgb,rrlyr_xgb,wp_xgb,eb_xgb,hp_xgb,blyr_xgb,rscvn_xgb,rrd_xgb,mir_xgb,rrc_xgb,bogus_xgb,rrab_xgb,agn_xgb
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diff --git a/tools/DEMO_SCoPe_data_analysis_plots.ipynb b/tools/DEMO_SCoPe_data_analysis_plots.ipynb
new file mode 100644
index 0000000..1fe2e80
--- /dev/null
+++ b/tools/DEMO_SCoPe_data_analysis_plots.ipynb
@@ -0,0 +1,1651 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9ec4ce85-21e7-4b8b-98ad-8b7c2e110340",
+   "metadata": {},
+   "source": [
+    "#### Commands to read in extra tools or files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e4c8a783-200a-4762-8b66-cf13cf8b3aa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "58aea22c-f7e3-4415-bd3c-f30c3ff1202f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-02-27 15:17:22.736998: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scope.utils import read_parquet\n",
+    "from scope.utils import read_hdf\n",
+    "\n",
+    "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
+    "colors = prop_cycle.by_key()['color']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06ba2568-f53e-44c9-9cba-4b99bad53ded",
+   "metadata": {},
+   "source": [
+    "#### Command to read in file, using the filepath where it is stored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e8a073fd-3f33-4671-97d5-48f670b97433",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "field_296 = pd.read_csv(f'/Users/jillhughes/scope/field_296.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e137ddf7-bff4-4afb-a00e-4ec2e1581cc4",
+   "metadata": {},
+   "source": [
+    "#### Displays data stored in variable 'field_296'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1386d78d-745c-4483-8a53-5dddffee1842",
+   "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>_id</th>\n",
+       "      <th>Gaia_EDR3___id</th>\n",
+       "      <th>AllWISE___id</th>\n",
+       "      <th>PS1_DR1___id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>period</th>\n",
+       "      <th>field</th>\n",
+       "      <th>ccd</th>\n",
+       "      <th>quad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>eb_xgb</th>\n",
+       "      <th>hp_xgb</th>\n",
+       "      <th>blyr_xgb</th>\n",
+       "      <th>rscvn_xgb</th>\n",
+       "      <th>rrd_xgb</th>\n",
+       "      <th>mir_xgb</th>\n",
+       "      <th>rrc_xgb</th>\n",
+       "      <th>bogus_xgb</th>\n",
+       "      <th>rrab_xgb</th>\n",
+       "      <th>agn_xgb</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10296361000000</td>\n",
+       "      <td>2359798278171989120</td>\n",
+       "      <td>172015201351031200</td>\n",
+       "      <td>89720170607189136</td>\n",
+       "      <td>17.060726</td>\n",
+       "      <td>-15.226034</td>\n",
+       "      <td>0.048755</td>\n",
+       "      <td>296</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10296361000002</td>\n",
+       "      <td>2359602603757804672</td>\n",
+       "      <td>172015201351028672</td>\n",
+       "      <td>89730175658850512</td>\n",
+       "      <td>17.566260</td>\n",
+       "      <td>-15.225224</td>\n",
+       "      <td>0.023448</td>\n",
+       "      <td>296</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10296361000003</td>\n",
+       "      <td>2359784538572830848</td>\n",
+       "      <td>172015201351031232</td>\n",
+       "      <td>89720173313407344</td>\n",
+       "      <td>17.331424</td>\n",
+       "      <td>-15.227558</td>\n",
+       "      <td>0.024145</td>\n",
+       "      <td>296</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10296361000004</td>\n",
+       "      <td>2359775738183618560</td>\n",
+       "      <td>172015201351033600</td>\n",
+       "      <td>89720169677823360</td>\n",
+       "      <td>16.967959</td>\n",
+       "      <td>-15.230840</td>\n",
+       "      <td>0.022884</td>\n",
+       "      <td>296</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.19</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10296361000005</td>\n",
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+       "      <td>89720174699178224</td>\n",
+       "      <td>17.469907</td>\n",
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+       "      <td>0.255252</td>\n",
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+       "    <tr>\n",
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+       "      <td>87870195202311664</td>\n",
+       "      <td>19.520232</td>\n",
+       "      <td>-16.773930</td>\n",
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+       "      <td>296</td>\n",
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+       "      <th>380567</th>\n",
+       "      <td>10296353028735</td>\n",
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+       "      <td>88170188239197136</td>\n",
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+       "      <td>0.01</td>\n",
+       "      <td>0.20</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380568</th>\n",
+       "      <td>10296353029189</td>\n",
+       "      <td>2358478417542203776</td>\n",
+       "      <td>188016701351044576</td>\n",
+       "      <td>88330190070667680</td>\n",
+       "      <td>19.007115</td>\n",
+       "      <td>-16.385592</td>\n",
+       "      <td>0.124979</td>\n",
+       "      <td>296</td>\n",
+       "      <td>9</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.00</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380569</th>\n",
+       "      <td>10296353029551</td>\n",
+       "      <td>2358473233517664128</td>\n",
+       "      <td>188016701351047168</td>\n",
+       "      <td>88230191262994288</td>\n",
+       "      <td>19.126316</td>\n",
+       "      <td>-16.471768</td>\n",
+       "      <td>0.021737</td>\n",
+       "      <td>296</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380570</th>\n",
+       "      <td>10296353030000</td>\n",
+       "      <td>2358469728823012096</td>\n",
+       "      <td>188016701351031072</td>\n",
+       "      <td>88170188241352464</td>\n",
+       "      <td>18.824175</td>\n",
+       "      <td>-16.523205</td>\n",
+       "      <td>0.036375</td>\n",
+       "      <td>296</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>380571 rows × 99 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   _id       Gaia_EDR3___id        AllWISE___id  \\\n",
+       "0       10296361000000  2359798278171989120  172015201351031200   \n",
+       "1       10296361000002  2359602603757804672  172015201351028672   \n",
+       "2       10296361000003  2359784538572830848  172015201351031232   \n",
+       "3       10296361000004  2359775738183618560  172015201351033600   \n",
+       "4       10296361000005                    0                   0   \n",
+       "...                ...                  ...                 ...   \n",
+       "380566  10296353028158  2454479496478149248  188016701351025984   \n",
+       "380567  10296353028735  2358469728824348416  188016701351031072   \n",
+       "380568  10296353029189  2358478417542203776  188016701351044576   \n",
+       "380569  10296353029551  2358473233517664128  188016701351047168   \n",
+       "380570  10296353030000  2358469728823012096  188016701351031072   \n",
+       "\n",
+       "             PS1_DR1___id         ra        dec    period  field  ccd  quad  \\\n",
+       "0       89720170607189136  17.060726 -15.226034  0.048755    296   10     1   \n",
+       "1       89730175658850512  17.566260 -15.225224  0.023448    296   10     1   \n",
+       "2       89720173313407344  17.331424 -15.227558  0.024145    296   10     1   \n",
+       "3       89720169677823360  16.967959 -15.230840  0.022884    296   10     1   \n",
+       "4       89720174699178224  17.469907 -15.226814  0.255252    296   10     1   \n",
+       "...                   ...        ...        ...       ...    ...  ...   ...   \n",
+       "380566  87870195202311664  19.520232 -16.773930  0.029231    296    9     4   \n",
+       "380567  88170188239197136  18.823937 -16.519325  0.165426    296    9     4   \n",
+       "380568  88330190070667680  19.007115 -16.385592  0.124979    296    9     4   \n",
+       "380569  88230191262994288  19.126316 -16.471768  0.021737    296    9     4   \n",
+       "380570  88170188241352464  18.824175 -16.523205  0.036375    296    9     4   \n",
+       "\n",
+       "        ...  eb_xgb  hp_xgb  blyr_xgb  rscvn_xgb  rrd_xgb  mir_xgb  rrc_xgb  \\\n",
+       "0       ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "1       ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "2       ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "3       ...     0.0     0.0       0.0       0.01      0.0      0.0      0.0   \n",
+       "4       ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "...     ...     ...     ...       ...        ...      ...      ...      ...   \n",
+       "380566  ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "380567  ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "380568  ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "380569  ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "380570  ...     0.0     0.0       0.0       0.00      0.0      0.0      0.0   \n",
+       "\n",
+       "        bogus_xgb  rrab_xgb  agn_xgb  \n",
+       "0            0.01      0.19     0.00  \n",
+       "1            0.01      0.19     0.00  \n",
+       "2            0.01      0.20     0.00  \n",
+       "3            0.00      0.19     0.00  \n",
+       "4            0.01      0.20     0.00  \n",
+       "...           ...       ...      ...  \n",
+       "380566       0.01      0.23     0.00  \n",
+       "380567       0.01      0.20     0.00  \n",
+       "380568       0.00      0.21     0.05  \n",
+       "380569       0.01      0.20     0.00  \n",
+       "380570       0.03      0.20     0.00  \n",
+       "\n",
+       "[380571 rows x 99 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "field_296"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e42f2fc-2818-40bf-804c-16c1caac3a9b",
+   "metadata": {},
+   "source": [
+    "#### By using .describe() on 'field_296' we are able to calculate some statistical data, like count, mean, std, etc. shown in the rows below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f4b3f66b-3f64-43f4-b376-46768be7d164",
+   "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",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_id</th>\n",
+       "      <th>Gaia_EDR3___id</th>\n",
+       "      <th>AllWISE___id</th>\n",
+       "      <th>PS1_DR1___id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>period</th>\n",
+       "      <th>field</th>\n",
+       "      <th>ccd</th>\n",
+       "      <th>quad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>eb_xgb</th>\n",
+       "      <th>hp_xgb</th>\n",
+       "      <th>blyr_xgb</th>\n",
+       "      <th>rscvn_xgb</th>\n",
+       "      <th>rrd_xgb</th>\n",
+       "      <th>mir_xgb</th>\n",
+       "      <th>rrc_xgb</th>\n",
+       "      <th>bogus_xgb</th>\n",
+       "      <th>rrab_xgb</th>\n",
+       "      <th>agn_xgb</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3.805710e+05</td>\n",
+       "      <td>3.805710e+05</td>\n",
+       "      <td>3.805710e+05</td>\n",
+       "      <td>3.805710e+05</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.0</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>3.805710e+05</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "      <td>380571.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.029633e+13</td>\n",
+       "      <td>1.603066e+18</td>\n",
+       "      <td>1.249383e+17</td>\n",
+       "      <td>8.740139e+16</td>\n",
+       "      <td>15.752017</td>\n",
+       "      <td>-16.871064</td>\n",
+       "      <td>10.437486</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>8.842852</td>\n",
+       "      <td>2.485205</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000733</td>\n",
+       "      <td>0.000879</td>\n",
+       "      <td>0.000031</td>\n",
+       "      <td>0.001117</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>5.255261e-08</td>\n",
+       "      <td>0.000313</td>\n",
+       "      <td>0.014853</td>\n",
+       "      <td>0.195852</td>\n",
+       "      <td>0.006528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.842417e+08</td>\n",
+       "      <td>1.113065e+18</td>\n",
+       "      <td>6.663733e+16</td>\n",
+       "      <td>6.094434e+15</td>\n",
+       "      <td>2.220835</td>\n",
+       "      <td>2.163312</td>\n",
+       "      <td>72.846354</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.580723</td>\n",
+       "      <td>1.108620</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003333</td>\n",
+       "      <td>0.002836</td>\n",
+       "      <td>0.002480</td>\n",
+       "      <td>0.010713</td>\n",
+       "      <td>0.001667</td>\n",
+       "      <td>2.292433e-05</td>\n",
+       "      <td>0.009243</td>\n",
+       "      <td>0.032710</td>\n",
+       "      <td>0.016193</td>\n",
+       "      <td>0.041306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.029600e+13</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>11.894935</td>\n",
+       "      <td>-20.787756</td>\n",
+       "      <td>0.020833</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.029617e+13</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.250152e+17</td>\n",
+       "      <td>8.555012e+16</td>\n",
+       "      <td>13.774321</td>\n",
+       "      <td>-18.686362</td>\n",
+       "      <td>0.027562</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.029634e+13</td>\n",
+       "      <td>2.356816e+18</td>\n",
+       "      <td>1.440197e+17</td>\n",
+       "      <td>8.798016e+16</td>\n",
+       "      <td>15.634759</td>\n",
+       "      <td>-16.666411</td>\n",
+       "      <td>0.041652</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.029649e+13</td>\n",
+       "      <td>2.370786e+18</td>\n",
+       "      <td>1.720167e+17</td>\n",
+       "      <td>9.011014e+16</td>\n",
+       "      <td>17.614688</td>\n",
+       "      <td>-14.900211</td>\n",
+       "      <td>0.085670</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.029663e+13</td>\n",
+       "      <td>2.468343e+18</td>\n",
+       "      <td>2.030152e+17</td>\n",
+       "      <td>9.206019e+16</td>\n",
+       "      <td>19.670602</td>\n",
+       "      <td>-13.282531</td>\n",
+       "      <td>828.318965</td>\n",
+       "      <td>296.0</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.410000</td>\n",
+       "      <td>0.030000</td>\n",
+       "      <td>0.960000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.770000</td>\n",
+       "      <td>1.000000e-02</td>\n",
+       "      <td>0.990000</td>\n",
+       "      <td>0.590000</td>\n",
+       "      <td>0.660000</td>\n",
+       "      <td>0.980000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 99 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                _id  Gaia_EDR3___id  AllWISE___id  PS1_DR1___id  \\\n",
+       "count  3.805710e+05    3.805710e+05  3.805710e+05  3.805710e+05   \n",
+       "mean   1.029633e+13    1.603066e+18  1.249383e+17  8.740139e+16   \n",
+       "std    1.842417e+08    1.113065e+18  6.663733e+16  6.094434e+15   \n",
+       "min    1.029600e+13    0.000000e+00  0.000000e+00  0.000000e+00   \n",
+       "25%    1.029617e+13    0.000000e+00  1.250152e+17  8.555012e+16   \n",
+       "50%    1.029634e+13    2.356816e+18  1.440197e+17  8.798016e+16   \n",
+       "75%    1.029649e+13    2.370786e+18  1.720167e+17  9.011014e+16   \n",
+       "max    1.029663e+13    2.468343e+18  2.030152e+17  9.206019e+16   \n",
+       "\n",
+       "                  ra            dec         period     field            ccd  \\\n",
+       "count  380571.000000  380571.000000  380571.000000  380571.0  380571.000000   \n",
+       "mean       15.752017     -16.871064      10.437486     296.0       8.842852   \n",
+       "std         2.220835       2.163312      72.846354       0.0       4.580723   \n",
+       "min        11.894935     -20.787756       0.020833     296.0       1.000000   \n",
+       "25%        13.774321     -18.686362       0.027562     296.0       5.000000   \n",
+       "50%        15.634759     -16.666411       0.041652     296.0       9.000000   \n",
+       "75%        17.614688     -14.900211       0.085670     296.0      13.000000   \n",
+       "max        19.670602     -13.282531     828.318965     296.0      16.000000   \n",
+       "\n",
+       "                quad  ...         eb_xgb         hp_xgb       blyr_xgb  \\\n",
+       "count  380571.000000  ...  380571.000000  380571.000000  380571.000000   \n",
+       "mean        2.485205  ...       0.000733       0.000879       0.000031   \n",
+       "std         1.108620  ...       0.003333       0.002836       0.002480   \n",
+       "min         1.000000  ...       0.000000       0.000000       0.000000   \n",
+       "25%         2.000000  ...       0.000000       0.000000       0.000000   \n",
+       "50%         2.000000  ...       0.000000       0.000000       0.000000   \n",
+       "75%         3.000000  ...       0.000000       0.000000       0.000000   \n",
+       "max         4.000000  ...       0.410000       0.030000       0.960000   \n",
+       "\n",
+       "           rscvn_xgb        rrd_xgb       mir_xgb        rrc_xgb  \\\n",
+       "count  380571.000000  380571.000000  3.805710e+05  380571.000000   \n",
+       "mean        0.001117       0.000005  5.255261e-08       0.000313   \n",
+       "std         0.010713       0.001667  2.292433e-05       0.009243   \n",
+       "min         0.000000       0.000000  0.000000e+00       0.000000   \n",
+       "25%         0.000000       0.000000  0.000000e+00       0.000000   \n",
+       "50%         0.000000       0.000000  0.000000e+00       0.000000   \n",
+       "75%         0.000000       0.000000  0.000000e+00       0.000000   \n",
+       "max         0.900000       0.770000  1.000000e-02       0.990000   \n",
+       "\n",
+       "           bogus_xgb       rrab_xgb        agn_xgb  \n",
+       "count  380571.000000  380571.000000  380571.000000  \n",
+       "mean        0.014853       0.195852       0.006528  \n",
+       "std         0.032710       0.016193       0.041306  \n",
+       "min         0.000000       0.190000       0.000000  \n",
+       "25%         0.000000       0.190000       0.000000  \n",
+       "50%         0.010000       0.190000       0.000000  \n",
+       "75%         0.010000       0.200000       0.000000  \n",
+       "max         0.590000       0.660000       0.980000  \n",
+       "\n",
+       "[8 rows x 99 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "field_296.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32116af4-c2c9-418e-899d-f66292966588",
+   "metadata": {},
+   "source": [
+    "#### Creates a list of all of the classifications found in 'field_296'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5e9b6e37-a30a-44cb-9c88-4c989ad8e2b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['_id',\n",
+       " 'Gaia_EDR3___id',\n",
+       " 'AllWISE___id',\n",
+       " 'PS1_DR1___id',\n",
+       " 'ra',\n",
+       " 'dec',\n",
+       " 'period',\n",
+       " 'field',\n",
+       " 'ccd',\n",
+       " 'quad',\n",
+       " 'filter',\n",
+       " 'e_dnn',\n",
+       " 'cv_dnn',\n",
+       " 'el_dnn',\n",
+       " 'osarg_dnn',\n",
+       " 'ea_dnn',\n",
+       " 'rrblz_dnn',\n",
+       " 'ceph2_dnn',\n",
+       " 'bis_dnn',\n",
+       " 'puls_dnn',\n",
+       " 'agn_dnn',\n",
+       " 'longt_dnn',\n",
+       " 'mir_dnn',\n",
+       " 'dscu_dnn',\n",
+       " 'rrc_dnn',\n",
+       " 'eb_dnn',\n",
+       " 'wp_dnn',\n",
+       " 'rrd_dnn',\n",
+       " 'hp_dnn',\n",
+       " 'blend_dnn',\n",
+       " 'wvir_dnn',\n",
+       " 'lpv_dnn',\n",
+       " 'emsms_dnn',\n",
+       " 'i_dnn',\n",
+       " 'rrlyr_dnn',\n",
+       " 'rrab_dnn',\n",
+       " 'sin_dnn',\n",
+       " 'rscvn_dnn',\n",
+       " 'bright_dnn',\n",
+       " 'bogus_dnn',\n",
+       " 'yso_dnn',\n",
+       " 'blyr_dnn',\n",
+       " 'dp_dnn',\n",
+       " 'saw_dnn',\n",
+       " 'ceph_dnn',\n",
+       " 'blher_dnn',\n",
+       " 'ext_dnn',\n",
+       " 'wuma_dnn',\n",
+       " 'ew_dnn',\n",
+       " 'srv_dnn',\n",
+       " 'fla_dnn',\n",
+       " 'vnv_dnn',\n",
+       " 'pnp_dnn',\n",
+       " 'dip_dnn',\n",
+       " 'mp_dnn',\n",
+       " 'saw_xgb',\n",
+       " 'dp_xgb',\n",
+       " 'i_xgb',\n",
+       " 'longt_xgb',\n",
+       " 'yso_xgb',\n",
+       " 'wvir_xgb',\n",
+       " 'puls_xgb',\n",
+       " 'sin_xgb',\n",
+       " 'dscu_xgb',\n",
+       " 'blend_xgb',\n",
+       " 'dip_xgb',\n",
+       " 'mp_xgb',\n",
+       " 'pnp_xgb',\n",
+       " 'vnv_xgb',\n",
+       " 'rrblz_xgb',\n",
+       " 'ew_xgb',\n",
+       " 'osarg_xgb',\n",
+       " 'fla_xgb',\n",
+       " 'srv_xgb',\n",
+       " 'ext_xgb',\n",
+       " 'ceph2_xgb',\n",
+       " 'bis_xgb',\n",
+       " 'blher_xgb',\n",
+       " 'e_xgb',\n",
+       " 'ea_xgb',\n",
+       " 'wuma_xgb',\n",
+       " 'ceph_xgb',\n",
+       " 'el_xgb',\n",
+       " 'bright_xgb',\n",
+       " 'cv_xgb',\n",
+       " 'lpv_xgb',\n",
+       " 'emsms_xgb',\n",
+       " 'rrlyr_xgb',\n",
+       " 'wp_xgb',\n",
+       " 'eb_xgb',\n",
+       " 'hp_xgb',\n",
+       " 'blyr_xgb',\n",
+       " 'rscvn_xgb',\n",
+       " 'rrd_xgb',\n",
+       " 'mir_xgb',\n",
+       " 'rrc_xgb',\n",
+       " 'bogus_xgb',\n",
+       " 'rrab_xgb',\n",
+       " 'agn_xgb']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifications = [x for x in field_296.columns]\n",
+    "classifications"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69df69-f902-4a35-b1f3-1427c8d885ab",
+   "metadata": {},
+   "source": [
+    "#### Command to show the length of the variable list 'classifications'. Helpful to make sure all of the indices were included in their respective lists below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "43383b84-6b46-4e6b-98c9-2ba9b5384403",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(classifications)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b6bd50f-4b4a-4f4b-a139-5293574c8309",
+   "metadata": {},
+   "source": [
+    "#### Variable 'ids' creates a list of all the id classifiers found in 'classifications'. Takes inputs starting at index 0 until index 4 (not inclusive)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "05c0d38b-7cc0-4887-b06b-e0e1b5f78567",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['_id', 'Gaia_EDR3', 'AllWISE', 'PS1_DR1']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ids = [] # create a list of the ids\n",
+    "for entry in field_296.columns:\n",
+    "    ids.append(entry.replace('___id',''))\n",
+    "\n",
+    "ids[0:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac65805-769e-4183-abb7-5bb10bdf9dfd",
+   "metadata": {},
+   "source": [
+    "#### Variable 'misc' creates a list of all the misc classifiers found in 'classifications'. Takes inputs starting at index 4 until index 11 (not inclusive)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4056636d-032e-44e4-ab08-d7d31e9b76c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ra', 'dec', 'period', 'field', 'ccd', 'quad', 'filter']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "misc = [] #create a list of misc\n",
+    "for entry in field_296.columns:\n",
+    "    misc.append(entry)\n",
+    "\n",
+    "misc[4:11]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18906ea8-0be4-45a8-ab9d-442bcf728d73",
+   "metadata": {},
+   "source": [
+    "#### Variable 'dnn_classifications' creates a list of all the dnn classifiers found in 'classifications'. Takes inputs starting at index 11 until index 55 (not inclusive)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e789cfc0-a7a4-4950-9c4e-976db80c2bd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['e',\n",
+       " 'cv',\n",
+       " 'el',\n",
+       " 'osarg',\n",
+       " 'ea',\n",
+       " 'rrblz',\n",
+       " 'ceph2',\n",
+       " 'bis',\n",
+       " 'puls',\n",
+       " 'agn',\n",
+       " 'longt',\n",
+       " 'mir',\n",
+       " 'dscu',\n",
+       " 'rrc',\n",
+       " 'eb',\n",
+       " 'wp',\n",
+       " 'rrd',\n",
+       " 'hp',\n",
+       " 'blend',\n",
+       " 'wvir',\n",
+       " 'lpv',\n",
+       " 'emsms',\n",
+       " 'i',\n",
+       " 'rrlyr',\n",
+       " 'rrab',\n",
+       " 'sin',\n",
+       " 'rscvn',\n",
+       " 'bright',\n",
+       " 'bogus',\n",
+       " 'yso',\n",
+       " 'blyr',\n",
+       " 'dp',\n",
+       " 'saw',\n",
+       " 'ceph',\n",
+       " 'blher',\n",
+       " 'ext',\n",
+       " 'wuma',\n",
+       " 'ew',\n",
+       " 'srv',\n",
+       " 'fla',\n",
+       " 'vnv',\n",
+       " 'pnp',\n",
+       " 'dip',\n",
+       " 'mp']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dnn_classifications = [] # create a list of the dnn classifications\n",
+    "for entry in field_296.columns:\n",
+    "        dnn_classifications.append(entry.replace('_dnn',''))\n",
+    "    \n",
+    "dnn_classifications[11:55]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9586adb7-c7e0-451e-85bf-cdf2a1118771",
+   "metadata": {},
+   "source": [
+    "#### Variable 'xgb_classifications' creates a list of all the xgb classifiers found in 'classifications'. Takes inputs starting at index 55 until index 98 (not inclusive)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f4738d54-032e-43e4-89a8-6b77c593a23d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['saw',\n",
+       " 'dp',\n",
+       " 'i',\n",
+       " 'longt',\n",
+       " 'yso',\n",
+       " 'wvir',\n",
+       " 'puls',\n",
+       " 'sin',\n",
+       " 'dscu',\n",
+       " 'blend',\n",
+       " 'dip',\n",
+       " 'mp',\n",
+       " 'pnp',\n",
+       " 'vnv',\n",
+       " 'rrblz',\n",
+       " 'ew',\n",
+       " 'osarg',\n",
+       " 'fla',\n",
+       " 'srv',\n",
+       " 'ext',\n",
+       " 'ceph2',\n",
+       " 'bis',\n",
+       " 'blher',\n",
+       " 'e',\n",
+       " 'ea',\n",
+       " 'wuma',\n",
+       " 'ceph',\n",
+       " 'el',\n",
+       " 'bright',\n",
+       " 'cv',\n",
+       " 'lpv',\n",
+       " 'emsms',\n",
+       " 'rrlyr',\n",
+       " 'wp',\n",
+       " 'eb',\n",
+       " 'hp',\n",
+       " 'blyr',\n",
+       " 'rscvn',\n",
+       " 'rrd',\n",
+       " 'mir',\n",
+       " 'rrc',\n",
+       " 'bogus',\n",
+       " 'rrab',\n",
+       " 'agn']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xgb_classifications = [] # create a list of the xgb classifications\n",
+    "for entry in field_296.columns:\n",
+    "    xgb_classifications.append(entry.replace('_xgb',''))\n",
+    "    \n",
+    "xgb_classifications[55:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24440618-baa8-473a-bbb6-17d16d39436f",
+   "metadata": {},
+   "source": [
+    "#### Here, we downsize the data from field_296 from 380571 rows to 100 rows. By assigning variable 'random' to field_296.sample, we are able to return a sample of items, in this case 100, from field_296."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8271f4d7-e726-4f35-a874-ff4aa8d5ff75",
+   "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>_id</th>\n",
+       "      <th>Gaia_EDR3___id</th>\n",
+       "      <th>AllWISE___id</th>\n",
+       "      <th>PS1_DR1___id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>period</th>\n",
+       "      <th>field</th>\n",
+       "      <th>ccd</th>\n",
+       "      <th>quad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>eb_xgb</th>\n",
+       "      <th>hp_xgb</th>\n",
+       "      <th>blyr_xgb</th>\n",
+       "      <th>rscvn_xgb</th>\n",
+       "      <th>rrd_xgb</th>\n",
+       "      <th>mir_xgb</th>\n",
+       "      <th>rrc_xgb</th>\n",
+       "      <th>bogus_xgb</th>\n",
+       "      <th>rrab_xgb</th>\n",
+       "      <th>agn_xgb</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>149545</th>\n",
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+       "      <td>10296531000371</td>\n",
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+       "      <td>170013701351041248</td>\n",
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+       "      <td>16.655579</td>\n",
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+       "      <td>188016701351063040</td>\n",
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+       "      <td>18.235299</td>\n",
+       "      <td>-15.908833</td>\n",
+       "      <td>0.025950</td>\n",
+       "      <td>296</td>\n",
+       "      <td>9</td>\n",
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+       "      <td>...</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289775</th>\n",
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+       "      <td>18.946842</td>\n",
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+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351108</th>\n",
+       "      <td>10296311002011</td>\n",
+       "      <td>0</td>\n",
+       "      <td>142018201351013568</td>\n",
+       "      <td>85930135304772064</td>\n",
+       "      <td>13.530474</td>\n",
+       "      <td>-18.390206</td>\n",
+       "      <td>0.099423</td>\n",
+       "      <td>296</td>\n",
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+       "      <td>4</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270303</th>\n",
+       "      <td>10296162004575</td>\n",
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+       "      <td>190018201351060032</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363397</th>\n",
+       "      <td>10296332000029</td>\n",
+       "      <td>0</td>\n",
+       "      <td>172015201351026912</td>\n",
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+       "      <td>0.079058</td>\n",
+       "      <td>296</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.05</td>\n",
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+       "      <td>0.32</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133334</th>\n",
+       "      <td>10296561018331</td>\n",
+       "      <td>2373028431006093312</td>\n",
+       "      <td>155013701351040992</td>\n",
+       "      <td>92000149780744640</td>\n",
+       "      <td>14.978093</td>\n",
+       "      <td>-13.329795</td>\n",
+       "      <td>0.034946</td>\n",
+       "      <td>296</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27225</th>\n",
+       "      <td>10296392037084</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88190168016806816</td>\n",
+       "      <td>16.801518</td>\n",
+       "      <td>-16.503011</td>\n",
+       "      <td>0.366488</td>\n",
+       "      <td>296</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 99 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   _id       Gaia_EDR3___id        AllWISE___id  \\\n",
+       "149545  10296582009946                    0  140015201351056624   \n",
+       "111426  10296531000371  2468292420539961088  170013701351041248   \n",
+       "365108  10296332007046  2358756082883576832  188016701351063040   \n",
+       "375247  10296352004611                    0                   0   \n",
+       "289775  10296193002860  2355129515577873280  190018201351021696   \n",
+       "...                ...                  ...                 ...   \n",
+       "351108  10296311002011                    0  142018201351013568   \n",
+       "270303  10296162004575  2355334535841260160  190018201351060032   \n",
+       "363397  10296332000029                    0  172015201351026912   \n",
+       "133334  10296561018331  2373028431006093312  155013701351040992   \n",
+       "27225   10296392037084                    0                   0   \n",
+       "\n",
+       "             PS1_DR1___id         ra        dec      period  field  ccd  quad  \\\n",
+       "149545  90610143756592496  14.375673 -14.489669    5.751999    296   15     3   \n",
+       "111426  91920166559039984  16.655579 -13.392433    0.253672    296   14     2   \n",
+       "365108  88900182352849744  18.235299 -15.908833    0.025950    296    9     2   \n",
+       "375247  88150190046276512  19.004638 -16.536509    0.033279    296    9     4   \n",
+       "289775  85910189467390912  18.946842 -18.407839    0.025762    296    5     4   \n",
+       "...                   ...        ...        ...         ...    ...  ...   ...   \n",
+       "351108  85930135304772064  13.530474 -18.390206    0.099423    296    8     4   \n",
+       "270303  86850190585978048  19.058643 -17.618552  637.168335    296    5     1   \n",
+       "363397  89710179705849520  17.970427 -15.234044    0.079058    296    9     2   \n",
+       "133334  92000149780744640  14.978093 -13.329795    0.034946    296   15     1   \n",
+       "27225   88190168016806816  16.801518 -16.503011    0.366488    296   10     4   \n",
+       "\n",
+       "        ...  eb_xgb  hp_xgb  blyr_xgb  rscvn_xgb  rrd_xgb  mir_xgb  rrc_xgb  \\\n",
+       "149545  ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "111426  ...    0.00    0.00       0.0        0.0     0.77      0.0     0.34   \n",
+       "365108  ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "375247  ...    0.00    0.01       0.0        0.0     0.00      0.0     0.00   \n",
+       "289775  ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "...     ...     ...     ...       ...        ...      ...      ...      ...   \n",
+       "351108  ...    0.00    0.01       0.0        0.0     0.00      0.0     0.00   \n",
+       "270303  ...    0.01    0.01       0.0        0.0     0.00      0.0     0.00   \n",
+       "363397  ...    0.05    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "133334  ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "27225   ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "\n",
+       "        bogus_xgb  rrab_xgb  agn_xgb  \n",
+       "149545       0.01      0.19     0.09  \n",
+       "111426       0.00      0.19     0.00  \n",
+       "365108       0.00      0.19     0.00  \n",
+       "375247       0.01      0.20     0.00  \n",
+       "289775       0.00      0.19     0.00  \n",
+       "...           ...       ...      ...  \n",
+       "351108       0.01      0.19     0.00  \n",
+       "270303       0.03      0.20     0.02  \n",
+       "363397       0.03      0.32     0.00  \n",
+       "133334       0.02      0.24     0.00  \n",
+       "27225        0.00      0.20     0.00  \n",
+       "\n",
+       "[100 rows x 99 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "random = field_296.sample(n=100, frac=None, replace=False, weights=None, random_state=1, axis=None, ignore_index=False)\n",
+    "random.to_csv(f'/Users/jillhughes/scope/random.csv', index=False)\n",
+    "random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba978685-59ba-4550-95bf-c67002910d70",
+   "metadata": {},
+   "source": [
+    "#### Scatter plot created to show the dec classifier values vs the ra classifer values of the 100 randomized rows above. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9e6cc6d7-2153-4567-8f5e-46426748c9cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ra = random.iloc[0:,4]\n",
+    "dec = random.iloc[0:,5]\n",
+    "plt.scatter(ra,dec)\n",
+    "plt.xlabel('ra Classifier',fontsize=14)\n",
+    "plt.ylabel('dec Classifier',fontsize=14)\n",
+    "plt.xticks(fontsize=14)\n",
+    "plt.yticks(fontsize=14)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "262e4e19-5e0d-4de2-a3ff-bd805b4c0391",
+   "metadata": {},
+   "source": [
+    "#### Creates a histogram that shows the classification probability of a specific dnn and xgb classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5673afe3-a97f-4685-a0e0-14c460877b00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Number of Sources')"
+      ]
+     },
+     "execution_count": 25,
+     "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"
+    }
+   ],
+   "source": [
+    "#plots - histogram (class probs) - variable or periodic - xaxis 0-1\n",
+    "plt.hist(random['vnv_dnn'], bins = 10, range = (0, 1), label = 'vnv_dnn')\n",
+    "plt.hist(random['vnv_xgb'], bins = 10, range = (0, 1), alpha = 0.7, label = 'vnv_xgb')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Classification Probability')\n",
+    "plt.ylabel('Number of Sources')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}