{
 "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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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "575000.0 265000.0                  35                    0       0   \n",
       "165000.0 675000.0                   0                    0       0   \n",
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       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "575000.0 265000.0                 48                  0                     0   \n",
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       "565000.0 585000.0                  0                  0                     0   \n",
       "205000.0 495000.0                 51                  0                     0   \n",
       "215000.0 615000.0                 43                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
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       "205000.0 495000.0               0    0        0                  0  ...   \n",
       "215000.0 615000.0               0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_10km  Mancozeb_10km  Mecoprop-P_10km  \\\n",
       "y        x                                                           \n",
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       "\n",
       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
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       "215000.0 615000.0    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
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       "215000.0 615000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                   Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y        x                                                    \n",
       "575000.0 265000.0 -3.400000e+38    -3.400000e+38           0  \n",
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       "215000.0 615000.0 -3.400000e+38    -3.400000e+38           1  \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "\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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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1265000.0 585000.0                   0                    0       0   \n",
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       "565000.0  265000.0                   1                   99       0   \n",
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       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1295000.0 605000.0                  0                  0   \n",
       "1265000.0 585000.0                  0                  0   \n",
       "375000.0  585000.0                  0                  0   \n",
       "565000.0  265000.0                  0                  0   \n",
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       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
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       "1265000.0 585000.0                     0               0    0        0   \n",
       "375000.0  585000.0                     0               0    0        0   \n",
       "565000.0  265000.0                     0               0    0        0   \n",
       "755000.0  615000.0                     0               0    0        0   \n",
       "\n",
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       "y         x                            ...                                   \n",
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       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
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       "755000.0  615000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
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       "1265000.0 585000.0                  -3.400000e+38      -3.400000e+38   \n",
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       "755000.0  615000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\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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       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125000.0</th>\n",
       "      <th>205000.0</th>\n",
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       "      <td>1.621378e+00</td>\n",
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       "      <td>3.642301e-01</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "    <tr>\n",
       "      <th>305000.0</th>\n",
       "      <th>565000.0</th>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1025000.0 55000.0                    0                    0       0   \n",
       "125000.0  205000.0                   0                    0       0   \n",
       "305000.0  565000.0                   3                    0      91   \n",
       "1235000.0 195000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "225000.0  115000.0                  0                  0   \n",
       "1025000.0 55000.0                   0                  0   \n",
       "125000.0  205000.0                  0                  0   \n",
       "305000.0  565000.0                  6                  0   \n",
       "1235000.0 195000.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
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       "1025000.0 55000.0                      0               0    0        0   \n",
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       "305000.0  565000.0                     0               0    0        0   \n",
       "1235000.0 195000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
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       "1025000.0 55000.0                   0  ...    -3.400000e+38  -3.400000e+38   \n",
       "125000.0  205000.0                  0  ...     1.609200e+00   9.198602e-02   \n",
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       "1235000.0 195000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "225000.0  115000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "1025000.0 55000.0     -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "125000.0  205000.0     6.563689e-01    -3.400000e+38        1.621378e+00   \n",
       "305000.0  565000.0     3.974655e-02     2.524318e+01        3.438350e-01   \n",
       "1235000.0 195000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
       "225000.0  115000.0                  -3.400000e+38       5.173159e-01   \n",
       "1025000.0 55000.0                   -3.400000e+38      -3.400000e+38   \n",
       "125000.0  205000.0                   7.263544e-01       2.354775e+00   \n",
       "305000.0  565000.0                   7.384946e+00       1.150152e+01   \n",
       "1235000.0 195000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
       "225000.0  115000.0  9.168798e-01     1.176593e-01           0  \n",
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       "125000.0  205000.0  3.642301e-01    -3.400000e+38           0  \n",
       "305000.0  565000.0  1.020674e+01     1.920763e+01           1  \n",
       "1235000.0 195000.0 -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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       "y         x                                                \n",
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       "655000.0  675000.0                  0                  0   \n",
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       "y         x                                                              \n",
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       "655000.0  675000.0                     0               0    0        0   \n",
       "1135000.0 75000.0                      0               0    0        0   \n",
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       "y         x                                                                \n",
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       "\n",
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       "      <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>535000.0</th>\n",
       "      <th>245000.0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</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>3.529527e-01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.662563e-01</td>\n",
       "      <td>6.416388e+00</td>\n",
       "      <td>1.433365e+00</td>\n",
       "      <td>4.209006e-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035000.0</th>\n",
       "      <th>505000.0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735000.0</th>\n",
       "      <th>505000.0</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>865000.0</th>\n",
       "      <th>185000.0</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>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</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>515000.0</th>\n",
       "      <th>595000.0</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",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "535000.0  245000.0                   5                    0       0   \n",
       "1035000.0 505000.0                   0                    0       0   \n",
       "735000.0  505000.0                   0                    0       0   \n",
       "865000.0  185000.0                   0                    0       0   \n",
       "515000.0  595000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "535000.0  245000.0                 65                  0   \n",
       "1035000.0 505000.0                  0                  0   \n",
       "735000.0  505000.0                  0                  0   \n",
       "865000.0  185000.0                  0                  0   \n",
       "515000.0  595000.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "535000.0  245000.0                     0               0    0        0   \n",
       "1035000.0 505000.0                     0               0    0        0   \n",
       "735000.0  505000.0                     0               0    0        0   \n",
       "865000.0  185000.0                     0               6    0        0   \n",
       "515000.0  595000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
       "535000.0  245000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "1035000.0 505000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "735000.0  505000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "865000.0  185000.0                 60  ...    -3.400000e+38  -3.400000e+38   \n",
       "515000.0  595000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "535000.0  245000.0    -3.400000e+38     3.529527e-01       -3.400000e+38   \n",
       "1035000.0 505000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "735000.0  505000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "865000.0  185000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "515000.0  595000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
       "535000.0  245000.0                   1.662563e-01       6.416388e+00   \n",
       "1035000.0 505000.0                  -3.400000e+38      -3.400000e+38   \n",
       "735000.0  505000.0                  -3.400000e+38      -3.400000e+38   \n",
       "865000.0  185000.0                  -3.400000e+38      -3.400000e+38   \n",
       "515000.0  595000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
       "535000.0  245000.0  1.433365e+00     4.209006e-01           0  \n",
       "1035000.0 505000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "735000.0  505000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "865000.0  185000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "515000.0  595000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "  <thead>\n",
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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>45000.0</th>\n",
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       "      <th>645000.0</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>255000.0</th>\n",
       "      <th>315000.0</th>\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",
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       "      <td>1.745215e+01</td>\n",
       "      <td>3.667922e+00</td>\n",
       "      <td>3.258508e+00</td>\n",
       "      <td>1.286882e+01</td>\n",
       "      <td>1.388559e+00</td>\n",
       "      <td>7.408013e+00</td>\n",
       "      <td>4.606191e+00</td>\n",
       "      <td>7.226281e+00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>895000.0</th>\n",
       "      <th>215000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>53</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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       "<p>5 rows × 40 columns</p>\n",
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      "text/plain": [
       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "45000.0  255000.0                   0                    0       0   \n",
       "645000.0 385000.0                   5                    0      95   \n",
       "255000.0 315000.0                   0                    0       0   \n",
       "895000.0 215000.0                   0                   53       0   \n",
       "685000.0 85000.0                    0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "45000.0  255000.0                  0                  0                     0   \n",
       "645000.0 385000.0                  0                  0                     0   \n",
       "255000.0 315000.0                  0                  0                     0   \n",
       "895000.0 215000.0                  1                  0                     0   \n",
       "685000.0 85000.0                   0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "45000.0  255000.0               0    0        0                  0  ...   \n",
       "645000.0 385000.0               0    0        0                  0  ...   \n",
       "255000.0 315000.0              50    0       50                  0  ...   \n",
       "895000.0 215000.0               0    0        0                 46  ...   \n",
       "685000.0 85000.0                0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_10km  Mancozeb_10km  Mecoprop-P_10km  \\\n",
       "y        x                                                           \n",
       "45000.0  255000.0    -3.400000e+38  -3.400000e+38    -3.400000e+38   \n",
       "645000.0 385000.0    -3.400000e+38  -3.400000e+38    -3.400000e+38   \n",
       "255000.0 315000.0     1.257356e+01   1.745215e+01     3.667922e+00   \n",
       "895000.0 215000.0     2.407615e-01   5.050598e-02     2.042672e-01   \n",
       "685000.0 85000.0     -3.400000e+38  -3.400000e+38    -3.400000e+38   \n",
       "\n",
       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
       "45000.0  255000.0    -3.400000e+38       -3.400000e+38   \n",
       "645000.0 385000.0    -3.400000e+38       -3.400000e+38   \n",
       "255000.0 315000.0     3.258508e+00        1.286882e+01   \n",
       "895000.0 215000.0    -3.400000e+38        1.190478e-01   \n",
       "685000.0 85000.0     -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
       "45000.0  255000.0                  -3.400000e+38      -3.400000e+38   \n",
       "645000.0 385000.0                  -3.400000e+38      -3.400000e+38   \n",
       "255000.0 315000.0                   1.388559e+00       7.408013e+00   \n",
       "895000.0 215000.0                  -3.400000e+38      -3.400000e+38   \n",
       "685000.0 85000.0                   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                   Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y        x                                                    \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
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       "y         x                                                            \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "295000.0  335000.0                   0                    1       0   \n",
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       "1255000.0 675000.0                   0                    0       0   \n",
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       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "125000.0  185000.0                  0                  0   \n",
       "295000.0  335000.0                 22                  0   \n",
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       "1255000.0 675000.0                  0                  0   \n",
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       "\n",
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       "y         x                                                              \n",
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       "1255000.0 675000.0                     0               0    0        0   \n",
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       "y         x                            ...                                   \n",
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       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "125000.0  185000.0     7.403410e-02     3.515963e-01        3.825245e-02   \n",
       "295000.0  335000.0     8.424385e-02     1.091831e+01        6.491575e-02   \n",
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       "1295000.0 525000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
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       "y         x                                                    \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\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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       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
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       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "y         x                                                                \n",
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       "      <th>335000.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</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>245000.0</th>\n",
       "      <th>375000.0</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>73</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>1.643640e+01</td>\n",
       "      <td>8.107372e+00</td>\n",
       "      <td>3.085294e+00</td>\n",
       "      <td>2.327609e+00</td>\n",
       "      <td>9.291373e+00</td>\n",
       "      <td>1.105644e+00</td>\n",
       "      <td>1.160969e+01</td>\n",
       "      <td>1.165004e+01</td>\n",
       "      <td>7.668460e+00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>665000.0</th>\n",
       "      <th>65000.0</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>445000.0</th>\n",
       "      <th>665000.0</th>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "      <th>1035000.0</th>\n",
       "      <th>395000.0</th>\n",
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       "      <td>...</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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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "995000.0  335000.0                   1                    0       0   \n",
       "245000.0  375000.0                   9                    0       9   \n",
       "665000.0  65000.0                    0                    0       0   \n",
       "445000.0  665000.0                   0                    0       0   \n",
       "1035000.0 395000.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "995000.0  335000.0                 44                  0   \n",
       "245000.0  375000.0                 73                  0   \n",
       "665000.0  65000.0                   0                  0   \n",
       "445000.0  665000.0                  0                  0   \n",
       "1035000.0 395000.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "995000.0  335000.0                     0               0    0       11   \n",
       "245000.0  375000.0                     0               0    0        0   \n",
       "665000.0  65000.0                      0               0    0        0   \n",
       "445000.0  665000.0                     0               0    0        0   \n",
       "1035000.0 395000.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
       "995000.0  335000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "245000.0  375000.0                  0  ...     1.643640e+01   8.107372e+00   \n",
       "665000.0  65000.0                   0  ...    -3.400000e+38  -3.400000e+38   \n",
       "445000.0  665000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "1035000.0 395000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "995000.0  335000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "245000.0  375000.0     3.085294e+00     2.327609e+00        9.291373e+00   \n",
       "665000.0  65000.0     -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "445000.0  665000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "1035000.0 395000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
       "995000.0  335000.0                  -3.400000e+38      -3.400000e+38   \n",
       "245000.0  375000.0                   1.105644e+00       1.160969e+01   \n",
       "665000.0  65000.0                   -3.400000e+38      -3.400000e+38   \n",
       "445000.0  665000.0                  -3.400000e+38      -3.400000e+38   \n",
       "1035000.0 395000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
       "995000.0  335000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "245000.0  375000.0  1.165004e+01     7.668460e+00           0  \n",
       "665000.0  65000.0  -3.400000e+38    -3.400000e+38           0  \n",
       "445000.0  665000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "1035000.0 395000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "      <th>385000.0</th>\n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "45000.0  335000.0                   0                    0       0   \n",
       "915000.0 455000.0                   0                    0       0   \n",
       "165000.0 445000.0                   0                    1       5   \n",
       "315000.0 385000.0                   3                    0      46   \n",
       "305000.0 35000.0                    0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "45000.0  335000.0                  0                  0                     0   \n",
       "915000.0 455000.0                  0                  0                     0   \n",
       "165000.0 445000.0                 16                  0                     0   \n",
       "315000.0 385000.0                 51                  0                     0   \n",
       "305000.0 35000.0                   0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "45000.0  335000.0               0    0        0                  0  ...   \n",
       "915000.0 455000.0               0    0        0                  0  ...   \n",
       "165000.0 445000.0               0    0        0                  0  ...   \n",
       "315000.0 385000.0               0    0        0                  0  ...   \n",
       "305000.0 35000.0                0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_10km  Mancozeb_10km  Mecoprop-P_10km  \\\n",
       "y        x                                                           \n",
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       "\n",
       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
       "45000.0  335000.0    -3.400000e+38       -3.400000e+38   \n",
       "915000.0 455000.0    -3.400000e+38       -3.400000e+38   \n",
       "165000.0 445000.0     3.506572e+00        6.399115e-02   \n",
       "315000.0 385000.0     2.546534e-01        1.847880e+01   \n",
       "305000.0 35000.0     -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
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       "\n",
       "                   Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y        x                                                    \n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Deciduous woodland</th>\n",
       "      <th>Coniferous woodland</th>\n",
       "      <th>Arable</th>\n",
       "      <th>Improve grassland</th>\n",
       "      <th>Neutral grassland</th>\n",
       "      <th>Calcareous grassland</th>\n",
       "      <th>Acid grassland</th>\n",
       "      <th>Fen</th>\n",
       "      <th>Heather</th>\n",
       "      <th>Heather grassland</th>\n",
       "      <th>...</th>\n",
       "      <th>Glyphosate_10km</th>\n",
       "      <th>Mancozeb_10km</th>\n",
       "      <th>Mecoprop-P_10km</th>\n",
       "      <th>Metamitron_10km</th>\n",
       "      <th>Pendimethalin_10km</th>\n",
       "      <th>PropamocarbHydrochloride_10km</th>\n",
       "      <th>Prosulfocarb_10km</th>\n",
       "      <th>Sulphur_10km</th>\n",
       "      <th>Tri-allate_10km</th>\n",
       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1125000.0</th>\n",
       "      <th>525000.0</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>285000.0</th>\n",
       "      <th>415000.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</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>2.699923e+01</td>\n",
       "      <td>7.159234e-01</td>\n",
       "      <td>2.832157e+00</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.705337e+01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.344955e+01</td>\n",
       "      <td>8.048236e+00</td>\n",
       "      <td>2.779240e+01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085000.0</th>\n",
       "      <th>305000.0</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>515000.0</th>\n",
       "      <th>255000.0</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>7.289742e-02</td>\n",
       "      <td>1.422214e-02</td>\n",
       "      <td>8.243214e-02</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>4.177649e-02</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>545000.0</th>\n",
       "      <th>65000.0</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",
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      ],
      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "1125000.0 525000.0                   0                    0       0   \n",
       "285000.0  415000.0                   0                    0       0   \n",
       "1085000.0 305000.0                   0                    0       0   \n",
       "515000.0  255000.0                   0                    0       0   \n",
       "545000.0  65000.0                    0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1125000.0 525000.0                  0                  0   \n",
       "285000.0  415000.0                  9                  0   \n",
       "1085000.0 305000.0                  0                  0   \n",
       "515000.0  255000.0                  0                  0   \n",
       "545000.0  65000.0                   0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "1125000.0 525000.0                     0               0    0        0   \n",
       "285000.0  415000.0                     0               0    0        0   \n",
       "1085000.0 305000.0                     0               0    0        0   \n",
       "515000.0  255000.0                     0               0    0        0   \n",
       "545000.0  65000.0                      0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_10km  Mancozeb_10km  \\\n",
       "y         x                            ...                                   \n",
       "1125000.0 525000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "285000.0  415000.0                  0  ...     2.699923e+01   7.159234e-01   \n",
       "1085000.0 305000.0                  0  ...    -3.400000e+38  -3.400000e+38   \n",
       "515000.0  255000.0                  0  ...     7.289742e-02   1.422214e-02   \n",
       "545000.0  65000.0                   0  ...    -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_10km  Metamitron_10km  Pendimethalin_10km  \\\n",
       "y         x                                                                \n",
       "1125000.0 525000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "285000.0  415000.0     2.832157e+00    -3.400000e+38        1.705337e+01   \n",
       "1085000.0 305000.0    -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "515000.0  255000.0     8.243214e-02    -3.400000e+38        4.177649e-02   \n",
       "545000.0  65000.0     -3.400000e+38    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y         x                                                            \n",
       "1125000.0 525000.0                  -3.400000e+38      -3.400000e+38   \n",
       "285000.0  415000.0                  -3.400000e+38       1.344955e+01   \n",
       "1085000.0 305000.0                  -3.400000e+38      -3.400000e+38   \n",
       "515000.0  255000.0                  -3.400000e+38      -3.400000e+38   \n",
       "545000.0  65000.0                   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y         x                                                    \n",
       "1125000.0 525000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "285000.0  415000.0  8.048236e+00     2.779240e+01           0  \n",
       "1085000.0 305000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "515000.0  255000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "545000.0  65000.0  -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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      "text/plain": [
       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "905000.0 235000.0                   0                    0       0   \n",
       "405000.0 665000.0                   0                    0       0   \n",
       "425000.0 605000.0                   0                    0       0   \n",
       "625000.0 145000.0                   0                    0       0   \n",
       "765000.0 575000.0                   0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "905000.0 235000.0                  0                  0                     0   \n",
       "405000.0 665000.0                  0                  0                     0   \n",
       "425000.0 605000.0                  0                  0                     0   \n",
       "625000.0 145000.0                  0                  0                     0   \n",
       "765000.0 575000.0                  0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "905000.0 235000.0               0    0        0                  1  ...   \n",
       "405000.0 665000.0               0    0        0                  0  ...   \n",
       "425000.0 605000.0               0    0        0                  0  ...   \n",
       "625000.0 145000.0               0    0        0                  0  ...   \n",
       "765000.0 575000.0               0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_10km  Mancozeb_10km  Mecoprop-P_10km  \\\n",
       "y        x                                                           \n",
       "905000.0 235000.0     2.596413e-01   4.824913e-02     2.193868e-01   \n",
       "405000.0 665000.0    -3.400000e+38  -3.400000e+38    -3.400000e+38   \n",
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       "765000.0 575000.0    -3.400000e+38  -3.400000e+38    -3.400000e+38   \n",
       "\n",
       "                   Metamitron_10km  Pendimethalin_10km  \\\n",
       "y        x                                               \n",
       "905000.0 235000.0    -3.400000e+38        1.357381e-01   \n",
       "405000.0 665000.0    -3.400000e+38       -3.400000e+38   \n",
       "425000.0 605000.0    -3.400000e+38       -3.400000e+38   \n",
       "625000.0 145000.0    -3.400000e+38        2.156298e-04   \n",
       "765000.0 575000.0    -3.400000e+38       -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_10km  Prosulfocarb_10km  \\\n",
       "y        x                                                            \n",
       "905000.0 235000.0                  -3.400000e+38      -3.400000e+38   \n",
       "405000.0 665000.0                  -3.400000e+38      -3.400000e+38   \n",
       "425000.0 605000.0                  -3.400000e+38      -3.400000e+38   \n",
       "625000.0 145000.0                  -3.400000e+38      -3.400000e+38   \n",
       "765000.0 575000.0                  -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                   Sulphur_10km  Tri-allate_10km  Occurrence  \n",
       "y        x                                                    \n",
       "905000.0 235000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "405000.0 665000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "425000.0 605000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "625000.0 145000.0 -3.400000e+38    -3.400000e+38           0  \n",
       "765000.0 575000.0 -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/10km Rasters/Birds'\n",
    "# Use this if using coordinates as separate columns\n",
    "# df_10km = pd.read_csv('Datasets/Machine Learning/Dataframes/10km_All_Birds_DF.csv')\n",
    "\n",
    "# Use this if using coordinates as indices\n",
    "df_10km = pd.read_csv('Datasets/Machine Learning/Dataframes/10km_All_Birds_DF.csv', index_col=[0,1])\n",
    "\n",
    "total_birds = (df_10km['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_10km.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_10km.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_10km.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 10km data before drop: \n",
      " Occurrence\n",
      "0    8634\n",
      "1     466\n",
      "dtype: int64 \n",
      "\n",
      "Barnacle Goose 10km data after drop: \n",
      " Occurrence\n",
      "0    2206\n",
      "1     466\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 10km data before drop: \n",
      " Occurrence\n",
      "0    7311\n",
      "1    1789\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 10km data after drop: \n",
      " Occurrence\n",
      "1    1789\n",
      "0     883\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 10km data before drop: \n",
      " Occurrence\n",
      "0    8800\n",
      "1     300\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 10km data after drop: \n",
      " Occurrence\n",
      "0    2372\n",
      "1     300\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 10km data before drop: \n",
      " Occurrence\n",
      "0    8270\n",
      "1     830\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 10km data after drop: \n",
      " Occurrence\n",
      "0    1842\n",
      "1     830\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 10km data before drop: \n",
      " Occurrence\n",
      "0    8654\n",
      "1     446\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 10km data after drop: \n",
      " Occurrence\n",
      "0    2226\n",
      "1     446\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 10km data before drop: \n",
      " Occurrence\n",
      "0    8098\n",
      "1    1002\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 10km data after drop: \n",
      " Occurrence\n",
      "0    1670\n",
      "1    1002\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 10km data before drop: \n",
      " Occurrence\n",
      "0    8853\n",
      "1     247\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 10km data after drop: \n",
      " Occurrence\n",
      "0    2425\n",
      "1     247\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 10km data before drop: \n",
      " Occurrence\n",
      "0    8023\n",
      "1    1077\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 10km data after drop: \n",
      " Occurrence\n",
      "0    1595\n",
      "1    1077\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 10km data before drop: \n",
      " Occurrence\n",
      "0    8622\n",
      "1     478\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 10km data after drop: \n",
      " Occurrence\n",
      "0    2194\n",
      "1     478\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 10km data before drop: \n",
      " Occurrence\n",
      "0    7093\n",
      "1    2007\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 10km data after drop: \n",
      " Occurrence\n",
      "1    2007\n",
      "0     665\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 10km data before drop: \n",
      " Occurrence\n",
      "0    7182\n",
      "1    1918\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 10km data after drop: \n",
      " Occurrence\n",
      "1    1918\n",
      "0     754\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 10km data before drop: \n",
      " Occurrence\n",
      "0    8370\n",
      "1     730\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 10km data after drop: \n",
      " Occurrence\n",
      "0    1942\n",
      "1     730\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 10km data before drop: \n",
      " Occurrence\n",
      "0    8587\n",
      "1     513\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 10km data after drop: \n",
      " Occurrence\n",
      "0    2159\n",
      "1     513\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 10km data before drop: \n",
      " Occurrence\n",
      "0    8362\n",
      "1     738\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 10km data after drop: \n",
      " Occurrence\n",
      "0    1934\n",
      "1     738\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 10km data before drop: \n",
      " Occurrence\n",
      "0    7892\n",
      "1    1208\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 10km data after drop: \n",
      " Occurrence\n",
      "0    1464\n",
      "1    1208\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 10km data before drop: \n",
      " Occurrence\n",
      "0    9004\n",
      "1      96\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 10km data after drop: \n",
      " Occurrence\n",
      "0    2576\n",
      "1      96\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 10km data before drop: \n",
      " Occurrence\n",
      "0    7480\n",
      "1    1620\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 10km data after drop: \n",
      " Occurrence\n",
      "1    1620\n",
      "0    1052\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 10km data before drop: \n",
      " Occurrence\n",
      "0    9003\n",
      "1      97\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 10km data after drop: \n",
      " Occurrence\n",
      "0    2575\n",
      "1      97\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 10km data before drop: \n",
      " Occurrence\n",
      "0    8441\n",
      "1     659\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 10km data after drop: \n",
      " Occurrence\n",
      "0    2013\n",
      "1     659\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 10km data before drop: \n",
      " Occurrence\n",
      "0    7833\n",
      "1    1267\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 10km data after drop: \n",
      " Occurrence\n",
      "0    1405\n",
      "1    1267\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 10km</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.751123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 10km</td>\n",
       "      <td>1918</td>\n",
       "      <td>0.717814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 10km</td>\n",
       "      <td>1789</td>\n",
       "      <td>0.669536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 10km</td>\n",
       "      <td>1620</td>\n",
       "      <td>0.606287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 10km</td>\n",
       "      <td>1267</td>\n",
       "      <td>0.474177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 10km</td>\n",
       "      <td>1208</td>\n",
       "      <td>0.452096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 10km</td>\n",
       "      <td>1077</td>\n",
       "      <td>0.403069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 10km</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 10km</td>\n",
       "      <td>830</td>\n",
       "      <td>0.310629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 10km</td>\n",
       "      <td>738</td>\n",
       "      <td>0.276198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 10km</td>\n",
       "      <td>730</td>\n",
       "      <td>0.273204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 10km</td>\n",
       "      <td>659</td>\n",
       "      <td>0.246632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 10km</td>\n",
       "      <td>513</td>\n",
       "      <td>0.191991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 10km</td>\n",
       "      <td>478</td>\n",
       "      <td>0.178892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 10km</td>\n",
       "      <td>466</td>\n",
       "      <td>0.174401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 10km</td>\n",
       "      <td>446</td>\n",
       "      <td>0.166916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 10km</td>\n",
       "      <td>300</td>\n",
       "      <td>0.112275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 10km</td>\n",
       "      <td>247</td>\n",
       "      <td>0.092440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 10km</td>\n",
       "      <td>97</td>\n",
       "      <td>0.036302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 10km</td>\n",
       "      <td>96</td>\n",
       "      <td>0.035928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name  Occurrence Count  Percentage\n",
       "9              Mute Swan 10km              2007    0.751123\n",
       "10              Pheasant 10km              1918    0.717814\n",
       "1           Canada Goose 10km              1789    0.669536\n",
       "16             Rock Dove 10km              1620    0.606287\n",
       "19                Wigeon 10km              1267    0.474177\n",
       "14  Red-legged Partridge 10km              1208    0.452096\n",
       "7             Little Owl 10km              1077    0.403069\n",
       "5         Grey Partridge 10km              1002    0.375000\n",
       "3                Gadwall 10km               830    0.310629\n",
       "13               Pochard 10km               738    0.276198\n",
       "11     Pink-footed Goose 10km               730    0.273204\n",
       "18          Whooper Swan 10km               659    0.246632\n",
       "12               Pintail 10km               513    0.191991\n",
       "8          Mandarin Duck 10km               478    0.178892\n",
       "0         Barnacle Goose 10km               466    0.174401\n",
       "4                Goshawk 10km               446    0.166916\n",
       "2         Egyptian Goose 10km               300    0.112275\n",
       "6         Indian Peafowl 10km               247    0.092440\n",
       "17            Ruddy Duck 10km                97    0.036302\n",
       "15  Ring-necked Parakeet 10km                96    0.035928"
      ]
     },
     "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 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8983364140480592,\n",
      "        \"recall\": 0.8741007194244604,\n",
      "        \"f1-score\": 0.8860528714676389,\n",
      "        \"support\": 556\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.44881889763779526,\n",
      "        \"recall\": 0.5089285714285714,\n",
      "        \"f1-score\": 0.47698744769874474,\n",
      "        \"support\": 112\n",
      "    },\n",
      "    \"accuracy\": 0.812874251497006,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6735776558429272,\n",
      "        \"recall\": 0.691514645426516,\n",
      "        \"f1-score\": 0.6815201595831918,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8229682077038233,\n",
      "        \"recall\": 0.812874251497006,\n",
      "        \"f1-score\": 0.8174670519135727,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Barnacle Goose 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9015151515151515,\n",
      "        \"recall\": 0.8561151079136691,\n",
      "        \"f1-score\": 0.8782287822878229,\n",
      "        \"support\": 556\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.42857142857142855,\n",
      "        \"recall\": 0.5357142857142857,\n",
      "        \"f1-score\": 0.47619047619047616,\n",
      "        \"support\": 112\n",
      "    },\n",
      "    \"accuracy\": 0.8023952095808383,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.66504329004329,\n",
      "        \"recall\": 0.6959146968139773,\n",
      "        \"f1-score\": 0.6772096292391495,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8222191979677009,\n",
      "        \"recall\": 0.8023952095808383,\n",
      "        \"f1-score\": 0.8108211621038367,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Canada Goose 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9219512195121952,\n",
      "        \"recall\": 0.8957345971563981,\n",
      "        \"f1-score\": 0.9086538461538463,\n",
      "        \"support\": 211\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9524838012958964,\n",
      "        \"recall\": 0.9649890590809628,\n",
      "        \"f1-score\": 0.9586956521739131,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.9431137724550899,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9372175104040458,\n",
      "        \"recall\": 0.9303618281186805,\n",
      "        \"f1-score\": 0.9336747491638797,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9428395277085297,\n",
      "        \"recall\": 0.9431137724550899,\n",
      "        \"f1-score\": 0.9428890338052992,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Canada Goose 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9056603773584906,\n",
      "        \"recall\": 0.909952606635071,\n",
      "        \"f1-score\": 0.9078014184397164,\n",
      "        \"support\": 211\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9583333333333334,\n",
      "        \"recall\": 0.9562363238512035,\n",
      "        \"f1-score\": 0.9572836801752466,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.9416167664670658,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9319968553459119,\n",
      "        \"recall\": 0.9330944652431372,\n",
      "        \"f1-score\": 0.9325425493074815,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9416956181975672,\n",
      "        \"recall\": 0.9416167664670658,\n",
      "        \"f1-score\": 0.9416538040881255,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Egyptian Goose 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9588336192109777,\n",
      "        \"recall\": 0.9442567567567568,\n",
      "        \"f1-score\": 0.9514893617021277,\n",
      "        \"support\": 592\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.611764705882353,\n",
      "        \"recall\": 0.6842105263157895,\n",
      "        \"f1-score\": 0.6459627329192548,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.9146706586826348,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7852991625466653,\n",
      "        \"recall\": 0.8142336415362732,\n",
      "        \"f1-score\": 0.7987260473106912,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9193467368562239,\n",
      "        \"recall\": 0.9146706586826348,\n",
      "        \"f1-score\": 0.9167288470501842,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Egyptian Goose 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9518900343642611,\n",
      "        \"recall\": 0.9358108108108109,\n",
      "        \"f1-score\": 0.9437819420783646,\n",
      "        \"support\": 592\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5581395348837209,\n",
      "        \"recall\": 0.631578947368421,\n",
      "        \"f1-score\": 0.5925925925925927,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.9011976047904192,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.755014784623991,\n",
      "        \"recall\": 0.7836948790896159,\n",
      "        \"f1-score\": 0.7681872673354786,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9070920733455171,\n",
      "        \"recall\": 0.9011976047904192,\n",
      "        \"f1-score\": 0.9038262675859714,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Gadwall 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9010989010989011,\n",
      "        \"recall\": 0.8951965065502183,\n",
      "        \"f1-score\": 0.8981380065717415,\n",
      "        \"support\": 458\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7746478873239436,\n",
      "        \"recall\": 0.7857142857142857,\n",
      "        \"f1-score\": 0.7801418439716311,\n",
      "        \"support\": 210\n",
      "    },\n",
      "    \"accuracy\": 0.8607784431137725,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8378733942114224,\n",
      "        \"recall\": 0.8404553961322521,\n",
      "        \"f1-score\": 0.8391399252716862,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8613463368882108,\n",
      "        \"recall\": 0.8607784431137725,\n",
      "        \"f1-score\": 0.8610434045567368,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Gadwall 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9191685912240185,\n",
      "        \"recall\": 0.868995633187773,\n",
      "        \"f1-score\": 0.89337822671156,\n",
      "        \"support\": 458\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7446808510638298,\n",
      "        \"recall\": 0.8333333333333334,\n",
      "        \"f1-score\": 0.7865168539325842,\n",
      "        \"support\": 210\n",
      "    },\n",
      "    \"accuracy\": 0.8577844311377245,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8319247211439241,\n",
      "        \"recall\": 0.8511644832605532,\n",
      "        \"f1-score\": 0.8399475403220721,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8643146609341388,\n",
      "        \"recall\": 0.8577844311377245,\n",
      "        \"f1-score\": 0.8597840825744567,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Goshawk 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9375,\n",
      "        \"recall\": 0.9026548672566371,\n",
      "        \"f1-score\": 0.9197475202885482,\n",
      "        \"support\": 565\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5564516129032258,\n",
      "        \"recall\": 0.6699029126213593,\n",
      "        \"f1-score\": 0.6079295154185023,\n",
      "        \"support\": 103\n",
      "    },\n",
      "    \"accuracy\": 0.8667664670658682,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7469758064516129,\n",
      "        \"recall\": 0.7862788899389982,\n",
      "        \"f1-score\": 0.7638385178535252,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8787455331272938,\n",
      "        \"recall\": 0.8667664670658682,\n",
      "        \"f1-score\": 0.8716677979807418,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Goshawk 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9243243243243243,\n",
      "        \"recall\": 0.9079646017699115,\n",
      "        \"f1-score\": 0.9160714285714285,\n",
      "        \"support\": 565\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5398230088495575,\n",
      "        \"recall\": 0.5922330097087378,\n",
      "        \"f1-score\": 0.5648148148148148,\n",
      "        \"support\": 103\n",
      "    },\n",
      "    \"accuracy\": 0.8592814371257484,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.732073666586941,\n",
      "        \"recall\": 0.7500988057393246,\n",
      "        \"f1-score\": 0.7404431216931217,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8650374448424366,\n",
      "        \"recall\": 0.8592814371257484,\n",
      "        \"f1-score\": 0.8619106033963817,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Grey Partridge 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.945,\n",
      "        \"recall\": 0.8936170212765957,\n",
      "        \"f1-score\": 0.9185905224787363,\n",
      "        \"support\": 423\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.832089552238806,\n",
      "        \"recall\": 0.9102040816326531,\n",
      "        \"f1-score\": 0.8693957115009747,\n",
      "        \"support\": 245\n",
      "    },\n",
      "    \"accuracy\": 0.8997005988023952,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.888544776119403,\n",
      "        \"recall\": 0.9019105514546244,\n",
      "        \"f1-score\": 0.8939931169898555,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9035882339798016,\n",
      "        \"recall\": 0.8997005988023952,\n",
      "        \"f1-score\": 0.9005475154584496,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Grey Partridge 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.949874686716792,\n",
      "        \"recall\": 0.8959810874704491,\n",
      "        \"f1-score\": 0.9221411192214111,\n",
      "        \"support\": 423\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8364312267657993,\n",
      "        \"recall\": 0.9183673469387755,\n",
      "        \"f1-score\": 0.8754863813229572,\n",
      "        \"support\": 245\n",
      "    },\n",
      "    \"accuracy\": 0.9041916167664671,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8931529567412957,\n",
      "        \"recall\": 0.9071742172046123,\n",
      "        \"f1-score\": 0.8988137502721841,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9082674296988381,\n",
      "        \"recall\": 0.9041916167664671,\n",
      "        \"f1-score\": 0.9050297258305111,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Indian Peafowl 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9234449760765551,\n",
      "        \"recall\": 0.9682274247491639,\n",
      "        \"f1-score\": 0.9453061224489796,\n",
      "        \"support\": 598\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5365853658536586,\n",
      "        \"recall\": 0.3142857142857143,\n",
      "        \"f1-score\": 0.39639639639639646,\n",
      "        \"support\": 70\n",
      "    },\n",
      "    \"accuracy\": 0.8997005988023952,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7300151709651068,\n",
      "        \"recall\": 0.641256569517439,\n",
      "        \"f1-score\": 0.6708512594226881,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8829057953645749,\n",
      "        \"recall\": 0.8997005988023952,\n",
      "        \"f1-score\": 0.8877856421740082,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Indian Peafowl 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9244372990353698,\n",
      "        \"recall\": 0.9615384615384616,\n",
      "        \"f1-score\": 0.9426229508196722,\n",
      "        \"support\": 598\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5,\n",
      "        \"recall\": 0.32857142857142857,\n",
      "        \"f1-score\": 0.39655172413793105,\n",
      "        \"support\": 70\n",
      "    },\n",
      "    \"accuracy\": 0.8952095808383234,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7122186495176849,\n",
      "        \"recall\": 0.6450549450549451,\n",
      "        \"f1-score\": 0.6695873374788016,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8799603365616034,\n",
      "        \"recall\": 0.8952095808383234,\n",
      "        \"f1-score\": 0.8853999180835616,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Little Owl 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.946949602122016,\n",
      "        \"recall\": 0.9037974683544304,\n",
      "        \"f1-score\": 0.9248704663212435,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8694158075601375,\n",
      "        \"recall\": 0.9267399267399268,\n",
      "        \"f1-score\": 0.8971631205673759,\n",
      "        \"support\": 273\n",
      "    },\n",
      "    \"accuracy\": 0.9131736526946108,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9081827048410767,\n",
      "        \"recall\": 0.9152686975471787,\n",
      "        \"f1-score\": 0.9110167934443096,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9152628866798111,\n",
      "        \"recall\": 0.9131736526946108,\n",
      "        \"f1-score\": 0.913546955257163,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Little Owl 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9554973821989529,\n",
      "        \"recall\": 0.9240506329113924,\n",
      "        \"f1-score\": 0.9395109395109396,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8951048951048951,\n",
      "        \"recall\": 0.9377289377289377,\n",
      "        \"f1-score\": 0.9159212880143113,\n",
      "        \"support\": 273\n",
      "    },\n",
      "    \"accuracy\": 0.9296407185628742,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.925301138651924,\n",
      "        \"recall\": 0.9308897853201651,\n",
      "        \"f1-score\": 0.9277161137626254,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9308160214554233,\n",
      "        \"recall\": 0.9296407185628742,\n",
      "        \"f1-score\": 0.9298702585849223,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mandarin Duck 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.949438202247191,\n",
      "        \"recall\": 0.9354243542435424,\n",
      "        \"f1-score\": 0.9423791821561337,\n",
      "        \"support\": 542\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7388059701492538,\n",
      "        \"recall\": 0.7857142857142857,\n",
      "        \"f1-score\": 0.7615384615384615,\n",
      "        \"support\": 126\n",
      "    },\n",
      "    \"accuracy\": 0.907185628742515,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8441220861982224,\n",
      "        \"recall\": 0.8605693199789141,\n",
      "        \"f1-score\": 0.8519588218472975,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9097081704442866,\n",
      "        \"recall\": 0.907185628742515,\n",
      "        \"f1-score\": 0.9082685073090877,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Mandarin Duck 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9287020109689214,\n",
      "        \"recall\": 0.9372693726937269,\n",
      "        \"f1-score\": 0.9329660238751146,\n",
      "        \"support\": 542\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.71900826446281,\n",
      "        \"recall\": 0.6904761904761905,\n",
      "        \"f1-score\": 0.7044534412955465,\n",
      "        \"support\": 126\n",
      "    },\n",
      "    \"accuracy\": 0.8907185628742516,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8238551377158656,\n",
      "        \"recall\": 0.8138727815849587,\n",
      "        \"f1-score\": 0.8187097325853305,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8891489989033974,\n",
      "        \"recall\": 0.8907185628742516,\n",
      "        \"f1-score\": 0.8898633511131003,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mute Swan 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8372093023255814,\n",
      "        \"recall\": 0.8571428571428571,\n",
      "        \"f1-score\": 0.8470588235294119,\n",
      "        \"support\": 168\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9516129032258065,\n",
      "        \"recall\": 0.944,\n",
      "        \"f1-score\": 0.9477911646586344,\n",
      "        \"support\": 500\n",
      "    },\n",
      "    \"accuracy\": 0.9221556886227545,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.894411102775694,\n",
      "        \"recall\": 0.9005714285714286,\n",
      "        \"f1-score\": 0.8974249940940231,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.922840740125151,\n",
      "        \"recall\": 0.9221556886227545,\n",
      "        \"f1-score\": 0.9224572824584706,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Mute Swan 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.7912087912087912,\n",
      "        \"recall\": 0.8571428571428571,\n",
      "        \"f1-score\": 0.8228571428571427,\n",
      "        \"support\": 168\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9506172839506173,\n",
      "        \"recall\": 0.924,\n",
      "        \"f1-score\": 0.9371196754563895,\n",
      "        \"support\": 500\n",
      "    },\n",
      "    \"accuracy\": 0.907185628742515,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8709130375797043,\n",
      "        \"recall\": 0.8905714285714286,\n",
      "        \"f1-score\": 0.8799884091567661,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9105265252969844,\n",
      "        \"recall\": 0.907185628742515,\n",
      "        \"f1-score\": 0.9083829906110699,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pheasant 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.826530612244898,\n",
      "        \"recall\": 0.8663101604278075,\n",
      "        \"f1-score\": 0.8459530026109661,\n",
      "        \"support\": 187\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9470338983050848,\n",
      "        \"recall\": 0.9293139293139293,\n",
      "        \"f1-score\": 0.938090241343127,\n",
      "        \"support\": 481\n",
      "    },\n",
      "    \"accuracy\": 0.9116766467065869,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8867822552749913,\n",
      "        \"recall\": 0.8978120448708684,\n",
      "        \"f1-score\": 0.8920216219770465,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9133001939738649,\n",
      "        \"recall\": 0.9116766467065869,\n",
      "        \"f1-score\": 0.912297331698046,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Pheasant 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.783410138248848,\n",
      "        \"recall\": 0.9090909090909091,\n",
      "        \"f1-score\": 0.8415841584158417,\n",
      "        \"support\": 187\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9623059866962306,\n",
      "        \"recall\": 0.9022869022869023,\n",
      "        \"f1-score\": 0.9313304721030042,\n",
      "        \"support\": 481\n",
      "    },\n",
      "    \"accuracy\": 0.9041916167664671,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8728580624725393,\n",
      "        \"recall\": 0.9056889056889057,\n",
      "        \"f1-score\": 0.886457315259423,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9122258614572177,\n",
      "        \"recall\": 0.9041916167664671,\n",
      "        \"f1-score\": 0.9062068783013584,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pink-footed Goose 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8768267223382046,\n",
      "        \"recall\": 0.8786610878661087,\n",
      "        \"f1-score\": 0.8777429467084639,\n",
      "        \"support\": 478\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6931216931216931,\n",
      "        \"recall\": 0.6894736842105263,\n",
      "        \"f1-score\": 0.6912928759894459,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.8248502994011976,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7849742077299489,\n",
      "        \"recall\": 0.7840673860383176,\n",
      "        \"f1-score\": 0.7845179113489549,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8245752918724304,\n",
      "        \"recall\": 0.8248502994011976,\n",
      "        \"f1-score\": 0.8247107409650306,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Pink-footed Goose 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8805970149253731,\n",
      "        \"recall\": 0.8640167364016736,\n",
      "        \"f1-score\": 0.8722280887011616,\n",
      "        \"support\": 478\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6733668341708543,\n",
      "        \"recall\": 0.7052631578947368,\n",
      "        \"f1-score\": 0.6889460154241646,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.8188622754491018,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7769819245481138,\n",
      "        \"recall\": 0.7846399471482053,\n",
      "        \"f1-score\": 0.7805870520626631,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8216542988425011,\n",
      "        \"recall\": 0.8188622754491018,\n",
      "        \"f1-score\": 0.820096960074471,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pintail 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8618881118881119,\n",
      "        \"recall\": 0.9354838709677419,\n",
      "        \"f1-score\": 0.8971792538671519,\n",
      "        \"support\": 527\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6458333333333334,\n",
      "        \"recall\": 0.4397163120567376,\n",
      "        \"f1-score\": 0.5232067510548524,\n",
      "        \"support\": 141\n",
      "    },\n",
      "    \"accuracy\": 0.8308383233532934,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7538607226107226,\n",
      "        \"recall\": 0.6876000915122398,\n",
      "        \"f1-score\": 0.7101930024610021,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8162837349775971,\n",
      "        \"recall\": 0.8308383233532934,\n",
      "        \"f1-score\": 0.8182419441418013,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Pintail 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9124513618677043,\n",
      "        \"recall\": 0.889943074003795,\n",
      "        \"f1-score\": 0.9010566762728146,\n",
      "        \"support\": 527\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6233766233766234,\n",
      "        \"recall\": 0.6808510638297872,\n",
      "        \"f1-score\": 0.6508474576271187,\n",
      "        \"support\": 141\n",
      "    },\n",
      "    \"accuracy\": 0.8458083832335329,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7679139926221639,\n",
      "        \"recall\": 0.7853970689167911,\n",
      "        \"f1-score\": 0.7759520669499667,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8514340892221318,\n",
      "        \"recall\": 0.8458083832335329,\n",
      "        \"f1-score\": 0.848243053774247,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pochard 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9142259414225942,\n",
      "        \"recall\": 0.9066390041493776,\n",
      "        \"f1-score\": 0.9104166666666668,\n",
      "        \"support\": 482\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7631578947368421,\n",
      "        \"recall\": 0.7795698924731183,\n",
      "        \"f1-score\": 0.7712765957446808,\n",
      "        \"support\": 186\n",
      "    },\n",
      "    \"accuracy\": 0.8712574850299402,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8386919180797181,\n",
      "        \"recall\": 0.843104448311248,\n",
      "        \"f1-score\": 0.8408466312056737,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8721620841118908,\n",
      "        \"recall\": 0.8712574850299402,\n",
      "        \"f1-score\": 0.8716740720686287,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Pochard 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9287305122494433,\n",
      "        \"recall\": 0.8651452282157677,\n",
      "        \"f1-score\": 0.895810955961332,\n",
      "        \"support\": 482\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7031963470319634,\n",
      "        \"recall\": 0.8279569892473119,\n",
      "        \"f1-score\": 0.7604938271604939,\n",
      "        \"support\": 186\n",
      "    },\n",
      "    \"accuracy\": 0.8547904191616766,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8159634296407033,\n",
      "        \"recall\": 0.8465511087315398,\n",
      "        \"f1-score\": 0.8281523915609129,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8659320770242169,\n",
      "        \"recall\": 0.8547904191616766,\n",
      "        \"f1-score\": 0.8581328332712783,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Red-legged Partridge 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9152046783625731,\n",
      "        \"recall\": 0.884180790960452,\n",
      "        \"f1-score\": 0.8994252873563219,\n",
      "        \"support\": 354\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8742331288343558,\n",
      "        \"recall\": 0.9076433121019108,\n",
      "        \"f1-score\": 0.890625,\n",
      "        \"support\": 314\n",
      "    },\n",
      "    \"accuracy\": 0.8952095808383234,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8947189035984644,\n",
      "        \"recall\": 0.8959120515311814,\n",
      "        \"f1-score\": 0.8950251436781609,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8959455966981116,\n",
      "        \"recall\": 0.8952095808383234,\n",
      "        \"f1-score\": 0.8952886253355358,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Red-legged Partridge 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9201183431952663,\n",
      "        \"recall\": 0.8785310734463276,\n",
      "        \"f1-score\": 0.8988439306358381,\n",
      "        \"support\": 354\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8696969696969697,\n",
      "        \"recall\": 0.9140127388535032,\n",
      "        \"f1-score\": 0.8913043478260869,\n",
      "        \"support\": 314\n",
      "    },\n",
      "    \"accuracy\": 0.8952095808383234,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.894907656446118,\n",
      "        \"recall\": 0.8962719061499154,\n",
      "        \"f1-score\": 0.8950741392309625,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8964172784071448,\n",
      "        \"recall\": 0.8952095808383234,\n",
      "        \"f1-score\": 0.8952998752432306,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ring-necked Parakeet 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9654135338345865,\n",
      "        \"recall\": 0.9968944099378882,\n",
      "        \"f1-score\": 0.9809014514896868,\n",
      "        \"support\": 644\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3333333333333333,\n",
      "        \"recall\": 0.041666666666666664,\n",
      "        \"f1-score\": 0.07407407407407407,\n",
      "        \"support\": 24\n",
      "    },\n",
      "    \"accuracy\": 0.9625748502994012,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6493734335839599,\n",
      "        \"recall\": 0.5192805383022775,\n",
      "        \"f1-score\": 0.5274877627818804,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9427040655531044,\n",
      "        \"recall\": 0.9625748502994012,\n",
      "        \"f1-score\": 0.9483208271514014,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Ring-necked Parakeet 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9667170953101362,\n",
      "        \"recall\": 0.9922360248447205,\n",
      "        \"f1-score\": 0.9793103448275863,\n",
      "        \"support\": 644\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2857142857142857,\n",
      "        \"recall\": 0.08333333333333333,\n",
      "        \"f1-score\": 0.12903225806451613,\n",
      "        \"support\": 24\n",
      "    },\n",
      "    \"accuracy\": 0.9595808383233533,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6262156905122109,\n",
      "        \"recall\": 0.5377846790890269,\n",
      "        \"f1-score\": 0.5541713014460512,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9422499284983094,\n",
      "        \"recall\": 0.9595808383233533,\n",
      "        \"f1-score\": 0.9487614315307096,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Rock Dove 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8938775510204081,\n",
      "        \"recall\": 0.8358778625954199,\n",
      "        \"f1-score\": 0.863905325443787,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8983451536643026,\n",
      "        \"recall\": 0.9359605911330049,\n",
      "        \"f1-score\": 0.916767189384801,\n",
      "        \"support\": 406\n",
      "    },\n",
      "    \"accuracy\": 0.8967065868263473,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8961113523423554,\n",
      "        \"recall\": 0.8859192268642124,\n",
      "        \"f1-score\": 0.8903362574142939,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.896592890351877,\n",
      "        \"recall\": 0.8967065868263473,\n",
      "        \"f1-score\": 0.896033943348056,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Rock Dove 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8308823529411765,\n",
      "        \"recall\": 0.8625954198473282,\n",
      "        \"f1-score\": 0.8464419475655431,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9090909090909091,\n",
      "        \"recall\": 0.8866995073891626,\n",
      "        \"f1-score\": 0.8977556109725687,\n",
      "        \"support\": 406\n",
      "    },\n",
      "    \"accuracy\": 0.8772455089820359,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8699866310160428,\n",
      "        \"recall\": 0.8746474636182454,\n",
      "        \"f1-score\": 0.872098779269056,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8784162957507444,\n",
      "        \"recall\": 0.8772455089820359,\n",
      "        \"f1-score\": 0.8776295932889748,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ruddy Duck 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9846625766871165,\n",
      "        \"recall\": 0.9801526717557252,\n",
      "        \"f1-score\": 0.9824024483550114,\n",
      "        \"support\": 655\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.1875,\n",
      "        \"recall\": 0.23076923076923078,\n",
      "        \"f1-score\": 0.20689655172413793,\n",
      "        \"support\": 13\n",
      "    },\n",
      "    \"accuracy\": 0.9655688622754491,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5860812883435582,\n",
      "        \"recall\": 0.605460951262478,\n",
      "        \"f1-score\": 0.5946495000395747,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9691489337276368,\n",
      "        \"recall\": 0.9655688622754491,\n",
      "        \"f1-score\": 0.9673102677319556,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Ruddy Duck 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9846390168970814,\n",
      "        \"recall\": 0.9786259541984733,\n",
      "        \"f1-score\": 0.9816232771822359,\n",
      "        \"support\": 655\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.17647058823529413,\n",
      "        \"recall\": 0.23076923076923078,\n",
      "        \"f1-score\": 0.20000000000000004,\n",
      "        \"support\": 13\n",
      "    },\n",
      "    \"accuracy\": 0.9640718562874252,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5805548025661877,\n",
      "        \"recall\": 0.604697592483852,\n",
      "        \"f1-score\": 0.590811638591118,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9689111881955795,\n",
      "        \"recall\": 0.9640718562874252,\n",
      "        \"f1-score\": 0.9664120457400666,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Whooper Swan 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8390804597701149,\n",
      "        \"recall\": 0.8938775510204081,\n",
      "        \"f1-score\": 0.8656126482213438,\n",
      "        \"support\": 490\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6438356164383562,\n",
      "        \"recall\": 0.5280898876404494,\n",
      "        \"f1-score\": 0.5802469135802469,\n",
      "        \"support\": 178\n",
      "    },\n",
      "    \"accuracy\": 0.7964071856287425,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7414580381042355,\n",
      "        \"recall\": 0.7109837193304287,\n",
      "        \"f1-score\": 0.7229297809007953,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.7870541392415925,\n",
      "        \"recall\": 0.7964071856287425,\n",
      "        \"f1-score\": 0.7895720782121891,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Whooper Swan 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.848605577689243,\n",
      "        \"recall\": 0.8693877551020408,\n",
      "        \"f1-score\": 0.8588709677419356,\n",
      "        \"support\": 490\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6144578313253012,\n",
      "        \"recall\": 0.5730337078651685,\n",
      "        \"f1-score\": 0.5930232558139534,\n",
      "        \"support\": 178\n",
      "    },\n",
      "    \"accuracy\": 0.7904191616766467,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7315317045072721,\n",
      "        \"recall\": 0.7212107314836047,\n",
      "        \"f1-score\": 0.7259471117779446,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.7862129147359771,\n",
      "        \"recall\": 0.7904191616766467,\n",
      "        \"f1-score\": 0.7880313079766947,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Wigeon 10km 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 10km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.956953642384106,\n",
      "        \"recall\": 0.7789757412398922,\n",
      "        \"f1-score\": 0.8588410104011887,\n",
      "        \"support\": 371\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7759562841530054,\n",
      "        \"recall\": 0.9562289562289562,\n",
      "        \"f1-score\": 0.8567119155354449,\n",
      "        \"support\": 297\n",
      "    },\n",
      "    \"accuracy\": 0.8577844311377245,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8664549632685556,\n",
      "        \"recall\": 0.8676023487344242,\n",
      "        \"f1-score\": 0.8577764629683168,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8764802660448293,\n",
      "        \"recall\": 0.8577844311377245,\n",
      "        \"f1-score\": 0.857894391875551,\n",
      "        \"support\": 668\n",
      "    }\n",
      "} \n",
      "\n",
      "Wigeon 10km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9503311258278145,\n",
      "        \"recall\": 0.7735849056603774,\n",
      "        \"f1-score\": 0.8528974739970282,\n",
      "        \"support\": 371\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7704918032786885,\n",
      "        \"recall\": 0.9494949494949495,\n",
      "        \"f1-score\": 0.8506787330316742,\n",
      "        \"support\": 297\n",
      "    },\n",
      "    \"accuracy\": 0.8517964071856288,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8604114645532515,\n",
      "        \"recall\": 0.8615399275776634,\n",
      "        \"f1-score\": 0.8517881035143512,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8703726246345653,\n",
      "        \"recall\": 0.8517964071856288,\n",
      "        \"f1-score\": 0.8519109978492585,\n",
      "        \"support\": 668\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 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8983364140480592,\n",
      "        \"recall\": 0.8741007194244604,\n",
      "        \"f1-score\": 0.8860528714676389,\n",
      "        \"support\": 556\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.44881889763779526,\n",
      "        \"recall\": 0.5089285714285714,\n",
      "        \"f1-score\": 0.47698744769874474,\n",
      "        \"support\": 112\n",
      "    },\n",
      "    \"accuracy\": 0.812874251497006,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6735776558429272,\n",
      "        \"recall\": 0.691514645426516,\n",
      "        \"f1-score\": 0.6815201595831918,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8229682077038233,\n",
      "        \"recall\": 0.812874251497006,\n",
      "        \"f1-score\": 0.8174670519135727,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Canada Goose 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9219512195121952,\n",
      "        \"recall\": 0.8957345971563981,\n",
      "        \"f1-score\": 0.9086538461538463,\n",
      "        \"support\": 211\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9524838012958964,\n",
      "        \"recall\": 0.9649890590809628,\n",
      "        \"f1-score\": 0.9586956521739131,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.9431137724550899,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9372175104040458,\n",
      "        \"recall\": 0.9303618281186805,\n",
      "        \"f1-score\": 0.9336747491638797,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9428395277085297,\n",
      "        \"recall\": 0.9431137724550899,\n",
      "        \"f1-score\": 0.9428890338052992,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Egyptian Goose 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9588336192109777,\n",
      "        \"recall\": 0.9442567567567568,\n",
      "        \"f1-score\": 0.9514893617021277,\n",
      "        \"support\": 592\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.611764705882353,\n",
      "        \"recall\": 0.6842105263157895,\n",
      "        \"f1-score\": 0.6459627329192548,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.9146706586826348,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7852991625466653,\n",
      "        \"recall\": 0.8142336415362732,\n",
      "        \"f1-score\": 0.7987260473106912,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9193467368562239,\n",
      "        \"recall\": 0.9146706586826348,\n",
      "        \"f1-score\": 0.9167288470501842,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Gadwall 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9010989010989011,\n",
      "        \"recall\": 0.8951965065502183,\n",
      "        \"f1-score\": 0.8981380065717415,\n",
      "        \"support\": 458\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7746478873239436,\n",
      "        \"recall\": 0.7857142857142857,\n",
      "        \"f1-score\": 0.7801418439716311,\n",
      "        \"support\": 210\n",
      "    },\n",
      "    \"accuracy\": 0.8607784431137725,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8378733942114224,\n",
      "        \"recall\": 0.8404553961322521,\n",
      "        \"f1-score\": 0.8391399252716862,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8613463368882108,\n",
      "        \"recall\": 0.8607784431137725,\n",
      "        \"f1-score\": 0.8610434045567368,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Goshawk 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9375,\n",
      "        \"recall\": 0.9026548672566371,\n",
      "        \"f1-score\": 0.9197475202885482,\n",
      "        \"support\": 565\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5564516129032258,\n",
      "        \"recall\": 0.6699029126213593,\n",
      "        \"f1-score\": 0.6079295154185023,\n",
      "        \"support\": 103\n",
      "    },\n",
      "    \"accuracy\": 0.8667664670658682,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7469758064516129,\n",
      "        \"recall\": 0.7862788899389982,\n",
      "        \"f1-score\": 0.7638385178535252,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8787455331272938,\n",
      "        \"recall\": 0.8667664670658682,\n",
      "        \"f1-score\": 0.8716677979807418,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Grey Partridge 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.945,\n",
      "        \"recall\": 0.8936170212765957,\n",
      "        \"f1-score\": 0.9185905224787363,\n",
      "        \"support\": 423\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.832089552238806,\n",
      "        \"recall\": 0.9102040816326531,\n",
      "        \"f1-score\": 0.8693957115009747,\n",
      "        \"support\": 245\n",
      "    },\n",
      "    \"accuracy\": 0.8997005988023952,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.888544776119403,\n",
      "        \"recall\": 0.9019105514546244,\n",
      "        \"f1-score\": 0.8939931169898555,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9035882339798016,\n",
      "        \"recall\": 0.8997005988023952,\n",
      "        \"f1-score\": 0.9005475154584496,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Indian Peafowl 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9234449760765551,\n",
      "        \"recall\": 0.9682274247491639,\n",
      "        \"f1-score\": 0.9453061224489796,\n",
      "        \"support\": 598\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5365853658536586,\n",
      "        \"recall\": 0.3142857142857143,\n",
      "        \"f1-score\": 0.39639639639639646,\n",
      "        \"support\": 70\n",
      "    },\n",
      "    \"accuracy\": 0.8997005988023952,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7300151709651068,\n",
      "        \"recall\": 0.641256569517439,\n",
      "        \"f1-score\": 0.6708512594226881,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8829057953645749,\n",
      "        \"recall\": 0.8997005988023952,\n",
      "        \"f1-score\": 0.8877856421740082,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Little Owl 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.946949602122016,\n",
      "        \"recall\": 0.9037974683544304,\n",
      "        \"f1-score\": 0.9248704663212435,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8694158075601375,\n",
      "        \"recall\": 0.9267399267399268,\n",
      "        \"f1-score\": 0.8971631205673759,\n",
      "        \"support\": 273\n",
      "    },\n",
      "    \"accuracy\": 0.9131736526946108,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9081827048410767,\n",
      "        \"recall\": 0.9152686975471787,\n",
      "        \"f1-score\": 0.9110167934443096,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9152628866798111,\n",
      "        \"recall\": 0.9131736526946108,\n",
      "        \"f1-score\": 0.913546955257163,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Mandarin Duck 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.949438202247191,\n",
      "        \"recall\": 0.9354243542435424,\n",
      "        \"f1-score\": 0.9423791821561337,\n",
      "        \"support\": 542\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7388059701492538,\n",
      "        \"recall\": 0.7857142857142857,\n",
      "        \"f1-score\": 0.7615384615384615,\n",
      "        \"support\": 126\n",
      "    },\n",
      "    \"accuracy\": 0.907185628742515,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8441220861982224,\n",
      "        \"recall\": 0.8605693199789141,\n",
      "        \"f1-score\": 0.8519588218472975,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9097081704442866,\n",
      "        \"recall\": 0.907185628742515,\n",
      "        \"f1-score\": 0.9082685073090877,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Mute Swan 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8372093023255814,\n",
      "        \"recall\": 0.8571428571428571,\n",
      "        \"f1-score\": 0.8470588235294119,\n",
      "        \"support\": 168\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9516129032258065,\n",
      "        \"recall\": 0.944,\n",
      "        \"f1-score\": 0.9477911646586344,\n",
      "        \"support\": 500\n",
      "    },\n",
      "    \"accuracy\": 0.9221556886227545,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.894411102775694,\n",
      "        \"recall\": 0.9005714285714286,\n",
      "        \"f1-score\": 0.8974249940940231,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.922840740125151,\n",
      "        \"recall\": 0.9221556886227545,\n",
      "        \"f1-score\": 0.9224572824584706,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Pheasant 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.826530612244898,\n",
      "        \"recall\": 0.8663101604278075,\n",
      "        \"f1-score\": 0.8459530026109661,\n",
      "        \"support\": 187\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9470338983050848,\n",
      "        \"recall\": 0.9293139293139293,\n",
      "        \"f1-score\": 0.938090241343127,\n",
      "        \"support\": 481\n",
      "    },\n",
      "    \"accuracy\": 0.9116766467065869,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8867822552749913,\n",
      "        \"recall\": 0.8978120448708684,\n",
      "        \"f1-score\": 0.8920216219770465,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9133001939738649,\n",
      "        \"recall\": 0.9116766467065869,\n",
      "        \"f1-score\": 0.912297331698046,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Pink-footed Goose 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8768267223382046,\n",
      "        \"recall\": 0.8786610878661087,\n",
      "        \"f1-score\": 0.8777429467084639,\n",
      "        \"support\": 478\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6931216931216931,\n",
      "        \"recall\": 0.6894736842105263,\n",
      "        \"f1-score\": 0.6912928759894459,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.8248502994011976,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7849742077299489,\n",
      "        \"recall\": 0.7840673860383176,\n",
      "        \"f1-score\": 0.7845179113489549,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8245752918724304,\n",
      "        \"recall\": 0.8248502994011976,\n",
      "        \"f1-score\": 0.8247107409650306,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Pintail 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8618881118881119,\n",
      "        \"recall\": 0.9354838709677419,\n",
      "        \"f1-score\": 0.8971792538671519,\n",
      "        \"support\": 527\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6458333333333334,\n",
      "        \"recall\": 0.4397163120567376,\n",
      "        \"f1-score\": 0.5232067510548524,\n",
      "        \"support\": 141\n",
      "    },\n",
      "    \"accuracy\": 0.8308383233532934,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7538607226107226,\n",
      "        \"recall\": 0.6876000915122398,\n",
      "        \"f1-score\": 0.7101930024610021,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8162837349775971,\n",
      "        \"recall\": 0.8308383233532934,\n",
      "        \"f1-score\": 0.8182419441418013,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Pochard 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9142259414225942,\n",
      "        \"recall\": 0.9066390041493776,\n",
      "        \"f1-score\": 0.9104166666666668,\n",
      "        \"support\": 482\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7631578947368421,\n",
      "        \"recall\": 0.7795698924731183,\n",
      "        \"f1-score\": 0.7712765957446808,\n",
      "        \"support\": 186\n",
      "    },\n",
      "    \"accuracy\": 0.8712574850299402,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8386919180797181,\n",
      "        \"recall\": 0.843104448311248,\n",
      "        \"f1-score\": 0.8408466312056737,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8721620841118908,\n",
      "        \"recall\": 0.8712574850299402,\n",
      "        \"f1-score\": 0.8716740720686287,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Red-legged Partridge 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9152046783625731,\n",
      "        \"recall\": 0.884180790960452,\n",
      "        \"f1-score\": 0.8994252873563219,\n",
      "        \"support\": 354\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8742331288343558,\n",
      "        \"recall\": 0.9076433121019108,\n",
      "        \"f1-score\": 0.890625,\n",
      "        \"support\": 314\n",
      "    },\n",
      "    \"accuracy\": 0.8952095808383234,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8947189035984644,\n",
      "        \"recall\": 0.8959120515311814,\n",
      "        \"f1-score\": 0.8950251436781609,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8959455966981116,\n",
      "        \"recall\": 0.8952095808383234,\n",
      "        \"f1-score\": 0.8952886253355358,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Ring-necked Parakeet 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9654135338345865,\n",
      "        \"recall\": 0.9968944099378882,\n",
      "        \"f1-score\": 0.9809014514896868,\n",
      "        \"support\": 644\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3333333333333333,\n",
      "        \"recall\": 0.041666666666666664,\n",
      "        \"f1-score\": 0.07407407407407407,\n",
      "        \"support\": 24\n",
      "    },\n",
      "    \"accuracy\": 0.9625748502994012,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6493734335839599,\n",
      "        \"recall\": 0.5192805383022775,\n",
      "        \"f1-score\": 0.5274877627818804,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9427040655531044,\n",
      "        \"recall\": 0.9625748502994012,\n",
      "        \"f1-score\": 0.9483208271514014,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Rock Dove 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8938775510204081,\n",
      "        \"recall\": 0.8358778625954199,\n",
      "        \"f1-score\": 0.863905325443787,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8983451536643026,\n",
      "        \"recall\": 0.9359605911330049,\n",
      "        \"f1-score\": 0.916767189384801,\n",
      "        \"support\": 406\n",
      "    },\n",
      "    \"accuracy\": 0.8967065868263473,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8961113523423554,\n",
      "        \"recall\": 0.8859192268642124,\n",
      "        \"f1-score\": 0.8903362574142939,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.896592890351877,\n",
      "        \"recall\": 0.8967065868263473,\n",
      "        \"f1-score\": 0.896033943348056,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Ruddy Duck 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9846625766871165,\n",
      "        \"recall\": 0.9801526717557252,\n",
      "        \"f1-score\": 0.9824024483550114,\n",
      "        \"support\": 655\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.1875,\n",
      "        \"recall\": 0.23076923076923078,\n",
      "        \"f1-score\": 0.20689655172413793,\n",
      "        \"support\": 13\n",
      "    },\n",
      "    \"accuracy\": 0.9655688622754491,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5860812883435582,\n",
      "        \"recall\": 0.605460951262478,\n",
      "        \"f1-score\": 0.5946495000395747,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9691489337276368,\n",
      "        \"recall\": 0.9655688622754491,\n",
      "        \"f1-score\": 0.9673102677319556,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Whooper Swan 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8390804597701149,\n",
      "        \"recall\": 0.8938775510204081,\n",
      "        \"f1-score\": 0.8656126482213438,\n",
      "        \"support\": 490\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6438356164383562,\n",
      "        \"recall\": 0.5280898876404494,\n",
      "        \"f1-score\": 0.5802469135802469,\n",
      "        \"support\": 178\n",
      "    },\n",
      "    \"accuracy\": 0.7964071856287425,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7414580381042355,\n",
      "        \"recall\": 0.7109837193304287,\n",
      "        \"f1-score\": 0.7229297809007953,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.7870541392415925,\n",
      "        \"recall\": 0.7964071856287425,\n",
      "        \"f1-score\": 0.7895720782121891,\n",
      "        \"support\": 668\n",
      "    }\n",
      "}\n",
      "Wigeon 10km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.956953642384106,\n",
      "        \"recall\": 0.7789757412398922,\n",
      "        \"f1-score\": 0.8588410104011887,\n",
      "        \"support\": 371\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7759562841530054,\n",
      "        \"recall\": 0.9562289562289562,\n",
      "        \"f1-score\": 0.8567119155354449,\n",
      "        \"support\": 297\n",
      "    },\n",
      "    \"accuracy\": 0.8577844311377245,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8664549632685556,\n",
      "        \"recall\": 0.8676023487344242,\n",
      "        \"f1-score\": 0.8577764629683168,\n",
      "        \"support\": 668\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8764802660448293,\n",
      "        \"recall\": 0.8577844311377245,\n",
      "        \"f1-score\": 0.857894391875551,\n",
      "        \"support\": 668\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": 12,
   "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 10km</td>\n",
       "      <td>0.951613</td>\n",
       "      <td>0.950617</td>\n",
       "      <td>0.944000</td>\n",
       "      <td>0.924000</td>\n",
       "      <td>0.947791</td>\n",
       "      <td>0.937120</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.751123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 10km</td>\n",
       "      <td>0.947034</td>\n",
       "      <td>0.962306</td>\n",
       "      <td>0.929314</td>\n",
       "      <td>0.902287</td>\n",
       "      <td>0.938090</td>\n",
       "      <td>0.931330</td>\n",
       "      <td>1918</td>\n",
       "      <td>0.717814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 10km</td>\n",
       "      <td>0.952484</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.964989</td>\n",
       "      <td>0.956236</td>\n",
       "      <td>0.958696</td>\n",
       "      <td>0.957284</td>\n",
       "      <td>1789</td>\n",
       "      <td>0.669536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 10km</td>\n",
       "      <td>0.898345</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.935961</td>\n",
       "      <td>0.886700</td>\n",
       "      <td>0.916767</td>\n",
       "      <td>0.897756</td>\n",
       "      <td>1620</td>\n",
       "      <td>0.606287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 10km</td>\n",
       "      <td>0.775956</td>\n",
       "      <td>0.770492</td>\n",
       "      <td>0.956229</td>\n",
       "      <td>0.949495</td>\n",
       "      <td>0.856712</td>\n",
       "      <td>0.850679</td>\n",
       "      <td>1267</td>\n",
       "      <td>0.474177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 10km</td>\n",
       "      <td>0.874233</td>\n",
       "      <td>0.869697</td>\n",
       "      <td>0.907643</td>\n",
       "      <td>0.914013</td>\n",
       "      <td>0.890625</td>\n",
       "      <td>0.891304</td>\n",
       "      <td>1208</td>\n",
       "      <td>0.452096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 10km</td>\n",
       "      <td>0.869416</td>\n",
       "      <td>0.895105</td>\n",
       "      <td>0.926740</td>\n",
       "      <td>0.937729</td>\n",
       "      <td>0.897163</td>\n",
       "      <td>0.915921</td>\n",
       "      <td>1077</td>\n",
       "      <td>0.403069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 10km</td>\n",
       "      <td>0.832090</td>\n",
       "      <td>0.836431</td>\n",
       "      <td>0.910204</td>\n",
       "      <td>0.918367</td>\n",
       "      <td>0.869396</td>\n",
       "      <td>0.875486</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 10km</td>\n",
       "      <td>0.774648</td>\n",
       "      <td>0.744681</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.780142</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>830</td>\n",
       "      <td>0.310629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 10km</td>\n",
       "      <td>0.763158</td>\n",
       "      <td>0.703196</td>\n",
       "      <td>0.779570</td>\n",
       "      <td>0.827957</td>\n",
       "      <td>0.771277</td>\n",
       "      <td>0.760494</td>\n",
       "      <td>738</td>\n",
       "      <td>0.276198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 10km</td>\n",
       "      <td>0.693122</td>\n",
       "      <td>0.673367</td>\n",
       "      <td>0.689474</td>\n",
       "      <td>0.705263</td>\n",
       "      <td>0.691293</td>\n",
       "      <td>0.688946</td>\n",
       "      <td>730</td>\n",
       "      <td>0.273204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 10km</td>\n",
       "      <td>0.643836</td>\n",
       "      <td>0.614458</td>\n",
       "      <td>0.528090</td>\n",
       "      <td>0.573034</td>\n",
       "      <td>0.580247</td>\n",
       "      <td>0.593023</td>\n",
       "      <td>659</td>\n",
       "      <td>0.246632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 10km</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.623377</td>\n",
       "      <td>0.439716</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.523207</td>\n",
       "      <td>0.650847</td>\n",
       "      <td>513</td>\n",
       "      <td>0.191991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 10km</td>\n",
       "      <td>0.738806</td>\n",
       "      <td>0.719008</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.690476</td>\n",
       "      <td>0.761538</td>\n",
       "      <td>0.704453</td>\n",
       "      <td>478</td>\n",
       "      <td>0.178892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 10km</td>\n",
       "      <td>0.448819</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.508929</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.476987</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>466</td>\n",
       "      <td>0.174401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 10km</td>\n",
       "      <td>0.556452</td>\n",
       "      <td>0.539823</td>\n",
       "      <td>0.669903</td>\n",
       "      <td>0.592233</td>\n",
       "      <td>0.607930</td>\n",
       "      <td>0.564815</td>\n",
       "      <td>446</td>\n",
       "      <td>0.166916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 10km</td>\n",
       "      <td>0.611765</td>\n",
       "      <td>0.558140</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.645963</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>300</td>\n",
       "      <td>0.112275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 10km</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>0.328571</td>\n",
       "      <td>0.396396</td>\n",
       "      <td>0.396552</td>\n",
       "      <td>247</td>\n",
       "      <td>0.092440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 10km</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.206897</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>97</td>\n",
       "      <td>0.036302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 10km</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>96</td>\n",
       "      <td>0.035928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Labels  Precision  Precision (Smote)    Recall  \\\n",
       "9              Mute Swan 10km   0.951613           0.950617  0.944000   \n",
       "10              Pheasant 10km   0.947034           0.962306  0.929314   \n",
       "1           Canada Goose 10km   0.952484           0.958333  0.964989   \n",
       "16             Rock Dove 10km   0.898345           0.909091  0.935961   \n",
       "19                Wigeon 10km   0.775956           0.770492  0.956229   \n",
       "14  Red-legged Partridge 10km   0.874233           0.869697  0.907643   \n",
       "7             Little Owl 10km   0.869416           0.895105  0.926740   \n",
       "5         Grey Partridge 10km   0.832090           0.836431  0.910204   \n",
       "3                Gadwall 10km   0.774648           0.744681  0.785714   \n",
       "13               Pochard 10km   0.763158           0.703196  0.779570   \n",
       "11     Pink-footed Goose 10km   0.693122           0.673367  0.689474   \n",
       "18          Whooper Swan 10km   0.643836           0.614458  0.528090   \n",
       "12               Pintail 10km   0.645833           0.623377  0.439716   \n",
       "8          Mandarin Duck 10km   0.738806           0.719008  0.785714   \n",
       "0         Barnacle Goose 10km   0.448819           0.428571  0.508929   \n",
       "4                Goshawk 10km   0.556452           0.539823  0.669903   \n",
       "2         Egyptian Goose 10km   0.611765           0.558140  0.684211   \n",
       "6         Indian Peafowl 10km   0.536585           0.500000  0.314286   \n",
       "17            Ruddy Duck 10km   0.187500           0.176471  0.230769   \n",
       "15  Ring-necked Parakeet 10km   0.333333           0.285714  0.041667   \n",
       "\n",
       "    Recall (Smote)        F1  F1 (Smote)  Occurrence Count  Percentage  \n",
       "9         0.924000  0.947791    0.937120              2007    0.751123  \n",
       "10        0.902287  0.938090    0.931330              1918    0.717814  \n",
       "1         0.956236  0.958696    0.957284              1789    0.669536  \n",
       "16        0.886700  0.916767    0.897756              1620    0.606287  \n",
       "19        0.949495  0.856712    0.850679              1267    0.474177  \n",
       "14        0.914013  0.890625    0.891304              1208    0.452096  \n",
       "7         0.937729  0.897163    0.915921              1077    0.403069  \n",
       "5         0.918367  0.869396    0.875486              1002    0.375000  \n",
       "3         0.833333  0.780142    0.786517               830    0.310629  \n",
       "13        0.827957  0.771277    0.760494               738    0.276198  \n",
       "11        0.705263  0.691293    0.688946               730    0.273204  \n",
       "18        0.573034  0.580247    0.593023               659    0.246632  \n",
       "12        0.680851  0.523207    0.650847               513    0.191991  \n",
       "8         0.690476  0.761538    0.704453               478    0.178892  \n",
       "0         0.535714  0.476987    0.476190               466    0.174401  \n",
       "4         0.592233  0.607930    0.564815               446    0.166916  \n",
       "2         0.631579  0.645963    0.592593               300    0.112275  \n",
       "6         0.328571  0.396396    0.396552               247    0.092440  \n",
       "17        0.230769  0.206897    0.200000                97    0.036302  \n",
       "15        0.083333  0.074074    0.129032                96    0.035928  "
      ]
     },
     "execution_count": 12,
     "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_10km' (list)\n"
     ]
    }
   ],
   "source": [
    "# Store dictionaries for later use\n",
    "df_dicts_10km = df_dicts\n",
    "%store df_dicts_10km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "# Export predictions to CSV for QGIS\n",
    "RESULTS_PATH = 'Datasets/Machine Learning/Results/10km/'\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 10km\n"
     ]
    },
    {
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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",
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       "      <th>25</th>\n",
       "      <td>1009.098268</td>\n",
       "      <td>3.952755e-188</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>822.960158</td>\n",
       "      <td>5.383451e-158</td>\n",
       "      <td>Elevation</td>\n",
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       "      <th>23</th>\n",
       "      <td>773.642924</td>\n",
       "      <td>9.675580e-150</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
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       "      <th>26</th>\n",
       "      <td>387.030728</td>\n",
       "      <td>1.424303e-80</td>\n",
       "      <td>Fertiliser K</td>\n",
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       "      <th>27</th>\n",
       "      <td>387.030728</td>\n",
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       "      <td>387.030728</td>\n",
       "      <td>1.424303e-80</td>\n",
       "      <td>Fertiliser P</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>286.074265</td>\n",
       "      <td>4.806316e-61</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>268.765539</td>\n",
       "      <td>1.255395e-57</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>235.656713</td>\n",
       "      <td>4.939605e-51</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>199.364053</td>\n",
       "      <td>1.033356e-43</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>196.189782</td>\n",
       "      <td>4.562554e-43</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>196.189782</td>\n",
       "      <td>4.562554e-43</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>188.369003</td>\n",
       "      <td>1.784581e-41</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>188.369003</td>\n",
       "      <td>1.784581e-41</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>188.369003</td>\n",
       "      <td>1.784581e-41</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>188.369003</td>\n",
       "      <td>1.784581e-41</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>188.369003</td>\n",
       "      <td>1.784581e-41</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>161.344102</td>\n",
       "      <td>6.172420e-36</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>146.572064</td>\n",
       "      <td>6.962136e-33</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>128.415593</td>\n",
       "      <td>4.161077e-29</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>113.875404</td>\n",
       "      <td>4.614046e-26</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>92.362595</td>\n",
       "      <td>1.602427e-21</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>79.830321</td>\n",
       "      <td>7.431887e-19</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>65.850294</td>\n",
       "      <td>7.342235e-16</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>54.991282</td>\n",
       "      <td>1.617037e-13</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>47.707380</td>\n",
       "      <td>6.162071e-12</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35.077761</td>\n",
       "      <td>3.574253e-09</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32.408605</td>\n",
       "      <td>1.385572e-08</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27.999637</td>\n",
       "      <td>1.311868e-07</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.715444</td>\n",
       "      <td>2.531488e-07</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.814489</td>\n",
       "      <td>1.020153e-03</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.406941</td>\n",
       "      <td>2.183328e-03</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.853502</td>\n",
       "      <td>5.108829e-03</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.092810</td>\n",
       "      <td>2.410577e-02</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.030409</td>\n",
       "      <td>4.478769e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.305533</td>\n",
       "      <td>2.533074e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.503498</td>\n",
       "      <td>4.780290e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.304783</td>\n",
       "      <td>5.809453e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.030913</td>\n",
       "      <td>8.604477e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1009.098268  3.952755e-188   Inflowing drainage direction\n",
       "21   822.960158  5.383451e-158                      Elevation\n",
       "23   773.642924  9.675580e-150                   Surface type\n",
       "26   387.030728   1.424303e-80                   Fertiliser K\n",
       "27   387.030728   1.424303e-80                   Fertiliser N\n",
       "28   387.030728   1.424303e-80                   Fertiliser P\n",
       "3    286.074265   4.806316e-61              Improve grassland\n",
       "2    268.765539   1.255395e-57                         Arable\n",
       "22   235.656713   4.939605e-51      Cumulative catchment area\n",
       "24   199.364053   1.033356e-43  Outflowing drainage direction\n",
       "36   196.189782   4.562554e-43              Prosulfocarb_10km\n",
       "37   196.189782   4.562554e-43                   Sulphur_10km\n",
       "29   188.369003   1.784581e-41            Chlorothalonil_10km\n",
       "30   188.369003   1.784581e-41                Glyphosate_10km\n",
       "31   188.369003   1.784581e-41                  Mancozeb_10km\n",
       "32   188.369003   1.784581e-41                Mecoprop-P_10km\n",
       "34   188.369003   1.784581e-41             Pendimethalin_10km\n",
       "0    161.344102   6.172420e-36             Deciduous woodland\n",
       "38   146.572064   6.962136e-33                Tri-allate_10km\n",
       "33   128.415593   4.161077e-29                Metamitron_10km\n",
       "35   113.875404   4.614046e-26  PropamocarbHydrochloride_10km\n",
       "20    92.362595   1.602427e-21                       Suburban\n",
       "19    79.830321   7.431887e-19                          Urban\n",
       "17    65.850294   7.342235e-16              Littoral sediment\n",
       "18    54.991282   1.617037e-13                      Saltmarsh\n",
       "15    47.707380   6.162071e-12         Supralittoral sediment\n",
       "9     35.077761   3.574253e-09              Heather grassland\n",
       "14    32.408605   1.385572e-08             Supralittoral rock\n",
       "16    27.999637   1.311868e-07                  Littoral rock\n",
       "7     26.715444   2.531488e-07                            Fen\n",
       "13    10.814489   1.020153e-03                     Freshwater\n",
       "4      9.406941   2.183328e-03              Neutral grassland\n",
       "1      7.853502   5.108829e-03            Coniferous woodland\n",
       "5      5.092810   2.410577e-02           Calcareous grassland\n",
       "12     4.030409   4.478769e-02                      Saltwater\n",
       "10     1.305533   2.533074e-01                            Bog\n",
       "6      0.503498   4.780290e-01                 Acid grassland\n",
       "11     0.304783   5.809453e-01                    Inland rock\n",
       "8      0.030913   8.604477e-01                        Heather"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada Goose 10km\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>7343.666630</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5748.855110</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5520.138505</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1431.849351</td>\n",
       "      <td>3.030462e-251</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1431.849351</td>\n",
       "      <td>3.030462e-251</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1431.849351</td>\n",
       "      <td>3.030462e-251</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1114.539477</td>\n",
       "      <td>1.580665e-204</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1114.539477</td>\n",
       "      <td>1.580665e-204</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1114.539477</td>\n",
       "      <td>1.580665e-204</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1111.648247</td>\n",
       "      <td>4.388703e-204</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1111.648247</td>\n",
       "      <td>4.388703e-204</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>741.741367</td>\n",
       "      <td>2.448280e-144</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>741.741367</td>\n",
       "      <td>2.448280e-144</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>736.051579</td>\n",
       "      <td>2.279725e-143</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>732.084357</td>\n",
       "      <td>1.082679e-142</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>572.662758</td>\n",
       "      <td>7.999142e-115</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>560.712879</td>\n",
       "      <td>1.115248e-112</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>551.243743</td>\n",
       "      <td>5.652544e-111</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>537.822289</td>\n",
       "      <td>1.504558e-108</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>324.290358</td>\n",
       "      <td>1.622397e-68</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>180.176262</td>\n",
       "      <td>8.407061e-40</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>50.311028</td>\n",
       "      <td>1.673556e-12</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.107127</td>\n",
       "      <td>1.615132e-08</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>28.330681</td>\n",
       "      <td>1.107605e-07</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24.550281</td>\n",
       "      <td>7.691838e-07</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.588824</td>\n",
       "      <td>3.542001e-06</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.731579</td>\n",
       "      <td>9.274335e-06</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.901638</td>\n",
       "      <td>5.695458e-04</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.468525</td>\n",
       "      <td>7.180931e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.225843</td>\n",
       "      <td>1.401059e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.771196</td>\n",
       "      <td>3.087237e-03</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.234798</td>\n",
       "      <td>7.194880e-03</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.603089</td>\n",
       "      <td>1.799978e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.319150</td>\n",
       "      <td>6.858907e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.498448</td>\n",
       "      <td>2.210184e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.364810</td>\n",
       "      <td>2.428099e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.087899</td>\n",
       "      <td>2.970316e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.651580</td>\n",
       "      <td>4.196203e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.149181</td>\n",
       "      <td>6.993498e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "21  7343.666630   0.000000e+00                      Elevation\n",
       "23  5748.855110   0.000000e+00                   Surface type\n",
       "25  5520.138505   0.000000e+00   Inflowing drainage direction\n",
       "26  1431.849351  3.030462e-251                   Fertiliser K\n",
       "27  1431.849351  3.030462e-251                   Fertiliser N\n",
       "28  1431.849351  3.030462e-251                   Fertiliser P\n",
       "29  1114.539477  1.580665e-204            Chlorothalonil_10km\n",
       "30  1114.539477  1.580665e-204                Glyphosate_10km\n",
       "34  1114.539477  1.580665e-204             Pendimethalin_10km\n",
       "31  1111.648247  4.388703e-204                  Mancozeb_10km\n",
       "32  1111.648247  4.388703e-204                Mecoprop-P_10km\n",
       "36   741.741367  2.448280e-144              Prosulfocarb_10km\n",
       "37   741.741367  2.448280e-144                   Sulphur_10km\n",
       "38   736.051579  2.279725e-143                Tri-allate_10km\n",
       "3    732.084357  1.082679e-142              Improve grassland\n",
       "2    572.662758  7.999142e-115                         Arable\n",
       "24   560.712879  1.115248e-112  Outflowing drainage direction\n",
       "35   551.243743  5.652544e-111  PropamocarbHydrochloride_10km\n",
       "33   537.822289  1.504558e-108                Metamitron_10km\n",
       "0    324.290358   1.622397e-68             Deciduous woodland\n",
       "20   180.176262   8.407061e-40                       Suburban\n",
       "19    50.311028   1.673556e-12                          Urban\n",
       "1     32.107127   1.615132e-08            Coniferous woodland\n",
       "22    28.330681   1.107605e-07      Cumulative catchment area\n",
       "6     24.550281   7.691838e-07                 Acid grassland\n",
       "4     21.588824   3.542001e-06              Neutral grassland\n",
       "5     19.731579   9.274335e-06           Calcareous grassland\n",
       "18    11.901638   5.695458e-04                      Saltmarsh\n",
       "13    11.468525   7.180931e-04                     Freshwater\n",
       "17    10.225843   1.401059e-03              Littoral sediment\n",
       "8      8.771196   3.087237e-03                        Heather\n",
       "7      7.234798   7.194880e-03                            Fen\n",
       "12     5.603089   1.799978e-02                      Saltwater\n",
       "15     3.319150   6.858907e-02         Supralittoral sediment\n",
       "9      1.498448   2.210184e-01              Heather grassland\n",
       "14     1.364810   2.428099e-01             Supralittoral rock\n",
       "10     1.087899   2.970316e-01                            Bog\n",
       "11     0.651580   4.196203e-01                    Inland rock\n",
       "16     0.149181   6.993498e-01                  Littoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Egyptian Goose 10km\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1863.935757</td>\n",
       "      <td>2.395294e-309</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1863.935757</td>\n",
       "      <td>2.395294e-309</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1863.935757</td>\n",
       "      <td>2.395294e-309</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>827.599456</td>\n",
       "      <td>9.132354e-159</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>641.921266</td>\n",
       "      <td>4.265779e-127</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>641.772899</td>\n",
       "      <td>4.529104e-127</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>589.698428</td>\n",
       "      <td>7.242005e-118</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>469.691429</td>\n",
       "      <td>4.453426e-96</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>469.691429</td>\n",
       "      <td>4.453426e-96</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>463.483676</td>\n",
       "      <td>6.289484e-95</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>440.139552</td>\n",
       "      <td>1.392058e-90</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>434.469069</td>\n",
       "      <td>1.599940e-89</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>434.469069</td>\n",
       "      <td>1.599940e-89</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>434.469069</td>\n",
       "      <td>1.599940e-89</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>432.216501</td>\n",
       "      <td>4.225777e-89</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>428.297036</td>\n",
       "      <td>2.293958e-88</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>428.297036</td>\n",
       "      <td>2.293958e-88</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>425.375213</td>\n",
       "      <td>8.107329e-88</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>314.637554</td>\n",
       "      <td>1.224799e-66</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>305.393057</td>\n",
       "      <td>7.805663e-65</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>279.121118</td>\n",
       "      <td>1.127077e-59</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>163.844471</td>\n",
       "      <td>1.886102e-36</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>37.622328</td>\n",
       "      <td>9.858221e-10</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33.079310</td>\n",
       "      <td>9.853602e-09</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.003073</td>\n",
       "      <td>4.715957e-08</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.229728</td>\n",
       "      <td>8.161764e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.043311</td>\n",
       "      <td>1.546316e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.307928</td>\n",
       "      <td>2.130504e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.193269</td>\n",
       "      <td>2.275315e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.900666</td>\n",
       "      <td>2.693057e-02</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.648895</td>\n",
       "      <td>5.621345e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.501216</td>\n",
       "      <td>6.143306e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.434389</td>\n",
       "      <td>1.188187e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.371591</td>\n",
       "      <td>2.416432e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.094713</td>\n",
       "      <td>2.955240e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.977077</td>\n",
       "      <td>3.230110e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.938625</td>\n",
       "      <td>3.327196e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.283602</td>\n",
       "      <td>5.943946e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.136474</td>\n",
       "      <td>7.118410e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  1863.935757  2.395294e-309                   Fertiliser K\n",
       "27  1863.935757  2.395294e-309                   Fertiliser N\n",
       "28  1863.935757  2.395294e-309                   Fertiliser P\n",
       "23   827.599456  9.132354e-159                   Surface type\n",
       "2    641.921266  4.265779e-127                         Arable\n",
       "25   641.772899  4.529104e-127   Inflowing drainage direction\n",
       "21   589.698428  7.242005e-118                      Elevation\n",
       "36   469.691429   4.453426e-96              Prosulfocarb_10km\n",
       "37   469.691429   4.453426e-96                   Sulphur_10km\n",
       "33   463.483676   6.289484e-95                Metamitron_10km\n",
       "38   440.139552   1.392058e-90                Tri-allate_10km\n",
       "29   434.469069   1.599940e-89            Chlorothalonil_10km\n",
       "30   434.469069   1.599940e-89                Glyphosate_10km\n",
       "34   434.469069   1.599940e-89             Pendimethalin_10km\n",
       "20   432.216501   4.225777e-89                       Suburban\n",
       "31   428.297036   2.293958e-88                  Mancozeb_10km\n",
       "32   428.297036   2.293958e-88                Mecoprop-P_10km\n",
       "35   425.375213   8.107329e-88  PropamocarbHydrochloride_10km\n",
       "24   314.637554   1.224799e-66  Outflowing drainage direction\n",
       "3    305.393057   7.805663e-65              Improve grassland\n",
       "0    279.121118   1.127077e-59             Deciduous woodland\n",
       "19   163.844471   1.886102e-36                          Urban\n",
       "22    37.622328   9.858221e-10      Cumulative catchment area\n",
       "18    33.079310   9.853602e-09                      Saltmarsh\n",
       "4     30.003073   4.715957e-08              Neutral grassland\n",
       "13    11.229728   8.161764e-04                     Freshwater\n",
       "6     10.043311   1.546316e-03                 Acid grassland\n",
       "9      5.307928   2.130504e-02              Heather grassland\n",
       "7      5.193269   2.275315e-02                            Fen\n",
       "5      4.900666   2.693057e-02           Calcareous grassland\n",
       "10     3.648895   5.621345e-02                            Bog\n",
       "8      3.501216   6.143306e-02                        Heather\n",
       "1      2.434389   1.188187e-01            Coniferous woodland\n",
       "16     1.371591   2.416432e-01                  Littoral rock\n",
       "12     1.094713   2.955240e-01                      Saltwater\n",
       "14     0.977077   3.230110e-01             Supralittoral rock\n",
       "11     0.938625   3.327196e-01                    Inland rock\n",
       "17     0.283602   5.943946e-01              Littoral sediment\n",
       "15     0.136474   7.118410e-01         Supralittoral sediment"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gadwall 10km\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>23</th>\n",
       "      <td>1903.437525</td>\n",
       "      <td>2.224486e-314</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1849.754051</td>\n",
       "      <td>1.572831e-307</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1849.754051</td>\n",
       "      <td>1.572831e-307</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1849.754051</td>\n",
       "      <td>1.572831e-307</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1645.682577</td>\n",
       "      <td>9.981393e-281</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1515.568547</td>\n",
       "      <td>5.744852e-263</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>949.689658</td>\n",
       "      <td>1.109256e-178</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>594.080640</td>\n",
       "      <td>1.201251e-118</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>579.128295</td>\n",
       "      <td>5.574029e-116</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>579.128295</td>\n",
       "      <td>5.574029e-116</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>576.789734</td>\n",
       "      <td>1.459949e-115</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>576.789734</td>\n",
       "      <td>1.459949e-115</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>576.789734</td>\n",
       "      <td>1.459949e-115</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>548.515527</td>\n",
       "      <td>1.755440e-110</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>492.292460</td>\n",
       "      <td>3.030236e-100</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>490.394846</td>\n",
       "      <td>6.763937e-100</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>490.394846</td>\n",
       "      <td>6.763937e-100</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>486.646167</td>\n",
       "      <td>3.309139e-99</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>466.821680</td>\n",
       "      <td>1.513555e-95</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>367.505198</td>\n",
       "      <td>7.557822e-77</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>344.808292</td>\n",
       "      <td>1.734076e-72</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>127.613146</td>\n",
       "      <td>6.120135e-29</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.409704</td>\n",
       "      <td>8.812937e-11</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38.573928</td>\n",
       "      <td>6.094998e-10</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29.514784</td>\n",
       "      <td>6.049909e-08</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.969658</td>\n",
       "      <td>1.735944e-06</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.396323</td>\n",
       "      <td>3.130899e-05</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.556296</td>\n",
       "      <td>2.361016e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.254536</td>\n",
       "      <td>2.770855e-04</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.567615</td>\n",
       "      <td>1.165165e-03</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.284403</td>\n",
       "      <td>1.357450e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.050744</td>\n",
       "      <td>2.650406e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.110622</td>\n",
       "      <td>4.434319e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.085671</td>\n",
       "      <td>1.369060e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.201548</td>\n",
       "      <td>4.048459e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.766321</td>\n",
       "      <td>3.814364e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.296763</td>\n",
       "      <td>5.859643e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.285778</td>\n",
       "      <td>5.929833e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.088228</td>\n",
       "      <td>7.664655e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  1903.437525  2.224486e-314                   Surface type\n",
       "26  1849.754051  1.572831e-307                   Fertiliser K\n",
       "27  1849.754051  1.572831e-307                   Fertiliser N\n",
       "28  1849.754051  1.572831e-307                   Fertiliser P\n",
       "25  1645.682577  9.981393e-281   Inflowing drainage direction\n",
       "21  1515.568547  5.744852e-263                      Elevation\n",
       "2    949.689658  1.109256e-178                         Arable\n",
       "3    594.080640  1.201251e-118              Improve grassland\n",
       "31   579.128295  5.574029e-116                  Mancozeb_10km\n",
       "32   579.128295  5.574029e-116                Mecoprop-P_10km\n",
       "29   576.789734  1.459949e-115            Chlorothalonil_10km\n",
       "30   576.789734  1.459949e-115                Glyphosate_10km\n",
       "34   576.789734  1.459949e-115             Pendimethalin_10km\n",
       "38   548.515527  1.755440e-110                Tri-allate_10km\n",
       "33   492.292460  3.030236e-100                Metamitron_10km\n",
       "36   490.394846  6.763937e-100              Prosulfocarb_10km\n",
       "37   490.394846  6.763937e-100                   Sulphur_10km\n",
       "35   486.646167   3.309139e-99  PropamocarbHydrochloride_10km\n",
       "24   466.821680   1.513555e-95  Outflowing drainage direction\n",
       "20   367.505198   7.557822e-77                       Suburban\n",
       "0    344.808292   1.734076e-72             Deciduous woodland\n",
       "19   127.613146   6.120135e-29                          Urban\n",
       "4     42.409704   8.812937e-11              Neutral grassland\n",
       "18    38.573928   6.094998e-10                      Saltmarsh\n",
       "22    29.514784   6.049909e-08      Cumulative catchment area\n",
       "7     22.969658   1.735944e-06                            Fen\n",
       "17    17.396323   3.130899e-05              Littoral sediment\n",
       "13    13.556296   2.361016e-04                     Freshwater\n",
       "12    13.254536   2.770855e-04                      Saltwater\n",
       "8     10.567615   1.165165e-03                        Heather\n",
       "6     10.284403   1.357450e-03                 Acid grassland\n",
       "5      9.050744   2.650406e-03           Calcareous grassland\n",
       "10     8.110622   4.434319e-03                            Bog\n",
       "15     6.085671   1.369060e-02         Supralittoral sediment\n",
       "9      4.201548   4.048459e-02              Heather grassland\n",
       "11     0.766321   3.814364e-01                    Inland rock\n",
       "1      0.296763   5.859643e-01            Coniferous woodland\n",
       "16     0.285778   5.929833e-01                  Littoral rock\n",
       "14     0.088228   7.664655e-01             Supralittoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goshawk 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>23</th>\n",
       "      <td>1516.675979</td>\n",
       "      <td>4.034569e-263</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1440.341773</td>\n",
       "      <td>1.912231e-252</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1051.448282</td>\n",
       "      <td>9.009989e-195</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>721.884503</td>\n",
       "      <td>5.994541e-141</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>721.884503</td>\n",
       "      <td>5.994541e-141</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>719.541000</td>\n",
       "      <td>1.510181e-140</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>719.541000</td>\n",
       "      <td>1.510181e-140</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>719.541000</td>\n",
       "      <td>1.510181e-140</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615.251091</td>\n",
       "      <td>2.114437e-122</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>574.147816</td>\n",
       "      <td>4.336277e-115</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>513.236262</td>\n",
       "      <td>4.432676e-104</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>513.236262</td>\n",
       "      <td>4.432676e-104</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>480.598054</td>\n",
       "      <td>4.304565e-98</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>412.738950</td>\n",
       "      <td>1.932868e-85</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>375.496624</td>\n",
       "      <td>2.243687e-78</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>362.348467</td>\n",
       "      <td>7.348625e-76</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>257.265143</td>\n",
       "      <td>2.402678e-55</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>240.765684</td>\n",
       "      <td>4.687337e-52</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>196.967146</td>\n",
       "      <td>3.170955e-43</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>196.967146</td>\n",
       "      <td>3.170955e-43</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>196.967146</td>\n",
       "      <td>3.170955e-43</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93.612918</td>\n",
       "      <td>8.702609e-22</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>47.786916</td>\n",
       "      <td>5.921305e-12</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>41.501374</td>\n",
       "      <td>1.392317e-10</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.665672</td>\n",
       "      <td>9.597340e-06</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.908913</td>\n",
       "      <td>1.958784e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.905596</td>\n",
       "      <td>2.868761e-03</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.891045</td>\n",
       "      <td>8.918962e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.474858</td>\n",
       "      <td>1.157984e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.866841</td>\n",
       "      <td>1.719524e-01</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.799764</td>\n",
       "      <td>1.798551e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.525152</td>\n",
       "      <td>2.169502e-01</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.414285</td>\n",
       "      <td>2.344522e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.880416</td>\n",
       "      <td>3.481726e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.595848</td>\n",
       "      <td>4.402348e-01</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.430001</td>\n",
       "      <td>5.120451e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.143413</td>\n",
       "      <td>7.049410e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.042886</td>\n",
       "      <td>8.359561e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.035680</td>\n",
       "      <td>8.501928e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  1516.675979  4.034569e-263                   Surface type\n",
       "21  1440.341773  1.912231e-252                      Elevation\n",
       "25  1051.448282  9.009989e-195   Inflowing drainage direction\n",
       "31   721.884503  5.994541e-141                  Mancozeb_10km\n",
       "32   721.884503  5.994541e-141                Mecoprop-P_10km\n",
       "29   719.541000  1.510181e-140            Chlorothalonil_10km\n",
       "30   719.541000  1.510181e-140                Glyphosate_10km\n",
       "34   719.541000  1.510181e-140             Pendimethalin_10km\n",
       "3    615.251091  2.114437e-122              Improve grassland\n",
       "38   574.147816  4.336277e-115                Tri-allate_10km\n",
       "36   513.236262  4.432676e-104              Prosulfocarb_10km\n",
       "37   513.236262  4.432676e-104                   Sulphur_10km\n",
       "24   480.598054   4.304565e-98  Outflowing drainage direction\n",
       "0    412.738950   1.932868e-85             Deciduous woodland\n",
       "35   375.496624   2.243687e-78  PropamocarbHydrochloride_10km\n",
       "33   362.348467   7.348625e-76                Metamitron_10km\n",
       "1    257.265143   2.402678e-55            Coniferous woodland\n",
       "6    240.765684   4.687337e-52                 Acid grassland\n",
       "26   196.967146   3.170955e-43                   Fertiliser K\n",
       "27   196.967146   3.170955e-43                   Fertiliser N\n",
       "28   196.967146   3.170955e-43                   Fertiliser P\n",
       "2     93.612918   8.702609e-22                         Arable\n",
       "20    47.786916   5.921305e-12                       Suburban\n",
       "8     41.501374   1.392317e-10                        Heather\n",
       "5     19.665672   9.597340e-06           Calcareous grassland\n",
       "13    13.908913   1.958784e-04                     Freshwater\n",
       "22     8.905596   2.868761e-03      Cumulative catchment area\n",
       "10     2.891045   8.918962e-02                            Bog\n",
       "18     2.474858   1.157984e-01                      Saltmarsh\n",
       "19     1.866841   1.719524e-01                          Urban\n",
       "9      1.799764   1.798551e-01              Heather grassland\n",
       "7      1.525152   2.169502e-01                            Fen\n",
       "17     1.414285   2.344522e-01              Littoral sediment\n",
       "14     0.880416   3.481726e-01             Supralittoral rock\n",
       "4      0.595848   4.402348e-01              Neutral grassland\n",
       "16     0.430001   5.120451e-01                  Littoral rock\n",
       "15     0.143413   7.049410e-01         Supralittoral sediment\n",
       "11     0.042886   8.359561e-01                    Inland rock\n",
       "12     0.035680   8.501928e-01                      Saltwater"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grey Partridge 10km\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>3400.634758</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3012.624235</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3012.624235</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3012.624235</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2971.831794</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2704.779889</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1571.720673</td>\n",
       "      <td>1.066172e-270</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>781.528925</td>\n",
       "      <td>4.547596e-151</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>781.528925</td>\n",
       "      <td>4.547596e-151</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>781.528925</td>\n",
       "      <td>4.547596e-151</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>777.761278</td>\n",
       "      <td>1.958039e-150</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>777.761278</td>\n",
       "      <td>1.958039e-150</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>772.281638</td>\n",
       "      <td>1.641378e-149</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>726.649481</td>\n",
       "      <td>9.177369e-142</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>726.649481</td>\n",
       "      <td>9.177369e-142</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>698.039823</td>\n",
       "      <td>7.476889e-137</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>616.616839</td>\n",
       "      <td>1.212906e-122</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>615.647956</td>\n",
       "      <td>1.799064e-122</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>584.987101</td>\n",
       "      <td>5.010287e-117</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>327.534974</td>\n",
       "      <td>3.804785e-69</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>298.002024</td>\n",
       "      <td>2.183527e-63</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>77.634624</td>\n",
       "      <td>2.187610e-18</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.253781</td>\n",
       "      <td>3.486330e-11</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.060397</td>\n",
       "      <td>9.948735e-09</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.530191</td>\n",
       "      <td>7.771715e-07</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.459902</td>\n",
       "      <td>2.484816e-04</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.699635</td>\n",
       "      <td>3.721351e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.344633</td>\n",
       "      <td>7.673946e-04</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9.944567</td>\n",
       "      <td>1.631166e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.961874</td>\n",
       "      <td>4.664365e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.643353</td>\n",
       "      <td>5.640058e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.993600</td>\n",
       "      <td>8.370969e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.048774</td>\n",
       "      <td>1.524459e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.786211</td>\n",
       "      <td>1.815023e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.212361</td>\n",
       "      <td>2.709643e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.784977</td>\n",
       "      <td>3.757031e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.422222</td>\n",
       "      <td>5.158862e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.219961</td>\n",
       "      <td>6.391078e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.189243</td>\n",
       "      <td>6.635828e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  3400.634758   0.000000e+00                   Surface type\n",
       "26  3012.624235   0.000000e+00                   Fertiliser K\n",
       "27  3012.624235   0.000000e+00                   Fertiliser N\n",
       "28  3012.624235   0.000000e+00                   Fertiliser P\n",
       "21  2971.831794   0.000000e+00                      Elevation\n",
       "25  2704.779889   0.000000e+00   Inflowing drainage direction\n",
       "2   1571.720673  1.066172e-270                         Arable\n",
       "29   781.528925  4.547596e-151            Chlorothalonil_10km\n",
       "30   781.528925  4.547596e-151                Glyphosate_10km\n",
       "34   781.528925  4.547596e-151             Pendimethalin_10km\n",
       "31   777.761278  1.958039e-150                  Mancozeb_10km\n",
       "32   777.761278  1.958039e-150                Mecoprop-P_10km\n",
       "3    772.281638  1.641378e-149              Improve grassland\n",
       "36   726.649481  9.177369e-142              Prosulfocarb_10km\n",
       "37   726.649481  9.177369e-142                   Sulphur_10km\n",
       "38   698.039823  7.476889e-137                Tri-allate_10km\n",
       "33   616.616839  1.212906e-122                Metamitron_10km\n",
       "35   615.647956  1.799064e-122  PropamocarbHydrochloride_10km\n",
       "24   584.987101  5.010287e-117  Outflowing drainage direction\n",
       "0    327.534974   3.804785e-69             Deciduous woodland\n",
       "20   298.002024   2.183527e-63                       Suburban\n",
       "19    77.634624   2.187610e-18                          Urban\n",
       "4     44.253781   3.486330e-11              Neutral grassland\n",
       "5     33.060397   9.948735e-09           Calcareous grassland\n",
       "18    24.530191   7.771715e-07                      Saltmarsh\n",
       "22    13.459902   2.484816e-04      Cumulative catchment area\n",
       "13    12.699635   3.721351e-04                     Freshwater\n",
       "7     11.344633   7.673946e-04                            Fen\n",
       "17     9.944567   1.631166e-03              Littoral sediment\n",
       "11     3.961874   4.664365e-02                    Inland rock\n",
       "15     3.643353   5.640058e-02         Supralittoral sediment\n",
       "10     2.993600   8.370969e-02                            Bog\n",
       "6      2.048774   1.524459e-01                 Acid grassland\n",
       "14     1.786211   1.815023e-01             Supralittoral rock\n",
       "9      1.212361   2.709643e-01              Heather grassland\n",
       "16     0.784977   3.757031e-01                  Littoral rock\n",
       "8      0.422222   5.158862e-01                        Heather\n",
       "12     0.219961   6.391078e-01                      Saltwater\n",
       "1      0.189243   6.635828e-01            Coniferous woodland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indian Peafowl 10km\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>1144.800740</td>\n",
       "      <td>3.777807e-209</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1144.800740</td>\n",
       "      <td>3.777807e-209</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1144.800740</td>\n",
       "      <td>3.777807e-209</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>754.797758</td>\n",
       "      <td>1.483797e-146</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>562.392260</td>\n",
       "      <td>5.565883e-113</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>554.303821</td>\n",
       "      <td>1.587591e-111</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>494.199893</td>\n",
       "      <td>1.352579e-100</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>397.595728</td>\n",
       "      <td>1.406939e-82</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>397.595728</td>\n",
       "      <td>1.406939e-82</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>362.537756</td>\n",
       "      <td>6.759522e-76</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>359.272836</td>\n",
       "      <td>2.858800e-75</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>359.272836</td>\n",
       "      <td>2.858800e-75</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>359.272836</td>\n",
       "      <td>2.858800e-75</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>359.272836</td>\n",
       "      <td>2.858800e-75</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>359.272836</td>\n",
       "      <td>2.858800e-75</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>355.699518</td>\n",
       "      <td>1.388033e-74</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>349.877991</td>\n",
       "      <td>1.828680e-73</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>301.537542</td>\n",
       "      <td>4.432405e-64</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>296.541227</td>\n",
       "      <td>4.222218e-63</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>290.544642</td>\n",
       "      <td>6.348500e-62</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>246.800525</td>\n",
       "      <td>2.918994e-53</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>35.737943</td>\n",
       "      <td>2.558060e-09</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30.997451</td>\n",
       "      <td>2.841086e-08</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.689072</td>\n",
       "      <td>9.222468e-08</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.928487</td>\n",
       "      <td>5.614321e-04</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.214172</td>\n",
       "      <td>4.188976e-03</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.512478</td>\n",
       "      <td>1.895389e-02</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.709709</td>\n",
       "      <td>5.420288e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.099160</td>\n",
       "      <td>7.844719e-02</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.110375</td>\n",
       "      <td>1.464209e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.749114</td>\n",
       "      <td>1.861015e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.030285</td>\n",
       "      <td>3.101837e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.433261</td>\n",
       "      <td>5.104501e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.349987</td>\n",
       "      <td>5.541705e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.275544</td>\n",
       "      <td>5.996806e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.131766</td>\n",
       "      <td>7.166368e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.107604</td>\n",
       "      <td>7.429141e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.025027</td>\n",
       "      <td>8.743110e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.007901</td>\n",
       "      <td>9.291788e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  1144.800740  3.777807e-209                   Fertiliser K\n",
       "27  1144.800740  3.777807e-209                   Fertiliser N\n",
       "28  1144.800740  3.777807e-209                   Fertiliser P\n",
       "23   754.797758  1.483797e-146                   Surface type\n",
       "25   562.392260  5.565883e-113   Inflowing drainage direction\n",
       "21   554.303821  1.587591e-111                      Elevation\n",
       "2    494.199893  1.352579e-100                         Arable\n",
       "36   397.595728   1.406939e-82              Prosulfocarb_10km\n",
       "37   397.595728   1.406939e-82                   Sulphur_10km\n",
       "0    362.537756   6.759522e-76             Deciduous woodland\n",
       "29   359.272836   2.858800e-75            Chlorothalonil_10km\n",
       "30   359.272836   2.858800e-75                Glyphosate_10km\n",
       "31   359.272836   2.858800e-75                  Mancozeb_10km\n",
       "32   359.272836   2.858800e-75                Mecoprop-P_10km\n",
       "34   359.272836   2.858800e-75             Pendimethalin_10km\n",
       "3    355.699518   1.388033e-74              Improve grassland\n",
       "38   349.877991   1.828680e-73                Tri-allate_10km\n",
       "33   301.537542   4.432405e-64                Metamitron_10km\n",
       "20   296.541227   4.222218e-63                       Suburban\n",
       "35   290.544642   6.348500e-62  PropamocarbHydrochloride_10km\n",
       "24   246.800525   2.918994e-53  Outflowing drainage direction\n",
       "19    35.737943   2.558060e-09                          Urban\n",
       "22    30.997451   2.841086e-08      Cumulative catchment area\n",
       "5     28.689072   9.222468e-08           Calcareous grassland\n",
       "4     11.928487   5.614321e-04              Neutral grassland\n",
       "1      8.214172   4.188976e-03            Coniferous woodland\n",
       "13     5.512478   1.895389e-02                     Freshwater\n",
       "7      3.709709   5.420288e-02                            Fen\n",
       "18     3.099160   7.844719e-02                      Saltmarsh\n",
       "16     2.110375   1.464209e-01                  Littoral rock\n",
       "10     1.749114   1.861015e-01                            Bog\n",
       "14     1.030285   3.101837e-01             Supralittoral rock\n",
       "11     0.433261   5.104501e-01                    Inland rock\n",
       "6      0.349987   5.541705e-01                 Acid grassland\n",
       "15     0.275544   5.996806e-01         Supralittoral sediment\n",
       "12     0.131766   7.166368e-01                      Saltwater\n",
       "9      0.107604   7.429141e-01              Heather grassland\n",
       "17     0.025027   8.743110e-01              Littoral sediment\n",
       "8      0.007901   9.291788e-01                        Heather"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Little Owl 10km\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>5828.921155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5828.921155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5828.921155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3559.278760</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3136.796514</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3021.897153</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1533.729395</td>\n",
       "      <td>1.767848e-265</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1264.339002</td>\n",
       "      <td>4.715367e-227</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1264.339002</td>\n",
       "      <td>4.715367e-227</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1264.339002</td>\n",
       "      <td>4.715367e-227</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1264.339002</td>\n",
       "      <td>4.715367e-227</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1264.339002</td>\n",
       "      <td>4.715367e-227</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1105.071811</td>\n",
       "      <td>4.491465e-203</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1072.156171</td>\n",
       "      <td>5.425738e-198</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1072.156171</td>\n",
       "      <td>5.425738e-198</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>874.989492</td>\n",
       "      <td>1.407758e-166</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>850.464782</td>\n",
       "      <td>1.507077e-162</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>805.594552</td>\n",
       "      <td>4.209572e-155</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>578.181222</td>\n",
       "      <td>8.231576e-116</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>413.099124</td>\n",
       "      <td>1.653117e-85</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>334.433765</td>\n",
       "      <td>1.751859e-70</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>78.360343</td>\n",
       "      <td>1.530878e-18</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48.334893</td>\n",
       "      <td>4.499781e-12</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.548857</td>\n",
       "      <td>2.250039e-10</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>32.640589</td>\n",
       "      <td>1.231441e-08</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21.008830</td>\n",
       "      <td>4.781964e-06</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.572271</td>\n",
       "      <td>3.442080e-03</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.147986</td>\n",
       "      <td>4.178254e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.747195</td>\n",
       "      <td>5.300166e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.077734</td>\n",
       "      <td>7.948576e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.992167</td>\n",
       "      <td>8.378370e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.808580</td>\n",
       "      <td>9.387843e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.785733</td>\n",
       "      <td>9.522440e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.798244</td>\n",
       "      <td>1.800390e-01</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.897553</td>\n",
       "      <td>3.435244e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.723618</td>\n",
       "      <td>3.950360e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.238446</td>\n",
       "      <td>6.253705e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.090069</td>\n",
       "      <td>7.641124e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.003703</td>\n",
       "      <td>9.514821e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  5828.921155   0.000000e+00                   Fertiliser K\n",
       "27  5828.921155   0.000000e+00                   Fertiliser N\n",
       "28  5828.921155   0.000000e+00                   Fertiliser P\n",
       "23  3559.278760   0.000000e+00                   Surface type\n",
       "21  3136.796514   0.000000e+00                      Elevation\n",
       "25  3021.897153   0.000000e+00   Inflowing drainage direction\n",
       "2   1533.729395  1.767848e-265                         Arable\n",
       "29  1264.339002  4.715367e-227            Chlorothalonil_10km\n",
       "30  1264.339002  4.715367e-227                Glyphosate_10km\n",
       "31  1264.339002  4.715367e-227                  Mancozeb_10km\n",
       "32  1264.339002  4.715367e-227                Mecoprop-P_10km\n",
       "34  1264.339002  4.715367e-227             Pendimethalin_10km\n",
       "38  1105.071811  4.491465e-203                Tri-allate_10km\n",
       "36  1072.156171  5.425738e-198              Prosulfocarb_10km\n",
       "37  1072.156171  5.425738e-198                   Sulphur_10km\n",
       "33   874.989492  1.407758e-166                Metamitron_10km\n",
       "35   850.464782  1.507077e-162  PropamocarbHydrochloride_10km\n",
       "3    805.594552  4.209572e-155              Improve grassland\n",
       "24   578.181222  8.231576e-116  Outflowing drainage direction\n",
       "0    413.099124   1.653117e-85             Deciduous woodland\n",
       "20   334.433765   1.751859e-70                       Suburban\n",
       "19    78.360343   1.530878e-18                          Urban\n",
       "4     48.334893   4.499781e-12              Neutral grassland\n",
       "5     40.548857   2.250039e-10           Calcareous grassland\n",
       "22    32.640589   1.231441e-08      Cumulative catchment area\n",
       "18    21.008830   4.781964e-06                      Saltmarsh\n",
       "7      8.572271   3.442080e-03                            Fen\n",
       "11     4.147986   4.178254e-02                    Inland rock\n",
       "8      3.747195   5.300166e-02                        Heather\n",
       "16     3.077734   7.948576e-02                  Littoral rock\n",
       "17     2.992167   8.378370e-02              Littoral sediment\n",
       "15     2.808580   9.387843e-02         Supralittoral sediment\n",
       "9      2.785733   9.522440e-02              Heather grassland\n",
       "13     1.798244   1.800390e-01                     Freshwater\n",
       "1      0.897553   3.435244e-01            Coniferous woodland\n",
       "12     0.723618   3.950360e-01                      Saltwater\n",
       "6      0.238446   6.253705e-01                 Acid grassland\n",
       "14     0.090069   7.641124e-01             Supralittoral rock\n",
       "10     0.003703   9.514821e-01                            Bog"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 10km\n"
     ]
    },
    {
     "data": {
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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>26</th>\n",
       "      <td>2376.682544</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2376.682544</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2376.682544</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1570.251863</td>\n",
       "      <td>1.693384e-270</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1262.402052</td>\n",
       "      <td>9.104361e-227</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1175.076676</td>\n",
       "      <td>9.773437e-214</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1175.076676</td>\n",
       "      <td>9.773437e-214</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1175.076676</td>\n",
       "      <td>9.773437e-214</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1175.076676</td>\n",
       "      <td>9.773437e-214</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1175.076676</td>\n",
       "      <td>9.773437e-214</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1125.652741</td>\n",
       "      <td>3.142630e-206</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1111.543858</td>\n",
       "      <td>4.553603e-204</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1111.543858</td>\n",
       "      <td>4.553603e-204</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1034.256684</td>\n",
       "      <td>4.383922e-192</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>842.817101</td>\n",
       "      <td>2.757285e-161</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>838.584038</td>\n",
       "      <td>1.381625e-160</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>719.444452</td>\n",
       "      <td>1.568797e-140</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>542.531902</td>\n",
       "      <td>2.114696e-109</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>465.257609</td>\n",
       "      <td>2.949695e-95</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>449.870098</td>\n",
       "      <td>2.130590e-92</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>407.303250</td>\n",
       "      <td>2.050692e-84</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>128.616823</td>\n",
       "      <td>3.777432e-29</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42.361723</td>\n",
       "      <td>9.028347e-11</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.814586</td>\n",
       "      <td>8.945254e-10</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35.248440</td>\n",
       "      <td>3.278061e-09</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25.421537</td>\n",
       "      <td>4.915887e-07</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.882851</td>\n",
       "      <td>6.483057e-07</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.949163</td>\n",
       "      <td>2.801367e-03</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.116605</td>\n",
       "      <td>4.419752e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.180315</td>\n",
       "      <td>2.292305e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.543835</td>\n",
       "      <td>3.312856e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.696160</td>\n",
       "      <td>1.007080e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.941605</td>\n",
       "      <td>1.636104e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.695076</td>\n",
       "      <td>1.930455e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.982589</td>\n",
       "      <td>3.216502e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.912160</td>\n",
       "      <td>3.396284e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.529182</td>\n",
       "      <td>4.670147e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.054095</td>\n",
       "      <td>8.161030e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000975</td>\n",
       "      <td>9.750981e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  2376.682544   0.000000e+00                   Fertiliser K\n",
       "27  2376.682544   0.000000e+00                   Fertiliser N\n",
       "28  2376.682544   0.000000e+00                   Fertiliser P\n",
       "23  1570.251863  1.693384e-270                   Surface type\n",
       "21  1262.402052  9.104361e-227                      Elevation\n",
       "29  1175.076676  9.773437e-214            Chlorothalonil_10km\n",
       "30  1175.076676  9.773437e-214                Glyphosate_10km\n",
       "31  1175.076676  9.773437e-214                  Mancozeb_10km\n",
       "32  1175.076676  9.773437e-214                Mecoprop-P_10km\n",
       "34  1175.076676  9.773437e-214             Pendimethalin_10km\n",
       "25  1125.652741  3.142630e-206   Inflowing drainage direction\n",
       "36  1111.543858  4.553603e-204              Prosulfocarb_10km\n",
       "37  1111.543858  4.553603e-204                   Sulphur_10km\n",
       "38  1034.256684  4.383922e-192                Tri-allate_10km\n",
       "33   842.817101  2.757285e-161                Metamitron_10km\n",
       "35   838.584038  1.381625e-160  PropamocarbHydrochloride_10km\n",
       "3    719.444452  1.568797e-140              Improve grassland\n",
       "0    542.531902  2.114696e-109             Deciduous woodland\n",
       "2    465.257609   2.949695e-95                         Arable\n",
       "24   449.870098   2.130590e-92  Outflowing drainage direction\n",
       "20   407.303250   2.050692e-84                       Suburban\n",
       "19   128.616823   3.777432e-29                          Urban\n",
       "5     42.361723   9.028347e-11           Calcareous grassland\n",
       "4     37.814586   8.945254e-10              Neutral grassland\n",
       "22    35.248440   3.278061e-09      Cumulative catchment area\n",
       "13    25.421537   4.915887e-07                     Freshwater\n",
       "1     24.882851   6.483057e-07            Coniferous woodland\n",
       "7      8.949163   2.801367e-03                            Fen\n",
       "18     8.116605   4.419752e-03                      Saltmarsh\n",
       "10     5.180315   2.292305e-02                            Bog\n",
       "6      4.543835   3.312856e-02                 Acid grassland\n",
       "16     2.696160   1.007080e-01                  Littoral rock\n",
       "12     1.941605   1.636104e-01                      Saltwater\n",
       "14     1.695076   1.930455e-01             Supralittoral rock\n",
       "15     0.982589   3.216502e-01         Supralittoral sediment\n",
       "17     0.912160   3.396284e-01              Littoral sediment\n",
       "9      0.529182   4.670147e-01              Heather grassland\n",
       "8      0.054095   8.161030e-01                        Heather\n",
       "11     0.000975   9.750981e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mute Swan 10km\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>21</th>\n",
       "      <td>4285.029849</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3424.622932</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2829.482084</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>720.676937</td>\n",
       "      <td>9.649173e-141</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>720.676937</td>\n",
       "      <td>9.649173e-141</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>720.676937</td>\n",
       "      <td>9.649173e-141</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>564.357910</td>\n",
       "      <td>2.468576e-113</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>564.357910</td>\n",
       "      <td>2.468576e-113</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>564.357910</td>\n",
       "      <td>2.468576e-113</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>563.022203</td>\n",
       "      <td>4.288984e-113</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>563.022203</td>\n",
       "      <td>4.288984e-113</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>446.956456</td>\n",
       "      <td>7.437265e-92</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>385.502047</td>\n",
       "      <td>2.781917e-80</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>385.502047</td>\n",
       "      <td>2.781917e-80</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>381.848250</td>\n",
       "      <td>1.379960e-79</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>380.563329</td>\n",
       "      <td>2.424716e-79</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>322.537009</td>\n",
       "      <td>3.554675e-68</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>309.160900</td>\n",
       "      <td>1.433190e-65</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>304.993428</td>\n",
       "      <td>9.344151e-65</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>220.990258</td>\n",
       "      <td>4.368413e-48</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>128.128309</td>\n",
       "      <td>4.777336e-29</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36.403502</td>\n",
       "      <td>1.826230e-09</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.029498</td>\n",
       "      <td>2.155301e-07</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.473657</td>\n",
       "      <td>1.060429e-05</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.927381</td>\n",
       "      <td>2.372524e-05</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.878184</td>\n",
       "      <td>5.767304e-04</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.624678</td>\n",
       "      <td>6.604854e-04</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.096155</td>\n",
       "      <td>2.585619e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.262555</td>\n",
       "      <td>4.079125e-03</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.944114</td>\n",
       "      <td>4.859938e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.085884</td>\n",
       "      <td>7.815924e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.004938</td>\n",
       "      <td>8.176260e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.793136</td>\n",
       "      <td>1.615635e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.064779</td>\n",
       "      <td>2.449789e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.574230</td>\n",
       "      <td>3.254681e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.521071</td>\n",
       "      <td>3.357137e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.430012</td>\n",
       "      <td>6.413195e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.735838</td>\n",
       "      <td>1.877801e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.082067</td>\n",
       "      <td>7.745383e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "21  4285.029849   0.000000e+00                      Elevation\n",
       "25  3424.622932   0.000000e+00   Inflowing drainage direction\n",
       "23  2829.482084   0.000000e+00                   Surface type\n",
       "26   720.676937  9.649173e-141                   Fertiliser K\n",
       "27   720.676937  9.649173e-141                   Fertiliser N\n",
       "28   720.676937  9.649173e-141                   Fertiliser P\n",
       "29   564.357910  2.468576e-113            Chlorothalonil_10km\n",
       "30   564.357910  2.468576e-113                Glyphosate_10km\n",
       "34   564.357910  2.468576e-113             Pendimethalin_10km\n",
       "31   563.022203  4.288984e-113                  Mancozeb_10km\n",
       "32   563.022203  4.288984e-113                Mecoprop-P_10km\n",
       "3    446.956456   7.437265e-92              Improve grassland\n",
       "36   385.502047   2.781917e-80              Prosulfocarb_10km\n",
       "37   385.502047   2.781917e-80                   Sulphur_10km\n",
       "38   381.848250   1.379960e-79                Tri-allate_10km\n",
       "2    380.563329   2.424716e-79                         Arable\n",
       "24   322.537009   3.554675e-68  Outflowing drainage direction\n",
       "35   309.160900   1.433190e-65  PropamocarbHydrochloride_10km\n",
       "33   304.993428   9.344151e-65                Metamitron_10km\n",
       "0    220.990258   4.368413e-48             Deciduous woodland\n",
       "20   128.128309   4.777336e-29                       Suburban\n",
       "19    36.403502   1.826230e-09                          Urban\n",
       "1     27.029498   2.155301e-07            Coniferous woodland\n",
       "22    19.473657   1.060429e-05      Cumulative catchment area\n",
       "6     17.927381   2.372524e-05                 Acid grassland\n",
       "4     11.878184   5.767304e-04              Neutral grassland\n",
       "17    11.624678   6.604854e-04              Littoral sediment\n",
       "18     9.096155   2.585619e-03                      Saltmarsh\n",
       "13     8.262555   4.079125e-03                     Freshwater\n",
       "5      7.944114   4.859938e-03           Calcareous grassland\n",
       "12     7.085884   7.815924e-03                      Saltwater\n",
       "10     7.004938   8.176260e-03                            Bog\n",
       "8      5.793136   1.615635e-02                        Heather\n",
       "15     5.064779   2.449789e-02         Supralittoral sediment\n",
       "7      4.574230   3.254681e-02                            Fen\n",
       "16     4.521071   3.357137e-02                  Littoral rock\n",
       "9      3.430012   6.413195e-02              Heather grassland\n",
       "14     1.735838   1.877801e-01             Supralittoral rock\n",
       "11     0.082067   7.745383e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pheasant 10km\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>21</th>\n",
       "      <td>3918.088889</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3761.808452</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3082.737272</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>990.797875</td>\n",
       "      <td>3.096770e-185</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>990.797875</td>\n",
       "      <td>3.096770e-185</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>990.797875</td>\n",
       "      <td>3.096770e-185</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>754.564125</td>\n",
       "      <td>1.625473e-146</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>754.564125</td>\n",
       "      <td>1.625473e-146</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>754.564125</td>\n",
       "      <td>1.625473e-146</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>752.724864</td>\n",
       "      <td>3.333225e-146</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>752.724864</td>\n",
       "      <td>3.333225e-146</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>588.096290</td>\n",
       "      <td>1.397570e-117</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>527.728254</td>\n",
       "      <td>1.018826e-106</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>525.279971</td>\n",
       "      <td>2.837985e-106</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>525.279971</td>\n",
       "      <td>2.837985e-106</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>489.630631</td>\n",
       "      <td>9.347569e-100</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>456.120338</td>\n",
       "      <td>1.464185e-93</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>395.599590</td>\n",
       "      <td>3.362167e-82</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>393.254409</td>\n",
       "      <td>9.363558e-82</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>242.808633</td>\n",
       "      <td>1.830101e-52</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>133.391825</td>\n",
       "      <td>3.814028e-30</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.297078</td>\n",
       "      <td>3.198252e-09</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31.560526</td>\n",
       "      <td>2.132963e-08</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29.348583</td>\n",
       "      <td>6.585463e-08</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.940059</td>\n",
       "      <td>1.052892e-06</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.710172</td>\n",
       "      <td>4.483242e-05</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.309706</td>\n",
       "      <td>9.352465e-05</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.331895</td>\n",
       "      <td>2.659400e-04</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.343273</td>\n",
       "      <td>6.774440e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.407960</td>\n",
       "      <td>1.141792e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.214079</td>\n",
       "      <td>2.248293e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.454565</td>\n",
       "      <td>3.490084e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.143647</td>\n",
       "      <td>4.188959e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.258077</td>\n",
       "      <td>1.330373e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.621027</td>\n",
       "      <td>2.030596e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.122800</td>\n",
       "      <td>2.894122e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.946162</td>\n",
       "      <td>3.307864e-01</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.687315</td>\n",
       "      <td>4.071534e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.235982</td>\n",
       "      <td>6.271634e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "21  3918.088889   0.000000e+00                      Elevation\n",
       "23  3761.808452   0.000000e+00                   Surface type\n",
       "25  3082.737272   0.000000e+00   Inflowing drainage direction\n",
       "26   990.797875  3.096770e-185                   Fertiliser K\n",
       "27   990.797875  3.096770e-185                   Fertiliser N\n",
       "28   990.797875  3.096770e-185                   Fertiliser P\n",
       "29   754.564125  1.625473e-146            Chlorothalonil_10km\n",
       "30   754.564125  1.625473e-146                Glyphosate_10km\n",
       "34   754.564125  1.625473e-146             Pendimethalin_10km\n",
       "31   752.724864  3.333225e-146                  Mancozeb_10km\n",
       "32   752.724864  3.333225e-146                Mecoprop-P_10km\n",
       "3    588.096290  1.397570e-117              Improve grassland\n",
       "38   527.728254  1.018826e-106                Tri-allate_10km\n",
       "36   525.279971  2.837985e-106              Prosulfocarb_10km\n",
       "37   525.279971  2.837985e-106                   Sulphur_10km\n",
       "24   489.630631  9.347569e-100  Outflowing drainage direction\n",
       "2    456.120338   1.464185e-93                         Arable\n",
       "35   395.599590   3.362167e-82  PropamocarbHydrochloride_10km\n",
       "33   393.254409   9.363558e-82                Metamitron_10km\n",
       "0    242.808633   1.830101e-52             Deciduous woodland\n",
       "20   133.391825   3.814028e-30                       Suburban\n",
       "1     35.297078   3.198252e-09            Coniferous woodland\n",
       "19    31.560526   2.132963e-08                          Urban\n",
       "6     29.348583   6.585463e-08                 Acid grassland\n",
       "22    23.940059   1.052892e-06      Cumulative catchment area\n",
       "4     16.710172   4.483242e-05              Neutral grassland\n",
       "5     15.309706   9.352465e-05           Calcareous grassland\n",
       "8     13.331895   2.659400e-04                        Heather\n",
       "18     7.343273   6.774440e-03                      Saltmarsh\n",
       "9      6.407960   1.141792e-02              Heather grassland\n",
       "17     5.214079   2.248293e-02              Littoral sediment\n",
       "11     4.454565   3.490084e-02                    Inland rock\n",
       "7      4.143647   4.188959e-02                            Fen\n",
       "10     2.258077   1.330373e-01                            Bog\n",
       "14     1.621027   2.030596e-01             Supralittoral rock\n",
       "16     1.122800   2.894122e-01                  Littoral rock\n",
       "13     0.946162   3.307864e-01                     Freshwater\n",
       "15     0.687315   4.071534e-01         Supralittoral sediment\n",
       "12     0.235982   6.271634e-01                      Saltwater"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pink-footed Goose 10km\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",
       "    .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>1682.218122</td>\n",
       "      <td>1.284141e-285</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1663.088957</td>\n",
       "      <td>4.613765e-283</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1582.046768</td>\n",
       "      <td>4.142091e-272</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563.718068</td>\n",
       "      <td>3.216301e-113</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>397.048799</td>\n",
       "      <td>1.786134e-82</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>354.864185</td>\n",
       "      <td>2.008795e-74</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>298.859626</td>\n",
       "      <td>1.482852e-63</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>298.859626</td>\n",
       "      <td>1.482852e-63</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>298.859626</td>\n",
       "      <td>1.482852e-63</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>220.954337</td>\n",
       "      <td>4.441810e-48</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>220.954337</td>\n",
       "      <td>4.441810e-48</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>219.797957</td>\n",
       "      <td>7.595595e-48</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>219.797957</td>\n",
       "      <td>7.595595e-48</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>219.797957</td>\n",
       "      <td>7.595595e-48</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>168.562296</td>\n",
       "      <td>2.020251e-37</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>168.562296</td>\n",
       "      <td>2.020251e-37</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166.093714</td>\n",
       "      <td>6.498543e-37</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>154.320781</td>\n",
       "      <td>1.735544e-34</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>91.642076</td>\n",
       "      <td>2.278422e-21</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>89.444154</td>\n",
       "      <td>6.671769e-21</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>84.711792</td>\n",
       "      <td>6.769856e-20</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>61.599086</td>\n",
       "      <td>6.041977e-15</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>47.686527</td>\n",
       "      <td>6.226801e-12</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.574103</td>\n",
       "      <td>8.113151e-11</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28.459930</td>\n",
       "      <td>1.036802e-07</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22.349456</td>\n",
       "      <td>2.390759e-06</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.292642</td>\n",
       "      <td>4.128544e-06</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.624488</td>\n",
       "      <td>5.835555e-06</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.433700</td>\n",
       "      <td>1.082683e-05</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19.232874</td>\n",
       "      <td>1.201842e-05</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.744420</td>\n",
       "      <td>1.549662e-05</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.768206</td>\n",
       "      <td>3.588010e-04</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.405852</td>\n",
       "      <td>1.271339e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.323252</td>\n",
       "      <td>1.329281e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.309235</td>\n",
       "      <td>6.903595e-03</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.060852</td>\n",
       "      <td>7.925588e-03</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.804120</td>\n",
       "      <td>1.605596e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.561093</td>\n",
       "      <td>3.279693e-02</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.003258</td>\n",
       "      <td>9.544873e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1682.218122  1.284141e-285   Inflowing drainage direction\n",
       "21  1663.088957  4.613765e-283                      Elevation\n",
       "23  1582.046768  4.142091e-272                   Surface type\n",
       "2    563.718068  3.216301e-113                         Arable\n",
       "24   397.048799   1.786134e-82  Outflowing drainage direction\n",
       "3    354.864185   2.008795e-74              Improve grassland\n",
       "26   298.859626   1.482852e-63                   Fertiliser K\n",
       "27   298.859626   1.482852e-63                   Fertiliser N\n",
       "28   298.859626   1.482852e-63                   Fertiliser P\n",
       "31   220.954337   4.441810e-48                  Mancozeb_10km\n",
       "32   220.954337   4.441810e-48                Mecoprop-P_10km\n",
       "29   219.797957   7.595595e-48            Chlorothalonil_10km\n",
       "30   219.797957   7.595595e-48                Glyphosate_10km\n",
       "34   219.797957   7.595595e-48             Pendimethalin_10km\n",
       "36   168.562296   2.020251e-37              Prosulfocarb_10km\n",
       "37   168.562296   2.020251e-37                   Sulphur_10km\n",
       "0    166.093714   6.498543e-37             Deciduous woodland\n",
       "38   154.320781   1.735544e-34                Tri-allate_10km\n",
       "35    91.642076   2.278422e-21  PropamocarbHydrochloride_10km\n",
       "20    89.444154   6.671769e-21                       Suburban\n",
       "33    84.711792   6.769856e-20                Metamitron_10km\n",
       "17    61.599086   6.041977e-15              Littoral sediment\n",
       "18    47.686527   6.226801e-12                      Saltmarsh\n",
       "4     42.574103   8.113151e-11              Neutral grassland\n",
       "19    28.459930   1.036802e-07                          Urban\n",
       "13    22.349456   2.390759e-06                     Freshwater\n",
       "1     21.292642   4.128544e-06            Coniferous woodland\n",
       "6     20.624488   5.835555e-06                 Acid grassland\n",
       "7     19.433700   1.082683e-05                            Fen\n",
       "15    19.232874   1.201842e-05         Supralittoral sediment\n",
       "8     18.744420   1.549662e-05                        Heather\n",
       "10    12.768206   3.588010e-04                            Bog\n",
       "12    10.405852   1.271339e-03                      Saltwater\n",
       "9     10.323252   1.329281e-03              Heather grassland\n",
       "22     7.309235   6.903595e-03      Cumulative catchment area\n",
       "16     7.060852   7.925588e-03                  Littoral rock\n",
       "14     5.804120   1.605596e-02             Supralittoral rock\n",
       "5      4.561093   3.279693e-02           Calcareous grassland\n",
       "11     0.003258   9.544873e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pintail 10km\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>25</th>\n",
       "      <td>1063.762969</td>\n",
       "      <td>1.090230e-196</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>858.206427</td>\n",
       "      <td>7.999859e-164</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>827.358145</td>\n",
       "      <td>1.001460e-158</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>737.488462</td>\n",
       "      <td>1.297224e-143</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>737.488462</td>\n",
       "      <td>1.297224e-143</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>737.488462</td>\n",
       "      <td>1.297224e-143</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>467.480402</td>\n",
       "      <td>1.142876e-95</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>335.651708</td>\n",
       "      <td>1.018161e-70</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>335.651708</td>\n",
       "      <td>1.018161e-70</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>335.651708</td>\n",
       "      <td>1.018161e-70</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>335.651708</td>\n",
       "      <td>1.018161e-70</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>335.651708</td>\n",
       "      <td>1.018161e-70</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>323.738474</td>\n",
       "      <td>2.076614e-68</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>255.396926</td>\n",
       "      <td>5.652887e-55</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>253.487931</td>\n",
       "      <td>1.355850e-54</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>224.661389</td>\n",
       "      <td>7.966749e-49</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>223.400734</td>\n",
       "      <td>1.428744e-48</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>220.602457</td>\n",
       "      <td>5.229386e-48</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>219.507404</td>\n",
       "      <td>8.692034e-48</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>181.209514</td>\n",
       "      <td>5.168309e-40</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144.311367</td>\n",
       "      <td>2.048534e-32</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>112.352423</td>\n",
       "      <td>9.640675e-26</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>92.811561</td>\n",
       "      <td>1.286938e-21</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>72.835214</td>\n",
       "      <td>2.326583e-17</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.173412</td>\n",
       "      <td>8.971952e-14</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>43.439880</td>\n",
       "      <td>5.248672e-11</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29.234581</td>\n",
       "      <td>6.980062e-08</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>28.134624</td>\n",
       "      <td>1.224372e-07</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.136192</td>\n",
       "      <td>1.024709e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.288320</td>\n",
       "      <td>3.847095e-02</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.232586</td>\n",
       "      <td>2.670047e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.131802</td>\n",
       "      <td>2.874875e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.024465</td>\n",
       "      <td>3.115539e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.743897</td>\n",
       "      <td>3.884928e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.298707</td>\n",
       "      <td>5.847395e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.226972</td>\n",
       "      <td>6.338170e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.212749</td>\n",
       "      <td>6.446585e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.105490</td>\n",
       "      <td>7.453641e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.061888</td>\n",
       "      <td>8.035558e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1063.762969  1.090230e-196   Inflowing drainage direction\n",
       "23   858.206427  7.999859e-164                   Surface type\n",
       "21   827.358145  1.001460e-158                      Elevation\n",
       "26   737.488462  1.297224e-143                   Fertiliser K\n",
       "27   737.488462  1.297224e-143                   Fertiliser N\n",
       "28   737.488462  1.297224e-143                   Fertiliser P\n",
       "2    467.480402   1.142876e-95                         Arable\n",
       "29   335.651708   1.018161e-70            Chlorothalonil_10km\n",
       "30   335.651708   1.018161e-70                Glyphosate_10km\n",
       "31   335.651708   1.018161e-70                  Mancozeb_10km\n",
       "32   335.651708   1.018161e-70                Mecoprop-P_10km\n",
       "34   335.651708   1.018161e-70             Pendimethalin_10km\n",
       "3    323.738474   2.076614e-68              Improve grassland\n",
       "38   255.396926   5.652887e-55                Tri-allate_10km\n",
       "24   253.487931   1.355850e-54  Outflowing drainage direction\n",
       "36   224.661389   7.966749e-49              Prosulfocarb_10km\n",
       "37   223.400734   1.428744e-48                   Sulphur_10km\n",
       "33   220.602457   5.229386e-48                Metamitron_10km\n",
       "35   219.507404   8.692034e-48  PropamocarbHydrochloride_10km\n",
       "20   181.209514   5.168309e-40                       Suburban\n",
       "0    144.311367   2.048534e-32             Deciduous woodland\n",
       "17   112.352423   9.640675e-26              Littoral sediment\n",
       "18    92.811561   1.286938e-21                      Saltmarsh\n",
       "19    72.835214   2.326583e-17                          Urban\n",
       "4     56.173412   8.971952e-14              Neutral grassland\n",
       "12    43.439880   5.248672e-11                      Saltwater\n",
       "7     29.234581   6.980062e-08                            Fen\n",
       "15    28.134624   1.224372e-07         Supralittoral sediment\n",
       "13    15.136192   1.024709e-04                     Freshwater\n",
       "22     4.288320   3.847095e-02      Cumulative catchment area\n",
       "10     1.232586   2.670047e-01                            Bog\n",
       "8      1.131802   2.874875e-01                        Heather\n",
       "9      1.024465   3.115539e-01              Heather grassland\n",
       "16     0.743897   3.884928e-01                  Littoral rock\n",
       "6      0.298707   5.847395e-01                 Acid grassland\n",
       "11     0.226972   6.338170e-01                    Inland rock\n",
       "14     0.212749   6.446585e-01             Supralittoral rock\n",
       "5      0.105490   7.453641e-01           Calcareous grassland\n",
       "1      0.061888   8.035558e-01            Coniferous woodland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pochard 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>2050.388140</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2050.388140</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2050.388140</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1829.302778</td>\n",
       "      <td>6.722059e-305</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1683.265511</td>\n",
       "      <td>9.311467e-286</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1558.054709</td>\n",
       "      <td>7.942916e-269</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>787.153513</td>\n",
       "      <td>5.158704e-152</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>646.314832</td>\n",
       "      <td>7.247431e-128</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>646.314832</td>\n",
       "      <td>7.247431e-128</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>646.314832</td>\n",
       "      <td>7.247431e-128</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>646.314832</td>\n",
       "      <td>7.247431e-128</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>646.314832</td>\n",
       "      <td>7.247431e-128</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>570.988315</td>\n",
       "      <td>1.595989e-114</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>562.763291</td>\n",
       "      <td>4.773860e-113</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>540.405062</td>\n",
       "      <td>5.127710e-109</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>539.841810</td>\n",
       "      <td>6.483961e-109</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>537.165552</td>\n",
       "      <td>1.978517e-108</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>530.095858</td>\n",
       "      <td>3.785982e-107</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>522.663739</td>\n",
       "      <td>8.488321e-106</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>412.911486</td>\n",
       "      <td>1.793393e-85</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>317.068854</td>\n",
       "      <td>4.116031e-67</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>152.812725</td>\n",
       "      <td>3.557010e-34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.072834</td>\n",
       "      <td>1.885312e-12</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31.644878</td>\n",
       "      <td>2.043336e-08</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27.531121</td>\n",
       "      <td>1.667173e-07</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25.794352</td>\n",
       "      <td>4.059689e-07</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.028471</td>\n",
       "      <td>1.684083e-06</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.547624</td>\n",
       "      <td>4.881792e-05</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.922847</td>\n",
       "      <td>6.775311e-05</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.692650</td>\n",
       "      <td>3.222677e-03</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.001764</td>\n",
       "      <td>4.708055e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.821431</td>\n",
       "      <td>9.057488e-03</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.737654</td>\n",
       "      <td>1.667345e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.180887</td>\n",
       "      <td>1.398519e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.110568</td>\n",
       "      <td>1.464025e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.764399</td>\n",
       "      <td>1.841902e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.765235</td>\n",
       "      <td>3.817738e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.208957</td>\n",
       "      <td>6.476236e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.133778</td>\n",
       "      <td>7.145760e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  2050.388140   0.000000e+00                   Fertiliser K\n",
       "27  2050.388140   0.000000e+00                   Fertiliser N\n",
       "28  2050.388140   0.000000e+00                   Fertiliser P\n",
       "23  1829.302778  6.722059e-305                   Surface type\n",
       "25  1683.265511  9.311467e-286   Inflowing drainage direction\n",
       "21  1558.054709  7.942916e-269                      Elevation\n",
       "2    787.153513  5.158704e-152                         Arable\n",
       "29   646.314832  7.247431e-128            Chlorothalonil_10km\n",
       "30   646.314832  7.247431e-128                Glyphosate_10km\n",
       "31   646.314832  7.247431e-128                  Mancozeb_10km\n",
       "32   646.314832  7.247431e-128                Mecoprop-P_10km\n",
       "34   646.314832  7.247431e-128             Pendimethalin_10km\n",
       "38   570.988315  1.595989e-114                Tri-allate_10km\n",
       "3    562.763291  4.773860e-113              Improve grassland\n",
       "33   540.405062  5.127710e-109                Metamitron_10km\n",
       "36   539.841810  6.483961e-109              Prosulfocarb_10km\n",
       "37   537.165552  1.978517e-108                   Sulphur_10km\n",
       "24   530.095858  3.785982e-107  Outflowing drainage direction\n",
       "35   522.663739  8.488321e-106  PropamocarbHydrochloride_10km\n",
       "20   412.911486   1.793393e-85                       Suburban\n",
       "0    317.068854   4.116031e-67             Deciduous woodland\n",
       "19   152.812725   3.557010e-34                          Urban\n",
       "4     50.072834   1.885312e-12              Neutral grassland\n",
       "18    31.644878   2.043336e-08                      Saltmarsh\n",
       "22    27.531121   1.667173e-07      Cumulative catchment area\n",
       "13    25.794352   4.059689e-07                     Freshwater\n",
       "7     23.028471   1.684083e-06                            Fen\n",
       "17    16.547624   4.881792e-05              Littoral sediment\n",
       "12    15.922847   6.775311e-05                      Saltwater\n",
       "16     8.692650   3.222677e-03                  Littoral rock\n",
       "10     8.001764   4.708055e-03                            Bog\n",
       "1      6.821431   9.057488e-03            Coniferous woodland\n",
       "15     5.737654   1.667345e-02         Supralittoral sediment\n",
       "14     2.180887   1.398519e-01             Supralittoral rock\n",
       "6      2.110568   1.464025e-01                 Acid grassland\n",
       "5      1.764399   1.841902e-01           Calcareous grassland\n",
       "11     0.765235   3.817738e-01                    Inland rock\n",
       "8      0.208957   6.476236e-01                        Heather\n",
       "9      0.133778   7.145760e-01              Heather grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red-legged Partridge 10km\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>23</th>\n",
       "      <td>4665.558943</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3938.262278</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3622.944260</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3622.944260</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3622.944260</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3326.396445</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1357.041556</td>\n",
       "      <td>1.447426e-240</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1120.672617</td>\n",
       "      <td>1.816264e-205</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1120.672617</td>\n",
       "      <td>1.816264e-205</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1120.672617</td>\n",
       "      <td>1.816264e-205</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1116.380676</td>\n",
       "      <td>8.252559e-205</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1116.380676</td>\n",
       "      <td>8.252559e-205</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>881.088110</td>\n",
       "      <td>1.415221e-167</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>881.088110</td>\n",
       "      <td>1.415221e-167</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>877.316210</td>\n",
       "      <td>5.857134e-167</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>872.220376</td>\n",
       "      <td>4.000470e-166</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>800.790440</td>\n",
       "      <td>2.674164e-154</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>704.555301</td>\n",
       "      <td>5.644440e-138</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>695.277852</td>\n",
       "      <td>2.238974e-136</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>333.873027</td>\n",
       "      <td>2.249262e-70</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>215.130108</td>\n",
       "      <td>6.638999e-47</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>41.288957</td>\n",
       "      <td>1.549560e-10</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35.695864</td>\n",
       "      <td>2.613167e-09</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.346891</td>\n",
       "      <td>5.177697e-09</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31.037194</td>\n",
       "      <td>2.784158e-08</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.036564</td>\n",
       "      <td>3.779031e-05</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.320668</td>\n",
       "      <td>9.298663e-05</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.692745</td>\n",
       "      <td>1.294536e-04</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12.966514</td>\n",
       "      <td>3.228901e-04</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.630734</td>\n",
       "      <td>1.007677e-02</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.922399</td>\n",
       "      <td>1.501478e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.265842</td>\n",
       "      <td>3.898227e-02</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.963782</td>\n",
       "      <td>4.659090e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.159555</td>\n",
       "      <td>7.559756e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.079135</td>\n",
       "      <td>1.494420e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.751981</td>\n",
       "      <td>3.859278e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.685745</td>\n",
       "      <td>4.076894e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.684070</td>\n",
       "      <td>4.082628e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.068934</td>\n",
       "      <td>7.929161e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  4665.558943   0.000000e+00                   Surface type\n",
       "21  3938.262278   0.000000e+00                      Elevation\n",
       "26  3622.944260   0.000000e+00                   Fertiliser K\n",
       "27  3622.944260   0.000000e+00                   Fertiliser N\n",
       "28  3622.944260   0.000000e+00                   Fertiliser P\n",
       "25  3326.396445   0.000000e+00   Inflowing drainage direction\n",
       "2   1357.041556  1.447426e-240                         Arable\n",
       "29  1120.672617  1.816264e-205            Chlorothalonil_10km\n",
       "30  1120.672617  1.816264e-205                Glyphosate_10km\n",
       "34  1120.672617  1.816264e-205             Pendimethalin_10km\n",
       "31  1116.380676  8.252559e-205                  Mancozeb_10km\n",
       "32  1116.380676  8.252559e-205                Mecoprop-P_10km\n",
       "36   881.088110  1.415221e-167              Prosulfocarb_10km\n",
       "37   881.088110  1.415221e-167                   Sulphur_10km\n",
       "3    877.316210  5.857134e-167              Improve grassland\n",
       "38   872.220376  4.000470e-166                Tri-allate_10km\n",
       "24   800.790440  2.674164e-154  Outflowing drainage direction\n",
       "33   704.555301  5.644440e-138                Metamitron_10km\n",
       "35   695.277852  2.238974e-136  PropamocarbHydrochloride_10km\n",
       "0    333.873027   2.249262e-70             Deciduous woodland\n",
       "20   215.130108   6.638999e-47                       Suburban\n",
       "5     41.288957   1.549560e-10           Calcareous grassland\n",
       "22    35.695864   2.613167e-09      Cumulative catchment area\n",
       "4     34.346891   5.177697e-09              Neutral grassland\n",
       "19    31.037194   2.784158e-08                          Urban\n",
       "1     17.036564   3.779031e-05            Coniferous woodland\n",
       "8     15.320668   9.298663e-05                        Heather\n",
       "7     14.692745   1.294536e-04                            Fen\n",
       "18    12.966514   3.228901e-04                      Saltmarsh\n",
       "13     6.630734   1.007677e-02                     Freshwater\n",
       "10     5.922399   1.501478e-02                            Bog\n",
       "12     4.265842   3.898227e-02                      Saltwater\n",
       "11     3.963782   4.659090e-02                    Inland rock\n",
       "6      3.159555   7.559756e-02                 Acid grassland\n",
       "17     2.079135   1.494420e-01              Littoral sediment\n",
       "16     0.751981   3.859278e-01                  Littoral rock\n",
       "14     0.685745   4.076894e-01             Supralittoral rock\n",
       "9      0.684070   4.082628e-01              Heather grassland\n",
       "15     0.068934   7.929161e-01         Supralittoral sediment"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ring-necked Parakeet 10km\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>20</th>\n",
       "      <td>423.338015</td>\n",
       "      <td>1.956491e-87</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>403.889203</td>\n",
       "      <td>9.058641e-84</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>403.889203</td>\n",
       "      <td>9.058641e-84</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>403.889203</td>\n",
       "      <td>9.058641e-84</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>263.539369</td>\n",
       "      <td>1.363343e-56</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>223.881090</td>\n",
       "      <td>1.143620e-48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>198.446382</td>\n",
       "      <td>1.587181e-43</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>191.636187</td>\n",
       "      <td>3.852663e-42</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>178.348178</td>\n",
       "      <td>1.989223e-39</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>150.535710</td>\n",
       "      <td>1.051957e-33</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>139.678605</td>\n",
       "      <td>1.876150e-31</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>138.613261</td>\n",
       "      <td>3.123937e-31</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>138.613261</td>\n",
       "      <td>3.123937e-31</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>124.989699</td>\n",
       "      <td>2.162435e-28</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>115.763452</td>\n",
       "      <td>1.851985e-26</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>115.763452</td>\n",
       "      <td>1.851985e-26</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>115.408999</td>\n",
       "      <td>2.198059e-26</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>115.408999</td>\n",
       "      <td>2.198059e-26</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>115.408999</td>\n",
       "      <td>2.198059e-26</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>84.964310</td>\n",
       "      <td>5.981668e-20</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>81.556521</td>\n",
       "      <td>3.183136e-19</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>54.149221</td>\n",
       "      <td>2.460819e-13</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.990900</td>\n",
       "      <td>3.978728e-11</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15.485837</td>\n",
       "      <td>8.524703e-05</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.050661</td>\n",
       "      <td>2.650526e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.728565</td>\n",
       "      <td>2.975315e-02</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.543688</td>\n",
       "      <td>5.988106e-02</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.422349</td>\n",
       "      <td>6.442985e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.669694</td>\n",
       "      <td>1.964114e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.383683</td>\n",
       "      <td>2.395797e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.916534</td>\n",
       "      <td>3.384733e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.632534</td>\n",
       "      <td>4.264981e-01</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.426454</td>\n",
       "      <td>5.137902e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.367288</td>\n",
       "      <td>5.445378e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.326919</td>\n",
       "      <td>5.675272e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.301721</td>\n",
       "      <td>5.828517e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.181267</td>\n",
       "      <td>6.703211e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.060003</td>\n",
       "      <td>8.065096e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.056731</td>\n",
       "      <td>8.117571e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       F Score       P Value                      Attribute\n",
       "20  423.338015  1.956491e-87                       Suburban\n",
       "26  403.889203  9.058641e-84                   Fertiliser K\n",
       "27  403.889203  9.058641e-84                   Fertiliser N\n",
       "28  403.889203  9.058641e-84                   Fertiliser P\n",
       "23  263.539369  1.363343e-56                   Surface type\n",
       "19  223.881090  1.143620e-48                          Urban\n",
       "25  198.446382  1.587181e-43   Inflowing drainage direction\n",
       "0   191.636187  3.852663e-42             Deciduous woodland\n",
       "21  178.348178  1.989223e-39                      Elevation\n",
       "24  150.535710  1.051957e-33  Outflowing drainage direction\n",
       "3   139.678605  1.876150e-31              Improve grassland\n",
       "36  138.613261  3.123937e-31              Prosulfocarb_10km\n",
       "37  138.613261  3.123937e-31                   Sulphur_10km\n",
       "38  124.989699  2.162435e-28                Tri-allate_10km\n",
       "31  115.763452  1.851985e-26                  Mancozeb_10km\n",
       "32  115.763452  1.851985e-26                Mecoprop-P_10km\n",
       "29  115.408999  2.198059e-26            Chlorothalonil_10km\n",
       "30  115.408999  2.198059e-26                Glyphosate_10km\n",
       "34  115.408999  2.198059e-26             Pendimethalin_10km\n",
       "35   84.964310  5.981668e-20  PropamocarbHydrochloride_10km\n",
       "33   81.556521  3.183136e-19                Metamitron_10km\n",
       "22   54.149221  2.460819e-13      Cumulative catchment area\n",
       "2    43.990900  3.978728e-11                         Arable\n",
       "7    15.485837  8.524703e-05                            Fen\n",
       "12    9.050661  2.650526e-03                      Saltwater\n",
       "4     4.728565  2.975315e-02              Neutral grassland\n",
       "1     3.543688  5.988106e-02            Coniferous woodland\n",
       "6     3.422349  6.442985e-02                 Acid grassland\n",
       "9     1.669694  1.964114e-01              Heather grassland\n",
       "10    1.383683  2.395797e-01                            Bog\n",
       "8     0.916534  3.384733e-01                        Heather\n",
       "13    0.632534  4.264981e-01                     Freshwater\n",
       "11    0.426454  5.137902e-01                    Inland rock\n",
       "17    0.367288  5.445378e-01              Littoral sediment\n",
       "14    0.326919  5.675272e-01             Supralittoral rock\n",
       "16    0.301721  5.828517e-01                  Littoral rock\n",
       "18    0.181267  6.703211e-01                      Saltmarsh\n",
       "5     0.060003  8.065096e-01           Calcareous grassland\n",
       "15    0.056731  8.117571e-01         Supralittoral sediment"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rock Dove 10km\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>21</th>\n",
       "      <td>3705.998132</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3523.491473</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3167.612036</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1224.817561</td>\n",
       "      <td>3.401946e-221</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1224.817561</td>\n",
       "      <td>3.401946e-221</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1224.817561</td>\n",
       "      <td>3.401946e-221</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>661.138165</td>\n",
       "      <td>1.864195e-130</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>661.138165</td>\n",
       "      <td>1.864195e-130</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>661.138165</td>\n",
       "      <td>1.864195e-130</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>659.116253</td>\n",
       "      <td>4.197999e-130</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>659.116253</td>\n",
       "      <td>4.197999e-130</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>657.131199</td>\n",
       "      <td>9.319155e-130</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>574.453772</td>\n",
       "      <td>3.822481e-115</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>551.770288</td>\n",
       "      <td>4.542649e-111</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>511.929896</td>\n",
       "      <td>7.675625e-104</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>511.929896</td>\n",
       "      <td>7.675625e-104</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>494.012796</td>\n",
       "      <td>1.463912e-100</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>423.397530</td>\n",
       "      <td>1.906764e-87</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>410.403601</td>\n",
       "      <td>5.329040e-85</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>327.864746</td>\n",
       "      <td>3.283643e-69</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>215.806810</td>\n",
       "      <td>4.847374e-47</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>63.875294</td>\n",
       "      <td>1.953389e-15</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26.401491</td>\n",
       "      <td>2.973417e-07</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.443583</td>\n",
       "      <td>4.860554e-07</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.105638</td>\n",
       "      <td>6.155325e-05</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.987362</td>\n",
       "      <td>3.193321e-04</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.549173</td>\n",
       "      <td>4.031728e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.818544</td>\n",
       "      <td>1.746353e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.049166</td>\n",
       "      <td>2.652688e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.408340</td>\n",
       "      <td>3.765535e-03</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.278622</td>\n",
       "      <td>7.021903e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.951936</td>\n",
       "      <td>8.421371e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.807778</td>\n",
       "      <td>9.126817e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.410824</td>\n",
       "      <td>1.139956e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.211470</td>\n",
       "      <td>1.275272e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.769668</td>\n",
       "      <td>5.229508e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.376299</td>\n",
       "      <td>1.233076e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.048863</td>\n",
       "      <td>3.058619e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.076750</td>\n",
       "      <td>7.817728e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "21  3705.998132   0.000000e+00                      Elevation\n",
       "23  3523.491473   0.000000e+00                   Surface type\n",
       "25  3167.612036   0.000000e+00   Inflowing drainage direction\n",
       "26  1224.817561  3.401946e-221                   Fertiliser K\n",
       "27  1224.817561  3.401946e-221                   Fertiliser N\n",
       "28  1224.817561  3.401946e-221                   Fertiliser P\n",
       "29   661.138165  1.864195e-130            Chlorothalonil_10km\n",
       "30   661.138165  1.864195e-130                Glyphosate_10km\n",
       "34   661.138165  1.864195e-130             Pendimethalin_10km\n",
       "31   659.116253  4.197999e-130                  Mancozeb_10km\n",
       "32   659.116253  4.197999e-130                Mecoprop-P_10km\n",
       "3    657.131199  9.319155e-130              Improve grassland\n",
       "2    574.453772  3.822481e-115                         Arable\n",
       "24   551.770288  4.542649e-111  Outflowing drainage direction\n",
       "36   511.929896  7.675625e-104              Prosulfocarb_10km\n",
       "37   511.929896  7.675625e-104                   Sulphur_10km\n",
       "38   494.012796  1.463912e-100                Tri-allate_10km\n",
       "33   423.397530   1.906764e-87                Metamitron_10km\n",
       "35   410.403601   5.329040e-85  PropamocarbHydrochloride_10km\n",
       "0    327.864746   3.283643e-69             Deciduous woodland\n",
       "20   215.806810   4.847374e-47                       Suburban\n",
       "19    63.875294   1.953389e-15                          Urban\n",
       "22    26.401491   2.973417e-07      Cumulative catchment area\n",
       "4     25.443583   4.860554e-07              Neutral grassland\n",
       "1     16.105638   6.155325e-05            Coniferous woodland\n",
       "5     12.987362   3.193321e-04           Calcareous grassland\n",
       "13    12.549173   4.031728e-04                     Freshwater\n",
       "18     9.818544   1.746353e-03                      Saltmarsh\n",
       "6      9.049166   2.652688e-03                 Acid grassland\n",
       "7      8.408340   3.765535e-03                            Fen\n",
       "9      7.278622   7.021903e-03              Heather grassland\n",
       "10     6.951936   8.421371e-03                            Bog\n",
       "17     6.807778   9.126817e-03              Littoral sediment\n",
       "16     6.410824   1.139956e-02                  Littoral rock\n",
       "15     6.211470   1.275272e-02         Supralittoral sediment\n",
       "14     3.769668   5.229508e-02             Supralittoral rock\n",
       "12     2.376299   1.233076e-01                      Saltwater\n",
       "8      1.048863   3.058619e-01                        Heather\n",
       "11     0.076750   7.817728e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ruddy Duck 10km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>394.369237</td>\n",
       "      <td>5.753642e-82</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>394.369237</td>\n",
       "      <td>5.753642e-82</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>394.369237</td>\n",
       "      <td>5.753642e-82</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>222.092265</td>\n",
       "      <td>2.620429e-48</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>201.385984</td>\n",
       "      <td>4.016297e-44</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>172.842486</td>\n",
       "      <td>2.671584e-38</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165.229518</td>\n",
       "      <td>9.785071e-37</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>161.746426</td>\n",
       "      <td>5.100030e-36</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>132.834071</td>\n",
       "      <td>4.984261e-30</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>128.609266</td>\n",
       "      <td>3.791177e-29</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>128.609266</td>\n",
       "      <td>3.791177e-29</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>128.609266</td>\n",
       "      <td>3.791177e-29</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>128.609266</td>\n",
       "      <td>3.791177e-29</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>128.609266</td>\n",
       "      <td>3.791177e-29</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>113.209410</td>\n",
       "      <td>6.367982e-26</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>100.242727</td>\n",
       "      <td>3.438663e-23</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>100.242727</td>\n",
       "      <td>3.438663e-23</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>96.047352</td>\n",
       "      <td>2.653861e-22</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>88.028256</td>\n",
       "      <td>1.333796e-20</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>85.793209</td>\n",
       "      <td>3.984836e-20</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>71.341242</td>\n",
       "      <td>4.862584e-17</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66.548473</td>\n",
       "      <td>5.196588e-16</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.792935</td>\n",
       "      <td>7.978585e-13</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>42.712031</td>\n",
       "      <td>7.569164e-11</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.652417</td>\n",
       "      <td>1.113138e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.738880</td>\n",
       "      <td>9.485083e-03</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.537197</td>\n",
       "      <td>6.011553e-02</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.864963</td>\n",
       "      <td>1.721681e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.561720</td>\n",
       "      <td>2.115231e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.540714</td>\n",
       "      <td>2.146205e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.471490</td>\n",
       "      <td>2.252181e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.412058</td>\n",
       "      <td>2.348207e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.686957</td>\n",
       "      <td>4.072755e-01</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.581560</td>\n",
       "      <td>4.457690e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.540364</td>\n",
       "      <td>4.623466e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.278701</td>\n",
       "      <td>5.975977e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.059568</td>\n",
       "      <td>8.071982e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.055293</td>\n",
       "      <td>8.141138e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.054017</td>\n",
       "      <td>8.162336e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       F Score       P Value                      Attribute\n",
       "26  394.369237  5.753642e-82                   Fertiliser K\n",
       "27  394.369237  5.753642e-82                   Fertiliser N\n",
       "28  394.369237  5.753642e-82                   Fertiliser P\n",
       "20  222.092265  2.620429e-48                       Suburban\n",
       "23  201.385984  4.016297e-44                   Surface type\n",
       "25  172.842486  2.671584e-38   Inflowing drainage direction\n",
       "2   165.229518  9.785071e-37                         Arable\n",
       "21  161.746426  5.100030e-36                      Elevation\n",
       "19  132.834071  4.984261e-30                          Urban\n",
       "29  128.609266  3.791177e-29            Chlorothalonil_10km\n",
       "30  128.609266  3.791177e-29                Glyphosate_10km\n",
       "31  128.609266  3.791177e-29                  Mancozeb_10km\n",
       "32  128.609266  3.791177e-29                Mecoprop-P_10km\n",
       "34  128.609266  3.791177e-29             Pendimethalin_10km\n",
       "38  113.209410  6.367982e-26                Tri-allate_10km\n",
       "36  100.242727  3.438663e-23              Prosulfocarb_10km\n",
       "37  100.242727  3.438663e-23                   Sulphur_10km\n",
       "33   96.047352  2.653861e-22                Metamitron_10km\n",
       "35   88.028256  1.333796e-20  PropamocarbHydrochloride_10km\n",
       "24   85.793209  3.984836e-20  Outflowing drainage direction\n",
       "4    71.341242  4.862584e-17              Neutral grassland\n",
       "3    66.548473  5.196588e-16              Improve grassland\n",
       "0    51.792935  7.978585e-13             Deciduous woodland\n",
       "22   42.712031  7.569164e-11      Cumulative catchment area\n",
       "12   10.652417  1.113138e-03                      Saltwater\n",
       "17    6.738880  9.485083e-03              Littoral sediment\n",
       "13    3.537197  6.011553e-02                     Freshwater\n",
       "18    1.864963  1.721681e-01                      Saltmarsh\n",
       "9     1.561720  2.115231e-01              Heather grassland\n",
       "8     1.540714  2.146205e-01                        Heather\n",
       "10    1.471490  2.252181e-01                            Bog\n",
       "6     1.412058  2.348207e-01                 Acid grassland\n",
       "7     0.686957  4.072755e-01                            Fen\n",
       "11    0.581560  4.457690e-01                    Inland rock\n",
       "14    0.540364  4.623466e-01             Supralittoral rock\n",
       "16    0.278701  5.975977e-01                  Littoral rock\n",
       "15    0.059568  8.071982e-01         Supralittoral sediment\n",
       "1     0.055293  8.141138e-01            Coniferous woodland\n",
       "5     0.054017  8.162336e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whooper Swan 10km\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>25</th>\n",
       "      <td>1078.343958</td>\n",
       "      <td>5.965819e-199</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1035.304027</td>\n",
       "      <td>3.004691e-192</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>961.388454</td>\n",
       "      <td>1.486786e-180</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>318.424551</td>\n",
       "      <td>2.241705e-67</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>265.819805</td>\n",
       "      <td>4.812689e-57</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>203.103471</td>\n",
       "      <td>1.800704e-44</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>163.930255</td>\n",
       "      <td>1.810961e-36</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>163.930255</td>\n",
       "      <td>1.810961e-36</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>163.022772</td>\n",
       "      <td>2.784317e-36</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>163.022772</td>\n",
       "      <td>2.784317e-36</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>163.022772</td>\n",
       "      <td>2.784317e-36</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158.129913</td>\n",
       "      <td>2.838432e-35</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>131.504877</td>\n",
       "      <td>9.433368e-30</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>131.504877</td>\n",
       "      <td>9.433368e-30</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>131.504877</td>\n",
       "      <td>9.433368e-30</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>89.947700</td>\n",
       "      <td>5.215517e-21</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>74.870047</td>\n",
       "      <td>8.532626e-18</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>74.870047</td>\n",
       "      <td>8.532626e-18</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>66.652066</td>\n",
       "      <td>4.936860e-16</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>63.408944</td>\n",
       "      <td>2.461532e-15</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>62.509655</td>\n",
       "      <td>3.845242e-15</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55.135835</td>\n",
       "      <td>1.504615e-13</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>48.778897</td>\n",
       "      <td>3.602661e-12</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38.969209</td>\n",
       "      <td>4.992149e-10</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.589008</td>\n",
       "      <td>1.618513e-07</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.078754</td>\n",
       "      <td>1.640978e-06</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21.307649</td>\n",
       "      <td>4.096605e-06</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.965698</td>\n",
       "      <td>4.889980e-06</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.823870</td>\n",
       "      <td>5.262746e-06</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19.643447</td>\n",
       "      <td>9.708795e-06</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.278206</td>\n",
       "      <td>1.173841e-05</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.340362</td>\n",
       "      <td>9.202786e-05</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.220989</td>\n",
       "      <td>9.799603e-05</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.947603</td>\n",
       "      <td>1.919085e-04</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.718829</td>\n",
       "      <td>3.683531e-04</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.465741</td>\n",
       "      <td>4.214970e-04</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.357531</td>\n",
       "      <td>2.440699e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.941124</td>\n",
       "      <td>3.320768e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.003208</td>\n",
       "      <td>9.548383e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1078.343958  5.965819e-199   Inflowing drainage direction\n",
       "21  1035.304027  3.004691e-192                      Elevation\n",
       "23   961.388454  1.486786e-180                   Surface type\n",
       "3    318.424551   2.241705e-67              Improve grassland\n",
       "24   265.819805   4.812689e-57  Outflowing drainage direction\n",
       "2    203.103471   1.800704e-44                         Arable\n",
       "31   163.930255   1.810961e-36                  Mancozeb_10km\n",
       "32   163.930255   1.810961e-36                Mecoprop-P_10km\n",
       "29   163.022772   2.784317e-36            Chlorothalonil_10km\n",
       "30   163.022772   2.784317e-36                Glyphosate_10km\n",
       "34   163.022772   2.784317e-36             Pendimethalin_10km\n",
       "0    158.129913   2.838432e-35             Deciduous woodland\n",
       "26   131.504877   9.433368e-30                   Fertiliser K\n",
       "27   131.504877   9.433368e-30                   Fertiliser N\n",
       "28   131.504877   9.433368e-30                   Fertiliser P\n",
       "38    89.947700   5.215517e-21                Tri-allate_10km\n",
       "36    74.870047   8.532626e-18              Prosulfocarb_10km\n",
       "37    74.870047   8.532626e-18                   Sulphur_10km\n",
       "20    66.652066   4.936860e-16                       Suburban\n",
       "35    63.408944   2.461532e-15  PropamocarbHydrochloride_10km\n",
       "33    62.509655   3.845242e-15                Metamitron_10km\n",
       "9     55.135835   1.504615e-13              Heather grassland\n",
       "17    48.778897   3.602661e-12              Littoral sediment\n",
       "16    38.969209   4.992149e-10                  Littoral rock\n",
       "7     27.589008   1.618513e-07                            Fen\n",
       "1     23.078754   1.640978e-06            Coniferous woodland\n",
       "14    21.307649   4.096605e-06             Supralittoral rock\n",
       "19    20.965698   4.889980e-06                          Urban\n",
       "18    20.823870   5.262746e-06                      Saltmarsh\n",
       "15    19.643447   9.708795e-06         Supralittoral sediment\n",
       "22    19.278206   1.173841e-05      Cumulative catchment area\n",
       "6     15.340362   9.202786e-05                 Acid grassland\n",
       "10    15.220989   9.799603e-05                            Bog\n",
       "13    13.947603   1.919085e-04                     Freshwater\n",
       "8     12.718829   3.683531e-04                        Heather\n",
       "4     12.465741   4.214970e-04              Neutral grassland\n",
       "11     1.357531   2.440699e-01                    Inland rock\n",
       "12     0.941124   3.320768e-01                      Saltwater\n",
       "5      0.003208   9.548383e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wigeon 10km\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>2620.080822</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2480.090800</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2181.122669</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>741.087843</td>\n",
       "      <td>3.162856e-144</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>741.087843</td>\n",
       "      <td>3.162856e-144</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>741.087843</td>\n",
       "      <td>3.162856e-144</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>510.895940</td>\n",
       "      <td>1.185521e-103</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>465.092494</td>\n",
       "      <td>3.165023e-95</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>453.697709</td>\n",
       "      <td>4.131083e-93</td>\n",
       "      <td>Chlorothalonil_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>453.697709</td>\n",
       "      <td>4.131083e-93</td>\n",
       "      <td>Glyphosate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>453.697709</td>\n",
       "      <td>4.131083e-93</td>\n",
       "      <td>Mancozeb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>453.697709</td>\n",
       "      <td>4.131083e-93</td>\n",
       "      <td>Mecoprop-P_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>453.697709</td>\n",
       "      <td>4.131083e-93</td>\n",
       "      <td>Pendimethalin_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>411.757352</td>\n",
       "      <td>2.959990e-85</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>340.962220</td>\n",
       "      <td>9.579211e-72</td>\n",
       "      <td>Tri-allate_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>337.576522</td>\n",
       "      <td>4.320640e-71</td>\n",
       "      <td>Prosulfocarb_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>337.576522</td>\n",
       "      <td>4.320640e-71</td>\n",
       "      <td>Sulphur_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>267.589534</td>\n",
       "      <td>2.146332e-57</td>\n",
       "      <td>Metamitron_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>259.434733</td>\n",
       "      <td>8.902052e-56</td>\n",
       "      <td>PropamocarbHydrochloride_10km</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>204.705588</td>\n",
       "      <td>8.524358e-45</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>193.263684</td>\n",
       "      <td>1.796472e-42</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>77.447594</td>\n",
       "      <td>2.398464e-18</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>31.117309</td>\n",
       "      <td>2.672853e-08</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.396261</td>\n",
       "      <td>3.859326e-08</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28.206138</td>\n",
       "      <td>1.180414e-07</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24.248589</td>\n",
       "      <td>8.983058e-07</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.876736</td>\n",
       "      <td>3.052102e-06</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20.162283</td>\n",
       "      <td>7.416147e-06</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.487899</td>\n",
       "      <td>2.984583e-05</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.260356</td>\n",
       "      <td>5.675365e-05</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.823912</td>\n",
       "      <td>7.136713e-05</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.044356</td>\n",
       "      <td>1.823323e-04</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.813446</td>\n",
       "      <td>3.502691e-04</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.336977</td>\n",
       "      <td>3.915851e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.098755</td>\n",
       "      <td>4.463355e-03</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.360418</td>\n",
       "      <td>3.687781e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.656884</td>\n",
       "      <td>4.177344e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.171550</td>\n",
       "      <td>6.787714e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.002713</td>\n",
       "      <td>9.584656e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  2620.080822   0.000000e+00   Inflowing drainage direction\n",
       "21  2480.090800   0.000000e+00                      Elevation\n",
       "23  2181.122669   0.000000e+00                   Surface type\n",
       "26   741.087843  3.162856e-144                   Fertiliser K\n",
       "27   741.087843  3.162856e-144                   Fertiliser N\n",
       "28   741.087843  3.162856e-144                   Fertiliser P\n",
       "3    510.895940  1.185521e-103              Improve grassland\n",
       "2    465.092494   3.165023e-95                         Arable\n",
       "29   453.697709   4.131083e-93            Chlorothalonil_10km\n",
       "30   453.697709   4.131083e-93                Glyphosate_10km\n",
       "31   453.697709   4.131083e-93                  Mancozeb_10km\n",
       "32   453.697709   4.131083e-93                Mecoprop-P_10km\n",
       "34   453.697709   4.131083e-93             Pendimethalin_10km\n",
       "24   411.757352   2.959990e-85  Outflowing drainage direction\n",
       "38   340.962220   9.579211e-72                Tri-allate_10km\n",
       "36   337.576522   4.320640e-71              Prosulfocarb_10km\n",
       "37   337.576522   4.320640e-71                   Sulphur_10km\n",
       "33   267.589534   2.146332e-57                Metamitron_10km\n",
       "35   259.434733   8.902052e-56  PropamocarbHydrochloride_10km\n",
       "0    204.705588   8.524358e-45             Deciduous woodland\n",
       "20   193.263684   1.796472e-42                       Suburban\n",
       "19    77.447594   2.398464e-18                          Urban\n",
       "17    31.117309   2.672853e-08              Littoral sediment\n",
       "4     30.396261   3.859326e-08              Neutral grassland\n",
       "18    28.206138   1.180414e-07                      Saltmarsh\n",
       "22    24.248589   8.983058e-07      Cumulative catchment area\n",
       "12    21.876736   3.052102e-06                      Saltwater\n",
       "13    20.162283   7.416147e-06                     Freshwater\n",
       "7     17.487899   2.984583e-05                            Fen\n",
       "8     16.260356   5.675365e-05                        Heather\n",
       "1     15.823912   7.136713e-05            Coniferous woodland\n",
       "9     14.044356   1.823323e-04              Heather grassland\n",
       "15    12.813446   3.502691e-04         Supralittoral sediment\n",
       "5      8.336977   3.915851e-03           Calcareous grassland\n",
       "16     8.098755   4.463355e-03                  Littoral rock\n",
       "6      4.360418   3.687781e-02                 Acid grassland\n",
       "14     0.656884   4.177344e-01             Supralittoral rock\n",
       "10     0.171550   6.787714e-01                            Bog\n",
       "11     0.002713   9.584656e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dict in df_dicts:\n",
    "    print(dict['name'])\n",
    "    display(dict['kbest']['Dataframe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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