{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import rioxarray\n",
    "import json, os\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2, f_classif, mutual_info_classif\n",
    "from sklearn.metrics import f1_score, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "verbose = False\n",
    "details = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>Sulphur_5km</th>\n",
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       "      <th>Occurrence</th>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "607500.0  252500.0                   0                   97       0   \n",
       "972500.0  427500.0                   0                    0       0   \n",
       "542500.0  532500.0                   0                    0       0   \n",
       "1282500.0 117500.0                   0                    0       0   \n",
       "37500.0   272500.0                  33                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "607500.0  252500.0                  0                  0   \n",
       "972500.0  427500.0                  0                  0   \n",
       "542500.0  532500.0                  0                  0   \n",
       "1282500.0 117500.0                  0                  0   \n",
       "37500.0   272500.0                 40                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "607500.0  252500.0                     0               1    0        0   \n",
       "972500.0  427500.0                     0               0    0        0   \n",
       "542500.0  532500.0                     0               0    0        0   \n",
       "1282500.0 117500.0                     0               0    0        0   \n",
       "37500.0   272500.0                     3               0    0        0   \n",
       "\n",
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       "y         x                            ...                                 \n",
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       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
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       "37500.0   272500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
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       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
       "607500.0  252500.0 -3.400000e+38   -3.400000e+38           0  \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "\n",
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       "y         x                                                \n",
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       "y         x                                                             \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y         x                                                \n",
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       "y         x                                                              \n",
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       "y         x                                                             \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "    <tr>\n",
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       "      <th>317500.0</th>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1247500.0 432500.0                   0                    0       0   \n",
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       "\n",
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       "y         x                                                \n",
       "117500.0  527500.0                 63                  0   \n",
       "1247500.0 432500.0                  0                  0   \n",
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       "317500.0  487500.0                  5                  0   \n",
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       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
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       "1247500.0 432500.0                     0               0    0        0   \n",
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       "317500.0  487500.0                     0               0    0        0   \n",
       "282500.0  47500.0                      0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
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       "282500.0  47500.0                   0  ...   -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "117500.0  527500.0    1.173386e-01    3.322592e+01       4.158661e-01   \n",
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       "282500.0  47500.0    -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
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       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "212500.0  232500.0                   5                    0       7   \n",
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       "y         x                                                \n",
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       "22500.0   32500.0                      0               0    0        0   \n",
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       "y         x                            ...                                 \n",
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       "y         x                                                             \n",
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       "y         x                                                          \n",
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       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
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       "      <th>Prosulfocarb_5km</th>\n",
       "      <th>Sulphur_5km</th>\n",
       "      <th>Tri-allate_5km</th>\n",
       "      <th>Occurrence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>612500.0</th>\n",
       "      <th>7500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697500.0</th>\n",
       "      <th>292500.0</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.259985e+00</td>\n",
       "      <td>4.164787e+01</td>\n",
       "      <td>6.766146e+00</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>4.883677e+00</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>762500.0</th>\n",
       "      <th>477500.0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977500.0</th>\n",
       "      <th>132500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>952500.0</th>\n",
       "      <th>557500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 40 columns</p>\n",
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      "text/plain": [
       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "612500.0 7500.0                     0                    0       0   \n",
       "697500.0 292500.0                  20                    0       3   \n",
       "762500.0 477500.0                   0                    0       0   \n",
       "977500.0 132500.0                   0                    0       0   \n",
       "952500.0 557500.0                   0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "612500.0 7500.0                    0                  0                     0   \n",
       "697500.0 292500.0                 26                  0                     0   \n",
       "762500.0 477500.0                  0                  0                     0   \n",
       "977500.0 132500.0                  0                  0                     0   \n",
       "952500.0 557500.0                  0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "612500.0 7500.0                 0    0        0                  0  ...   \n",
       "697500.0 292500.0               3    0        0                  0  ...   \n",
       "762500.0 477500.0               0    0        0                  0  ...   \n",
       "977500.0 132500.0               0    0        0                  0  ...   \n",
       "952500.0 557500.0               0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_5km  Mancozeb_5km  Mecoprop-P_5km  \\\n",
       "y        x                                                        \n",
       "612500.0 7500.0     -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "697500.0 292500.0    9.259985e+00  4.164787e+01    6.766146e+00   \n",
       "762500.0 477500.0   -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "977500.0 132500.0   -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "952500.0 557500.0   -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "\n",
       "                   Metamitron_5km  Pendimethalin_5km  \\\n",
       "y        x                                             \n",
       "612500.0 7500.0     -3.400000e+38      -3.400000e+38   \n",
       "697500.0 292500.0   -3.400000e+38       4.883677e+00   \n",
       "762500.0 477500.0   -3.400000e+38      -3.400000e+38   \n",
       "977500.0 132500.0   -3.400000e+38      -3.400000e+38   \n",
       "952500.0 557500.0   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y        x                                                          \n",
       "612500.0 7500.0                   -3.400000e+38     -3.400000e+38   \n",
       "697500.0 292500.0                 -3.400000e+38     -3.400000e+38   \n",
       "762500.0 477500.0                 -3.400000e+38     -3.400000e+38   \n",
       "977500.0 132500.0                 -3.400000e+38     -3.400000e+38   \n",
       "952500.0 557500.0                 -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                    Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y        x                                                   \n",
       "612500.0 7500.0   -3.400000e+38   -3.400000e+38           0  \n",
       "697500.0 292500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "762500.0 477500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "977500.0 132500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "952500.0 557500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "      <th>Deciduous woodland</th>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <th>312500.0</th>\n",
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       "      <td>8</td>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "1007500.0 467500.0                   0                    0       0   \n",
       "1257500.0 42500.0                    0                    0       0   \n",
       "707500.0  462500.0                   0                    0       0   \n",
       "312500.0  587500.0                   8                    0      66   \n",
       "167500.0  167500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1007500.0 467500.0                  0                  0   \n",
       "1257500.0 42500.0                   0                  0   \n",
       "707500.0  462500.0                  0                  0   \n",
       "312500.0  587500.0                 25                  0   \n",
       "167500.0  167500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "1007500.0 467500.0                     0               0    0        0   \n",
       "1257500.0 42500.0                      0               0    0        0   \n",
       "707500.0  462500.0                     0               0    0        0   \n",
       "312500.0  587500.0                     0               0    0        0   \n",
       "167500.0  167500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
       "1007500.0 467500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "1257500.0 42500.0                   0  ...   -3.400000e+38 -3.400000e+38   \n",
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       "312500.0  587500.0                  0  ...    2.792899e+01  6.121789e+01   \n",
       "167500.0  167500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "1007500.0 467500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "1257500.0 42500.0    -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "707500.0  462500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
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       "167500.0  167500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
       "1007500.0 467500.0                 -3.400000e+38     -3.400000e+38   \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y         x                                                             \n",
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       "      <td>4.879790e+00</td>\n",
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       "      <td>7.785163e-04</td>\n",
       "      <td>4.027653e-05</td>\n",
       "      <td>4.539189e-04</td>\n",
       "      <td>2.452656e+01</td>\n",
       "      <td>3.015757e-04</td>\n",
       "      <td>1.320384e+01</td>\n",
       "      <td>3.131600e+01</td>\n",
       "      <td>7.590619e+00</td>\n",
       "      <td>4.133070e+00</td>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "237500.0  427500.0                   2                    0      45   \n",
       "292500.0  477500.0                   1                    0      43   \n",
       "197500.0  337500.0                   4                    0      41   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1102500.0 642500.0                  0                  0   \n",
       "822500.0  547500.0                  0                  0   \n",
       "237500.0  427500.0                 53                  0   \n",
       "292500.0  477500.0                 56                  0   \n",
       "197500.0  337500.0                 52                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
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       "292500.0  477500.0                     0               0    0        0   \n",
       "197500.0  337500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
       "1102500.0 642500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
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       "292500.0  477500.0                  0  ...    4.988595e+01  5.187139e+01   \n",
       "197500.0  337500.0                  0  ...    7.785163e-04  4.027653e-05   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "1102500.0 642500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
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       "237500.0  427500.0    2.723321e-01    1.764000e+01       9.751811e-01   \n",
       "292500.0  477500.0    4.879790e+00    2.720762e+01       2.823989e+01   \n",
       "197500.0  337500.0    4.539189e-04    2.452656e+01       3.015757e-04   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
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       "822500.0  547500.0                 -3.400000e+38     -3.400000e+38   \n",
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       "292500.0  477500.0                  3.644452e+01      4.140786e+01   \n",
       "197500.0  337500.0                  1.320384e+01      3.131600e+01   \n",
       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
       "1102500.0 642500.0 -3.400000e+38   -3.400000e+38           0  \n",
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       "292500.0  477500.0  1.372874e+01    1.559179e+01           0  \n",
       "197500.0  337500.0  7.590619e+00    4.133070e+00           0  \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "232500.0  607500.0                 61                  0   \n",
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       "y         x                                                             \n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1122500.0</th>\n",
       "      <th>657500.0</th>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1092500.0 542500.0                   0                    0       0   \n",
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       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
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       "1122500.0 657500.0                  0                  0   \n",
       "1092500.0 542500.0                  0                  0   \n",
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       "\n",
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       "y         x                                                              \n",
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       "1092500.0 542500.0                     0               0    0        0   \n",
       "1212500.0 522500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
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       "1212500.0 522500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "157500.0  197500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
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       "1212500.0 522500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
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       "1212500.0 522500.0                 -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1132500.0 242500.0                   0                    0       0   \n",
       "227500.0  337500.0                   5                    0       8   \n",
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       "y         x                                                \n",
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       "227500.0  337500.0                 83                  0   \n",
       "502500.0  147500.0                  0                  0   \n",
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       "y         x                                                              \n",
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       "\n",
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       "y         x                                                             \n",
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       "\n",
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       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
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       "      <th>Occurrence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>632500.0</th>\n",
       "      <th>157500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362500.0</th>\n",
       "      <th>462500.0</th>\n",
       "      <td>5</td>\n",
       "      <td>59</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.219382e+01</td>\n",
       "      <td>1.068436e+00</td>\n",
       "      <td>6.758402e+00</td>\n",
       "      <td>1.672401e+01</td>\n",
       "      <td>2.361158e+01</td>\n",
       "      <td>1.341892e+01</td>\n",
       "      <td>1.552295e+01</td>\n",
       "      <td>1.054125e+01</td>\n",
       "      <td>1.673775e+01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857500.0</th>\n",
       "      <th>432500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187500.0</th>\n",
       "      <th>507500.0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.194264e+00</td>\n",
       "      <td>3.816978e+00</td>\n",
       "      <td>1.333557e+00</td>\n",
       "      <td>6.474104e+00</td>\n",
       "      <td>6.045681e+00</td>\n",
       "      <td>5.836989e+00</td>\n",
       "      <td>9.897083e+00</td>\n",
       "      <td>3.260769e+00</td>\n",
       "      <td>1.010081e+01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37500.0</th>\n",
       "      <th>37500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "632500.0 157500.0                   0                    0       0   \n",
       "362500.0 462500.0                   5                   59      33   \n",
       "857500.0 432500.0                   0                    0       0   \n",
       "187500.0 507500.0                   5                    0       3   \n",
       "37500.0  37500.0                    0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "632500.0 157500.0                  0                  0                     0   \n",
       "362500.0 462500.0                  2                  0                     0   \n",
       "857500.0 432500.0                  0                  0                     0   \n",
       "187500.0 507500.0                 74                  0                     0   \n",
       "37500.0  37500.0                   0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "632500.0 157500.0               0    0        0                  0  ...   \n",
       "362500.0 462500.0               0    0        0                  0  ...   \n",
       "857500.0 432500.0               0    0        0                  0  ...   \n",
       "187500.0 507500.0               0    0        0                  0  ...   \n",
       "37500.0  37500.0                0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_5km  Mancozeb_5km  Mecoprop-P_5km  \\\n",
       "y        x                                                        \n",
       "632500.0 157500.0   -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "362500.0 462500.0    3.219382e+01  1.068436e+00    6.758402e+00   \n",
       "857500.0 432500.0   -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "187500.0 507500.0    9.194264e+00  3.816978e+00    1.333557e+00   \n",
       "37500.0  37500.0    -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "\n",
       "                   Metamitron_5km  Pendimethalin_5km  \\\n",
       "y        x                                             \n",
       "632500.0 157500.0   -3.400000e+38      -3.400000e+38   \n",
       "362500.0 462500.0    1.672401e+01       2.361158e+01   \n",
       "857500.0 432500.0   -3.400000e+38      -3.400000e+38   \n",
       "187500.0 507500.0    6.474104e+00       6.045681e+00   \n",
       "37500.0  37500.0    -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y        x                                                          \n",
       "632500.0 157500.0                 -3.400000e+38     -3.400000e+38   \n",
       "362500.0 462500.0                  1.341892e+01      1.552295e+01   \n",
       "857500.0 432500.0                 -3.400000e+38     -3.400000e+38   \n",
       "187500.0 507500.0                  5.836989e+00      9.897083e+00   \n",
       "37500.0  37500.0                  -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                    Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y        x                                                   \n",
       "632500.0 157500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "362500.0 462500.0  1.054125e+01    1.673775e+01           0  \n",
       "857500.0 432500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "187500.0 507500.0  3.260769e+00    1.010081e+01           1  \n",
       "37500.0  37500.0  -3.400000e+38   -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Deciduous woodland</th>\n",
       "      <th>Coniferous woodland</th>\n",
       "      <th>Arable</th>\n",
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       "      <th>1117500.0</th>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>197500.0</th>\n",
       "      <th>667500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>782500.0</th>\n",
       "      <th>277500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "1117500.0 477500.0                   0                    0       0   \n",
       "347500.0  112500.0                   0                    0       0   \n",
       "197500.0  667500.0                   0                    0       0   \n",
       "782500.0  277500.0                   0                    0       0   \n",
       "567500.0  192500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1117500.0 477500.0                  0                  0   \n",
       "347500.0  112500.0                  0                  0   \n",
       "197500.0  667500.0                  0                  0   \n",
       "782500.0  277500.0                  0                  0   \n",
       "567500.0  192500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "1117500.0 477500.0                     0               0    0        0   \n",
       "347500.0  112500.0                     0               0    0        0   \n",
       "197500.0  667500.0                     0               0    0        0   \n",
       "782500.0  277500.0                     0               0    0       96   \n",
       "567500.0  192500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
       "1117500.0 477500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "347500.0  112500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "197500.0  667500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "782500.0  277500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "567500.0  192500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "1117500.0 477500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "347500.0  112500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "197500.0  667500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "782500.0  277500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "567500.0  192500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
       "1117500.0 477500.0                 -3.400000e+38     -3.400000e+38   \n",
       "347500.0  112500.0                 -3.400000e+38     -3.400000e+38   \n",
       "197500.0  667500.0                 -3.400000e+38     -3.400000e+38   \n",
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       "87500.0   257500.0                  0                  0   \n",
       "402500.0  87500.0                   0                  0   \n",
       "1297500.0 347500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "687500.0  467500.0                     0               0    0        0   \n",
       "637500.0  207500.0                     0               0    0        0   \n",
       "87500.0   257500.0                     0              50    0        0   \n",
       "402500.0  87500.0                      0               0    0        0   \n",
       "1297500.0 347500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
       "687500.0  467500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "637500.0  207500.0                  0  ...    2.604617e+00  1.825517e-01   \n",
       "87500.0   257500.0                  0  ...    2.477325e+00  1.834522e+00   \n",
       "402500.0  87500.0                   0  ...   -3.400000e+38 -3.400000e+38   \n",
       "1297500.0 347500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "687500.0  467500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "637500.0  207500.0    1.142017e+00   -3.400000e+38       6.284199e-01   \n",
       "87500.0   257500.0    7.743283e-01    1.164793e+00       1.839896e+00   \n",
       "402500.0  87500.0    -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "1297500.0 347500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
       "687500.0  467500.0                 -3.400000e+38     -3.400000e+38   \n",
       "637500.0  207500.0                 -3.400000e+38     -3.400000e+38   \n",
       "87500.0   257500.0                  5.867671e-01      2.048754e+00   \n",
       "402500.0  87500.0                  -3.400000e+38     -3.400000e+38   \n",
       "1297500.0 347500.0                 -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
       "687500.0  467500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "637500.0  207500.0 -3.400000e+38    9.273250e-01           0  \n",
       "87500.0   257500.0  3.985094e-01    7.494090e-01           0  \n",
       "402500.0  87500.0  -3.400000e+38   -3.400000e+38           0  \n",
       "1297500.0 347500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "INVASIVE_BIRDS_PATH = 'Datasets/Machine Learning/5km Rasters/Birds'\n",
    "# Use this if using coordinates as separate columns\n",
    "# df_5km = pd.read_csv('Datasets/Machine Learning/Dataframes/5km_All_Birds_DF.csv')\n",
    "\n",
    "# Use this if using coordinates as indices\n",
    "df_5km = pd.read_csv('Datasets/Machine Learning/Dataframes/5km_All_Birds_DF.csv', index_col=[0,1])\n",
    "\n",
    "total_birds = (df_5km['Occurrence']==1).sum()\n",
    "df_dicts = []\n",
    "\n",
    "for file in os.listdir(INVASIVE_BIRDS_PATH):\n",
    "    filename = os.fsdecode(file)\n",
    "    if not filename.endswith('.tif') or filename.endswith('All_Invasive_Birds_5km.tif') :\n",
    "         continue\n",
    "\n",
    "\n",
    "\n",
    "    bird_name = filename[:-4].replace('_', ' ')\n",
    "\n",
    "    bird_dataset = rioxarray.open_rasterio(f'{INVASIVE_BIRDS_PATH}/{file}')\n",
    "    bird_dataset.name = 'data'\n",
    "    bird_df = bird_dataset.squeeze().drop(\"spatial_ref\").drop(\"band\").to_dataframe()\n",
    "\n",
    "    # Check if index matches\n",
    "    if not df_5km.index.equals(bird_df.index):\n",
    "        print('Warning: Index does not match')\n",
    "        continue\n",
    "\n",
    "    bird_df['Occurrence'] = [0 if x == -1 else 1 for x in bird_df['data']]\n",
    "    bird_df = df_5km.drop(columns='Occurrence').join(bird_df.drop(columns='data'))\n",
    "    \n",
    "    bird_dict = {'name' : bird_name, 'dataframe' : bird_df }\n",
    "    df_dicts.append(bird_dict)\n",
    "    display(bird_df.sample(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 5km data before drop: \n",
      " Occurrence\n",
      "0    35813\n",
      "1      587\n",
      "dtype: int64 \n",
      "\n",
      "Barnacle Goose 5km data after drop: \n",
      " Occurrence\n",
      "0    7378\n",
      "1     587\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 5km data before drop: \n",
      " Occurrence\n",
      "0    32095\n",
      "1     4305\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 5km data after drop: \n",
      " Occurrence\n",
      "1    4305\n",
      "0    3660\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 5km data before drop: \n",
      " Occurrence\n",
      "0    35915\n",
      "1      485\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 5km data after drop: \n",
      " Occurrence\n",
      "0    7480\n",
      "1     485\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 5km data before drop: \n",
      " Occurrence\n",
      "0    35001\n",
      "1     1399\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 5km data after drop: \n",
      " Occurrence\n",
      "0    6566\n",
      "1    1399\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 5km data before drop: \n",
      " Occurrence\n",
      "0    35954\n",
      "1      446\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 5km data after drop: \n",
      " Occurrence\n",
      "0    7519\n",
      "1     446\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 5km data before drop: \n",
      " Occurrence\n",
      "0    34771\n",
      "1     1629\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 5km data after drop: \n",
      " Occurrence\n",
      "0    6336\n",
      "1    1629\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 5km data before drop: \n",
      " Occurrence\n",
      "0    36116\n",
      "1      284\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 5km data after drop: \n",
      " Occurrence\n",
      "0    7681\n",
      "1     284\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 5km data before drop: \n",
      " Occurrence\n",
      "0    34242\n",
      "1     2158\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 5km data after drop: \n",
      " Occurrence\n",
      "0    5807\n",
      "1    2158\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 5km data before drop: \n",
      " Occurrence\n",
      "0    35686\n",
      "1      714\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 5km data after drop: \n",
      " Occurrence\n",
      "0    7251\n",
      "1     714\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 5km data before drop: \n",
      " Occurrence\n",
      "0    31133\n",
      "1     5267\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 5km data after drop: \n",
      " Occurrence\n",
      "1    5267\n",
      "0    2698\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 5km data before drop: \n",
      " Occurrence\n",
      "0    32552\n",
      "1     3848\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 5km data after drop: \n",
      " Occurrence\n",
      "0    4117\n",
      "1    3848\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 5km data before drop: \n",
      " Occurrence\n",
      "0    35087\n",
      "1     1313\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 5km data after drop: \n",
      " Occurrence\n",
      "0    6652\n",
      "1    1313\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 5km data before drop: \n",
      " Occurrence\n",
      "0    35751\n",
      "1      649\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 5km data after drop: \n",
      " Occurrence\n",
      "0    7316\n",
      "1     649\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 5km data before drop: \n",
      " Occurrence\n",
      "0    35458\n",
      "1      942\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 5km data after drop: \n",
      " Occurrence\n",
      "0    7023\n",
      "1     942\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 5km data before drop: \n",
      " Occurrence\n",
      "0    34250\n",
      "1     2150\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 5km data after drop: \n",
      " Occurrence\n",
      "0    5815\n",
      "1    2150\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 5km data before drop: \n",
      " Occurrence\n",
      "0    36194\n",
      "1      206\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 5km data after drop: \n",
      " Occurrence\n",
      "0    7759\n",
      "1     206\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 5km data before drop: \n",
      " Occurrence\n",
      "0    33570\n",
      "1     2830\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 5km data after drop: \n",
      " Occurrence\n",
      "0    5135\n",
      "1    2830\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 5km data before drop: \n",
      " Occurrence\n",
      "0    36291\n",
      "1      109\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 5km data after drop: \n",
      " Occurrence\n",
      "0    7856\n",
      "1     109\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 5km data before drop: \n",
      " Occurrence\n",
      "0    35558\n",
      "1      842\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 5km data after drop: \n",
      " Occurrence\n",
      "0    7123\n",
      "1     842\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 5km data before drop: \n",
      " Occurrence\n",
      "0    34543\n",
      "1     1857\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 5km data after drop: \n",
      " Occurrence\n",
      "0    6108\n",
      "1    1857\n",
      "dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data Cleaning\n",
    "np.random.seed(seed=seed)\n",
    "\n",
    "for dict in df_dicts:\n",
    "    cur_df = dict[\"dataframe\"]\n",
    "    cur_df_name = dict[\"name\"]\n",
    "\n",
    "    print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n",
    "    \n",
    "    no_occurences = cur_df[cur_df['Occurrence']==0].index \n",
    "    sample_size = sum(cur_df['Occurrence']==0) - total_birds + sum(cur_df['Occurrence']==1)\n",
    "    random_indices = np.random.choice(no_occurences, sample_size, replace=False)\n",
    "    dict[\"dataframe\"] =  cur_df.drop(random_indices)\n",
    "    \n",
    "    print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')\n",
    "\n",
    "\n",
    "# for dict in df_dicts:\n",
    "#     cur_df = dict[\"dataframe\"]\n",
    "#     cur_df_name = dict[\"name\"]\n",
    "\n",
    "#     print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n",
    "    \n",
    "#     no_occurences = cur_df[cur_df['Occurrence']==0].index\n",
    "#     sample_size = sum(cur_df['Occurrence']==0) - sum(cur_df['Occurrence']==1)\n",
    "#     random_indices = np.random.choice(no_occurences, sample_size, replace=False)\n",
    "#     dict[\"dataframe\"] =  cur_df.drop(random_indices)\n",
    "    \n",
    "#     print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardisation\n",
    "def standardise(X):\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "    # Add headers back\n",
    "    X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
    "\n",
    "    # Revert 'Surface type' back to non-standardised column as it is a categorical feature\n",
    "    X_scaled_df['Surface type'] = X['Surface type'].values\n",
    "    return X_scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Selection\n",
    "\n",
    "# Check if any columns have NaN in them\n",
    "# nan_columns = []\n",
    "# for column in X_scaled_df:\n",
    "#     if X_scaled_df[column].isnull().values.any():\n",
    "#         nan_columns.append(column)\n",
    "# print(nan_columns if len(nan_columns)!= 0 else 'None')\n",
    "\n",
    "\n",
    "# Using ANOVA F-Score as a feature selection method\n",
    "def feature_select(X, y):\n",
    "    k_nums = [10, 15, 20, 25, 30, 35]\n",
    "    kbest_dict = {}\n",
    "    for num in k_nums:\n",
    "        # Needs to be 1d array, y.values.ravel() converts y into a 1d array\n",
    "        best_X = SelectKBest(f_classif, k=num).fit(X, y.values.ravel())\n",
    "        # kbest_dict[str(num)] = best_X.get_feature_names_out().tolist()\n",
    "        kbest_dict[str(num)] = best_X\n",
    "    # kbest_dict['40'] = list(X.columns)\n",
    "\n",
    "    best_X = SelectKBest(f_classif, k='all').fit(X, y.values.ravel())\n",
    "\n",
    "    feat_scores = pd.DataFrame()\n",
    "    feat_scores[\"F Score\"] = best_X.scores_\n",
    "    feat_scores[\"P Value\"] = best_X.pvalues_\n",
    "    feat_scores[\"Attribute\"] = X.columns\n",
    "    kbest_dict['Dataframe'] = feat_scores.sort_values([\"F Score\", \"P Value\"], ascending=[False, False])\n",
    "\n",
    "\n",
    "    if details:\n",
    "        print(f'K-Best Features Dataframe: \\n{kbest_dict[\"Dataframe\"]} \\n')\n",
    "    # print(json.dumps(kbest_dict, indent=4))\n",
    "    return kbest_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample (upsample) minority data\n",
    "# for dict in df_dicts:\n",
    "#     if sum(dict['dataframe']['Occurence']==1) > sum(dict['dataframe']['Occurence']==0):\n",
    "#         continue\n",
    "\n",
    "# from sklearn.utils import resample\n",
    "\n",
    "# def upsample(X, y):\n",
    "#     X_1 = X[y['Occurrence'] == 1] # Getting positive occurrences (minority)\n",
    "#     X_0 = X[y['Occurrence'] == 0] # Getting negative occurrences (majority)\n",
    "    \n",
    "#     X_1_upsampled = resample(X_1 ,random_state=seed,n_samples=total_birds/2,replace=True)\n",
    "\n",
    "\n",
    "#     print(f'Resampling: \\n {y.value_counts()} \\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oversample(X_train, y_train):\n",
    "    over = RandomOverSampler(sampling_strategy='minority', random_state=seed)\n",
    "    smote = SMOTE(random_state=seed, sampling_strategy='minority')\n",
    "    X_smote, y_smote = smote.fit_resample(X_train, y_train)\n",
    "    \n",
    "    if details:\n",
    "        print(f'Resampled Value Counts: \\n {y_smote.value_counts()} \\n')\n",
    "\n",
    "    return X_smote, y_smote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Name</th>\n",
       "      <th>Occurrence Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mute Swan 5km</td>\n",
       "      <td>5267</td>\n",
       "      <td>0.661268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 5km</td>\n",
       "      <td>4305</td>\n",
       "      <td>0.540490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 5km</td>\n",
       "      <td>3848</td>\n",
       "      <td>0.483114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 5km</td>\n",
       "      <td>2830</td>\n",
       "      <td>0.355304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 5km</td>\n",
       "      <td>2158</td>\n",
       "      <td>0.270935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 5km</td>\n",
       "      <td>2150</td>\n",
       "      <td>0.269931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 5km</td>\n",
       "      <td>1857</td>\n",
       "      <td>0.233145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 5km</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.204520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 5km</td>\n",
       "      <td>1399</td>\n",
       "      <td>0.175643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 5km</td>\n",
       "      <td>1313</td>\n",
       "      <td>0.164846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 5km</td>\n",
       "      <td>942</td>\n",
       "      <td>0.118267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 5km</td>\n",
       "      <td>842</td>\n",
       "      <td>0.105712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 5km</td>\n",
       "      <td>714</td>\n",
       "      <td>0.089642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 5km</td>\n",
       "      <td>649</td>\n",
       "      <td>0.081481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 5km</td>\n",
       "      <td>587</td>\n",
       "      <td>0.073697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 5km</td>\n",
       "      <td>485</td>\n",
       "      <td>0.060891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 5km</td>\n",
       "      <td>446</td>\n",
       "      <td>0.055995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 5km</td>\n",
       "      <td>284</td>\n",
       "      <td>0.035656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 5km</td>\n",
       "      <td>206</td>\n",
       "      <td>0.025863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 5km</td>\n",
       "      <td>109</td>\n",
       "      <td>0.013685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name  Occurrence Count  Percentage\n",
       "9              Mute Swan 5km              5267    0.661268\n",
       "1           Canada Goose 5km              4305    0.540490\n",
       "10              Pheasant 5km              3848    0.483114\n",
       "16             Rock Dove 5km              2830    0.355304\n",
       "7             Little Owl 5km              2158    0.270935\n",
       "14  Red-legged Partridge 5km              2150    0.269931\n",
       "19                Wigeon 5km              1857    0.233145\n",
       "5         Grey Partridge 5km              1629    0.204520\n",
       "3                Gadwall 5km              1399    0.175643\n",
       "11     Pink-footed Goose 5km              1313    0.164846\n",
       "13               Pochard 5km               942    0.118267\n",
       "18          Whooper Swan 5km               842    0.105712\n",
       "8          Mandarin Duck 5km               714    0.089642\n",
       "12               Pintail 5km               649    0.081481\n",
       "0         Barnacle Goose 5km               587    0.073697\n",
       "2         Egyptian Goose 5km               485    0.060891\n",
       "4                Goshawk 5km               446    0.055995\n",
       "6         Indian Peafowl 5km               284    0.035656\n",
       "15  Ring-necked Parakeet 5km               206    0.025863\n",
       "17            Ruddy Duck 5km               109    0.013685"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_bird_occurrences = pd.DataFrame([(dict['name'],sum(dict['dataframe']['Occurrence'] == 1)) for dict in df_dicts], columns=['Name', 'Occurrence Count'])\n",
    "All_bird_occurrences['Percentage'] = All_bird_occurrences['Occurrence Count']/total_birds\n",
    "\n",
    "All_bird_occurrences.sort_values('Occurrence Count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training with Barnacle Goose 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9358316221765913,\n",
      "        \"recall\": 0.9886117136659436,\n",
      "        \"f1-score\": 0.9614978902953586,\n",
      "        \"support\": 1844\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5227272727272727,\n",
      "        \"recall\": 0.1554054054054054,\n",
      "        \"f1-score\": 0.23958333333333334,\n",
      "        \"support\": 148\n",
      "    },\n",
      "    \"accuracy\": 0.9267068273092369,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.729279447451932,\n",
      "        \"recall\": 0.5720085595356745,\n",
      "        \"f1-score\": 0.6005406118143459,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9051391303500355,\n",
      "        \"recall\": 0.9267068273092369,\n",
      "        \"f1-score\": 0.9078616681917543,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Barnacle Goose 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9397066811515481,\n",
      "        \"recall\": 0.938177874186551,\n",
      "        \"f1-score\": 0.9389416553595659,\n",
      "        \"support\": 1844\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.24503311258278146,\n",
      "        \"recall\": 0.25,\n",
      "        \"f1-score\": 0.2474916387959866,\n",
      "        \"support\": 148\n",
      "    },\n",
      "    \"accuracy\": 0.8870481927710844,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5923698968671648,\n",
      "        \"recall\": 0.5940889370932755,\n",
      "        \"f1-score\": 0.5932166470777762,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.888094387904471,\n",
      "        \"recall\": 0.8870481927710844,\n",
      "        \"f1-score\": 0.8875688629642798,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Canada Goose 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada Goose 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9355971896955504,\n",
      "        \"recall\": 0.871319520174482,\n",
      "        \"f1-score\": 0.9023150762281198,\n",
      "        \"support\": 917\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8963093145869947,\n",
      "        \"recall\": 0.9488372093023256,\n",
      "        \"f1-score\": 0.921825576140985,\n",
      "        \"support\": 1075\n",
      "    },\n",
      "    \"accuracy\": 0.9131526104417671,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9159532521412725,\n",
      "        \"recall\": 0.9100783647384039,\n",
      "        \"f1-score\": 0.9120703261845524,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9143951486605617,\n",
      "        \"recall\": 0.9131526104417671,\n",
      "        \"f1-score\": 0.9128440859702533,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Canada Goose 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9213483146067416,\n",
      "        \"recall\": 0.8942202835332607,\n",
      "        \"f1-score\": 0.9075816270060874,\n",
      "        \"support\": 917\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.911978221415608,\n",
      "        \"recall\": 0.9348837209302325,\n",
      "        \"f1-score\": 0.9232889297197979,\n",
      "        \"support\": 1075\n",
      "    },\n",
      "    \"accuracy\": 0.9161646586345381,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9166632680111748,\n",
      "        \"recall\": 0.9145520022317466,\n",
      "        \"f1-score\": 0.9154352783629427,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9162916629097192,\n",
      "        \"recall\": 0.9161646586345381,\n",
      "        \"f1-score\": 0.9160582085408459,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Egyptian Goose 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Egyptian Goose 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9595588235294118,\n",
      "        \"recall\": 0.981203007518797,\n",
      "        \"f1-score\": 0.9702602230483273,\n",
      "        \"support\": 1862\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6022727272727273,\n",
      "        \"recall\": 0.4076923076923077,\n",
      "        \"f1-score\": 0.4862385321100917,\n",
      "        \"support\": 130\n",
      "    },\n",
      "    \"accuracy\": 0.9437751004016064,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7809157754010696,\n",
      "        \"recall\": 0.6944476576055523,\n",
      "        \"f1-score\": 0.7282493775792095,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9362419598178812,\n",
      "        \"recall\": 0.9437751004016064,\n",
      "        \"f1-score\": 0.9386724620935227,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Egyptian Goose 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9638874137015401,\n",
      "        \"recall\": 0.9747583243823845,\n",
      "        \"f1-score\": 0.9692923898531375,\n",
      "        \"support\": 1862\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5688073394495413,\n",
      "        \"recall\": 0.47692307692307695,\n",
      "        \"f1-score\": 0.5188284518828452,\n",
      "        \"support\": 130\n",
      "    },\n",
      "    \"accuracy\": 0.9422690763052208,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7663473765755406,\n",
      "        \"recall\": 0.7258407006527308,\n",
      "        \"f1-score\": 0.7440604208679913,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9381040755224437,\n",
      "        \"recall\": 0.9422690763052208,\n",
      "        \"f1-score\": 0.939894642897245,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Gadwall 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gadwall 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9131195335276968,\n",
      "        \"recall\": 0.9566279780085523,\n",
      "        \"f1-score\": 0.9343675417661097,\n",
      "        \"support\": 1637\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7436823104693141,\n",
      "        \"recall\": 0.5802816901408451,\n",
      "        \"f1-score\": 0.6518987341772152,\n",
      "        \"support\": 355\n",
      "    },\n",
      "    \"accuracy\": 0.8895582329317269,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8284009219985055,\n",
      "        \"recall\": 0.7684548340746986,\n",
      "        \"f1-score\": 0.7931331379716624,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8829236428722119,\n",
      "        \"recall\": 0.8895582329317269,\n",
      "        \"f1-score\": 0.8840279701325467,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Gadwall 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9126040428061831,\n",
      "        \"recall\": 0.9376908979841173,\n",
      "        \"f1-score\": 0.9249774028321783,\n",
      "        \"support\": 1637\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6709677419354839,\n",
      "        \"recall\": 0.5859154929577465,\n",
      "        \"f1-score\": 0.6255639097744361,\n",
      "        \"support\": 355\n",
      "    },\n",
      "    \"accuracy\": 0.875,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7917858923708335,\n",
      "        \"recall\": 0.7618031954709319,\n",
      "        \"f1-score\": 0.7752706563033072,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.869541348624909,\n",
      "        \"recall\": 0.875,\n",
      "        \"f1-score\": 0.8716180704850405,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Goshawk 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goshawk 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.954337899543379,\n",
      "        \"recall\": 0.9926121372031662,\n",
      "        \"f1-score\": 0.9730988101396792,\n",
      "        \"support\": 1895\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3333333333333333,\n",
      "        \"recall\": 0.07216494845360824,\n",
      "        \"f1-score\": 0.11864406779661016,\n",
      "        \"support\": 97\n",
      "    },\n",
      "    \"accuracy\": 0.9477911646586346,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6438356164383562,\n",
      "        \"recall\": 0.5323885428283872,\n",
      "        \"f1-score\": 0.5458714389681447,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9240982193614641,\n",
      "        \"recall\": 0.9477911646586346,\n",
      "        \"f1-score\": 0.9314913251962669,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Goshawk 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9625195210827694,\n",
      "        \"recall\": 0.9757255936675462,\n",
      "        \"f1-score\": 0.9690775681341719,\n",
      "        \"support\": 1895\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.352112676056338,\n",
      "        \"recall\": 0.25773195876288657,\n",
      "        \"f1-score\": 0.2976190476190476,\n",
      "        \"support\": 97\n",
      "    },\n",
      "    \"accuracy\": 0.9407630522088354,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6573160985695536,\n",
      "        \"recall\": 0.6167287762152164,\n",
      "        \"f1-score\": 0.6333483078766098,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9327958945930285,\n",
      "        \"recall\": 0.9407630522088354,\n",
      "        \"f1-score\": 0.9363810437918191,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Grey Partridge 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grey Partridge 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9587491683300067,\n",
      "        \"recall\": 0.902316844082655,\n",
      "        \"f1-score\": 0.9296774193548387,\n",
      "        \"support\": 1597\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6809815950920245,\n",
      "        \"recall\": 0.8430379746835444,\n",
      "        \"f1-score\": 0.7533936651583709,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"accuracy\": 0.8905622489959839,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8198653817110155,\n",
      "        \"recall\": 0.8726774093830997,\n",
      "        \"f1-score\": 0.8415355422566049,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9036697549620333,\n",
      "        \"recall\": 0.8905622489959839,\n",
      "        \"f1-score\": 0.8947215544413825,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Grey Partridge 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9104385423100679,\n",
      "        \"recall\": 0.9229805886036319,\n",
      "        \"f1-score\": 0.9166666666666666,\n",
      "        \"support\": 1597\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6702412868632708,\n",
      "        \"recall\": 0.6329113924050633,\n",
      "        \"f1-score\": 0.6510416666666667,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"accuracy\": 0.8654618473895582,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7903399145866694,\n",
      "        \"recall\": 0.7779459905043475,\n",
      "        \"f1-score\": 0.7838541666666667,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8628090664559088,\n",
      "        \"recall\": 0.8654618473895582,\n",
      "        \"f1-score\": 0.8639950426706827,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Indian Peafowl 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indian Peafowl 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9617706237424547,\n",
      "        \"recall\": 0.9979123173277662,\n",
      "        \"f1-score\": 0.9795081967213115,\n",
      "        \"support\": 1916\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.9598393574297188,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.48088531187122735,\n",
      "        \"recall\": 0.4989561586638831,\n",
      "        \"f1-score\": 0.48975409836065575,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9250765638004735,\n",
      "        \"recall\": 0.9598393574297188,\n",
      "        \"f1-score\": 0.9421374020672856,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Indian Peafowl 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9646878198567042,\n",
      "        \"recall\": 0.9838204592901879,\n",
      "        \"f1-score\": 0.9741602067183461,\n",
      "        \"support\": 1916\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.18421052631578946,\n",
      "        \"recall\": 0.09210526315789473,\n",
      "        \"f1-score\": 0.12280701754385964,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.9497991967871486,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5744491730862468,\n",
      "        \"recall\": 0.5379628612240414,\n",
      "        \"f1-score\": 0.5484836121311029,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9349105737175929,\n",
      "        \"recall\": 0.9497991967871486,\n",
      "        \"f1-score\": 0.9416788601434158,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Little Owl 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Little Owl 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9746376811594203,\n",
      "        \"recall\": 0.9069453809844908,\n",
      "        \"f1-score\": 0.9395738735592035,\n",
      "        \"support\": 1483\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7745098039215687,\n",
      "        \"recall\": 0.931237721021611,\n",
      "        \"f1-score\": 0.8456735057983942,\n",
      "        \"support\": 509\n",
      "    },\n",
      "    \"accuracy\": 0.9131526104417671,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8745737425404945,\n",
      "        \"recall\": 0.9190915510030508,\n",
      "        \"f1-score\": 0.8926236896787989,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9235005880298688,\n",
      "        \"recall\": 0.9131526104417671,\n",
      "        \"f1-score\": 0.9155802554918079,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Little Owl 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9678800856531049,\n",
      "        \"recall\": 0.9143627781523938,\n",
      "        \"f1-score\": 0.9403606102635229,\n",
      "        \"support\": 1483\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7851099830795262,\n",
      "        \"recall\": 0.9115913555992141,\n",
      "        \"f1-score\": 0.8436363636363637,\n",
      "        \"support\": 509\n",
      "    },\n",
      "    \"accuracy\": 0.9136546184738956,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8764950343663156,\n",
      "        \"recall\": 0.9129770668758039,\n",
      "        \"f1-score\": 0.8919984869499433,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9211782873549366,\n",
      "        \"recall\": 0.9136546184738956,\n",
      "        \"f1-score\": 0.9156454287709406,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mandarin Duck 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9385775862068966,\n",
      "        \"recall\": 0.963495575221239,\n",
      "        \"f1-score\": 0.9508733624454149,\n",
      "        \"support\": 1808\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5147058823529411,\n",
      "        \"recall\": 0.3804347826086957,\n",
      "        \"f1-score\": 0.4375,\n",
      "        \"support\": 184\n",
      "    },\n",
      "    \"accuracy\": 0.9096385542168675,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7266417342799188,\n",
      "        \"recall\": 0.6719651789149673,\n",
      "        \"f1-score\": 0.6941866812227074,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8994247782203867,\n",
      "        \"recall\": 0.9096385542168675,\n",
      "        \"f1-score\": 0.9034533329825853,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Mandarin Duck 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9399449035812673,\n",
      "        \"recall\": 0.9435840707964602,\n",
      "        \"f1-score\": 0.9417609715705216,\n",
      "        \"support\": 1808\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.423728813559322,\n",
      "        \"recall\": 0.4076086956521739,\n",
      "        \"f1-score\": 0.41551246537396125,\n",
      "        \"support\": 184\n",
      "    },\n",
      "    \"accuracy\": 0.8940763052208835,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6818368585702946,\n",
      "        \"recall\": 0.675596383224317,\n",
      "        \"f1-score\": 0.6786367184722415,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8922622928563486,\n",
      "        \"recall\": 0.8940763052208835,\n",
      "        \"f1-score\": 0.8931516718013615,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mute Swan 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mute Swan 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.92776886035313,\n",
      "        \"recall\": 0.8328530259365994,\n",
      "        \"f1-score\": 0.8777524677296886,\n",
      "        \"support\": 694\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9152666179693206,\n",
      "        \"recall\": 0.9653312788906009,\n",
      "        \"f1-score\": 0.9396325459317585,\n",
      "        \"support\": 1298\n",
      "    },\n",
      "    \"accuracy\": 0.9191767068273092,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9215177391612253,\n",
      "        \"recall\": 0.8990921524136002,\n",
      "        \"f1-score\": 0.9086925068307236,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9196223188801458,\n",
      "        \"recall\": 0.9191767068273092,\n",
      "        \"f1-score\": 0.9180739243091497,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Mute Swan 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8452054794520548,\n",
      "        \"recall\": 0.8890489913544669,\n",
      "        \"f1-score\": 0.8665730337078652,\n",
      "        \"support\": 694\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.938985736925515,\n",
      "        \"recall\": 0.9129429892141756,\n",
      "        \"f1-score\": 0.92578125,\n",
      "        \"support\": 1298\n",
      "    },\n",
      "    \"accuracy\": 0.9046184738955824,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.892095608188785,\n",
      "        \"recall\": 0.9009959902843212,\n",
      "        \"f1-score\": 0.8961771418539326,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9063132978258255,\n",
      "        \"recall\": 0.9046184738955824,\n",
      "        \"f1-score\": 0.9051534878982221,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pheasant 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pheasant 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9282744282744283,\n",
      "        \"recall\": 0.8636363636363636,\n",
      "        \"f1-score\": 0.8947895791583166,\n",
      "        \"support\": 1034\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8631067961165049,\n",
      "        \"recall\": 0.9279749478079332,\n",
      "        \"f1-score\": 0.8943661971830986,\n",
      "        \"support\": 958\n",
      "    },\n",
      "    \"accuracy\": 0.8945783132530121,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8956906121954666,\n",
      "        \"recall\": 0.8958056557221484,\n",
      "        \"f1-score\": 0.8945778881707076,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8969337698370333,\n",
      "        \"recall\": 0.8945783132530121,\n",
      "        \"f1-score\": 0.8945859647344919,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Pheasant 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9408658922914467,\n",
      "        \"recall\": 0.8617021276595744,\n",
      "        \"f1-score\": 0.8995456839979807,\n",
      "        \"support\": 1034\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8631578947368421,\n",
      "        \"recall\": 0.941544885177453,\n",
      "        \"f1-score\": 0.9006490264603096,\n",
      "        \"support\": 958\n",
      "    },\n",
      "    \"accuracy\": 0.9001004016064257,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9020118935141443,\n",
      "        \"recall\": 0.9016235064185137,\n",
      "        \"f1-score\": 0.9000973552291451,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9034942749935997,\n",
      "        \"recall\": 0.9001004016064257,\n",
      "        \"f1-score\": 0.9000763075315706,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pink-footed Goose 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pink-footed Goose 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9011173184357542,\n",
      "        \"recall\": 0.9641362821279139,\n",
      "        \"f1-score\": 0.9315622292809702,\n",
      "        \"support\": 1673\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7029702970297029,\n",
      "        \"recall\": 0.445141065830721,\n",
      "        \"f1-score\": 0.5451055662188099,\n",
      "        \"support\": 319\n",
      "    },\n",
      "    \"accuracy\": 0.8810240963855421,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8020438077327285,\n",
      "        \"recall\": 0.7046386739793175,\n",
      "        \"f1-score\": 0.7383338977498901,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8693859430198253,\n",
      "        \"recall\": 0.8810240963855421,\n",
      "        \"f1-score\": 0.869674841973325,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Pink-footed Goose 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9275184275184275,\n",
      "        \"recall\": 0.9025702331141662,\n",
      "        \"f1-score\": 0.9148742805210542,\n",
      "        \"support\": 1673\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5521978021978022,\n",
      "        \"recall\": 0.6300940438871473,\n",
      "        \"f1-score\": 0.588579795021962,\n",
      "        \"support\": 319\n",
      "    },\n",
      "    \"accuracy\": 0.8589357429718876,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7398581148581149,\n",
      "        \"recall\": 0.7663321385006567,\n",
      "        \"f1-score\": 0.7517270377715082,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8674143715559378,\n",
      "        \"recall\": 0.8589357429718876,\n",
      "        \"f1-score\": 0.8626212981544827,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pintail 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pintail 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9353671147880042,\n",
      "        \"recall\": 0.9820846905537459,\n",
      "        \"f1-score\": 0.9581567796610171,\n",
      "        \"support\": 1842\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.43103448275862066,\n",
      "        \"recall\": 0.16666666666666666,\n",
      "        \"f1-score\": 0.2403846153846154,\n",
      "        \"support\": 150\n",
      "    },\n",
      "    \"accuracy\": 0.9206827309236948,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6832007987733124,\n",
      "        \"recall\": 0.5743756786102063,\n",
      "        \"f1-score\": 0.5992706975228163,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8973902599665144,\n",
      "        \"recall\": 0.9206827309236948,\n",
      "        \"f1-score\": 0.904107670905264,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Pintail 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9503311258278145,\n",
      "        \"recall\": 0.9348534201954397,\n",
      "        \"f1-score\": 0.9425287356321839,\n",
      "        \"support\": 1842\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3333333333333333,\n",
      "        \"recall\": 0.4,\n",
      "        \"f1-score\": 0.3636363636363636,\n",
      "        \"support\": 150\n",
      "    },\n",
      "    \"accuracy\": 0.8945783132530121,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.641832229580574,\n",
      "        \"recall\": 0.6674267100977198,\n",
      "        \"f1-score\": 0.6530825496342737,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9038704486821457,\n",
      "        \"recall\": 0.8945783132530121,\n",
      "        \"f1-score\": 0.8989374425602095,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pochard 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pochard 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9102564102564102,\n",
      "        \"recall\": 0.9709401709401709,\n",
      "        \"f1-score\": 0.9396195202646815,\n",
      "        \"support\": 1755\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.575,\n",
      "        \"recall\": 0.2911392405063291,\n",
      "        \"f1-score\": 0.38655462184873945,\n",
      "        \"support\": 237\n",
      "    },\n",
      "    \"accuracy\": 0.8900602409638554,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7426282051282052,\n",
      "        \"recall\": 0.63103970572325,\n",
      "        \"f1-score\": 0.6630870710567105,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8703689759036145,\n",
      "        \"recall\": 0.8900602409638554,\n",
      "        \"f1-score\": 0.8738181242182065,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Pochard 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9073464912280702,\n",
      "        \"recall\": 0.9430199430199431,\n",
      "        \"f1-score\": 0.9248393405979324,\n",
      "        \"support\": 1755\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.40476190476190477,\n",
      "        \"recall\": 0.2869198312236287,\n",
      "        \"f1-score\": 0.3358024691358025,\n",
      "        \"support\": 237\n",
      "    },\n",
      "    \"accuracy\": 0.8649598393574297,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6560541979949874,\n",
      "        \"recall\": 0.6149698871217859,\n",
      "        \"f1-score\": 0.6303209048668674,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8475510359105595,\n",
      "        \"recall\": 0.8649598393574297,\n",
      "        \"f1-score\": 0.8547581465534921,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Red-legged Partridge 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red-legged Partridge 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9474789915966386,\n",
      "        \"recall\": 0.9135719108710331,\n",
      "        \"f1-score\": 0.9302165692677895,\n",
      "        \"support\": 1481\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7730496453900709,\n",
      "        \"recall\": 0.8532289628180039,\n",
      "        \"f1-score\": 0.8111627906976744,\n",
      "        \"support\": 511\n",
      "    },\n",
      "    \"accuracy\": 0.8980923694779116,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8602643184933547,\n",
      "        \"recall\": 0.8834004368445185,\n",
      "        \"f1-score\": 0.870689679982732,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9027333109181466,\n",
      "        \"recall\": 0.8980923694779116,\n",
      "        \"f1-score\": 0.899676167234994,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Red-legged Partridge 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9483734087694484,\n",
      "        \"recall\": 0.9054692775151925,\n",
      "        \"f1-score\": 0.9264248704663213,\n",
      "        \"support\": 1481\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7577854671280276,\n",
      "        \"recall\": 0.8571428571428571,\n",
      "        \"f1-score\": 0.8044077134986225,\n",
      "        \"support\": 511\n",
      "    },\n",
      "    \"accuracy\": 0.8930722891566265,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8530794379487381,\n",
      "        \"recall\": 0.8813060673290247,\n",
      "        \"f1-score\": 0.8654162919824719,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8994826265511923,\n",
      "        \"recall\": 0.8930722891566265,\n",
      "        \"f1-score\": 0.8951242845172781,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ring-necked Parakeet 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ring-necked Parakeet 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9810159055926116,\n",
      "        \"recall\": 0.9881136950904392,\n",
      "        \"f1-score\": 0.984552008238929,\n",
      "        \"support\": 1935\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.46511627906976744,\n",
      "        \"recall\": 0.3508771929824561,\n",
      "        \"f1-score\": 0.4,\n",
      "        \"support\": 57\n",
      "    },\n",
      "    \"accuracy\": 0.9698795180722891,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7230660923311896,\n",
      "        \"recall\": 0.6694954440364477,\n",
      "        \"f1-score\": 0.6922760041194644,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9662537174842774,\n",
      "        \"recall\": 0.9698795180722891,\n",
      "        \"f1-score\": 0.9678253694489596,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Ring-necked Parakeet 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9809964047252183,\n",
      "        \"recall\": 0.9870801033591732,\n",
      "        \"f1-score\": 0.9840288511076766,\n",
      "        \"support\": 1935\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4444444444444444,\n",
      "        \"recall\": 0.3508771929824561,\n",
      "        \"f1-score\": 0.39215686274509803,\n",
      "        \"support\": 57\n",
      "    },\n",
      "    \"accuracy\": 0.9688755020080321,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7127204245848313,\n",
      "        \"recall\": 0.6689786481708146,\n",
      "        \"f1-score\": 0.6880928569263873,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9656432612834492,\n",
      "        \"recall\": 0.9688755020080321,\n",
      "        \"f1-score\": 0.9670927550551328,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Rock Dove 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rock Dove 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9165302782324058,\n",
      "        \"recall\": 0.8543096872616324,\n",
      "        \"f1-score\": 0.8843268851164627,\n",
      "        \"support\": 1311\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7519480519480519,\n",
      "        \"recall\": 0.8502202643171806,\n",
      "        \"f1-score\": 0.7980702963473466,\n",
      "        \"support\": 681\n",
      "    },\n",
      "    \"accuracy\": 0.8529116465863453,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8342391650902289,\n",
      "        \"recall\": 0.8522649757894065,\n",
      "        \"f1-score\": 0.8411985907319046,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8602649689454356,\n",
      "        \"recall\": 0.8529116465863453,\n",
      "        \"f1-score\": 0.8548385633535269,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Rock Dove 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9233937397034596,\n",
      "        \"recall\": 0.855072463768116,\n",
      "        \"f1-score\": 0.887920792079208,\n",
      "        \"support\": 1311\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7557840616966581,\n",
      "        \"recall\": 0.8634361233480177,\n",
      "        \"f1-score\": 0.8060315284441397,\n",
      "        \"support\": 681\n",
      "    },\n",
      "    \"accuracy\": 0.8579317269076305,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8395889007000589,\n",
      "        \"recall\": 0.8592542935580668,\n",
      "        \"f1-score\": 0.8469761602616739,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8660934431559536,\n",
      "        \"recall\": 0.8579317269076305,\n",
      "        \"f1-score\": 0.8599255167099904,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ruddy Duck 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ruddy Duck 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9834254143646409,\n",
      "        \"recall\": 0.9994895354772844,\n",
      "        \"f1-score\": 0.9913924050632913,\n",
      "        \"support\": 1959\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 33\n",
      "    },\n",
      "    \"accuracy\": 0.9829317269076305,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49171270718232046,\n",
      "        \"recall\": 0.4997447677386422,\n",
      "        \"f1-score\": 0.49569620253164565,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.967133728283299,\n",
      "        \"recall\": 0.9829317269076305,\n",
      "        \"f1-score\": 0.9749687357023031,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Ruddy Duck 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9848637739656912,\n",
      "        \"recall\": 0.9964267483409903,\n",
      "        \"f1-score\": 0.9906115199188025,\n",
      "        \"support\": 1959\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3,\n",
      "        \"recall\": 0.09090909090909091,\n",
      "        \"f1-score\": 0.13953488372093023,\n",
      "        \"support\": 33\n",
      "    },\n",
      "    \"accuracy\": 0.981425702811245,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6424318869828456,\n",
      "        \"recall\": 0.5436679196250406,\n",
      "        \"f1-score\": 0.5650732018198663,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9735181391560187,\n",
      "        \"recall\": 0.981425702811245,\n",
      "        \"f1-score\": 0.9765123587769704,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Whooper Swan 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whooper Swan 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9047864127637674,\n",
      "        \"recall\": 0.9854260089686099,\n",
      "        \"f1-score\": 0.9433861014220553,\n",
      "        \"support\": 1784\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.46938775510204084,\n",
      "        \"recall\": 0.11057692307692307,\n",
      "        \"f1-score\": 0.17898832684824903,\n",
      "        \"support\": 208\n",
      "    },\n",
      "    \"accuracy\": 0.8940763052208835,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6870870839329041,\n",
      "        \"recall\": 0.5480014660227664,\n",
      "        \"f1-score\": 0.5611872141351522,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8593230991123421,\n",
      "        \"recall\": 0.8940763052208835,\n",
      "        \"f1-score\": 0.8635694663259952,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Whooper Swan 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9271597967250141,\n",
      "        \"recall\": 0.9204035874439462,\n",
      "        \"f1-score\": 0.9237693389592125,\n",
      "        \"support\": 1784\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3574660633484163,\n",
      "        \"recall\": 0.3798076923076923,\n",
      "        \"f1-score\": 0.36829836829836826,\n",
      "        \"support\": 208\n",
      "    },\n",
      "    \"accuracy\": 0.8639558232931727,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6423129300367152,\n",
      "        \"recall\": 0.6501056398758193,\n",
      "        \"f1-score\": 0.6460338536287904,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.867673704083281,\n",
      "        \"recall\": 0.8639558232931727,\n",
      "        \"f1-score\": 0.8657683540709316,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Wigeon 5km cells... \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wigeon 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8940092165898618,\n",
      "        \"recall\": 0.8846905537459283,\n",
      "        \"f1-score\": 0.8893254747871644,\n",
      "        \"support\": 1535\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6257928118393234,\n",
      "        \"recall\": 0.6477024070021882,\n",
      "        \"f1-score\": 0.6365591397849463,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.8303212851405622,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7599010142145926,\n",
      "        \"recall\": 0.7661964803740582,\n",
      "        \"f1-score\": 0.7629423072860553,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8324756337730967,\n",
      "        \"recall\": 0.8303212851405622,\n",
      "        \"f1-score\": 0.8313364109839447,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Wigeon 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8759791122715405,\n",
      "        \"recall\": 0.8742671009771987,\n",
      "        \"f1-score\": 0.8751222693185523,\n",
      "        \"support\": 1535\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5804347826086956,\n",
      "        \"recall\": 0.5842450765864332,\n",
      "        \"f1-score\": 0.5823336968375136,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.8077309236947792,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.728206947440118,\n",
      "        \"recall\": 0.729256088781816,\n",
      "        \"f1-score\": 0.7287279830780329,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8081760205768014,\n",
      "        \"recall\": 0.8077309236947792,\n",
      "        \"f1-score\": 0.8079513970174305,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Add model pipeline\n",
    "estimators = [\n",
    "    ('lr', LogisticRegression(max_iter=10000, solver='saga', random_state=seed, penalty='l2', verbose=verbose)),\n",
    "    ('knn', KNeighborsClassifier(n_neighbors=5, weights='distance')),\n",
    "    ('sgd', SGDClassifier( max_iter=10000, loss='modified_huber', random_state=seed, penalty='l2', verbose=verbose)),\n",
    "    ('rf', RandomForestClassifier(n_estimators=20,max_features=None, random_state=seed, verbose=verbose))\n",
    "]\n",
    "\n",
    "\n",
    "for dict in df_dicts:\n",
    "    print(f'Training with {dict[\"name\"]} cells... \\n')\n",
    "    # Use this if using coordinates as separate columns\n",
    "    # coords, X, y = data['dataframe'].iloc[:, :2], data['dataframe'].iloc[:, 2:-1], data['dataframe'].iloc[:, [-1]]\n",
    "    # data['coords'] = coords\n",
    "    \n",
    "    # Use this if using coordinates as indices\n",
    "    X, y = dict['dataframe'].iloc[:, 0:-1], dict['dataframe'].iloc[:, [-1]], \n",
    "\n",
    "    dict['X'] = standardise(X)\n",
    "    dict['y'] = y\n",
    "    dict['kbest'] = feature_select(dict['X'], dict['y'])\n",
    "\n",
    "    # dict['X'] = dict['kbest']['10'].transform(dict['X'])\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(dict['X'], dict['y'], random_state=seed)\n",
    "    dict['X_train'], dict['X_test'], dict['y_train'], dict['y_test'] = X_train, X_test, y_train, y_test # for debugging purposes\n",
    "\n",
    "    dict['X_smote'], dict['y_smote'] = oversample(X_train, y_train)\n",
    "\n",
    "    stack_clf = StackingClassifier(\n",
    "        estimators=estimators, \n",
    "        final_estimator=GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=None, max_depth=2, random_state=seed)\n",
    "    )\n",
    "\n",
    "    # Classifier without SMOTE\n",
    "    stack_clf.fit(dict['X_train'], dict['y_train'])\n",
    "    y_pred = stack_clf.predict(X_test)\n",
    "    \n",
    "    dict['predictions'] = y_pred\n",
    "    dict['report'] = classification_report(y_test, y_pred, output_dict=True)\n",
    "    \n",
    "\n",
    "    # Classifier with SMOTE\n",
    "    stack_clf.fit(dict['X_smote'], dict['y_smote'])\n",
    "    y_pred_smote = stack_clf.predict(X_test)\n",
    "    \n",
    "    dict['predictions_smote'] = y_pred_smote\n",
    "    dict['report_smote'] = classification_report(y_test, y_pred_smote, output_dict=True)\n",
    "    \n",
    "    print(f'{dict[\"name\"]} Classification Report: \\n {json.dumps(dict[\"report\"], indent=4)} \\n')\n",
    "    print(f'{dict[\"name\"]} SMOTE Classification Report: \\n {json.dumps(dict[\"report_smote\"], indent=4)} \\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9358316221765913,\n",
      "        \"recall\": 0.9886117136659436,\n",
      "        \"f1-score\": 0.9614978902953586,\n",
      "        \"support\": 1844\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5227272727272727,\n",
      "        \"recall\": 0.1554054054054054,\n",
      "        \"f1-score\": 0.23958333333333334,\n",
      "        \"support\": 148\n",
      "    },\n",
      "    \"accuracy\": 0.9267068273092369,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.729279447451932,\n",
      "        \"recall\": 0.5720085595356745,\n",
      "        \"f1-score\": 0.6005406118143459,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9051391303500355,\n",
      "        \"recall\": 0.9267068273092369,\n",
      "        \"f1-score\": 0.9078616681917543,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Canada Goose 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9355971896955504,\n",
      "        \"recall\": 0.871319520174482,\n",
      "        \"f1-score\": 0.9023150762281198,\n",
      "        \"support\": 917\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8963093145869947,\n",
      "        \"recall\": 0.9488372093023256,\n",
      "        \"f1-score\": 0.921825576140985,\n",
      "        \"support\": 1075\n",
      "    },\n",
      "    \"accuracy\": 0.9131526104417671,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9159532521412725,\n",
      "        \"recall\": 0.9100783647384039,\n",
      "        \"f1-score\": 0.9120703261845524,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9143951486605617,\n",
      "        \"recall\": 0.9131526104417671,\n",
      "        \"f1-score\": 0.9128440859702533,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Egyptian Goose 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9595588235294118,\n",
      "        \"recall\": 0.981203007518797,\n",
      "        \"f1-score\": 0.9702602230483273,\n",
      "        \"support\": 1862\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6022727272727273,\n",
      "        \"recall\": 0.4076923076923077,\n",
      "        \"f1-score\": 0.4862385321100917,\n",
      "        \"support\": 130\n",
      "    },\n",
      "    \"accuracy\": 0.9437751004016064,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7809157754010696,\n",
      "        \"recall\": 0.6944476576055523,\n",
      "        \"f1-score\": 0.7282493775792095,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9362419598178812,\n",
      "        \"recall\": 0.9437751004016064,\n",
      "        \"f1-score\": 0.9386724620935227,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Gadwall 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9131195335276968,\n",
      "        \"recall\": 0.9566279780085523,\n",
      "        \"f1-score\": 0.9343675417661097,\n",
      "        \"support\": 1637\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7436823104693141,\n",
      "        \"recall\": 0.5802816901408451,\n",
      "        \"f1-score\": 0.6518987341772152,\n",
      "        \"support\": 355\n",
      "    },\n",
      "    \"accuracy\": 0.8895582329317269,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8284009219985055,\n",
      "        \"recall\": 0.7684548340746986,\n",
      "        \"f1-score\": 0.7931331379716624,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8829236428722119,\n",
      "        \"recall\": 0.8895582329317269,\n",
      "        \"f1-score\": 0.8840279701325467,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Goshawk 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.954337899543379,\n",
      "        \"recall\": 0.9926121372031662,\n",
      "        \"f1-score\": 0.9730988101396792,\n",
      "        \"support\": 1895\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3333333333333333,\n",
      "        \"recall\": 0.07216494845360824,\n",
      "        \"f1-score\": 0.11864406779661016,\n",
      "        \"support\": 97\n",
      "    },\n",
      "    \"accuracy\": 0.9477911646586346,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6438356164383562,\n",
      "        \"recall\": 0.5323885428283872,\n",
      "        \"f1-score\": 0.5458714389681447,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9240982193614641,\n",
      "        \"recall\": 0.9477911646586346,\n",
      "        \"f1-score\": 0.9314913251962669,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Grey Partridge 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9587491683300067,\n",
      "        \"recall\": 0.902316844082655,\n",
      "        \"f1-score\": 0.9296774193548387,\n",
      "        \"support\": 1597\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6809815950920245,\n",
      "        \"recall\": 0.8430379746835444,\n",
      "        \"f1-score\": 0.7533936651583709,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"accuracy\": 0.8905622489959839,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8198653817110155,\n",
      "        \"recall\": 0.8726774093830997,\n",
      "        \"f1-score\": 0.8415355422566049,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9036697549620333,\n",
      "        \"recall\": 0.8905622489959839,\n",
      "        \"f1-score\": 0.8947215544413825,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Indian Peafowl 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9617706237424547,\n",
      "        \"recall\": 0.9979123173277662,\n",
      "        \"f1-score\": 0.9795081967213115,\n",
      "        \"support\": 1916\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.9598393574297188,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.48088531187122735,\n",
      "        \"recall\": 0.4989561586638831,\n",
      "        \"f1-score\": 0.48975409836065575,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9250765638004735,\n",
      "        \"recall\": 0.9598393574297188,\n",
      "        \"f1-score\": 0.9421374020672856,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Little Owl 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9746376811594203,\n",
      "        \"recall\": 0.9069453809844908,\n",
      "        \"f1-score\": 0.9395738735592035,\n",
      "        \"support\": 1483\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7745098039215687,\n",
      "        \"recall\": 0.931237721021611,\n",
      "        \"f1-score\": 0.8456735057983942,\n",
      "        \"support\": 509\n",
      "    },\n",
      "    \"accuracy\": 0.9131526104417671,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8745737425404945,\n",
      "        \"recall\": 0.9190915510030508,\n",
      "        \"f1-score\": 0.8926236896787989,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9235005880298688,\n",
      "        \"recall\": 0.9131526104417671,\n",
      "        \"f1-score\": 0.9155802554918079,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Mandarin Duck 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9385775862068966,\n",
      "        \"recall\": 0.963495575221239,\n",
      "        \"f1-score\": 0.9508733624454149,\n",
      "        \"support\": 1808\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5147058823529411,\n",
      "        \"recall\": 0.3804347826086957,\n",
      "        \"f1-score\": 0.4375,\n",
      "        \"support\": 184\n",
      "    },\n",
      "    \"accuracy\": 0.9096385542168675,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7266417342799188,\n",
      "        \"recall\": 0.6719651789149673,\n",
      "        \"f1-score\": 0.6941866812227074,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8994247782203867,\n",
      "        \"recall\": 0.9096385542168675,\n",
      "        \"f1-score\": 0.9034533329825853,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Mute Swan 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.92776886035313,\n",
      "        \"recall\": 0.8328530259365994,\n",
      "        \"f1-score\": 0.8777524677296886,\n",
      "        \"support\": 694\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9152666179693206,\n",
      "        \"recall\": 0.9653312788906009,\n",
      "        \"f1-score\": 0.9396325459317585,\n",
      "        \"support\": 1298\n",
      "    },\n",
      "    \"accuracy\": 0.9191767068273092,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9215177391612253,\n",
      "        \"recall\": 0.8990921524136002,\n",
      "        \"f1-score\": 0.9086925068307236,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9196223188801458,\n",
      "        \"recall\": 0.9191767068273092,\n",
      "        \"f1-score\": 0.9180739243091497,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Pheasant 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9282744282744283,\n",
      "        \"recall\": 0.8636363636363636,\n",
      "        \"f1-score\": 0.8947895791583166,\n",
      "        \"support\": 1034\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8631067961165049,\n",
      "        \"recall\": 0.9279749478079332,\n",
      "        \"f1-score\": 0.8943661971830986,\n",
      "        \"support\": 958\n",
      "    },\n",
      "    \"accuracy\": 0.8945783132530121,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8956906121954666,\n",
      "        \"recall\": 0.8958056557221484,\n",
      "        \"f1-score\": 0.8945778881707076,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8969337698370333,\n",
      "        \"recall\": 0.8945783132530121,\n",
      "        \"f1-score\": 0.8945859647344919,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Pink-footed Goose 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9011173184357542,\n",
      "        \"recall\": 0.9641362821279139,\n",
      "        \"f1-score\": 0.9315622292809702,\n",
      "        \"support\": 1673\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7029702970297029,\n",
      "        \"recall\": 0.445141065830721,\n",
      "        \"f1-score\": 0.5451055662188099,\n",
      "        \"support\": 319\n",
      "    },\n",
      "    \"accuracy\": 0.8810240963855421,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8020438077327285,\n",
      "        \"recall\": 0.7046386739793175,\n",
      "        \"f1-score\": 0.7383338977498901,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8693859430198253,\n",
      "        \"recall\": 0.8810240963855421,\n",
      "        \"f1-score\": 0.869674841973325,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Pintail 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9353671147880042,\n",
      "        \"recall\": 0.9820846905537459,\n",
      "        \"f1-score\": 0.9581567796610171,\n",
      "        \"support\": 1842\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.43103448275862066,\n",
      "        \"recall\": 0.16666666666666666,\n",
      "        \"f1-score\": 0.2403846153846154,\n",
      "        \"support\": 150\n",
      "    },\n",
      "    \"accuracy\": 0.9206827309236948,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6832007987733124,\n",
      "        \"recall\": 0.5743756786102063,\n",
      "        \"f1-score\": 0.5992706975228163,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8973902599665144,\n",
      "        \"recall\": 0.9206827309236948,\n",
      "        \"f1-score\": 0.904107670905264,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Pochard 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9102564102564102,\n",
      "        \"recall\": 0.9709401709401709,\n",
      "        \"f1-score\": 0.9396195202646815,\n",
      "        \"support\": 1755\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.575,\n",
      "        \"recall\": 0.2911392405063291,\n",
      "        \"f1-score\": 0.38655462184873945,\n",
      "        \"support\": 237\n",
      "    },\n",
      "    \"accuracy\": 0.8900602409638554,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7426282051282052,\n",
      "        \"recall\": 0.63103970572325,\n",
      "        \"f1-score\": 0.6630870710567105,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8703689759036145,\n",
      "        \"recall\": 0.8900602409638554,\n",
      "        \"f1-score\": 0.8738181242182065,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Red-legged Partridge 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9474789915966386,\n",
      "        \"recall\": 0.9135719108710331,\n",
      "        \"f1-score\": 0.9302165692677895,\n",
      "        \"support\": 1481\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7730496453900709,\n",
      "        \"recall\": 0.8532289628180039,\n",
      "        \"f1-score\": 0.8111627906976744,\n",
      "        \"support\": 511\n",
      "    },\n",
      "    \"accuracy\": 0.8980923694779116,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8602643184933547,\n",
      "        \"recall\": 0.8834004368445185,\n",
      "        \"f1-score\": 0.870689679982732,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9027333109181466,\n",
      "        \"recall\": 0.8980923694779116,\n",
      "        \"f1-score\": 0.899676167234994,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Ring-necked Parakeet 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9810159055926116,\n",
      "        \"recall\": 0.9881136950904392,\n",
      "        \"f1-score\": 0.984552008238929,\n",
      "        \"support\": 1935\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.46511627906976744,\n",
      "        \"recall\": 0.3508771929824561,\n",
      "        \"f1-score\": 0.4,\n",
      "        \"support\": 57\n",
      "    },\n",
      "    \"accuracy\": 0.9698795180722891,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7230660923311896,\n",
      "        \"recall\": 0.6694954440364477,\n",
      "        \"f1-score\": 0.6922760041194644,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9662537174842774,\n",
      "        \"recall\": 0.9698795180722891,\n",
      "        \"f1-score\": 0.9678253694489596,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Rock Dove 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9165302782324058,\n",
      "        \"recall\": 0.8543096872616324,\n",
      "        \"f1-score\": 0.8843268851164627,\n",
      "        \"support\": 1311\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7519480519480519,\n",
      "        \"recall\": 0.8502202643171806,\n",
      "        \"f1-score\": 0.7980702963473466,\n",
      "        \"support\": 681\n",
      "    },\n",
      "    \"accuracy\": 0.8529116465863453,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8342391650902289,\n",
      "        \"recall\": 0.8522649757894065,\n",
      "        \"f1-score\": 0.8411985907319046,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8602649689454356,\n",
      "        \"recall\": 0.8529116465863453,\n",
      "        \"f1-score\": 0.8548385633535269,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Ruddy Duck 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9834254143646409,\n",
      "        \"recall\": 0.9994895354772844,\n",
      "        \"f1-score\": 0.9913924050632913,\n",
      "        \"support\": 1959\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 33\n",
      "    },\n",
      "    \"accuracy\": 0.9829317269076305,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49171270718232046,\n",
      "        \"recall\": 0.4997447677386422,\n",
      "        \"f1-score\": 0.49569620253164565,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.967133728283299,\n",
      "        \"recall\": 0.9829317269076305,\n",
      "        \"f1-score\": 0.9749687357023031,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Whooper Swan 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9047864127637674,\n",
      "        \"recall\": 0.9854260089686099,\n",
      "        \"f1-score\": 0.9433861014220553,\n",
      "        \"support\": 1784\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.46938775510204084,\n",
      "        \"recall\": 0.11057692307692307,\n",
      "        \"f1-score\": 0.17898832684824903,\n",
      "        \"support\": 208\n",
      "    },\n",
      "    \"accuracy\": 0.8940763052208835,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6870870839329041,\n",
      "        \"recall\": 0.5480014660227664,\n",
      "        \"f1-score\": 0.5611872141351522,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8593230991123421,\n",
      "        \"recall\": 0.8940763052208835,\n",
      "        \"f1-score\": 0.8635694663259952,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Wigeon 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8940092165898618,\n",
      "        \"recall\": 0.8846905537459283,\n",
      "        \"f1-score\": 0.8893254747871644,\n",
      "        \"support\": 1535\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6257928118393234,\n",
      "        \"recall\": 0.6477024070021882,\n",
      "        \"f1-score\": 0.6365591397849463,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.8303212851405622,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7599010142145926,\n",
      "        \"recall\": 0.7661964803740582,\n",
      "        \"f1-score\": 0.7629423072860553,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8324756337730967,\n",
      "        \"recall\": 0.8303212851405622,\n",
      "        \"f1-score\": 0.8313364109839447,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "for dict in df_dicts:\n",
    "    print(dict['name'], '\\n', json.dumps(dict['report'], indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Labels</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Precision (Smote)</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Recall (Smote)</th>\n",
       "      <th>F1</th>\n",
       "      <th>F1 (Smote)</th>\n",
       "      <th>Occurrence Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mute Swan 5km</td>\n",
       "      <td>0.915267</td>\n",
       "      <td>0.938986</td>\n",
       "      <td>0.965331</td>\n",
       "      <td>0.912943</td>\n",
       "      <td>0.939633</td>\n",
       "      <td>0.925781</td>\n",
       "      <td>5267</td>\n",
       "      <td>0.661268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 5km</td>\n",
       "      <td>0.896309</td>\n",
       "      <td>0.911978</td>\n",
       "      <td>0.948837</td>\n",
       "      <td>0.934884</td>\n",
       "      <td>0.921826</td>\n",
       "      <td>0.923289</td>\n",
       "      <td>4305</td>\n",
       "      <td>0.540490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 5km</td>\n",
       "      <td>0.863107</td>\n",
       "      <td>0.863158</td>\n",
       "      <td>0.927975</td>\n",
       "      <td>0.941545</td>\n",
       "      <td>0.894366</td>\n",
       "      <td>0.900649</td>\n",
       "      <td>3848</td>\n",
       "      <td>0.483114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 5km</td>\n",
       "      <td>0.751948</td>\n",
       "      <td>0.755784</td>\n",
       "      <td>0.850220</td>\n",
       "      <td>0.863436</td>\n",
       "      <td>0.798070</td>\n",
       "      <td>0.806032</td>\n",
       "      <td>2830</td>\n",
       "      <td>0.355304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 5km</td>\n",
       "      <td>0.774510</td>\n",
       "      <td>0.785110</td>\n",
       "      <td>0.931238</td>\n",
       "      <td>0.911591</td>\n",
       "      <td>0.845674</td>\n",
       "      <td>0.843636</td>\n",
       "      <td>2158</td>\n",
       "      <td>0.270935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 5km</td>\n",
       "      <td>0.773050</td>\n",
       "      <td>0.757785</td>\n",
       "      <td>0.853229</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.811163</td>\n",
       "      <td>0.804408</td>\n",
       "      <td>2150</td>\n",
       "      <td>0.269931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 5km</td>\n",
       "      <td>0.625793</td>\n",
       "      <td>0.580435</td>\n",
       "      <td>0.647702</td>\n",
       "      <td>0.584245</td>\n",
       "      <td>0.636559</td>\n",
       "      <td>0.582334</td>\n",
       "      <td>1857</td>\n",
       "      <td>0.233145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 5km</td>\n",
       "      <td>0.680982</td>\n",
       "      <td>0.670241</td>\n",
       "      <td>0.843038</td>\n",
       "      <td>0.632911</td>\n",
       "      <td>0.753394</td>\n",
       "      <td>0.651042</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.204520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 5km</td>\n",
       "      <td>0.743682</td>\n",
       "      <td>0.670968</td>\n",
       "      <td>0.580282</td>\n",
       "      <td>0.585915</td>\n",
       "      <td>0.651899</td>\n",
       "      <td>0.625564</td>\n",
       "      <td>1399</td>\n",
       "      <td>0.175643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 5km</td>\n",
       "      <td>0.702970</td>\n",
       "      <td>0.552198</td>\n",
       "      <td>0.445141</td>\n",
       "      <td>0.630094</td>\n",
       "      <td>0.545106</td>\n",
       "      <td>0.588580</td>\n",
       "      <td>1313</td>\n",
       "      <td>0.164846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 5km</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.404762</td>\n",
       "      <td>0.291139</td>\n",
       "      <td>0.286920</td>\n",
       "      <td>0.386555</td>\n",
       "      <td>0.335802</td>\n",
       "      <td>942</td>\n",
       "      <td>0.118267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 5km</td>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.357466</td>\n",
       "      <td>0.110577</td>\n",
       "      <td>0.379808</td>\n",
       "      <td>0.178988</td>\n",
       "      <td>0.368298</td>\n",
       "      <td>842</td>\n",
       "      <td>0.105712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 5km</td>\n",
       "      <td>0.514706</td>\n",
       "      <td>0.423729</td>\n",
       "      <td>0.380435</td>\n",
       "      <td>0.407609</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.415512</td>\n",
       "      <td>714</td>\n",
       "      <td>0.089642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 5km</td>\n",
       "      <td>0.431034</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.240385</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>649</td>\n",
       "      <td>0.081481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 5km</td>\n",
       "      <td>0.522727</td>\n",
       "      <td>0.245033</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.239583</td>\n",
       "      <td>0.247492</td>\n",
       "      <td>587</td>\n",
       "      <td>0.073697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 5km</td>\n",
       "      <td>0.602273</td>\n",
       "      <td>0.568807</td>\n",
       "      <td>0.407692</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>0.486239</td>\n",
       "      <td>0.518828</td>\n",
       "      <td>485</td>\n",
       "      <td>0.060891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 5km</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>0.072165</td>\n",
       "      <td>0.257732</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.297619</td>\n",
       "      <td>446</td>\n",
       "      <td>0.055995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 5km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>284</td>\n",
       "      <td>0.035656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 5km</td>\n",
       "      <td>0.465116</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>206</td>\n",
       "      <td>0.025863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 5km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>109</td>\n",
       "      <td>0.013685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Labels  Precision  Precision (Smote)    Recall  \\\n",
       "9              Mute Swan 5km   0.915267           0.938986  0.965331   \n",
       "1           Canada Goose 5km   0.896309           0.911978  0.948837   \n",
       "10              Pheasant 5km   0.863107           0.863158  0.927975   \n",
       "16             Rock Dove 5km   0.751948           0.755784  0.850220   \n",
       "7             Little Owl 5km   0.774510           0.785110  0.931238   \n",
       "14  Red-legged Partridge 5km   0.773050           0.757785  0.853229   \n",
       "19                Wigeon 5km   0.625793           0.580435  0.647702   \n",
       "5         Grey Partridge 5km   0.680982           0.670241  0.843038   \n",
       "3                Gadwall 5km   0.743682           0.670968  0.580282   \n",
       "11     Pink-footed Goose 5km   0.702970           0.552198  0.445141   \n",
       "13               Pochard 5km   0.575000           0.404762  0.291139   \n",
       "18          Whooper Swan 5km   0.469388           0.357466  0.110577   \n",
       "8          Mandarin Duck 5km   0.514706           0.423729  0.380435   \n",
       "12               Pintail 5km   0.431034           0.333333  0.166667   \n",
       "0         Barnacle Goose 5km   0.522727           0.245033  0.155405   \n",
       "2         Egyptian Goose 5km   0.602273           0.568807  0.407692   \n",
       "4                Goshawk 5km   0.333333           0.352113  0.072165   \n",
       "6         Indian Peafowl 5km   0.000000           0.184211  0.000000   \n",
       "15  Ring-necked Parakeet 5km   0.465116           0.444444  0.350877   \n",
       "17            Ruddy Duck 5km   0.000000           0.300000  0.000000   \n",
       "\n",
       "    Recall (Smote)        F1  F1 (Smote)  Occurrence Count  Percentage  \n",
       "9         0.912943  0.939633    0.925781              5267    0.661268  \n",
       "1         0.934884  0.921826    0.923289              4305    0.540490  \n",
       "10        0.941545  0.894366    0.900649              3848    0.483114  \n",
       "16        0.863436  0.798070    0.806032              2830    0.355304  \n",
       "7         0.911591  0.845674    0.843636              2158    0.270935  \n",
       "14        0.857143  0.811163    0.804408              2150    0.269931  \n",
       "19        0.584245  0.636559    0.582334              1857    0.233145  \n",
       "5         0.632911  0.753394    0.651042              1629    0.204520  \n",
       "3         0.585915  0.651899    0.625564              1399    0.175643  \n",
       "11        0.630094  0.545106    0.588580              1313    0.164846  \n",
       "13        0.286920  0.386555    0.335802               942    0.118267  \n",
       "18        0.379808  0.178988    0.368298               842    0.105712  \n",
       "8         0.407609  0.437500    0.415512               714    0.089642  \n",
       "12        0.400000  0.240385    0.363636               649    0.081481  \n",
       "0         0.250000  0.239583    0.247492               587    0.073697  \n",
       "2         0.476923  0.486239    0.518828               485    0.060891  \n",
       "4         0.257732  0.118644    0.297619               446    0.055995  \n",
       "6         0.092105  0.000000    0.122807               284    0.035656  \n",
       "15        0.350877  0.400000    0.392157               206    0.025863  \n",
       "17        0.090909  0.000000    0.139535               109    0.013685  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create graphs to show off data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [9, 12]\n",
    "\n",
    "occurrence_count, occurrence_percentage = All_bird_occurrences['Occurrence Count'], All_bird_occurrences['Percentage']\n",
    "precision = []\n",
    "precision_smote = []\n",
    "recall = []\n",
    "recall_smote = []\n",
    "f1 = []\n",
    "f1_smote = []\n",
    "labels = []\n",
    "for dict in df_dicts:\n",
    "    precision.append(dict['report']['1']['precision'])\n",
    "    precision_smote.append(dict['report_smote']['1']['precision'])\n",
    "    recall.append(dict['report']['1']['recall'])\n",
    "    recall_smote.append(dict['report_smote']['1']['recall'])\n",
    "    f1.append(dict['report']['1']['f1-score'])\n",
    "    f1_smote.append(dict['report_smote']['1']['f1-score'])\n",
    "    labels.append(dict['name'])\n",
    "\n",
    "\n",
    "\n",
    "scores = pd.DataFrame({'Labels' : labels, \n",
    "                        'Precision': precision, 'Precision (Smote)': precision_smote, \n",
    "                        'Recall': recall, 'Recall (Smote)': recall_smote, \n",
    "                        'F1': f1, 'F1 (Smote)': f1_smote,\n",
    "                        'Occurrence Count' : occurrence_count, 'Percentage' : occurrence_percentage} )\n",
    "                        \n",
    "scores.sort_values('Occurrence Count', inplace=True)\n",
    "\n",
    "n=20\n",
    "r = np.arange(n)\n",
    "height = 0.25\n",
    "\n",
    "plt.barh(r, 'Percentage', data=scores, label='Occurrence Percentage', height = height, color='g')\n",
    "plt.barh(r+height, 'F1', data=scores, label='F1-Score',  height= height, color='b')\n",
    "plt.barh(r+height*2, 'F1 (Smote)', data=scores, label='F1-Score (Smote)',  height = height, color='r')\n",
    "plt.legend(framealpha=1, frameon=True)\n",
    "plt.yticks(r+height*2, scores['Labels'])\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "scores.sort_values('Occurrence Count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'df_dicts_5km' (list)\n"
     ]
    }
   ],
   "source": [
    "# Store dictionaries for later use\n",
    "df_dicts_5km = df_dicts\n",
    "%store df_dicts_5km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "                    Occurrence  Predictions\n",
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       "...                        ...          ...\n",
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       "...                        ...          ...\n",
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       "\n",
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       "                    Occurrence  Predictions\n",
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       "...                        ...          ...\n",
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       "1237500.0 332500.0           0            0\n",
       "957500.0  122500.0           0            0\n",
       "182500.0  582500.0           1            0\n",
       "\n",
       "[1992 rows x 2 columns]"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Export predictions to CSV for QGIS\n",
    "RESULTS_PATH = 'Datasets/Machine Learning/Results/5km/'\n",
    "for dict in df_dicts:\n",
    "    # Join with y_test datafram\n",
    "    result_df = dict['y_test'] \n",
    "    result_df['Predictions'] = dict['predictions_smote']\n",
    "    display(result_df)\n",
    "    result_df.to_csv(RESULTS_PATH + dict['name'] + '.csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 5km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1227.949077</td>\n",
       "      <td>2.545634e-250</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1020.748820</td>\n",
       "      <td>7.391612e-211</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>983.053244</td>\n",
       "      <td>1.401801e-203</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>511.443416</td>\n",
       "      <td>8.364957e-110</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>511.443416</td>\n",
       "      <td>8.364957e-110</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>511.443416</td>\n",
       "      <td>8.364957e-110</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>462.292259</td>\n",
       "      <td>1.002663e-99</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>360.982411</td>\n",
       "      <td>9.334063e-79</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>322.588411</td>\n",
       "      <td>9.713871e-71</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>322.261898</td>\n",
       "      <td>1.136958e-70</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>293.180616</td>\n",
       "      <td>1.430076e-64</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>292.896402</td>\n",
       "      <td>1.640917e-64</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>292.896402</td>\n",
       "      <td>1.640917e-64</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>292.896402</td>\n",
       "      <td>1.640917e-64</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>292.896402</td>\n",
       "      <td>1.640917e-64</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>292.896402</td>\n",
       "      <td>1.640917e-64</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>289.722162</td>\n",
       "      <td>7.626146e-64</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>257.074186</td>\n",
       "      <td>5.771923e-57</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>203.769294</td>\n",
       "      <td>1.146737e-45</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>199.309746</td>\n",
       "      <td>1.019948e-44</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>182.849376</td>\n",
       "      <td>3.290272e-41</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>182.849376</td>\n",
       "      <td>3.290272e-41</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>169.084149</td>\n",
       "      <td>2.871633e-38</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>143.209180</td>\n",
       "      <td>1.008505e-32</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>124.416131</td>\n",
       "      <td>1.113518e-28</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>115.851777</td>\n",
       "      <td>7.830557e-27</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>113.318097</td>\n",
       "      <td>2.759452e-26</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68.189003</td>\n",
       "      <td>1.724869e-16</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47.338464</td>\n",
       "      <td>6.425274e-12</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19.857971</td>\n",
       "      <td>8.455492e-06</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.044878</td>\n",
       "      <td>1.292914e-05</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.513250</td>\n",
       "      <td>4.063766e-04</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11.644670</td>\n",
       "      <td>6.470725e-04</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.710060</td>\n",
       "      <td>9.975671e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.086756</td>\n",
       "      <td>2.972228e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.579650</td>\n",
       "      <td>4.464720e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.219295</td>\n",
       "      <td>6.395903e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.101198</td>\n",
       "      <td>7.504047e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.015774</td>\n",
       "      <td>9.000546e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1227.949077  2.545634e-250   Inflowing drainage direction\n",
       "23  1020.748820  7.391612e-211                   Surface type\n",
       "21   983.053244  1.401801e-203                      Elevation\n",
       "26   511.443416  8.364957e-110                   Fertiliser K\n",
       "27   511.443416  8.364957e-110                   Fertiliser N\n",
       "28   511.443416  8.364957e-110                   Fertiliser P\n",
       "3    462.292259   1.002663e-99              Improve grassland\n",
       "22   360.982411   9.334063e-79      Cumulative catchment area\n",
       "24   322.588411   9.713871e-71  Outflowing drainage direction\n",
       "2    322.261898   1.136958e-70                         Arable\n",
       "17   293.180616   1.430076e-64              Littoral sediment\n",
       "29   292.896402   1.640917e-64             Chlorothalonil_5km\n",
       "30   292.896402   1.640917e-64                 Glyphosate_5km\n",
       "31   292.896402   1.640917e-64                   Mancozeb_5km\n",
       "32   292.896402   1.640917e-64                 Mecoprop-P_5km\n",
       "34   292.896402   1.640917e-64              Pendimethalin_5km\n",
       "0    289.722162   7.626146e-64             Deciduous woodland\n",
       "18   257.074186   5.771923e-57                      Saltmarsh\n",
       "38   203.769294   1.146737e-45                 Tri-allate_5km\n",
       "20   199.309746   1.019948e-44                       Suburban\n",
       "36   182.849376   3.290272e-41               Prosulfocarb_5km\n",
       "37   182.849376   3.290272e-41                    Sulphur_5km\n",
       "13   169.084149   2.871633e-38                     Freshwater\n",
       "19   143.209180   1.008505e-32                          Urban\n",
       "33   124.416131   1.113518e-28                 Metamitron_5km\n",
       "35   115.851777   7.830557e-27   PropamocarbHydrochloride_5km\n",
       "15   113.318097   2.759452e-26         Supralittoral sediment\n",
       "7     68.189003   1.724869e-16                            Fen\n",
       "16    47.338464   6.425274e-12                  Littoral rock\n",
       "9     19.857971   8.455492e-06              Heather grassland\n",
       "4     19.044878   1.292914e-05              Neutral grassland\n",
       "12    12.513250   4.063766e-04                      Saltwater\n",
       "14    11.644670   6.470725e-04             Supralittoral rock\n",
       "10     2.710060   9.975671e-02                            Bog\n",
       "1      1.086756   2.972228e-01            Coniferous woodland\n",
       "8      0.579650   4.464720e-01                        Heather\n",
       "11     0.219295   6.395903e-01                    Inland rock\n",
       "5      0.101198   7.504047e-01           Calcareous grassland\n",
       "6      0.015774   9.000546e-01                 Acid grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada Goose 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13690.149107</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13106.121105</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10572.095214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7654.297733</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7654.297733</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7654.297733</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4195.605263</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4195.605263</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4195.605263</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4195.605263</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4195.605263</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2983.778416</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2914.229737</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2857.863587</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2857.863587</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2212.563533</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2201.543782</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1898.937619</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1801.902510</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1196.591276</td>\n",
       "      <td>2.090563e-244</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>930.073203</td>\n",
       "      <td>2.679934e-193</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>451.747492</td>\n",
       "      <td>1.483732e-97</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>231.411721</td>\n",
       "      <td>1.548806e-51</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.412278</td>\n",
       "      <td>1.704563e-23</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>90.659362</td>\n",
       "      <td>2.217209e-21</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>47.528190</td>\n",
       "      <td>5.835888e-12</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46.419332</td>\n",
       "      <td>1.024185e-11</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>38.812181</td>\n",
       "      <td>4.903074e-10</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37.359801</td>\n",
       "      <td>1.028497e-09</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31.034285</td>\n",
       "      <td>2.617748e-08</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.189900</td>\n",
       "      <td>5.782912e-05</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.017043</td>\n",
       "      <td>9.068339e-04</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.522332</td>\n",
       "      <td>3.348587e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.664775</td>\n",
       "      <td>5.560996e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.617360</td>\n",
       "      <td>2.034974e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.326860</td>\n",
       "      <td>2.493992e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.919556</td>\n",
       "      <td>3.376208e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.673409</td>\n",
       "      <td>4.118901e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.488612</td>\n",
       "      <td>4.845674e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "23  13690.149107   0.000000e+00                   Surface type\n",
       "21  13106.121105   0.000000e+00                      Elevation\n",
       "25  10572.095214   0.000000e+00   Inflowing drainage direction\n",
       "26   7654.297733   0.000000e+00                   Fertiliser K\n",
       "27   7654.297733   0.000000e+00                   Fertiliser N\n",
       "28   7654.297733   0.000000e+00                   Fertiliser P\n",
       "29   4195.605263   0.000000e+00             Chlorothalonil_5km\n",
       "30   4195.605263   0.000000e+00                 Glyphosate_5km\n",
       "31   4195.605263   0.000000e+00                   Mancozeb_5km\n",
       "32   4195.605263   0.000000e+00                 Mecoprop-P_5km\n",
       "34   4195.605263   0.000000e+00              Pendimethalin_5km\n",
       "3    2983.778416   0.000000e+00              Improve grassland\n",
       "38   2914.229737   0.000000e+00                 Tri-allate_5km\n",
       "36   2857.863587   0.000000e+00               Prosulfocarb_5km\n",
       "37   2857.863587   0.000000e+00                    Sulphur_5km\n",
       "35   2212.563533   0.000000e+00   PropamocarbHydrochloride_5km\n",
       "33   2201.543782   0.000000e+00                 Metamitron_5km\n",
       "2    1898.937619   0.000000e+00                         Arable\n",
       "24   1801.902510   0.000000e+00  Outflowing drainage direction\n",
       "0    1196.591276  2.090563e-244             Deciduous woodland\n",
       "20    930.073203  2.679934e-193                       Suburban\n",
       "22    451.747492   1.483732e-97      Cumulative catchment area\n",
       "19    231.411721   1.548806e-51                          Urban\n",
       "4     100.412278   1.704563e-23              Neutral grassland\n",
       "13     90.659362   2.217209e-21                     Freshwater\n",
       "7      47.528190   5.835888e-12                            Fen\n",
       "5      46.419332   1.024185e-11           Calcareous grassland\n",
       "17     38.812181   4.903074e-10              Littoral sediment\n",
       "18     37.359801   1.028497e-09                      Saltmarsh\n",
       "6      31.034285   2.617748e-08                 Acid grassland\n",
       "12     16.189900   5.782912e-05                      Saltwater\n",
       "1      11.017043   9.068339e-04            Coniferous woodland\n",
       "15      4.522332   3.348587e-02         Supralittoral sediment\n",
       "8       3.664775   5.560996e-02                        Heather\n",
       "14      1.617360   2.034974e-01             Supralittoral rock\n",
       "10      1.326860   2.493992e-01                            Bog\n",
       "9       0.919556   3.376208e-01              Heather grassland\n",
       "16      0.673409   4.118901e-01                  Littoral rock\n",
       "11      0.488612   4.845674e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Egyptian Goose 5km\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2983.128760</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2983.128760</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2983.128760</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1377.421605</td>\n",
       "      <td>3.086937e-278</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>992.142694</td>\n",
       "      <td>2.448748e-205</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>919.257319</td>\n",
       "      <td>3.425183e-191</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>913.921650</td>\n",
       "      <td>3.757007e-190</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>777.091220</td>\n",
       "      <td>2.934139e-163</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>777.091220</td>\n",
       "      <td>2.934139e-163</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>777.091220</td>\n",
       "      <td>2.934139e-163</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>777.091220</td>\n",
       "      <td>2.934139e-163</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>777.091220</td>\n",
       "      <td>2.934139e-163</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>722.722472</td>\n",
       "      <td>1.869674e-152</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>717.204913</td>\n",
       "      <td>2.355405e-151</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>654.748811</td>\n",
       "      <td>7.529947e-139</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>618.553745</td>\n",
       "      <td>1.465513e-131</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>582.829395</td>\n",
       "      <td>2.461927e-124</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>532.557771</td>\n",
       "      <td>4.086890e-114</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>532.344520</td>\n",
       "      <td>4.517297e-114</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>528.887194</td>\n",
       "      <td>2.291119e-113</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>528.131835</td>\n",
       "      <td>3.267028e-113</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>503.767649</td>\n",
       "      <td>3.107615e-108</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>287.157536</td>\n",
       "      <td>2.639912e-63</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>187.810292</td>\n",
       "      <td>2.876500e-42</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>97.443276</td>\n",
       "      <td>7.494184e-23</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85.855949</td>\n",
       "      <td>2.448264e-20</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.303917</td>\n",
       "      <td>6.700810e-06</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.659849</td>\n",
       "      <td>6.418219e-04</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.127169</td>\n",
       "      <td>7.607861e-03</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.918232</td>\n",
       "      <td>8.548545e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.259766</td>\n",
       "      <td>1.237095e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.742731</td>\n",
       "      <td>5.307308e-02</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.215402</td>\n",
       "      <td>7.298622e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.594709</td>\n",
       "      <td>1.072608e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.333754</td>\n",
       "      <td>1.266359e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.557288</td>\n",
       "      <td>2.120994e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.387037</td>\n",
       "      <td>2.389413e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.318619</td>\n",
       "      <td>2.508746e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.706995</td>\n",
       "      <td>4.004684e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  2983.128760   0.000000e+00                   Fertiliser K\n",
       "27  2983.128760   0.000000e+00                   Fertiliser N\n",
       "28  2983.128760   0.000000e+00                   Fertiliser P\n",
       "23  1377.421605  3.086937e-278                   Surface type\n",
       "25   992.142694  2.448748e-205   Inflowing drainage direction\n",
       "21   919.257319  3.425183e-191                      Elevation\n",
       "2    913.921650  3.757007e-190                         Arable\n",
       "29   777.091220  2.934139e-163             Chlorothalonil_5km\n",
       "30   777.091220  2.934139e-163                 Glyphosate_5km\n",
       "31   777.091220  2.934139e-163                   Mancozeb_5km\n",
       "32   777.091220  2.934139e-163                 Mecoprop-P_5km\n",
       "34   777.091220  2.934139e-163              Pendimethalin_5km\n",
       "20   722.722472  1.869674e-152                       Suburban\n",
       "0    717.204913  2.355405e-151             Deciduous woodland\n",
       "24   654.748811  7.529947e-139  Outflowing drainage direction\n",
       "19   618.553745  1.465513e-131                          Urban\n",
       "38   582.829395  2.461927e-124                 Tri-allate_5km\n",
       "33   532.557771  4.086890e-114                 Metamitron_5km\n",
       "35   532.344520  4.517297e-114   PropamocarbHydrochloride_5km\n",
       "36   528.887194  2.291119e-113               Prosulfocarb_5km\n",
       "37   528.131835  3.267028e-113                    Sulphur_5km\n",
       "3    503.767649  3.107615e-108              Improve grassland\n",
       "22   287.157536   2.639912e-63      Cumulative catchment area\n",
       "13   187.810292   2.876500e-42                     Freshwater\n",
       "7     97.443276   7.494184e-23                            Fen\n",
       "4     85.855949   2.448264e-20              Neutral grassland\n",
       "18    20.303917   6.700810e-06                      Saltmarsh\n",
       "6     11.659849   6.418219e-04                 Acid grassland\n",
       "15     7.127169   7.607861e-03         Supralittoral sediment\n",
       "9      6.918232   8.548545e-03              Heather grassland\n",
       "10     6.259766   1.237095e-02                            Bog\n",
       "1      3.742731   5.307308e-02            Coniferous woodland\n",
       "8      3.215402   7.298622e-02                        Heather\n",
       "17     2.594709   1.072608e-01              Littoral sediment\n",
       "11     2.333754   1.266359e-01                    Inland rock\n",
       "5      1.557288   2.120994e-01           Calcareous grassland\n",
       "12     1.387037   2.389413e-01                      Saltwater\n",
       "16     1.318619   2.508746e-01                  Littoral rock\n",
       "14     0.706995   4.004684e-01             Supralittoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gadwall 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4515.330213</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4515.330213</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4515.330213</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3184.992011</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2723.133762</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2406.022115</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1966.244495</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1231.056795</td>\n",
       "      <td>6.618329e-251</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1214.046569</td>\n",
       "      <td>1.060403e-247</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1214.046569</td>\n",
       "      <td>1.060403e-247</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1214.046569</td>\n",
       "      <td>1.060403e-247</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1214.046569</td>\n",
       "      <td>1.060403e-247</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1214.046569</td>\n",
       "      <td>1.060403e-247</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1197.486834</td>\n",
       "      <td>1.416014e-244</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1195.617415</td>\n",
       "      <td>3.193534e-244</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1131.878562</td>\n",
       "      <td>3.895579e-232</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1098.657559</td>\n",
       "      <td>8.392032e-226</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1097.500842</td>\n",
       "      <td>1.395745e-225</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1080.912434</td>\n",
       "      <td>2.072104e-222</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>981.022023</td>\n",
       "      <td>3.465257e-203</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>755.893708</td>\n",
       "      <td>4.696371e-159</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>606.274195</td>\n",
       "      <td>4.426528e-129</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>438.627859</td>\n",
       "      <td>7.506556e-95</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>217.264573</td>\n",
       "      <td>1.552531e-48</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>138.300402</td>\n",
       "      <td>1.144010e-31</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>133.023871</td>\n",
       "      <td>1.560292e-30</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>89.448105</td>\n",
       "      <td>4.061749e-21</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60.749893</td>\n",
       "      <td>7.300004e-15</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29.059887</td>\n",
       "      <td>7.218540e-08</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25.266049</td>\n",
       "      <td>5.103069e-07</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22.775112</td>\n",
       "      <td>1.853532e-06</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.501862</td>\n",
       "      <td>2.398918e-04</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13.004722</td>\n",
       "      <td>3.126078e-04</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.502561</td>\n",
       "      <td>4.087045e-04</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.393429</td>\n",
       "      <td>1.147351e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.049342</td>\n",
       "      <td>4.422218e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.100284</td>\n",
       "      <td>1.473112e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.268947</td>\n",
       "      <td>2.599982e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.295037</td>\n",
       "      <td>5.870261e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  4515.330213   0.000000e+00                   Fertiliser K\n",
       "27  4515.330213   0.000000e+00                   Fertiliser N\n",
       "28  4515.330213   0.000000e+00                   Fertiliser P\n",
       "23  3184.992011   0.000000e+00                   Surface type\n",
       "25  2723.133762   0.000000e+00   Inflowing drainage direction\n",
       "21  2406.022115   0.000000e+00                      Elevation\n",
       "2   1966.244495   0.000000e+00                         Arable\n",
       "3   1231.056795  6.618329e-251              Improve grassland\n",
       "29  1214.046569  1.060403e-247             Chlorothalonil_5km\n",
       "30  1214.046569  1.060403e-247                 Glyphosate_5km\n",
       "31  1214.046569  1.060403e-247                   Mancozeb_5km\n",
       "32  1214.046569  1.060403e-247                 Mecoprop-P_5km\n",
       "34  1214.046569  1.060403e-247              Pendimethalin_5km\n",
       "36  1197.486834  1.416014e-244               Prosulfocarb_5km\n",
       "37  1195.617415  3.193534e-244                    Sulphur_5km\n",
       "33  1131.878562  3.895579e-232                 Metamitron_5km\n",
       "35  1098.657559  8.392032e-226   PropamocarbHydrochloride_5km\n",
       "38  1097.500842  1.395745e-225                 Tri-allate_5km\n",
       "20  1080.912434  2.072104e-222                       Suburban\n",
       "24   981.022023  3.465257e-203  Outflowing drainage direction\n",
       "0    755.893708  4.696371e-159             Deciduous woodland\n",
       "22   606.274195  4.426528e-129      Cumulative catchment area\n",
       "19   438.627859   7.506556e-95                          Urban\n",
       "13   217.264573   1.552531e-48                     Freshwater\n",
       "4    138.300402   1.144010e-31              Neutral grassland\n",
       "7    133.023871   1.560292e-30                            Fen\n",
       "18    89.448105   4.061749e-21                      Saltmarsh\n",
       "17    60.749893   7.300004e-15              Littoral sediment\n",
       "6     29.059887   7.218540e-08                 Acid grassland\n",
       "15    25.266049   5.103069e-07         Supralittoral sediment\n",
       "12    22.775112   1.853532e-06                      Saltwater\n",
       "8     13.501862   2.398918e-04                        Heather\n",
       "10    13.004722   3.126078e-04                            Bog\n",
       "9     12.502561   4.087045e-04              Heather grassland\n",
       "11     6.393429   1.147351e-02                    Inland rock\n",
       "14     4.049342   4.422218e-02             Supralittoral rock\n",
       "5      2.100284   1.473112e-01           Calcareous grassland\n",
       "16     1.268947   2.599982e-01                  Littoral rock\n",
       "1      0.295037   5.870261e-01            Coniferous woodland"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goshawk 5km\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1152.957368</td>\n",
       "      <td>3.838405e-236</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1141.475518</td>\n",
       "      <td>5.825497e-234</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>949.198977</td>\n",
       "      <td>5.121685e-197</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>787.722786</td>\n",
       "      <td>2.305292e-165</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>787.722786</td>\n",
       "      <td>2.305292e-165</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>787.722786</td>\n",
       "      <td>2.305292e-165</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>787.722786</td>\n",
       "      <td>2.305292e-165</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>787.722786</td>\n",
       "      <td>2.305292e-165</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>657.400077</td>\n",
       "      <td>2.208469e-139</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>627.392411</td>\n",
       "      <td>2.416062e-133</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>583.784688</td>\n",
       "      <td>1.576392e-124</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>568.093600</td>\n",
       "      <td>2.402274e-121</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>568.093600</td>\n",
       "      <td>2.402274e-121</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>454.918810</td>\n",
       "      <td>3.298942e-98</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>431.343580</td>\n",
       "      <td>2.391684e-93</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>405.811895</td>\n",
       "      <td>4.555045e-88</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>319.953790</td>\n",
       "      <td>3.459450e-70</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>272.336940</td>\n",
       "      <td>3.480601e-60</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>200.347726</td>\n",
       "      <td>6.132138e-45</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>200.347726</td>\n",
       "      <td>6.132138e-45</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>200.347726</td>\n",
       "      <td>6.132138e-45</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>96.338273</td>\n",
       "      <td>1.300727e-22</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.501954</td>\n",
       "      <td>1.195416e-17</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>71.543628</td>\n",
       "      <td>3.195845e-17</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36.219779</td>\n",
       "      <td>1.840737e-09</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.158766</td>\n",
       "      <td>1.147703e-07</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.321960</td>\n",
       "      <td>1.395826e-06</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.113433</td>\n",
       "      <td>4.405104e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.606575</td>\n",
       "      <td>1.791696e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.841764</td>\n",
       "      <td>2.780707e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.506314</td>\n",
       "      <td>6.117194e-02</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.445394</td>\n",
       "      <td>6.346521e-02</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.834645</td>\n",
       "      <td>1.756183e-01</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.357066</td>\n",
       "      <td>2.440815e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.249499</td>\n",
       "      <td>2.636818e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.237899</td>\n",
       "      <td>2.659099e-01</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.727229</td>\n",
       "      <td>3.938086e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.205592</td>\n",
       "      <td>6.502571e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.024729</td>\n",
       "      <td>8.750493e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "21  1152.957368  3.838405e-236                      Elevation\n",
       "23  1141.475518  5.825497e-234                   Surface type\n",
       "25   949.198977  5.121685e-197   Inflowing drainage direction\n",
       "29   787.722786  2.305292e-165             Chlorothalonil_5km\n",
       "30   787.722786  2.305292e-165                 Glyphosate_5km\n",
       "31   787.722786  2.305292e-165                   Mancozeb_5km\n",
       "32   787.722786  2.305292e-165                 Mecoprop-P_5km\n",
       "34   787.722786  2.305292e-165              Pendimethalin_5km\n",
       "0    657.400077  2.208469e-139             Deciduous woodland\n",
       "3    627.392411  2.416062e-133              Improve grassland\n",
       "38   583.784688  1.576392e-124                 Tri-allate_5km\n",
       "36   568.093600  2.402274e-121               Prosulfocarb_5km\n",
       "37   568.093600  2.402274e-121                    Sulphur_5km\n",
       "35   454.918810   3.298942e-98   PropamocarbHydrochloride_5km\n",
       "33   431.343580   2.391684e-93                 Metamitron_5km\n",
       "24   405.811895   4.555045e-88  Outflowing drainage direction\n",
       "1    319.953790   3.459450e-70            Coniferous woodland\n",
       "6    272.336940   3.480601e-60                 Acid grassland\n",
       "26   200.347726   6.132138e-45                   Fertiliser K\n",
       "27   200.347726   6.132138e-45                   Fertiliser N\n",
       "28   200.347726   6.132138e-45                   Fertiliser P\n",
       "22    96.338273   1.300727e-22      Cumulative catchment area\n",
       "2     73.501954   1.195416e-17                         Arable\n",
       "20    71.543628   3.195845e-17                       Suburban\n",
       "8     36.219779   1.840737e-09                        Heather\n",
       "5     28.158766   1.147703e-07           Calcareous grassland\n",
       "7     23.321960   1.395826e-06                            Fen\n",
       "17     8.113433   4.405104e-03              Littoral sediment\n",
       "10     5.606575   1.791696e-02                            Bog\n",
       "9      4.841764   2.780707e-02              Heather grassland\n",
       "18     3.506314   6.117194e-02                      Saltmarsh\n",
       "4      3.445394   6.346521e-02              Neutral grassland\n",
       "13     1.834645   1.756183e-01                     Freshwater\n",
       "16     1.357066   2.440815e-01                  Littoral rock\n",
       "14     1.249499   2.636818e-01             Supralittoral rock\n",
       "19     1.237899   2.659099e-01                          Urban\n",
       "15     0.727229   3.938086e-01         Supralittoral sediment\n",
       "12     0.205592   6.502571e-01                      Saltwater\n",
       "11     0.024729   8.750493e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grey Partridge 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6271.935655</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6271.935655</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6271.935655</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4597.238354</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4174.660137</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3785.784065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3602.849751</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1561.563980</td>\n",
       "      <td>5.115844e-312</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1561.563980</td>\n",
       "      <td>5.115844e-312</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1561.563980</td>\n",
       "      <td>5.115844e-312</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1561.563980</td>\n",
       "      <td>5.115844e-312</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1561.563980</td>\n",
       "      <td>5.115844e-312</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1404.440831</td>\n",
       "      <td>3.099099e-283</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1380.011301</td>\n",
       "      <td>1.022893e-278</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1349.536551</td>\n",
       "      <td>4.605203e-273</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1349.536551</td>\n",
       "      <td>4.605203e-273</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1263.054607</td>\n",
       "      <td>6.434266e-257</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1235.719640</td>\n",
       "      <td>8.776294e-252</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1226.751670</td>\n",
       "      <td>4.278275e-250</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>692.233242</td>\n",
       "      <td>2.294955e-146</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>574.680939</td>\n",
       "      <td>1.105711e-122</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>546.043425</td>\n",
       "      <td>7.306351e-117</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>140.354125</td>\n",
       "      <td>4.140347e-32</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93.814958</td>\n",
       "      <td>4.583868e-22</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>77.610902</td>\n",
       "      <td>1.521430e-18</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>29.201063</td>\n",
       "      <td>6.713033e-08</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.048887</td>\n",
       "      <td>5.709287e-07</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14.797441</td>\n",
       "      <td>1.206320e-04</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.477578</td>\n",
       "      <td>1.429000e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.123802</td>\n",
       "      <td>1.335804e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.589450</td>\n",
       "      <td>5.818367e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.815814</td>\n",
       "      <td>9.337877e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.652885</td>\n",
       "      <td>1.034017e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.400615</td>\n",
       "      <td>1.213273e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.413722</td>\n",
       "      <td>5.201047e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.131228</td>\n",
       "      <td>7.171721e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.091617</td>\n",
       "      <td>7.621390e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.078178</td>\n",
       "      <td>7.797887e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.041344</td>\n",
       "      <td>8.388804e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  6271.935655   0.000000e+00                   Fertiliser K\n",
       "27  6271.935655   0.000000e+00                   Fertiliser N\n",
       "28  6271.935655   0.000000e+00                   Fertiliser P\n",
       "23  4597.238354   0.000000e+00                   Surface type\n",
       "2   4174.660137   0.000000e+00                         Arable\n",
       "21  3785.784065   0.000000e+00                      Elevation\n",
       "25  3602.849751   0.000000e+00   Inflowing drainage direction\n",
       "29  1561.563980  5.115844e-312             Chlorothalonil_5km\n",
       "30  1561.563980  5.115844e-312                 Glyphosate_5km\n",
       "31  1561.563980  5.115844e-312                   Mancozeb_5km\n",
       "32  1561.563980  5.115844e-312                 Mecoprop-P_5km\n",
       "34  1561.563980  5.115844e-312              Pendimethalin_5km\n",
       "3   1404.440831  3.099099e-283              Improve grassland\n",
       "38  1380.011301  1.022893e-278                 Tri-allate_5km\n",
       "36  1349.536551  4.605203e-273               Prosulfocarb_5km\n",
       "37  1349.536551  4.605203e-273                    Sulphur_5km\n",
       "33  1263.054607  6.434266e-257                 Metamitron_5km\n",
       "35  1235.719640  8.776294e-252   PropamocarbHydrochloride_5km\n",
       "24  1226.751670  4.278275e-250  Outflowing drainage direction\n",
       "0    692.233242  2.294955e-146             Deciduous woodland\n",
       "22   574.680939  1.105711e-122      Cumulative catchment area\n",
       "20   546.043425  7.306351e-117                       Suburban\n",
       "5    140.354125   4.140347e-32           Calcareous grassland\n",
       "4     93.814958   4.583868e-22              Neutral grassland\n",
       "19    77.610902   1.521430e-18                          Urban\n",
       "18    29.201063   6.713033e-08                      Saltmarsh\n",
       "7     25.048887   5.709287e-07                            Fen\n",
       "17    14.797441   1.206320e-04              Littoral sediment\n",
       "13    14.477578   1.429000e-04                     Freshwater\n",
       "15     6.123802   1.335804e-02         Supralittoral sediment\n",
       "10     3.589450   5.818367e-02                            Bog\n",
       "11     2.815814   9.337877e-02                    Inland rock\n",
       "14     2.652885   1.034017e-01             Supralittoral rock\n",
       "1      2.400615   1.213273e-01            Coniferous woodland\n",
       "6      0.413722   5.201047e-01                 Acid grassland\n",
       "8      0.131228   7.171721e-01                        Heather\n",
       "16     0.091617   7.621390e-01                  Littoral rock\n",
       "9      0.078178   7.797887e-01              Heather grassland\n",
       "12     0.041344   8.388804e-01                      Saltwater"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indian Peafowl 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1401.150933</td>\n",
       "      <td>1.256201e-282</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1401.150933</td>\n",
       "      <td>1.256201e-282</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1401.150933</td>\n",
       "      <td>1.256201e-282</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>792.263164</td>\n",
       "      <td>2.915059e-166</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>701.104487</td>\n",
       "      <td>3.862323e-148</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>578.148305</td>\n",
       "      <td>2.189384e-123</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>545.827122</td>\n",
       "      <td>8.086054e-117</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>524.382331</td>\n",
       "      <td>1.902480e-112</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>487.290667</td>\n",
       "      <td>7.370916e-105</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>444.347581</td>\n",
       "      <td>4.966029e-96</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>444.347581</td>\n",
       "      <td>4.966029e-96</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>444.347581</td>\n",
       "      <td>4.966029e-96</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>444.347581</td>\n",
       "      <td>4.966029e-96</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>444.347581</td>\n",
       "      <td>4.966029e-96</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>419.586073</td>\n",
       "      <td>6.426713e-91</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>419.006535</td>\n",
       "      <td>8.468818e-91</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>411.705997</td>\n",
       "      <td>2.742416e-89</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>402.167370</td>\n",
       "      <td>2.591400e-87</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>331.150515</td>\n",
       "      <td>1.570489e-72</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>310.917668</td>\n",
       "      <td>2.706177e-68</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>305.059186</td>\n",
       "      <td>4.582354e-67</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>218.350415</td>\n",
       "      <td>9.130323e-49</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>47.921042</td>\n",
       "      <td>4.781891e-12</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.829471</td>\n",
       "      <td>3.814352e-07</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.143796</td>\n",
       "      <td>7.284259e-06</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.729135</td>\n",
       "      <td>1.250737e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.535159</td>\n",
       "      <td>6.011705e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.346729</td>\n",
       "      <td>6.737607e-02</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.567200</td>\n",
       "      <td>2.106509e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.464449</td>\n",
       "      <td>2.262594e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.427318</td>\n",
       "      <td>2.322381e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.216857</td>\n",
       "      <td>2.700117e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.203109</td>\n",
       "      <td>2.727343e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.819230</td>\n",
       "      <td>3.654327e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.675335</td>\n",
       "      <td>4.112224e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.173224</td>\n",
       "      <td>6.772735e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.163487</td>\n",
       "      <td>6.859768e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.111157</td>\n",
       "      <td>7.388395e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.018397</td>\n",
       "      <td>8.921123e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  1401.150933  1.256201e-282                   Fertiliser K\n",
       "27  1401.150933  1.256201e-282                   Fertiliser N\n",
       "28  1401.150933  1.256201e-282                   Fertiliser P\n",
       "23   792.263164  2.915059e-166                   Surface type\n",
       "2    701.104487  3.862323e-148                         Arable\n",
       "21   578.148305  2.189384e-123                      Elevation\n",
       "3    545.827122  8.086054e-117              Improve grassland\n",
       "25   524.382331  1.902480e-112   Inflowing drainage direction\n",
       "38   487.290667  7.370916e-105                 Tri-allate_5km\n",
       "29   444.347581   4.966029e-96             Chlorothalonil_5km\n",
       "30   444.347581   4.966029e-96                 Glyphosate_5km\n",
       "31   444.347581   4.966029e-96                   Mancozeb_5km\n",
       "32   444.347581   4.966029e-96                 Mecoprop-P_5km\n",
       "34   444.347581   4.966029e-96              Pendimethalin_5km\n",
       "36   419.586073   6.426713e-91               Prosulfocarb_5km\n",
       "37   419.006535   8.468818e-91                    Sulphur_5km\n",
       "33   411.705997   2.742416e-89                 Metamitron_5km\n",
       "35   402.167370   2.591400e-87   PropamocarbHydrochloride_5km\n",
       "0    331.150515   1.570489e-72             Deciduous woodland\n",
       "24   310.917668   2.706177e-68  Outflowing drainage direction\n",
       "20   305.059186   4.582354e-67                       Suburban\n",
       "22   218.350415   9.130323e-49      Cumulative catchment area\n",
       "19    47.921042   4.781891e-12                          Urban\n",
       "7     25.829471   3.814352e-07                            Fen\n",
       "4     20.143796   7.284259e-06              Neutral grassland\n",
       "13    14.729135   1.250737e-04                     Freshwater\n",
       "9      3.535159   6.011705e-02              Heather grassland\n",
       "5      3.346729   6.737607e-02           Calcareous grassland\n",
       "10     1.567200   2.106509e-01                            Bog\n",
       "16     1.464449   2.262594e-01                  Littoral rock\n",
       "6      1.427318   2.322381e-01                 Acid grassland\n",
       "17     1.216857   2.700117e-01              Littoral sediment\n",
       "8      1.203109   2.727343e-01                        Heather\n",
       "14     0.819230   3.654327e-01             Supralittoral rock\n",
       "15     0.675335   4.112224e-01         Supralittoral sediment\n",
       "12     0.173224   6.772735e-01                      Saltwater\n",
       "1      0.163487   6.859768e-01            Coniferous woodland\n",
       "18     0.111157   7.388395e-01                      Saltmarsh\n",
       "11     0.018397   8.921123e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Little Owl 5km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14865.981572</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>14865.981572</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14865.981572</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6857.748193</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5386.245994</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5359.355245</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4395.443997</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3029.229250</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3029.229250</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3029.229250</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3029.229250</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3029.229250</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2708.701907</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2708.701907</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2676.100879</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2338.758475</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2301.433951</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2284.811376</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1686.978667</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1070.418247</td>\n",
       "      <td>2.117710e-220</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>964.944912</td>\n",
       "      <td>4.507157e-200</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>328.787852</td>\n",
       "      <td>4.899488e-72</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>216.363484</td>\n",
       "      <td>2.412088e-48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>173.795722</td>\n",
       "      <td>2.823657e-39</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>153.749243</td>\n",
       "      <td>5.519369e-35</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>61.708907</td>\n",
       "      <td>4.501866e-15</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39.967805</td>\n",
       "      <td>2.720897e-10</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17.263205</td>\n",
       "      <td>3.288316e-05</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.298137</td>\n",
       "      <td>5.462439e-05</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.600066</td>\n",
       "      <td>6.627543e-04</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.429545</td>\n",
       "      <td>1.245185e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.681820</td>\n",
       "      <td>5.590941e-03</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6.733951</td>\n",
       "      <td>9.476830e-03</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.448022</td>\n",
       "      <td>1.112645e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.260300</td>\n",
       "      <td>1.236722e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.630479</td>\n",
       "      <td>3.143876e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.803586</td>\n",
       "      <td>5.117799e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.700481</td>\n",
       "      <td>5.443238e-02</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.159413</td>\n",
       "      <td>1.417383e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "26  14865.981572   0.000000e+00                   Fertiliser K\n",
       "27  14865.981572   0.000000e+00                   Fertiliser N\n",
       "28  14865.981572   0.000000e+00                   Fertiliser P\n",
       "23   6857.748193   0.000000e+00                   Surface type\n",
       "25   5386.245994   0.000000e+00   Inflowing drainage direction\n",
       "21   5359.355245   0.000000e+00                      Elevation\n",
       "2    4395.443997   0.000000e+00                         Arable\n",
       "29   3029.229250   0.000000e+00             Chlorothalonil_5km\n",
       "30   3029.229250   0.000000e+00                 Glyphosate_5km\n",
       "31   3029.229250   0.000000e+00                   Mancozeb_5km\n",
       "32   3029.229250   0.000000e+00                 Mecoprop-P_5km\n",
       "34   3029.229250   0.000000e+00              Pendimethalin_5km\n",
       "36   2708.701907   0.000000e+00               Prosulfocarb_5km\n",
       "37   2708.701907   0.000000e+00                    Sulphur_5km\n",
       "38   2676.100879   0.000000e+00                 Tri-allate_5km\n",
       "33   2338.758475   0.000000e+00                 Metamitron_5km\n",
       "35   2301.433951   0.000000e+00   PropamocarbHydrochloride_5km\n",
       "3    2284.811376   0.000000e+00              Improve grassland\n",
       "24   1686.978667   0.000000e+00  Outflowing drainage direction\n",
       "20   1070.418247  2.117710e-220                       Suburban\n",
       "0     964.944912  4.507157e-200             Deciduous woodland\n",
       "22    328.787852   4.899488e-72      Cumulative catchment area\n",
       "19    216.363484   2.412088e-48                          Urban\n",
       "4     173.795722   2.823657e-39              Neutral grassland\n",
       "5     153.749243   5.519369e-35           Calcareous grassland\n",
       "13     61.708907   4.501866e-15                     Freshwater\n",
       "7      39.967805   2.720897e-10                            Fen\n",
       "18     17.263205   3.288316e-05                      Saltmarsh\n",
       "6      16.298137   5.462439e-05                 Acid grassland\n",
       "10     11.600066   6.627543e-04                            Bog\n",
       "9      10.429545   1.245185e-03              Heather grassland\n",
       "8       7.681820   5.590941e-03                        Heather\n",
       "14      6.733951   9.476830e-03             Supralittoral rock\n",
       "11      6.448022   1.112645e-02                    Inland rock\n",
       "16      6.260300   1.236722e-02                  Littoral rock\n",
       "17      4.630479   3.143876e-02              Littoral sediment\n",
       "12      3.803586   5.117799e-02                      Saltwater\n",
       "1       3.700481   5.443238e-02            Coniferous woodland\n",
       "15      2.159413   1.417383e-01         Supralittoral sediment"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3499.336953</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3499.336953</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3499.336953</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2033.821770</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1803.002674</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1803.002674</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1803.002674</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1803.002674</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1803.002674</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1714.730650</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1714.730650</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1692.207160</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1610.098872</td>\n",
       "      <td>8.211371e-321</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1587.607155</td>\n",
       "      <td>9.646045e-317</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1464.813759</td>\n",
       "      <td>2.371236e-294</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1418.099414</td>\n",
       "      <td>9.333415e-286</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1415.671099</td>\n",
       "      <td>2.618218e-285</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1284.895260</td>\n",
       "      <td>5.209143e-261</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>929.329547</td>\n",
       "      <td>3.740071e-193</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>655.392304</td>\n",
       "      <td>5.590929e-139</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>510.589782</td>\n",
       "      <td>1.250228e-109</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>432.717693</td>\n",
       "      <td>1.244580e-93</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>225.523833</td>\n",
       "      <td>2.743268e-50</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123.306007</td>\n",
       "      <td>1.931753e-28</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57.284789</td>\n",
       "      <td>4.192614e-14</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>57.065530</td>\n",
       "      <td>4.683352e-14</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.442727</td>\n",
       "      <td>6.233113e-06</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.281864</td>\n",
       "      <td>1.221775e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.955921</td>\n",
       "      <td>4.674092e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.204290</td>\n",
       "      <td>1.376669e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.062553</td>\n",
       "      <td>1.509963e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.973009</td>\n",
       "      <td>1.601679e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.283070</td>\n",
       "      <td>2.573631e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.941324</td>\n",
       "      <td>3.319671e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.931651</td>\n",
       "      <td>3.344636e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.503272</td>\n",
       "      <td>4.780867e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.326150</td>\n",
       "      <td>5.679510e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.229897</td>\n",
       "      <td>6.316136e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000028</td>\n",
       "      <td>9.957553e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  3499.336953   0.000000e+00                   Fertiliser K\n",
       "27  3499.336953   0.000000e+00                   Fertiliser N\n",
       "28  3499.336953   0.000000e+00                   Fertiliser P\n",
       "23  2033.821770   0.000000e+00                   Surface type\n",
       "29  1803.002674   0.000000e+00             Chlorothalonil_5km\n",
       "30  1803.002674   0.000000e+00                 Glyphosate_5km\n",
       "31  1803.002674   0.000000e+00                   Mancozeb_5km\n",
       "32  1803.002674   0.000000e+00                 Mecoprop-P_5km\n",
       "34  1803.002674   0.000000e+00              Pendimethalin_5km\n",
       "36  1714.730650   0.000000e+00               Prosulfocarb_5km\n",
       "37  1714.730650   0.000000e+00                    Sulphur_5km\n",
       "38  1692.207160   0.000000e+00                 Tri-allate_5km\n",
       "3   1610.098872  8.211371e-321              Improve grassland\n",
       "21  1587.607155  9.646045e-317                      Elevation\n",
       "25  1464.813759  2.371236e-294   Inflowing drainage direction\n",
       "33  1418.099414  9.333415e-286                 Metamitron_5km\n",
       "35  1415.671099  2.618218e-285   PropamocarbHydrochloride_5km\n",
       "0   1284.895260  5.209143e-261             Deciduous woodland\n",
       "20   929.329547  3.740071e-193                       Suburban\n",
       "24   655.392304  5.590929e-139  Outflowing drainage direction\n",
       "22   510.589782  1.250228e-109      Cumulative catchment area\n",
       "2    432.717693   1.244580e-93                         Arable\n",
       "19   225.523833   2.743268e-50                          Urban\n",
       "4    123.306007   1.931753e-28              Neutral grassland\n",
       "5     57.284789   4.192614e-14           Calcareous grassland\n",
       "13    57.065530   4.683352e-14                     Freshwater\n",
       "1     20.442727   6.233113e-06            Coniferous woodland\n",
       "7      6.281864   1.221775e-02                            Fen\n",
       "10     3.955921   4.674092e-02                            Bog\n",
       "11     2.204290   1.376669e-01                    Inland rock\n",
       "9      2.062553   1.509963e-01              Heather grassland\n",
       "14     1.973009   1.601679e-01             Supralittoral rock\n",
       "16     1.283070   2.573631e-01                  Littoral rock\n",
       "18     0.941324   3.319671e-01                      Saltmarsh\n",
       "15     0.931651   3.344636e-01         Supralittoral sediment\n",
       "17     0.503272   4.780867e-01              Littoral sediment\n",
       "6      0.326150   5.679510e-01                 Acid grassland\n",
       "12     0.229897   6.316136e-01                      Saltwater\n",
       "8      0.000028   9.957553e-01                        Heather"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mute Swan 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13258.103579</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11515.117494</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10658.285644</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4008.850443</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4008.850443</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4008.850443</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2394.708388</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2394.708388</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2394.708388</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2394.708388</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2394.708388</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2061.087790</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1696.172627</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1696.172627</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1662.847893</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1662.030433</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1429.112950</td>\n",
       "      <td>8.705042e-288</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1265.276094</td>\n",
       "      <td>2.465302e-257</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1248.669640</td>\n",
       "      <td>3.226801e-254</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>897.430418</td>\n",
       "      <td>6.218893e-187</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>696.898175</td>\n",
       "      <td>2.677486e-147</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>366.516265</td>\n",
       "      <td>6.572976e-80</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>200.841130</td>\n",
       "      <td>4.814901e-45</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75.098468</td>\n",
       "      <td>5.364636e-18</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>67.741670</td>\n",
       "      <td>2.159976e-16</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>56.508193</td>\n",
       "      <td>6.205420e-14</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34.869146</td>\n",
       "      <td>3.670966e-09</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>34.191736</td>\n",
       "      <td>5.191196e-09</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24.549494</td>\n",
       "      <td>7.391787e-07</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.411080</td>\n",
       "      <td>1.332813e-06</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18.991803</td>\n",
       "      <td>1.329292e-05</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.649440</td>\n",
       "      <td>6.454178e-04</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.254357</td>\n",
       "      <td>7.980745e-04</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.405239</td>\n",
       "      <td>3.751635e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.500497</td>\n",
       "      <td>1.138501e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.358636</td>\n",
       "      <td>2.438089e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.929841</td>\n",
       "      <td>3.349335e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.491047</td>\n",
       "      <td>4.834813e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.447600</td>\n",
       "      <td>5.034964e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "21  13258.103579   0.000000e+00                      Elevation\n",
       "23  11515.117494   0.000000e+00                   Surface type\n",
       "25  10658.285644   0.000000e+00   Inflowing drainage direction\n",
       "26   4008.850443   0.000000e+00                   Fertiliser K\n",
       "27   4008.850443   0.000000e+00                   Fertiliser N\n",
       "28   4008.850443   0.000000e+00                   Fertiliser P\n",
       "29   2394.708388   0.000000e+00             Chlorothalonil_5km\n",
       "30   2394.708388   0.000000e+00                 Glyphosate_5km\n",
       "31   2394.708388   0.000000e+00                   Mancozeb_5km\n",
       "32   2394.708388   0.000000e+00                 Mecoprop-P_5km\n",
       "34   2394.708388   0.000000e+00              Pendimethalin_5km\n",
       "3    2061.087790   0.000000e+00              Improve grassland\n",
       "36   1696.172627   0.000000e+00               Prosulfocarb_5km\n",
       "37   1696.172627   0.000000e+00                    Sulphur_5km\n",
       "38   1662.847893   0.000000e+00                 Tri-allate_5km\n",
       "2    1662.030433   0.000000e+00                         Arable\n",
       "24   1429.112950  8.705042e-288  Outflowing drainage direction\n",
       "35   1265.276094  2.465302e-257   PropamocarbHydrochloride_5km\n",
       "33   1248.669640  3.226801e-254                 Metamitron_5km\n",
       "0     897.430418  6.218893e-187             Deciduous woodland\n",
       "20    696.898175  2.677486e-147                       Suburban\n",
       "22    366.516265   6.572976e-80      Cumulative catchment area\n",
       "19    200.841130   4.814901e-45                          Urban\n",
       "4      75.098468   5.364636e-18              Neutral grassland\n",
       "13     67.741670   2.159976e-16                     Freshwater\n",
       "17     56.508193   6.205420e-14              Littoral sediment\n",
       "7      34.869146   3.670966e-09                            Fen\n",
       "18     34.191736   5.191196e-09                      Saltmarsh\n",
       "12     24.549494   7.391787e-07                      Saltwater\n",
       "5      23.411080   1.332813e-06           Calcareous grassland\n",
       "15     18.991803   1.329292e-05         Supralittoral sediment\n",
       "1      11.649440   6.454178e-04            Coniferous woodland\n",
       "16     11.254357   7.980745e-04                  Littoral rock\n",
       "10      8.405239   3.751635e-03                            Bog\n",
       "14      2.500497   1.138501e-01             Supralittoral rock\n",
       "11      1.358636   2.438089e-01                    Inland rock\n",
       "6       0.929841   3.349335e-01                 Acid grassland\n",
       "8       0.491047   4.834813e-01                        Heather\n",
       "9       0.447600   5.034964e-01              Heather grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pheasant 5km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10050.271292</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8841.943280</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7309.294901</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5420.435199</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5420.435199</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5420.435199</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3270.520292</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3270.520292</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3270.520292</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3270.520292</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3270.520292</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2659.803503</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2399.875580</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2281.505401</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2278.677851</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1870.157119</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1859.291990</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1812.032710</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1787.097580</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1174.299291</td>\n",
       "      <td>3.445172e-240</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>962.080147</td>\n",
       "      <td>1.619598e-199</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>712.985461</td>\n",
       "      <td>1.636740e-150</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>108.950885</td>\n",
       "      <td>2.423079e-25</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>97.227340</td>\n",
       "      <td>8.346693e-23</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77.118783</td>\n",
       "      <td>1.947244e-18</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72.789784</td>\n",
       "      <td>1.709221e-17</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>61.756741</td>\n",
       "      <td>4.394643e-15</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>34.749423</td>\n",
       "      <td>3.902749e-09</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30.155778</td>\n",
       "      <td>4.109808e-08</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.652838</td>\n",
       "      <td>1.587154e-05</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.957160</td>\n",
       "      <td>3.861930e-05</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.467693</td>\n",
       "      <td>4.995926e-05</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.416989</td>\n",
       "      <td>5.131069e-05</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.693737</td>\n",
       "      <td>5.554180e-03</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.051453</td>\n",
       "      <td>7.935868e-03</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.723840</td>\n",
       "      <td>2.977661e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.427993</td>\n",
       "      <td>1.192252e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.957411</td>\n",
       "      <td>1.618294e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.074882</td>\n",
       "      <td>7.843642e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "23  10050.271292   0.000000e+00                   Surface type\n",
       "21   8841.943280   0.000000e+00                      Elevation\n",
       "25   7309.294901   0.000000e+00   Inflowing drainage direction\n",
       "26   5420.435199   0.000000e+00                   Fertiliser K\n",
       "27   5420.435199   0.000000e+00                   Fertiliser N\n",
       "28   5420.435199   0.000000e+00                   Fertiliser P\n",
       "29   3270.520292   0.000000e+00             Chlorothalonil_5km\n",
       "30   3270.520292   0.000000e+00                 Glyphosate_5km\n",
       "31   3270.520292   0.000000e+00                   Mancozeb_5km\n",
       "32   3270.520292   0.000000e+00                 Mecoprop-P_5km\n",
       "34   3270.520292   0.000000e+00              Pendimethalin_5km\n",
       "3    2659.803503   0.000000e+00              Improve grassland\n",
       "38   2399.875580   0.000000e+00                 Tri-allate_5km\n",
       "37   2281.505401   0.000000e+00                    Sulphur_5km\n",
       "36   2278.677851   0.000000e+00               Prosulfocarb_5km\n",
       "35   1870.157119   0.000000e+00   PropamocarbHydrochloride_5km\n",
       "33   1859.291990   0.000000e+00                 Metamitron_5km\n",
       "2    1812.032710   0.000000e+00                         Arable\n",
       "24   1787.097580   0.000000e+00  Outflowing drainage direction\n",
       "0    1174.299291  3.445172e-240             Deciduous woodland\n",
       "22    962.080147  1.619598e-199      Cumulative catchment area\n",
       "20    712.985461  1.636740e-150                       Suburban\n",
       "19    108.950885   2.423079e-25                          Urban\n",
       "5      97.227340   8.346693e-23           Calcareous grassland\n",
       "1      77.118783   1.947244e-18            Coniferous woodland\n",
       "4      72.789784   1.709221e-17              Neutral grassland\n",
       "6      61.756741   4.394643e-15                 Acid grassland\n",
       "13     34.749423   3.902749e-09                     Freshwater\n",
       "7      30.155778   4.109808e-08                            Fen\n",
       "8      18.652838   1.587154e-05                        Heather\n",
       "18     16.957160   3.861930e-05                      Saltmarsh\n",
       "17     16.467693   4.995926e-05              Littoral sediment\n",
       "10     16.416989   5.131069e-05                            Bog\n",
       "15      7.693737   5.554180e-03         Supralittoral sediment\n",
       "11      7.051453   7.935868e-03                    Inland rock\n",
       "12      4.723840   2.977661e-02                      Saltwater\n",
       "14      2.427993   1.192252e-01             Supralittoral rock\n",
       "9       1.957411   1.618294e-01              Heather grassland\n",
       "16      0.074882   7.843642e-01                  Littoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pink-footed Goose 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3058.357364</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2873.839868</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2752.743624</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1607.983542</td>\n",
       "      <td>1.980709e-320</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>900.562185</td>\n",
       "      <td>1.520435e-187</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>796.768219</td>\n",
       "      <td>3.749922e-167</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>532.616285</td>\n",
       "      <td>3.976135e-114</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>532.616285</td>\n",
       "      <td>3.976135e-114</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>532.616285</td>\n",
       "      <td>3.976135e-114</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>495.339143</td>\n",
       "      <td>1.652140e-106</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>274.520690</td>\n",
       "      <td>1.206526e-60</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>270.648302</td>\n",
       "      <td>7.898492e-60</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>247.805699</td>\n",
       "      <td>5.245303e-55</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>247.805699</td>\n",
       "      <td>5.245303e-55</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>247.805699</td>\n",
       "      <td>5.245303e-55</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>247.805699</td>\n",
       "      <td>5.245303e-55</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>247.805699</td>\n",
       "      <td>5.245303e-55</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>209.765273</td>\n",
       "      <td>6.085933e-47</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>178.358152</td>\n",
       "      <td>2.992833e-40</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>177.859694</td>\n",
       "      <td>3.824203e-40</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>156.940500</td>\n",
       "      <td>1.142502e-35</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>130.830883</td>\n",
       "      <td>4.625347e-30</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>129.928183</td>\n",
       "      <td>7.235400e-30</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>126.675723</td>\n",
       "      <td>3.629620e-29</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>117.532842</td>\n",
       "      <td>3.396210e-27</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>88.671048</td>\n",
       "      <td>5.989848e-21</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>48.043370</td>\n",
       "      <td>4.494345e-12</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.170380</td>\n",
       "      <td>2.454146e-10</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.082017</td>\n",
       "      <td>9.161935e-09</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>31.848804</td>\n",
       "      <td>1.723696e-08</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28.362986</td>\n",
       "      <td>1.033171e-07</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.746215</td>\n",
       "      <td>6.127553e-04</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.587245</td>\n",
       "      <td>6.673323e-04</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.998434</td>\n",
       "      <td>4.693475e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.172007</td>\n",
       "      <td>2.298017e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.371159</td>\n",
       "      <td>1.236350e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.135878</td>\n",
       "      <td>2.865572e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.372574</td>\n",
       "      <td>5.416222e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.126953</td>\n",
       "      <td>7.216219e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  3058.357364   0.000000e+00   Inflowing drainage direction\n",
       "23  2873.839868   0.000000e+00                   Surface type\n",
       "21  2752.743624   0.000000e+00                      Elevation\n",
       "2   1607.983542  1.980709e-320                         Arable\n",
       "24   900.562185  1.520435e-187  Outflowing drainage direction\n",
       "3    796.768219  3.749922e-167              Improve grassland\n",
       "26   532.616285  3.976135e-114                   Fertiliser K\n",
       "27   532.616285  3.976135e-114                   Fertiliser N\n",
       "28   532.616285  3.976135e-114                   Fertiliser P\n",
       "0    495.339143  1.652140e-106             Deciduous woodland\n",
       "17   274.520690   1.206526e-60              Littoral sediment\n",
       "20   270.648302   7.898492e-60                       Suburban\n",
       "29   247.805699   5.245303e-55             Chlorothalonil_5km\n",
       "30   247.805699   5.245303e-55                 Glyphosate_5km\n",
       "31   247.805699   5.245303e-55                   Mancozeb_5km\n",
       "32   247.805699   5.245303e-55                 Mecoprop-P_5km\n",
       "34   247.805699   5.245303e-55              Pendimethalin_5km\n",
       "22   209.765273   6.085933e-47      Cumulative catchment area\n",
       "36   178.358152   2.992833e-40               Prosulfocarb_5km\n",
       "37   177.859694   3.824203e-40                    Sulphur_5km\n",
       "38   156.940500   1.142502e-35                 Tri-allate_5km\n",
       "35   130.830883   4.625347e-30   PropamocarbHydrochloride_5km\n",
       "19   129.928183   7.235400e-30                          Urban\n",
       "33   126.675723   3.629620e-29                 Metamitron_5km\n",
       "18   117.532842   3.396210e-27                      Saltmarsh\n",
       "15    88.671048   5.989848e-21         Supralittoral sediment\n",
       "16    48.043370   4.494345e-12                  Littoral rock\n",
       "4     40.170380   2.454146e-10              Neutral grassland\n",
       "1     33.082017   9.161935e-09            Coniferous woodland\n",
       "13    31.848804   1.723696e-08                     Freshwater\n",
       "7     28.362986   1.033171e-07                            Fen\n",
       "8     11.746215   6.127553e-04                        Heather\n",
       "10    11.587245   6.673323e-04                            Bog\n",
       "6      7.998434   4.693475e-03                 Acid grassland\n",
       "9      5.172007   2.298017e-02              Heather grassland\n",
       "12     2.371159   1.236350e-01                      Saltwater\n",
       "11     1.135878   2.865572e-01                    Inland rock\n",
       "14     0.372574   5.416222e-01             Supralittoral rock\n",
       "5      0.126953   7.216219e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pintail 5km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1193.390686</td>\n",
       "      <td>8.415495e-244</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1059.117690</td>\n",
       "      <td>3.107133e-218</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1059.117690</td>\n",
       "      <td>3.107133e-218</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1059.117690</td>\n",
       "      <td>3.107133e-218</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1044.622083</td>\n",
       "      <td>1.885397e-215</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>953.980643</td>\n",
       "      <td>6.038807e-198</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>678.915030</td>\n",
       "      <td>1.065146e-143</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>511.570131</td>\n",
       "      <td>7.880582e-110</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>437.458138</td>\n",
       "      <td>1.308414e-94</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>437.458138</td>\n",
       "      <td>1.308414e-94</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>437.458138</td>\n",
       "      <td>1.308414e-94</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>437.458138</td>\n",
       "      <td>1.308414e-94</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>437.458138</td>\n",
       "      <td>1.308414e-94</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>365.266834</td>\n",
       "      <td>1.196351e-79</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>328.700531</td>\n",
       "      <td>5.109936e-72</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>311.163722</td>\n",
       "      <td>2.403093e-68</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>308.836341</td>\n",
       "      <td>7.392246e-68</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>300.708807</td>\n",
       "      <td>3.750857e-66</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>300.167250</td>\n",
       "      <td>4.873345e-66</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>280.600130</td>\n",
       "      <td>6.328572e-62</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>278.656397</td>\n",
       "      <td>1.623719e-61</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>271.215067</td>\n",
       "      <td>5.999103e-60</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>269.394523</td>\n",
       "      <td>1.451568e-59</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>185.237707</td>\n",
       "      <td>1.017662e-41</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>155.189502</td>\n",
       "      <td>2.711014e-35</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143.715093</td>\n",
       "      <td>7.852766e-33</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>97.095040</td>\n",
       "      <td>8.916265e-23</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>92.278551</td>\n",
       "      <td>9.873618e-22</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.914191</td>\n",
       "      <td>1.026991e-06</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.462169</td>\n",
       "      <td>8.488716e-05</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.551016</td>\n",
       "      <td>6.804429e-04</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.159156</td>\n",
       "      <td>2.315071e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.607237</td>\n",
       "      <td>3.186741e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.382959</td>\n",
       "      <td>1.227047e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.157468</td>\n",
       "      <td>1.419178e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.820981</td>\n",
       "      <td>1.772349e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.400084</td>\n",
       "      <td>2.367449e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.321758</td>\n",
       "      <td>5.705693e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.100196</td>\n",
       "      <td>7.516032e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1193.390686  8.415495e-244   Inflowing drainage direction\n",
       "26  1059.117690  3.107133e-218                   Fertiliser K\n",
       "27  1059.117690  3.107133e-218                   Fertiliser N\n",
       "28  1059.117690  3.107133e-218                   Fertiliser P\n",
       "23  1044.622083  1.885397e-215                   Surface type\n",
       "21   953.980643  6.038807e-198                      Elevation\n",
       "2    678.915030  1.065146e-143                         Arable\n",
       "3    511.570131  7.880582e-110              Improve grassland\n",
       "29   437.458138   1.308414e-94             Chlorothalonil_5km\n",
       "30   437.458138   1.308414e-94                 Glyphosate_5km\n",
       "31   437.458138   1.308414e-94                   Mancozeb_5km\n",
       "32   437.458138   1.308414e-94                 Mecoprop-P_5km\n",
       "34   437.458138   1.308414e-94              Pendimethalin_5km\n",
       "20   365.266834   1.196351e-79                       Suburban\n",
       "24   328.700531   5.109936e-72  Outflowing drainage direction\n",
       "38   311.163722   2.403093e-68                 Tri-allate_5km\n",
       "19   308.836341   7.392246e-68                          Urban\n",
       "36   300.708807   3.750857e-66               Prosulfocarb_5km\n",
       "37   300.167250   4.873345e-66                    Sulphur_5km\n",
       "33   280.600130   6.328572e-62                 Metamitron_5km\n",
       "17   278.656397   1.623719e-61              Littoral sediment\n",
       "22   271.215067   5.999103e-60      Cumulative catchment area\n",
       "35   269.394523   1.451568e-59   PropamocarbHydrochloride_5km\n",
       "18   185.237707   1.017662e-41                      Saltmarsh\n",
       "0    155.189502   2.711014e-35             Deciduous woodland\n",
       "4    143.715093   7.852766e-33              Neutral grassland\n",
       "12    97.095040   8.916265e-23                      Saltwater\n",
       "7     92.278551   9.873618e-22                            Fen\n",
       "13    23.914191   1.026991e-06                     Freshwater\n",
       "15    15.462169   8.488716e-05         Supralittoral sediment\n",
       "6     11.551016   6.804429e-04                 Acid grassland\n",
       "8      5.159156   2.315071e-02                        Heather\n",
       "14     4.607237   3.186741e-02             Supralittoral rock\n",
       "11     2.382959   1.227047e-01                    Inland rock\n",
       "10     2.157468   1.419178e-01                            Bog\n",
       "9      1.820981   1.772349e-01              Heather grassland\n",
       "1      1.400084   2.367449e-01            Coniferous woodland\n",
       "16     0.321758   5.705693e-01                  Littoral rock\n",
       "5      0.100196   7.516032e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pochard 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2671.442257</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2671.442257</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2671.442257</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1871.268813</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1596.293317</td>\n",
       "      <td>2.578949e-318</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1458.059785</td>\n",
       "      <td>4.121005e-293</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1323.591102</td>\n",
       "      <td>3.098156e-268</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>806.540025</td>\n",
       "      <td>4.403351e-169</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>806.540025</td>\n",
       "      <td>4.403351e-169</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>806.540025</td>\n",
       "      <td>4.403351e-169</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>806.540025</td>\n",
       "      <td>4.403351e-169</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>806.540025</td>\n",
       "      <td>4.403351e-169</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>791.573223</td>\n",
       "      <td>3.991040e-166</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>791.573223</td>\n",
       "      <td>3.991040e-166</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>782.836054</td>\n",
       "      <td>2.137179e-164</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>780.887257</td>\n",
       "      <td>5.196046e-164</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>771.463114</td>\n",
       "      <td>3.825963e-162</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>734.117892</td>\n",
       "      <td>1.003605e-154</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>732.060624</td>\n",
       "      <td>2.577445e-154</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>658.633204</td>\n",
       "      <td>1.248489e-139</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>618.437904</td>\n",
       "      <td>1.546567e-131</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>481.916188</td>\n",
       "      <td>9.330097e-104</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>430.417497</td>\n",
       "      <td>3.714651e-93</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.555013</td>\n",
       "      <td>1.090677e-25</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84.237933</td>\n",
       "      <td>5.501599e-20</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>61.691034</td>\n",
       "      <td>4.542600e-15</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>48.836131</td>\n",
       "      <td>3.007200e-12</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20.906609</td>\n",
       "      <td>4.895094e-06</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14.195959</td>\n",
       "      <td>1.659125e-04</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.095658</td>\n",
       "      <td>1.491870e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.310865</td>\n",
       "      <td>6.868452e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.252435</td>\n",
       "      <td>7.135523e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.895485</td>\n",
       "      <td>8.886706e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.338684</td>\n",
       "      <td>1.262358e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.728844</td>\n",
       "      <td>1.885965e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.189055</td>\n",
       "      <td>2.755532e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.349072</td>\n",
       "      <td>5.546558e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.137187</td>\n",
       "      <td>7.111031e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.126274</td>\n",
       "      <td>7.223367e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  2671.442257   0.000000e+00                   Fertiliser K\n",
       "27  2671.442257   0.000000e+00                   Fertiliser N\n",
       "28  2671.442257   0.000000e+00                   Fertiliser P\n",
       "23  1871.268813   0.000000e+00                   Surface type\n",
       "25  1596.293317  2.578949e-318   Inflowing drainage direction\n",
       "21  1458.059785  4.121005e-293                      Elevation\n",
       "2   1323.591102  3.098156e-268                         Arable\n",
       "29   806.540025  4.403351e-169             Chlorothalonil_5km\n",
       "30   806.540025  4.403351e-169                 Glyphosate_5km\n",
       "31   806.540025  4.403351e-169                   Mancozeb_5km\n",
       "32   806.540025  4.403351e-169                 Mecoprop-P_5km\n",
       "34   806.540025  4.403351e-169              Pendimethalin_5km\n",
       "36   791.573223  3.991040e-166               Prosulfocarb_5km\n",
       "37   791.573223  3.991040e-166                    Sulphur_5km\n",
       "38   782.836054  2.137179e-164                 Tri-allate_5km\n",
       "3    780.887257  5.196046e-164              Improve grassland\n",
       "33   771.463114  3.825963e-162                 Metamitron_5km\n",
       "35   734.117892  1.003605e-154   PropamocarbHydrochloride_5km\n",
       "20   732.060624  2.577445e-154                       Suburban\n",
       "22   658.633204  1.248489e-139      Cumulative catchment area\n",
       "24   618.437904  1.546567e-131  Outflowing drainage direction\n",
       "19   481.916188  9.330097e-104                          Urban\n",
       "0    430.417497   3.714651e-93             Deciduous woodland\n",
       "4    110.555013   1.090677e-25              Neutral grassland\n",
       "7     84.237933   5.501599e-20                            Fen\n",
       "18    61.691034   4.542600e-15                      Saltmarsh\n",
       "13    48.836131   3.007200e-12                     Freshwater\n",
       "17    20.906609   4.895094e-06              Littoral sediment\n",
       "12    14.195959   1.659125e-04                      Saltwater\n",
       "10    10.095658   1.491870e-03                            Bog\n",
       "6      7.310865   6.868452e-03                 Acid grassland\n",
       "9      3.252435   7.135523e-02              Heather grassland\n",
       "8      2.895485   8.886706e-02                        Heather\n",
       "11     2.338684   1.262358e-01                    Inland rock\n",
       "15     1.728844   1.885965e-01         Supralittoral sediment\n",
       "5      1.189055   2.755532e-01           Calcareous grassland\n",
       "14     0.349072   5.546558e-01             Supralittoral rock\n",
       "1      0.137187   7.111031e-01            Coniferous woodland\n",
       "16     0.126274   7.223367e-01                  Littoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red-legged Partridge 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10028.081544</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10028.081544</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10028.081544</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6569.078657</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5268.430630</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4818.581261</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4323.612248</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2736.285944</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2736.285944</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2736.285944</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2736.285944</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2736.285944</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2359.016809</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2303.416408</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2250.336701</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2245.412837</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1967.904394</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1927.307830</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1545.134347</td>\n",
       "      <td>4.967533e-309</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1070.992467</td>\n",
       "      <td>1.643813e-220</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>779.627069</td>\n",
       "      <td>9.230330e-164</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>675.306381</td>\n",
       "      <td>5.632102e-143</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>107.311955</td>\n",
       "      <td>5.478702e-25</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>67.177375</td>\n",
       "      <td>2.868836e-16</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62.384237</td>\n",
       "      <td>3.203360e-15</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32.256137</td>\n",
       "      <td>1.398832e-08</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31.379082</td>\n",
       "      <td>2.193271e-08</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13.866634</td>\n",
       "      <td>1.976067e-04</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.906308</td>\n",
       "      <td>2.850500e-03</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.105522</td>\n",
       "      <td>2.387671e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.467719</td>\n",
       "      <td>3.457209e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.083385</td>\n",
       "      <td>7.913418e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.844292</td>\n",
       "      <td>9.173816e-02</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.063899</td>\n",
       "      <td>1.508631e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.979514</td>\n",
       "      <td>3.223488e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.876741</td>\n",
       "      <td>3.491243e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.359643</td>\n",
       "      <td>5.487215e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.075404</td>\n",
       "      <td>7.836319e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.037231</td>\n",
       "      <td>8.470000e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "26  10028.081544   0.000000e+00                   Fertiliser K\n",
       "27  10028.081544   0.000000e+00                   Fertiliser N\n",
       "28  10028.081544   0.000000e+00                   Fertiliser P\n",
       "23   6569.078657   0.000000e+00                   Surface type\n",
       "21   5268.430630   0.000000e+00                      Elevation\n",
       "25   4818.581261   0.000000e+00   Inflowing drainage direction\n",
       "2    4323.612248   0.000000e+00                         Arable\n",
       "29   2736.285944   0.000000e+00             Chlorothalonil_5km\n",
       "30   2736.285944   0.000000e+00                 Glyphosate_5km\n",
       "31   2736.285944   0.000000e+00                   Mancozeb_5km\n",
       "32   2736.285944   0.000000e+00                 Mecoprop-P_5km\n",
       "34   2736.285944   0.000000e+00              Pendimethalin_5km\n",
       "38   2359.016809   0.000000e+00                 Tri-allate_5km\n",
       "3    2303.416408   0.000000e+00              Improve grassland\n",
       "37   2250.336701   0.000000e+00                    Sulphur_5km\n",
       "36   2245.412837   0.000000e+00               Prosulfocarb_5km\n",
       "33   1967.904394   0.000000e+00                 Metamitron_5km\n",
       "35   1927.307830   0.000000e+00   PropamocarbHydrochloride_5km\n",
       "24   1545.134347  4.967533e-309  Outflowing drainage direction\n",
       "0    1070.992467  1.643813e-220             Deciduous woodland\n",
       "22    779.627069  9.230330e-164      Cumulative catchment area\n",
       "20    675.306381  5.632102e-143                       Suburban\n",
       "5     107.311955   5.478702e-25           Calcareous grassland\n",
       "19     67.177375   2.868836e-16                          Urban\n",
       "4      62.384237   3.203360e-15              Neutral grassland\n",
       "13     32.256137   1.398832e-08                     Freshwater\n",
       "7      31.379082   2.193271e-08                            Fen\n",
       "18     13.866634   1.976067e-04                      Saltmarsh\n",
       "11      8.906308   2.850500e-03                    Inland rock\n",
       "8       5.105522   2.387671e-02                        Heather\n",
       "14      4.467719   3.457209e-02             Supralittoral rock\n",
       "17      3.083385   7.913418e-02              Littoral sediment\n",
       "1       2.844292   9.173816e-02            Coniferous woodland\n",
       "9       2.063899   1.508631e-01              Heather grassland\n",
       "12      0.979514   3.223488e-01                      Saltwater\n",
       "15      0.876741   3.491243e-01         Supralittoral sediment\n",
       "10      0.359643   5.487215e-01                            Bog\n",
       "16      0.075404   7.836319e-01                  Littoral rock\n",
       "6       0.037231   8.470000e-01                 Acid grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ring-necked Parakeet 5km\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1621.475487</td>\n",
       "      <td>7.410985e-323</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1336.970983</td>\n",
       "      <td>9.993620e-271</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1074.183474</td>\n",
       "      <td>4.023650e-221</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1074.183474</td>\n",
       "      <td>4.023650e-221</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1074.183474</td>\n",
       "      <td>4.023650e-221</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>565.996873</td>\n",
       "      <td>6.403263e-121</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>427.527239</td>\n",
       "      <td>1.468349e-92</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>400.020717</td>\n",
       "      <td>7.218079e-87</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>391.123758</td>\n",
       "      <td>5.053244e-85</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>391.123758</td>\n",
       "      <td>5.053244e-85</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>391.123758</td>\n",
       "      <td>5.053244e-85</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>391.123758</td>\n",
       "      <td>5.053244e-85</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>391.123758</td>\n",
       "      <td>5.053244e-85</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>352.039450</td>\n",
       "      <td>6.823027e-77</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>339.108972</td>\n",
       "      <td>3.409990e-74</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>319.146189</td>\n",
       "      <td>5.106623e-70</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>319.146189</td>\n",
       "      <td>5.106623e-70</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>318.152714</td>\n",
       "      <td>8.245376e-70</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>317.219812</td>\n",
       "      <td>1.293078e-69</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>285.355903</td>\n",
       "      <td>6.317471e-63</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>283.340499</td>\n",
       "      <td>1.677140e-62</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.357389</td>\n",
       "      <td>1.391539e-12</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>48.811699</td>\n",
       "      <td>3.044664e-12</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35.867447</td>\n",
       "      <td>2.203758e-09</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.300329</td>\n",
       "      <td>1.131332e-05</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.952640</td>\n",
       "      <td>8.385814e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.782438</td>\n",
       "      <td>5.182829e-02</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.524436</td>\n",
       "      <td>6.050691e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.733169</td>\n",
       "      <td>9.832354e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.532926</td>\n",
       "      <td>1.115330e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.272192</td>\n",
       "      <td>1.317531e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.899822</td>\n",
       "      <td>3.428583e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.861968</td>\n",
       "      <td>3.532166e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.790142</td>\n",
       "      <td>3.740841e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.589704</td>\n",
       "      <td>4.425563e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.412370</td>\n",
       "      <td>5.207875e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.008544</td>\n",
       "      <td>9.263560e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.001371</td>\n",
       "      <td>9.704634e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000396</td>\n",
       "      <td>9.841293e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "20  1621.475487  7.410985e-323                       Suburban\n",
       "19  1336.970983  9.993620e-271                          Urban\n",
       "26  1074.183474  4.023650e-221                   Fertiliser K\n",
       "27  1074.183474  4.023650e-221                   Fertiliser N\n",
       "28  1074.183474  4.023650e-221                   Fertiliser P\n",
       "23   565.996873  6.403263e-121                   Surface type\n",
       "25   427.527239   1.468349e-92   Inflowing drainage direction\n",
       "21   400.020717   7.218079e-87                      Elevation\n",
       "29   391.123758   5.053244e-85             Chlorothalonil_5km\n",
       "30   391.123758   5.053244e-85                 Glyphosate_5km\n",
       "31   391.123758   5.053244e-85                   Mancozeb_5km\n",
       "32   391.123758   5.053244e-85                 Mecoprop-P_5km\n",
       "34   391.123758   5.053244e-85              Pendimethalin_5km\n",
       "0    352.039450   6.823027e-77             Deciduous woodland\n",
       "38   339.108972   3.409990e-74                 Tri-allate_5km\n",
       "36   319.146189   5.106623e-70               Prosulfocarb_5km\n",
       "37   319.146189   5.106623e-70                    Sulphur_5km\n",
       "35   318.152714   8.245376e-70   PropamocarbHydrochloride_5km\n",
       "33   317.219812   1.293078e-69                 Metamitron_5km\n",
       "24   285.355903   6.317471e-63  Outflowing drainage direction\n",
       "3    283.340499   1.677140e-62              Improve grassland\n",
       "2     50.357389   1.391539e-12                         Arable\n",
       "13    48.811699   3.044664e-12                     Freshwater\n",
       "22    35.867447   2.203758e-09      Cumulative catchment area\n",
       "7     19.300329   1.131332e-05                            Fen\n",
       "6      6.952640   8.385814e-03                 Acid grassland\n",
       "4      3.782438   5.182829e-02              Neutral grassland\n",
       "9      3.524436   6.050691e-02              Heather grassland\n",
       "8      2.733169   9.832354e-02                        Heather\n",
       "10     2.532926   1.115330e-01                            Bog\n",
       "1      2.272192   1.317531e-01            Coniferous woodland\n",
       "17     0.899822   3.428583e-01              Littoral sediment\n",
       "11     0.861968   3.532166e-01                    Inland rock\n",
       "16     0.790142   3.740841e-01                  Littoral rock\n",
       "14     0.589704   4.425563e-01             Supralittoral rock\n",
       "18     0.412370   5.207875e-01                      Saltmarsh\n",
       "15     0.008544   9.263560e-01         Supralittoral sediment\n",
       "12     0.001371   9.704634e-01                      Saltwater\n",
       "5      0.000396   9.841293e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rock Dove 5km\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6619.008028</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5838.703214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5360.017492</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4904.971327</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4904.971327</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4904.971327</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2334.727881</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2334.727881</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2334.727881</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2334.727881</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2334.727881</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2261.308708</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1862.743756</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1750.454521</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1750.454521</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1729.647756</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1470.194191</td>\n",
       "      <td>2.442119e-295</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1464.256923</td>\n",
       "      <td>3.000378e-294</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1406.973455</td>\n",
       "      <td>1.055522e-283</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1158.528776</td>\n",
       "      <td>3.363250e-237</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1035.953183</td>\n",
       "      <td>8.748264e-214</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>751.130029</td>\n",
       "      <td>4.149641e-158</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>315.777931</td>\n",
       "      <td>2.592399e-69</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93.807385</td>\n",
       "      <td>4.601235e-22</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64.397083</td>\n",
       "      <td>1.162344e-15</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>53.306354</td>\n",
       "      <td>3.129215e-13</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34.904593</td>\n",
       "      <td>3.605024e-09</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20.266808</td>\n",
       "      <td>6.831707e-06</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.523632</td>\n",
       "      <td>1.698188e-05</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.262056</td>\n",
       "      <td>3.290300e-05</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.619793</td>\n",
       "      <td>1.325292e-04</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.607879</td>\n",
       "      <td>3.356669e-03</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.197187</td>\n",
       "      <td>1.281560e-02</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.712949</td>\n",
       "      <td>1.686333e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.682524</td>\n",
       "      <td>1.715800e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.134740</td>\n",
       "      <td>2.347832e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.917829</td>\n",
       "      <td>1.661350e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.550400</td>\n",
       "      <td>2.131130e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.292612</td>\n",
       "      <td>5.885664e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  6619.008028   0.000000e+00                   Surface type\n",
       "21  5838.703214   0.000000e+00                      Elevation\n",
       "25  5360.017492   0.000000e+00   Inflowing drainage direction\n",
       "26  4904.971327   0.000000e+00                   Fertiliser K\n",
       "27  4904.971327   0.000000e+00                   Fertiliser N\n",
       "28  4904.971327   0.000000e+00                   Fertiliser P\n",
       "29  2334.727881   0.000000e+00             Chlorothalonil_5km\n",
       "30  2334.727881   0.000000e+00                 Glyphosate_5km\n",
       "31  2334.727881   0.000000e+00                   Mancozeb_5km\n",
       "32  2334.727881   0.000000e+00                 Mecoprop-P_5km\n",
       "34  2334.727881   0.000000e+00              Pendimethalin_5km\n",
       "3   2261.308708   0.000000e+00              Improve grassland\n",
       "38  1862.743756   0.000000e+00                 Tri-allate_5km\n",
       "36  1750.454521   0.000000e+00               Prosulfocarb_5km\n",
       "37  1750.454521   0.000000e+00                    Sulphur_5km\n",
       "2   1729.647756   0.000000e+00                         Arable\n",
       "35  1470.194191  2.442119e-295   PropamocarbHydrochloride_5km\n",
       "33  1464.256923  3.000378e-294                 Metamitron_5km\n",
       "24  1406.973455  1.055522e-283  Outflowing drainage direction\n",
       "20  1158.528776  3.363250e-237                       Suburban\n",
       "0   1035.953183  8.748264e-214             Deciduous woodland\n",
       "22   751.130029  4.149641e-158      Cumulative catchment area\n",
       "19   315.777931   2.592399e-69                          Urban\n",
       "4     93.807385   4.601235e-22              Neutral grassland\n",
       "5     64.397083   1.162344e-15           Calcareous grassland\n",
       "13    53.306354   3.129215e-13                     Freshwater\n",
       "7     34.904593   3.605024e-09                            Fen\n",
       "17    20.266808   6.831707e-06              Littoral sediment\n",
       "18    18.523632   1.698188e-05                      Saltmarsh\n",
       "16    17.262056   3.290300e-05                  Littoral rock\n",
       "15    14.619793   1.325292e-04         Supralittoral sediment\n",
       "14     8.607879   3.356669e-03             Supralittoral rock\n",
       "1      6.197187   1.281560e-02            Coniferous woodland\n",
       "11     5.712949   1.686333e-02                    Inland rock\n",
       "9      5.682524   1.715800e-02              Heather grassland\n",
       "10     5.134740   2.347832e-02                            Bog\n",
       "12     1.917829   1.661350e-01                      Saltwater\n",
       "6      1.550400   2.131130e-01                 Acid grassland\n",
       "8      0.292612   5.885664e-01                        Heather"
      ]
     },
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    },
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     "text": [
      "Ruddy Duck 5km\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>506.011518</td>\n",
       "      <td>1.079756e-108</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>506.011518</td>\n",
       "      <td>1.079756e-108</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>506.011518</td>\n",
       "      <td>1.079756e-108</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>283.173640</td>\n",
       "      <td>1.818362e-62</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>266.772577</td>\n",
       "      <td>5.184196e-59</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>265.160749</td>\n",
       "      <td>1.134062e-58</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>254.219428</td>\n",
       "      <td>2.313511e-56</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>195.841301</td>\n",
       "      <td>5.587129e-44</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>180.145187</td>\n",
       "      <td>1.243031e-40</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160.625779</td>\n",
       "      <td>1.855127e-36</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>147.983254</td>\n",
       "      <td>9.521754e-34</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>127.251076</td>\n",
       "      <td>2.728551e-29</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>127.251076</td>\n",
       "      <td>2.728551e-29</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>127.251076</td>\n",
       "      <td>2.728551e-29</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>127.251076</td>\n",
       "      <td>2.728551e-29</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>127.251076</td>\n",
       "      <td>2.728551e-29</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>124.028628</td>\n",
       "      <td>1.349605e-28</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>120.800275</td>\n",
       "      <td>6.701485e-28</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>120.800275</td>\n",
       "      <td>6.701485e-28</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120.623646</td>\n",
       "      <td>7.315784e-28</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>116.409165</td>\n",
       "      <td>5.935908e-27</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>114.624634</td>\n",
       "      <td>1.441129e-26</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87.739974</td>\n",
       "      <td>9.541108e-21</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>47.929479</td>\n",
       "      <td>4.761481e-12</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.300605</td>\n",
       "      <td>6.378079e-08</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>23.105165</td>\n",
       "      <td>1.561879e-06</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12.077647</td>\n",
       "      <td>5.130188e-04</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.537990</td>\n",
       "      <td>6.001455e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.370084</td>\n",
       "      <td>1.237201e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.211937</td>\n",
       "      <td>1.369862e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.997667</td>\n",
       "      <td>1.575807e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.742925</td>\n",
       "      <td>1.868066e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.515171</td>\n",
       "      <td>2.183881e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.414463</td>\n",
       "      <td>5.197314e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.412700</td>\n",
       "      <td>5.206209e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.407971</td>\n",
       "      <td>5.230193e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.387826</td>\n",
       "      <td>5.334622e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.378989</td>\n",
       "      <td>5.381623e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.015552</td>\n",
       "      <td>9.007570e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       F Score        P Value                      Attribute\n",
       "26  506.011518  1.079756e-108                   Fertiliser K\n",
       "27  506.011518  1.079756e-108                   Fertiliser N\n",
       "28  506.011518  1.079756e-108                   Fertiliser P\n",
       "20  283.173640   1.818362e-62                       Suburban\n",
       "19  266.772577   5.184196e-59                          Urban\n",
       "23  265.160749   1.134062e-58                   Surface type\n",
       "13  254.219428   2.313511e-56                     Freshwater\n",
       "25  195.841301   5.587129e-44   Inflowing drainage direction\n",
       "21  180.145187   1.243031e-40                      Elevation\n",
       "3   160.625779   1.855127e-36              Improve grassland\n",
       "24  147.983254   9.521754e-34  Outflowing drainage direction\n",
       "29  127.251076   2.728551e-29             Chlorothalonil_5km\n",
       "30  127.251076   2.728551e-29                 Glyphosate_5km\n",
       "31  127.251076   2.728551e-29                   Mancozeb_5km\n",
       "32  127.251076   2.728551e-29                 Mecoprop-P_5km\n",
       "34  127.251076   2.728551e-29              Pendimethalin_5km\n",
       "33  124.028628   1.349605e-28                 Metamitron_5km\n",
       "36  120.800275   6.701485e-28               Prosulfocarb_5km\n",
       "37  120.800275   6.701485e-28                    Sulphur_5km\n",
       "2   120.623646   7.315784e-28                         Arable\n",
       "38  116.409165   5.935908e-27                 Tri-allate_5km\n",
       "35  114.624634   1.441129e-26   PropamocarbHydrochloride_5km\n",
       "4    87.739974   9.541108e-21              Neutral grassland\n",
       "22   47.929479   4.761481e-12      Cumulative catchment area\n",
       "0    29.300605   6.378079e-08             Deciduous woodland\n",
       "15   23.105165   1.561879e-06         Supralittoral sediment\n",
       "7    12.077647   5.130188e-04                            Fen\n",
       "6     3.537990   6.001455e-02                 Acid grassland\n",
       "9     2.370084   1.237201e-01              Heather grassland\n",
       "18    2.211937   1.369862e-01                      Saltmarsh\n",
       "8     1.997667   1.575807e-01                        Heather\n",
       "1     1.742925   1.868066e-01            Coniferous woodland\n",
       "10    1.515171   2.183881e-01                            Bog\n",
       "17    0.414463   5.197314e-01              Littoral sediment\n",
       "5     0.412700   5.206209e-01           Calcareous grassland\n",
       "14    0.407971   5.230193e-01             Supralittoral rock\n",
       "16    0.387826   5.334622e-01                  Littoral rock\n",
       "11    0.378989   5.381623e-01                    Inland rock\n",
       "12    0.015552   9.007570e-01                      Saltwater"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whooper Swan 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1446.473544</td>\n",
       "      <td>5.550292e-291</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1251.247669</td>\n",
       "      <td>1.058098e-254</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1191.375081</td>\n",
       "      <td>2.023384e-243</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>445.722249</td>\n",
       "      <td>2.586255e-96</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>374.066418</td>\n",
       "      <td>1.765675e-81</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>320.036326</td>\n",
       "      <td>3.324479e-70</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>238.820965</td>\n",
       "      <td>4.174954e-53</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>223.204418</td>\n",
       "      <td>8.516960e-50</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>223.204418</td>\n",
       "      <td>8.516960e-50</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>223.204418</td>\n",
       "      <td>8.516960e-50</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>197.475322</td>\n",
       "      <td>2.507116e-44</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>197.475322</td>\n",
       "      <td>2.507116e-44</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>197.475322</td>\n",
       "      <td>2.507116e-44</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>197.475322</td>\n",
       "      <td>2.507116e-44</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>197.475322</td>\n",
       "      <td>2.507116e-44</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>121.314510</td>\n",
       "      <td>5.191402e-28</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>114.782090</td>\n",
       "      <td>1.332627e-26</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>107.759719</td>\n",
       "      <td>4.383959e-25</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>107.759719</td>\n",
       "      <td>4.383959e-25</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>97.774587</td>\n",
       "      <td>6.352410e-23</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>94.043625</td>\n",
       "      <td>4.089261e-22</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>90.045300</td>\n",
       "      <td>3.013566e-21</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>79.687198</td>\n",
       "      <td>5.373532e-19</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>77.348071</td>\n",
       "      <td>1.735747e-18</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>63.066151</td>\n",
       "      <td>2.272035e-15</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>53.271204</td>\n",
       "      <td>3.185333e-13</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.201527</td>\n",
       "      <td>3.299568e-13</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50.830701</td>\n",
       "      <td>1.095016e-12</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>45.236869</td>\n",
       "      <td>1.866498e-11</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42.512494</td>\n",
       "      <td>7.451361e-11</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.916161</td>\n",
       "      <td>9.974305e-09</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32.907150</td>\n",
       "      <td>1.002046e-08</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>29.904098</td>\n",
       "      <td>4.677093e-08</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24.512053</td>\n",
       "      <td>7.536372e-07</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.352755</td>\n",
       "      <td>3.698086e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.633900</td>\n",
       "      <td>1.046440e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.226658</td>\n",
       "      <td>1.356866e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.591194</td>\n",
       "      <td>2.071926e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.020465</td>\n",
       "      <td>8.862510e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1446.473544  5.550292e-291   Inflowing drainage direction\n",
       "21  1251.247669  1.058098e-254                      Elevation\n",
       "23  1191.375081  2.023384e-243                   Surface type\n",
       "3    445.722249   2.586255e-96              Improve grassland\n",
       "2    374.066418   1.765675e-81                         Arable\n",
       "24   320.036326   3.324479e-70  Outflowing drainage direction\n",
       "0    238.820965   4.174954e-53             Deciduous woodland\n",
       "26   223.204418   8.516960e-50                   Fertiliser K\n",
       "27   223.204418   8.516960e-50                   Fertiliser N\n",
       "28   223.204418   8.516960e-50                   Fertiliser P\n",
       "29   197.475322   2.507116e-44             Chlorothalonil_5km\n",
       "30   197.475322   2.507116e-44                 Glyphosate_5km\n",
       "31   197.475322   2.507116e-44                   Mancozeb_5km\n",
       "32   197.475322   2.507116e-44                 Mecoprop-P_5km\n",
       "34   197.475322   2.507116e-44              Pendimethalin_5km\n",
       "17   121.314510   5.191402e-28              Littoral sediment\n",
       "38   114.782090   1.332627e-26                 Tri-allate_5km\n",
       "36   107.759719   4.383959e-25               Prosulfocarb_5km\n",
       "37   107.759719   4.383959e-25                    Sulphur_5km\n",
       "35    97.774587   6.352410e-23   PropamocarbHydrochloride_5km\n",
       "33    94.043625   4.089261e-22                 Metamitron_5km\n",
       "20    90.045300   3.013566e-21                       Suburban\n",
       "22    79.687198   5.373532e-19      Cumulative catchment area\n",
       "13    77.348071   1.735747e-18                     Freshwater\n",
       "19    63.066151   2.272035e-15                          Urban\n",
       "18    53.271204   3.185333e-13                      Saltmarsh\n",
       "1     53.201527   3.299568e-13            Coniferous woodland\n",
       "10    50.830701   1.095016e-12                            Bog\n",
       "15    45.236869   1.866498e-11         Supralittoral sediment\n",
       "9     42.512494   7.451361e-11              Heather grassland\n",
       "4     32.916161   9.974305e-09              Neutral grassland\n",
       "14    32.907150   1.002046e-08             Supralittoral rock\n",
       "16    29.904098   4.677093e-08                  Littoral rock\n",
       "7     24.512053   7.536372e-07                            Fen\n",
       "12     4.352755   3.698086e-02                      Saltwater\n",
       "8      2.633900   1.046440e-01                        Heather\n",
       "5      2.226658   1.356866e-01           Calcareous grassland\n",
       "6      1.591194   2.071926e-01                 Acid grassland\n",
       "11     0.020465   8.862510e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wigeon 5km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3527.304911</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3350.481828</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3275.722008</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1560.675179</td>\n",
       "      <td>7.419696e-312</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1560.675179</td>\n",
       "      <td>7.419696e-312</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1560.675179</td>\n",
       "      <td>7.419696e-312</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1300.349746</td>\n",
       "      <td>6.719167e-264</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1127.764279</td>\n",
       "      <td>2.364062e-231</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>888.494256</td>\n",
       "      <td>3.471083e-185</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>847.052726</td>\n",
       "      <td>4.617711e-177</td>\n",
       "      <td>Chlorothalonil_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>847.052726</td>\n",
       "      <td>4.617711e-177</td>\n",
       "      <td>Glyphosate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>847.052726</td>\n",
       "      <td>4.617711e-177</td>\n",
       "      <td>Mancozeb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>847.052726</td>\n",
       "      <td>4.617711e-177</td>\n",
       "      <td>Mecoprop-P_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>847.052726</td>\n",
       "      <td>4.617711e-177</td>\n",
       "      <td>Pendimethalin_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>676.520460</td>\n",
       "      <td>3.215919e-143</td>\n",
       "      <td>Prosulfocarb_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>675.198692</td>\n",
       "      <td>5.919142e-143</td>\n",
       "      <td>Sulphur_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>659.465995</td>\n",
       "      <td>8.494090e-140</td>\n",
       "      <td>Tri-allate_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>583.145685</td>\n",
       "      <td>2.124075e-124</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>550.331786</td>\n",
       "      <td>9.792385e-118</td>\n",
       "      <td>Metamitron_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>548.586416</td>\n",
       "      <td>2.218553e-117</td>\n",
       "      <td>PropamocarbHydrochloride_5km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>461.333112</td>\n",
       "      <td>1.579205e-99</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>215.255159</td>\n",
       "      <td>4.147517e-48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>187.302538</td>\n",
       "      <td>3.691076e-42</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>171.051423</td>\n",
       "      <td>1.090041e-38</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>92.813673</td>\n",
       "      <td>7.557806e-22</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86.129805</td>\n",
       "      <td>2.134802e-20</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>71.432156</td>\n",
       "      <td>3.379892e-17</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>52.356807</td>\n",
       "      <td>5.058361e-13</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>40.733985</td>\n",
       "      <td>1.841863e-10</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>39.128665</td>\n",
       "      <td>4.172586e-10</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>38.306779</td>\n",
       "      <td>6.344354e-10</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22.013738</td>\n",
       "      <td>2.752216e-06</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.602833</td>\n",
       "      <td>2.273482e-04</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.198857</td>\n",
       "      <td>4.807841e-04</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.118067</td>\n",
       "      <td>4.393870e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.660225</td>\n",
       "      <td>5.658186e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.878914</td>\n",
       "      <td>2.721508e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.972496</td>\n",
       "      <td>3.240885e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000720</td>\n",
       "      <td>9.785954e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  3527.304911   0.000000e+00   Inflowing drainage direction\n",
       "23  3350.481828   0.000000e+00                   Surface type\n",
       "21  3275.722008   0.000000e+00                      Elevation\n",
       "26  1560.675179  7.419696e-312                   Fertiliser K\n",
       "27  1560.675179  7.419696e-312                   Fertiliser N\n",
       "28  1560.675179  7.419696e-312                   Fertiliser P\n",
       "3   1300.349746  6.719167e-264              Improve grassland\n",
       "2   1127.764279  2.364062e-231                         Arable\n",
       "24   888.494256  3.471083e-185  Outflowing drainage direction\n",
       "29   847.052726  4.617711e-177             Chlorothalonil_5km\n",
       "30   847.052726  4.617711e-177                 Glyphosate_5km\n",
       "31   847.052726  4.617711e-177                   Mancozeb_5km\n",
       "32   847.052726  4.617711e-177                 Mecoprop-P_5km\n",
       "34   847.052726  4.617711e-177              Pendimethalin_5km\n",
       "36   676.520460  3.215919e-143               Prosulfocarb_5km\n",
       "37   675.198692  5.919142e-143                    Sulphur_5km\n",
       "38   659.465995  8.494090e-140                 Tri-allate_5km\n",
       "0    583.145685  2.124075e-124             Deciduous woodland\n",
       "33   550.331786  9.792385e-118                 Metamitron_5km\n",
       "35   548.586416  2.218553e-117   PropamocarbHydrochloride_5km\n",
       "20   461.333112   1.579205e-99                       Suburban\n",
       "19   215.255159   4.147517e-48                          Urban\n",
       "22   187.302538   3.691076e-42      Cumulative catchment area\n",
       "17   171.051423   1.090041e-38              Littoral sediment\n",
       "18    92.813673   7.557806e-22                      Saltmarsh\n",
       "4     86.129805   2.134802e-20              Neutral grassland\n",
       "7     71.432156   3.379892e-17                            Fen\n",
       "13    52.356807   5.058361e-13                     Freshwater\n",
       "16    40.733985   1.841863e-10                  Littoral rock\n",
       "15    39.128665   4.172586e-10         Supralittoral sediment\n",
       "12    38.306779   6.344354e-10                      Saltwater\n",
       "9     22.013738   2.752216e-06              Heather grassland\n",
       "14    13.602833   2.273482e-04             Supralittoral rock\n",
       "1     12.198857   4.807841e-04            Coniferous woodland\n",
       "10     8.118067   4.393870e-03                            Bog\n",
       "5      7.660225   5.658186e-03           Calcareous grassland\n",
       "8      4.878914   2.721508e-02                        Heather\n",
       "6      0.972496   3.240885e-01                 Acid grassland\n",
       "11     0.000720   9.785954e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dict in df_dicts:\n",
    "    print(dict['name'])\n",
    "    display(dict['kbest']['Dataframe'])"
   ]
  }
 ],
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