nx-defis-eau.ipynb

{
 "metadata": {
  "kernelspec": {
   "language": "python",
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.7.12",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat_minor": 4,
 "nbformat": 4,
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "execution": {
     "iopub.status.busy": "2022-12-19T14:30:26.902142Z",
     "iopub.execute_input": "2022-12-19T14:30:26.902588Z",
     "iopub.status.idle": "2022-12-19T14:30:26.907765Z",
     "shell.execute_reply.started": "2022-12-19T14:30:26.902549Z",
     "shell.execute_reply": "2022-12-19T14:30:26.906866Z"
    },
    "trusted": true
   },
   "execution_count": 122,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "class_alt = pd.read_csv('/kaggle/input/eau-dataset/données/classe_alteration_seq_eau.csv')\n",
    "list_faune = pd.read_csv('/kaggle/input/eau-dataset/données/liste_faunistique_floristique.csv')\n",
    "physchim = pd.read_csv('/kaggle/input/eau-dataset/données/physicochimie.csv')\n",
    "station = pd.read_csv('/kaggle/input/eau-dataset/données/station.csv')\n",
    "taxon = pd.read_csv('/kaggle/input/eau-dataset/données/taxon.csv')\n",
    "taxon_denom = pd.read_csv('/kaggle/input/eau-dataset/données/taxon_denombre.csv')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:36:38.814335Z",
     "iopub.execute_input": "2022-12-19T13:36:38.814629Z",
     "iopub.status.idle": "2022-12-19T13:36:41.101639Z",
     "shell.execute_reply.started": "2022-12-19T13:36:38.814605Z",
     "shell.execute_reply": "2022-12-19T13:36:41.100388Z"
    },
    "trusted": true
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "class_alt"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:36:41.103875Z",
     "iopub.execute_input": "2022-12-19T13:36:41.104227Z",
     "iopub.status.idle": "2022-12-19T13:36:41.135299Z",
     "shell.execute_reply.started": "2022-12-19T13:36:41.104203Z",
     "shell.execute_reply": "2022-12-19T13:36:41.134042Z"
    },
    "trusted": true
   },
   "execution_count": 6,
   "outputs": [
    {
     "execution_count": 6,
     "output_type": "execute_result",
     "data": {
      "text/plain": "         id  parametre_id                   nom  alteration_id nom.1  valeur  \\\n0     10231          1301  Température de l'Eau              0  TEMP       1   \n1     10232          1301  Température de l'Eau              0  TEMP       2   \n2     10233          1301  Température de l'Eau              0  TEMP       3   \n3     10234          1301  Température de l'Eau              0  TEMP       4   \n4     10235          1301  Température de l'Eau              0  TEMP       5   \n...     ...           ...                   ...            ...   ...     ...   \n1096  10659          1383                  Zinc             16  MPMI       2   \n1097  10660          1383                  Zinc             16  MPMI       3   \n1098  10661          1383                  Zinc             16  MPMI       4   \n1099  10662          1383                  Zinc             16  MPMI       5   \n1100  10597          1389                Chrome             16  MPMI       5   \n\n     libelle borne_inf_incluse borne_sup_incluse           borne_inf  \\\n0       Bleu                 t                 f                  \\N   \n1       Vert                 t                 f                  24   \n2      Jaune                 t                 f                25.5   \n3     Orange                 t                 f                  27   \n4      Rouge                 t                 f                  28   \n...      ...               ...               ...                 ...   \n1096    Vert                 t                 f  1.3999999999999999   \n1097   Jaune                 t                 f                  14   \n1098  Orange                 t                 f                 140   \n1099   Rouge                 t                 f                 330   \n1100   Rouge                 t                 f                  50   \n\n     borne_sup  unite_mesure_code              libelle.1 norme  \n0           24                 27          degré Celsius   DCE  \n1         25.5                 27          degré Celsius   DCE  \n2           27                 27          degré Celsius   DCE  \n3           28                 27          degré Celsius   DCE  \n4           \\N                 27          degré Celsius   DCE  \n...        ...                ...                    ...   ...  \n1096        14                133  microgramme par litre   SEQ  \n1097       140                133  microgramme par litre   SEQ  \n1098       330                133  microgramme par litre   SEQ  \n1099        \\N                133  microgramme par litre   SEQ  \n1100        \\N                133  microgramme par litre   SEQ  \n\n[1101 rows x 14 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>parametre_id</th>\n      <th>nom</th>\n      <th>alteration_id</th>\n      <th>nom.1</th>\n      <th>valeur</th>\n      <th>libelle</th>\n      <th>borne_inf_incluse</th>\n      <th>borne_sup_incluse</th>\n      <th>borne_inf</th>\n      <th>borne_sup</th>\n      <th>unite_mesure_code</th>\n      <th>libelle.1</th>\n      <th>norme</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10231</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>0</td>\n      <td>TEMP</td>\n      <td>1</td>\n      <td>Bleu</td>\n      <td>t</td>\n      <td>f</td>\n      <td>\\N</td>\n      <td>24</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n      <td>DCE</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10232</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>0</td>\n      <td>TEMP</td>\n      <td>2</td>\n      <td>Vert</td>\n      <td>t</td>\n      <td>f</td>\n      <td>24</td>\n      <td>25.5</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n      <td>DCE</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10233</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>0</td>\n      <td>TEMP</td>\n      <td>3</td>\n      <td>Jaune</td>\n      <td>t</td>\n      <td>f</td>\n      <td>25.5</td>\n      <td>27</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n      <td>DCE</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10234</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>0</td>\n      <td>TEMP</td>\n      <td>4</td>\n      <td>Orange</td>\n      <td>t</td>\n      <td>f</td>\n      <td>27</td>\n      <td>28</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n      <td>DCE</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10235</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>0</td>\n      <td>TEMP</td>\n      <td>5</td>\n      <td>Rouge</td>\n      <td>t</td>\n      <td>f</td>\n      <td>28</td>\n      <td>\\N</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n      <td>DCE</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1096</th>\n      <td>10659</td>\n      <td>1383</td>\n      <td>Zinc</td>\n      <td>16</td>\n      <td>MPMI</td>\n      <td>2</td>\n      <td>Vert</td>\n      <td>t</td>\n      <td>f</td>\n      <td>1.3999999999999999</td>\n      <td>14</td>\n      <td>133</td>\n      <td>microgramme par litre</td>\n      <td>SEQ</td>\n    </tr>\n    <tr>\n      <th>1097</th>\n      <td>10660</td>\n      <td>1383</td>\n      <td>Zinc</td>\n      <td>16</td>\n      <td>MPMI</td>\n      <td>3</td>\n      <td>Jaune</td>\n      <td>t</td>\n      <td>f</td>\n      <td>14</td>\n      <td>140</td>\n      <td>133</td>\n      <td>microgramme par litre</td>\n      <td>SEQ</td>\n    </tr>\n    <tr>\n      <th>1098</th>\n      <td>10661</td>\n      <td>1383</td>\n      <td>Zinc</td>\n      <td>16</td>\n      <td>MPMI</td>\n      <td>4</td>\n      <td>Orange</td>\n      <td>t</td>\n      <td>f</td>\n      <td>140</td>\n      <td>330</td>\n      <td>133</td>\n      <td>microgramme par litre</td>\n      <td>SEQ</td>\n    </tr>\n    <tr>\n      <th>1099</th>\n      <td>10662</td>\n      <td>1383</td>\n      <td>Zinc</td>\n      <td>16</td>\n      <td>MPMI</td>\n      <td>5</td>\n      <td>Rouge</td>\n      <td>t</td>\n      <td>f</td>\n      <td>330</td>\n      <td>\\N</td>\n      <td>133</td>\n      <td>microgramme par litre</td>\n      <td>SEQ</td>\n    </tr>\n    <tr>\n      <th>1100</th>\n      <td>10597</td>\n      <td>1389</td>\n      <td>Chrome</td>\n      <td>16</td>\n      <td>MPMI</td>\n      <td>5</td>\n      <td>Rouge</td>\n      <td>t</td>\n      <td>f</td>\n      <td>50</td>\n      <td>\\N</td>\n      <td>133</td>\n      <td>microgramme par litre</td>\n      <td>SEQ</td>\n    </tr>\n  </tbody>\n</table>\n<p>1101 rows × 14 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "list_faune"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:36:41.136553Z",
     "iopub.execute_input": "2022-12-19T13:36:41.136929Z",
     "iopub.status.idle": "2022-12-19T13:36:41.158773Z",
     "shell.execute_reply.started": "2022-12-19T13:36:41.136879Z",
     "shell.execute_reply": "2022-12-19T13:36:41.157562Z"
    },
    "trusted": true
   },
   "execution_count": 7,
   "outputs": [
    {
     "execution_count": 7,
     "output_type": "execute_result",
     "data": {
      "text/plain": "           id  date_year                date_prel  \\\n0      398943       2010  2010-08-11 00:00:00.000   \n1      398943       2013  2013-07-23 00:00:00.000   \n2      398944       2007  2007-08-29 00:00:00.000   \n3      398944       2007  2007-08-29 00:00:00.000   \n4      398944       2009  2009-09-28 00:00:00.000   \n...       ...        ...                      ...   \n55687  421014       2013  2013-06-27 00:00:00.000   \n55688  421014       2010  2010-06-09 00:00:00.000   \n55689  421014       2012  2012-06-21 00:00:00.000   \n55690  421014       2010  2010-06-09 00:00:00.000   \n55691  421014       2008  2008-05-30 00:00:00.000   \n\n       liste_faunistique_floristique  facies_substrat    facies_vitesse  \\\n0                            3541596  Pierres, galets  Vitesse < 5 cm/s   \n1                            3545195   Roches, dalles  Vitesse < 5 cm/s   \n2                            3525909              NaN               NaN   \n3                            3525910              NaN               NaN   \n4                            3527276              NaN               NaN   \n...                              ...              ...               ...   \n55687                        3627210              NaN               NaN   \n55688                        3637746              NaN               NaN   \n55689                        3655503              NaN               NaN   \n55690                        3664704              NaN               NaN   \n55691                        3672844              NaN               NaN   \n\n      facies_profondeur  \n0                   NaN  \n1                   NaN  \n2                   NaN  \n3                   NaN  \n4                   NaN  \n...                 ...  \n55687               NaN  \n55688               NaN  \n55689               NaN  \n55690               NaN  \n55691               NaN  \n\n[55692 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>date_year</th>\n      <th>date_prel</th>\n      <th>liste_faunistique_floristique</th>\n      <th>facies_substrat</th>\n      <th>facies_vitesse</th>\n      <th>facies_profondeur</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>398943</td>\n      <td>2010</td>\n      <td>2010-08-11 00:00:00.000</td>\n      <td>3541596</td>\n      <td>Pierres, galets</td>\n      <td>Vitesse &lt; 5 cm/s</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>398943</td>\n      <td>2013</td>\n      <td>2013-07-23 00:00:00.000</td>\n      <td>3545195</td>\n      <td>Roches, dalles</td>\n      <td>Vitesse &lt; 5 cm/s</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>398944</td>\n      <td>2007</td>\n      <td>2007-08-29 00:00:00.000</td>\n      <td>3525909</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>398944</td>\n      <td>2007</td>\n      <td>2007-08-29 00:00:00.000</td>\n      <td>3525910</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>398944</td>\n      <td>2009</td>\n      <td>2009-09-28 00:00:00.000</td>\n      <td>3527276</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>55687</th>\n      <td>421014</td>\n      <td>2013</td>\n      <td>2013-06-27 00:00:00.000</td>\n      <td>3627210</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>55688</th>\n      <td>421014</td>\n      <td>2010</td>\n      <td>2010-06-09 00:00:00.000</td>\n      <td>3637746</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>55689</th>\n      <td>421014</td>\n      <td>2012</td>\n      <td>2012-06-21 00:00:00.000</td>\n      <td>3655503</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>55690</th>\n      <td>421014</td>\n      <td>2010</td>\n      <td>2010-06-09 00:00:00.000</td>\n      <td>3664704</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>55691</th>\n      <td>421014</td>\n      <td>2008</td>\n      <td>2008-05-30 00:00:00.000</td>\n      <td>3672844</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>55692 rows × 7 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "physchim"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:36:41.161222Z",
     "iopub.execute_input": "2022-12-19T13:36:41.162045Z",
     "iopub.status.idle": "2022-12-19T13:36:41.178255Z",
     "shell.execute_reply.started": "2022-12-19T13:36:41.162018Z",
     "shell.execute_reply": "2022-12-19T13:36:41.176932Z"
    },
    "trusted": true
   },
   "execution_count": 8,
   "outputs": [
    {
     "execution_count": 8,
     "output_type": "execute_result",
     "data": {
      "text/plain": "        stn_id  date_year     valeur  parameter        parameter_name  unite  \\\n0       398944       2007  10.825000       1301  Température de l'Eau     27   \n1       398944       2008  10.300000       1301  Température de l'Eau     27   \n2       398944       2009  11.208333       1301  Température de l'Eau     27   \n3       398944       2010  10.930000       1301  Température de l'Eau     27   \n4       398944       2011  10.433333       1301  Température de l'Eau     27   \n...        ...        ...        ...        ...                   ...    ...   \n187437  421014       2008  11.250000       1375                Sodium    162   \n187438  421014       2009  21.150000       1375                Sodium    162   \n187439  421014       2010   5.950000       1375                Sodium    162   \n187440  421014       2011   6.550000       1375                Sodium    162   \n187441  421014       2012   5.650000       1375                Sodium    162   \n\n                   unite_name  \n0               degré Celsius  \n1               degré Celsius  \n2               degré Celsius  \n3               degré Celsius  \n4               degré Celsius  \n...                       ...  \n187437  milligramme par litre  \n187438  milligramme par litre  \n187439  milligramme par litre  \n187440  milligramme par litre  \n187441  milligramme par litre  \n\n[187442 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>stn_id</th>\n      <th>date_year</th>\n      <th>valeur</th>\n      <th>parameter</th>\n      <th>parameter_name</th>\n      <th>unite</th>\n      <th>unite_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>398944</td>\n      <td>2007</td>\n      <td>10.825000</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>398944</td>\n      <td>2008</td>\n      <td>10.300000</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>398944</td>\n      <td>2009</td>\n      <td>11.208333</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>398944</td>\n      <td>2010</td>\n      <td>10.930000</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>398944</td>\n      <td>2011</td>\n      <td>10.433333</td>\n      <td>1301</td>\n      <td>Température de l'Eau</td>\n      <td>27</td>\n      <td>degré Celsius</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>187437</th>\n      <td>421014</td>\n      <td>2008</td>\n      <td>11.250000</td>\n      <td>1375</td>\n      <td>Sodium</td>\n      <td>162</td>\n      <td>milligramme par litre</td>\n    </tr>\n    <tr>\n      <th>187438</th>\n      <td>421014</td>\n      <td>2009</td>\n      <td>21.150000</td>\n      <td>1375</td>\n      <td>Sodium</td>\n      <td>162</td>\n      <td>milligramme par litre</td>\n    </tr>\n    <tr>\n      <th>187439</th>\n      <td>421014</td>\n      <td>2010</td>\n      <td>5.950000</td>\n      <td>1375</td>\n      <td>Sodium</td>\n      <td>162</td>\n      <td>milligramme par litre</td>\n    </tr>\n    <tr>\n      <th>187440</th>\n      <td>421014</td>\n      <td>2011</td>\n      <td>6.550000</td>\n      <td>1375</td>\n      <td>Sodium</td>\n      <td>162</td>\n      <td>milligramme par litre</td>\n    </tr>\n    <tr>\n      <th>187441</th>\n      <td>421014</td>\n      <td>2012</td>\n      <td>5.650000</td>\n      <td>1375</td>\n      <td>Sodium</td>\n      <td>162</td>\n      <td>milligramme par litre</td>\n    </tr>\n  </tbody>\n</table>\n<p>187442 rows × 7 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "station"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:36:41.180001Z",
     "iopub.execute_input": "2022-12-19T13:36:41.180535Z",
     "iopub.status.idle": "2022-12-19T13:36:41.200088Z",
     "shell.execute_reply.started": "2022-12-19T13:36:41.180503Z",
     "shell.execute_reply": "2022-12-19T13:36:41.198295Z"
    },
    "trusted": true
   },
   "execution_count": 9,
   "outputs": [
    {
     "execution_count": 9,
     "output_type": "execute_result",
     "data": {
      "text/plain": "           id  date_year                                    nom_masse_eau  \\\n0      398943       2007                                           SAMBRE   \n1      398943       2008                                           SAMBRE   \n2      398943       2010                                           SAMBRE   \n3      398943       2013                                           SAMBRE   \n4      398944       2007                                    HELPE MAJEURE   \n...       ...        ...                                              ...   \n12056  421014       2009  Allondon et Lion de leur confluence a la Suisse   \n12057  421014       2010  Allondon et Lion de leur confluence a la Suisse   \n12058  421014       2011  Allondon et Lion de leur confluence a la Suisse   \n12059  421014       2012  Allondon et Lion de leur confluence a la Suisse   \n12060  421014       2013  Allondon et Lion de leur confluence a la Suisse   \n\n       her           st_x          st_y  \n0       20  751467.817242  7.004902e+06  \n1       20  751467.817242  7.004902e+06  \n2       20  751467.817242  7.004902e+06  \n3       20  751467.817242  7.004902e+06  \n4       22  784221.677267  7.002720e+06  \n...    ...            ...           ...  \n12056    5  932199.000000  6.574390e+06  \n12057    5  932199.000000  6.574390e+06  \n12058    5  932199.000000  6.574390e+06  \n12059    5  932199.000000  6.574390e+06  \n12060    5  932199.000000  6.574390e+06  \n\n[12061 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>date_year</th>\n      <th>nom_masse_eau</th>\n      <th>her</th>\n      <th>st_x</th>\n      <th>st_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>398943</td>\n      <td>2007</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>398943</td>\n      <td>2008</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>398943</td>\n      <td>2010</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>398943</td>\n      <td>2013</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>398944</td>\n      <td>2007</td>\n      <td>HELPE MAJEURE</td>\n      <td>22</td>\n      <td>784221.677267</td>\n      <td>7.002720e+06</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>12056</th>\n      <td>421014</td>\n      <td>2009</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n    </tr>\n    <tr>\n      <th>12057</th>\n      <td>421014</td>\n      <td>2010</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n    </tr>\n    <tr>\n      <th>12058</th>\n      <td>421014</td>\n      <td>2011</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n    </tr>\n    <tr>\n      <th>12059</th>\n      <td>421014</td>\n      <td>2012</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n    </tr>\n    <tr>\n      <th>12060</th>\n      <td>421014</td>\n      <td>2013</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n    </tr>\n  </tbody>\n</table>\n<p>12061 rows × 6 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "taxon"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:36:41.217356Z",
     "iopub.execute_input": "2022-12-19T13:36:41.217700Z",
     "iopub.status.idle": "2022-12-19T13:36:41.231332Z",
     "shell.execute_reply.started": "2022-12-19T13:36:41.217676Z",
     "shell.execute_reply": "2022-12-19T13:36:41.230240Z"
    },
    "trusted": true
   },
   "execution_count": 10,
   "outputs": [
    {
     "execution_count": 10,
     "output_type": "execute_result",
     "data": {
      "text/plain": "         id                nom_latin  taxon_parent_id\n0       327                Beraeidae          20545.0\n1       212              Hydropsyche            211.0\n2      1072             Dendrocoelum           1071.0\n3      1064                Polycelis           1061.0\n4      1044                Sphaerium           1042.0\n...     ...                      ...              ...\n3395  29918                   Bromus          23597.0\n3396   1234            Drepanocladus           1218.0\n3397   1594       Nymphoides peltata           1593.0\n3398  10211    Drepanocladus aduncus           1234.0\n3399  19654  Eucladium verticillatum           1364.0\n\n[3400 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>nom_latin</th>\n      <th>taxon_parent_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>327</td>\n      <td>Beraeidae</td>\n      <td>20545.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>212</td>\n      <td>Hydropsyche</td>\n      <td>211.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1072</td>\n      <td>Dendrocoelum</td>\n      <td>1071.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1064</td>\n      <td>Polycelis</td>\n      <td>1061.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1044</td>\n      <td>Sphaerium</td>\n      <td>1042.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3395</th>\n      <td>29918</td>\n      <td>Bromus</td>\n      <td>23597.0</td>\n    </tr>\n    <tr>\n      <th>3396</th>\n      <td>1234</td>\n      <td>Drepanocladus</td>\n      <td>1218.0</td>\n    </tr>\n    <tr>\n      <th>3397</th>\n      <td>1594</td>\n      <td>Nymphoides peltata</td>\n      <td>1593.0</td>\n    </tr>\n    <tr>\n      <th>3398</th>\n      <td>10211</td>\n      <td>Drepanocladus aduncus</td>\n      <td>1234.0</td>\n    </tr>\n    <tr>\n      <th>3399</th>\n      <td>19654</td>\n      <td>Eucladium verticillatum</td>\n      <td>1364.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3400 rows × 3 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "taxon_denom"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:53:47.441143Z",
     "iopub.execute_input": "2022-12-19T13:53:47.441464Z",
     "iopub.status.idle": "2022-12-19T13:53:47.458831Z",
     "shell.execute_reply.started": "2022-12-19T13:53:47.441441Z",
     "shell.execute_reply": "2022-12-19T13:53:47.457466Z"
    },
    "trusted": true
   },
   "execution_count": 42,
   "outputs": [
    {
     "execution_count": 42,
     "output_type": "execute_result",
     "data": {
      "text/plain": "         liste_ff_id  taxon_id  valeur  \\\n0            3551360       322     1.0   \n1            3551268       837     8.0   \n2            3663043      2080    60.0   \n3            3551258       906   120.0   \n4            3671035      2012     1.0   \n...              ...       ...     ...   \n1569195      3666127      2088    14.0   \n1569196      3626819        69     2.0   \n1569197      3626765       183     3.0   \n1569198      3671544      2197    18.0   \n1569199      3655535      2122     3.8   \n\n                                             critere_taxon  critere_taxon_id  \n0                                 Nombre exact d'individus                 1  \n1                                 Nombre exact d'individus                 1  \n2                  Longueur Minimale totale exprimée en mm                 2  \n3                                 Nombre exact d'individus                 1  \n4                            Poids individuel exprimé en g                 3  \n...                                                    ...               ...  \n1569195  Poids total de l'ensemble des individus appart...                 5  \n1569196                           Nombre exact d'individus                 1  \n1569197                           Nombre exact d'individus                 1  \n1569198                      Poids individuel exprimé en g                 3  \n1569199  Poids individuel exprimé en g partir d'un autr...                10  \n\n[1569200 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>liste_ff_id</th>\n      <th>taxon_id</th>\n      <th>valeur</th>\n      <th>critere_taxon</th>\n      <th>critere_taxon_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3551360</td>\n      <td>322</td>\n      <td>1.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3551268</td>\n      <td>837</td>\n      <td>8.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3663043</td>\n      <td>2080</td>\n      <td>60.0</td>\n      <td>Longueur Minimale totale exprimée en mm</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3551258</td>\n      <td>906</td>\n      <td>120.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3671035</td>\n      <td>2012</td>\n      <td>1.0</td>\n      <td>Poids individuel exprimé en g</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1569195</th>\n      <td>3666127</td>\n      <td>2088</td>\n      <td>14.0</td>\n      <td>Poids total de l'ensemble des individus appart...</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1569196</th>\n      <td>3626819</td>\n      <td>69</td>\n      <td>2.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1569197</th>\n      <td>3626765</td>\n      <td>183</td>\n      <td>3.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1569198</th>\n      <td>3671544</td>\n      <td>2197</td>\n      <td>18.0</td>\n      <td>Poids individuel exprimé en g</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1569199</th>\n      <td>3655535</td>\n      <td>2122</td>\n      <td>3.8</td>\n      <td>Poids individuel exprimé en g partir d'un autr...</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n<p>1569200 rows × 5 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "criteres_tailles = taxon_denom[taxon_denom[\"critere_taxon\"].str.contains(r\"(?i)longueur\")] \\\n",
    "                   [\"critere_taxon_id\"] \\\n",
    "                   .unique() \\\n",
    "                   .tolist()\n",
    "\n",
    "criteres_tailles"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:36:45.930217Z",
     "iopub.execute_input": "2022-12-19T13:36:45.930552Z",
     "iopub.status.idle": "2022-12-19T13:36:47.028875Z",
     "shell.execute_reply.started": "2022-12-19T13:36:45.930528Z",
     "shell.execute_reply": "2022-12-19T13:36:47.027690Z"
    },
    "trusted": true
   },
   "execution_count": 12,
   "outputs": [
    {
     "execution_count": 12,
     "output_type": "execute_result",
     "data": {
      "text/plain": "[2, 4, 6, 11, 13, 15]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Par taxons, par critère, pour une liste ff : une unique valeur"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "length_taxons = taxon_denom[taxon_denom[\"critere_taxon_id\"].isin(criteres_tailles)]\n",
    "# print(length_taxons.sort_values(\"taxon_id\"))\n",
    "length_taxons\\\n",
    "    .drop(columns=[\"critere_taxon\"])\\\n",
    "    .groupby([\"taxon_id\", \"critere_taxon_id\", \"liste_ff_id\"])\\\n",
    "    .count()\\\n",
    "    [\"valeur\"]\\\n",
    "    .unique()\n"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:37:01.762378Z",
     "iopub.execute_input": "2022-12-19T13:37:01.762724Z",
     "iopub.status.idle": "2022-12-19T13:37:01.817330Z",
     "shell.execute_reply.started": "2022-12-19T13:37:01.762700Z",
     "shell.execute_reply": "2022-12-19T13:37:01.815935Z"
    },
    "trusted": true
   },
   "execution_count": 14,
   "outputs": [
    {
     "execution_count": 14,
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([1])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "length_taxons"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:37:08.576123Z",
     "iopub.execute_input": "2022-12-19T13:37:08.576473Z",
     "iopub.status.idle": "2022-12-19T13:37:08.592678Z",
     "shell.execute_reply.started": "2022-12-19T13:37:08.576450Z",
     "shell.execute_reply": "2022-12-19T13:37:08.591725Z"
    },
    "trusted": true
   },
   "execution_count": 15,
   "outputs": [
    {
     "execution_count": 15,
     "output_type": "execute_result",
     "data": {
      "text/plain": "         liste_ff_id  taxon_id  valeur  \\\n2            3663043      2080    60.0   \n9            3671035      2080    32.0   \n32           3671035      2221    40.0   \n50           3663043      2221    53.0   \n58           3671035      2080    87.0   \n...              ...       ...     ...   \n1569172      3676992      2071    86.0   \n1569183      3647224      2131    25.0   \n1569185      3666127      2104    71.0   \n1569189      3676992      2096   258.0   \n1569193      3644508      2129    56.0   \n\n                                       critere_taxon  critere_taxon_id  \n2            Longueur Minimale totale exprimée en mm                 2  \n9        Longueur Individuelle totale exprimée en mm                 4  \n32       Longueur Individuelle totale exprimée en mm                 4  \n50       Longueur Individuelle totale exprimée en mm                 4  \n58           Longueur Maximale totale exprimée en mm                 6  \n...                                              ...               ...  \n1569172      Longueur Maximale totale exprimée en mm                 6  \n1569183      Longueur Minimale totale exprimée en mm                 2  \n1569185  Longueur Individuelle totale exprimée en mm                 4  \n1569189  Longueur Individuelle totale exprimée en mm                 4  \n1569193      Longueur Maximale totale exprimée en mm                 6  \n\n[90552 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>liste_ff_id</th>\n      <th>taxon_id</th>\n      <th>valeur</th>\n      <th>critere_taxon</th>\n      <th>critere_taxon_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>3663043</td>\n      <td>2080</td>\n      <td>60.0</td>\n      <td>Longueur Minimale totale exprimée en mm</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>3671035</td>\n      <td>2080</td>\n      <td>32.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>3671035</td>\n      <td>2221</td>\n      <td>40.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>3663043</td>\n      <td>2221</td>\n      <td>53.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>3671035</td>\n      <td>2080</td>\n      <td>87.0</td>\n      <td>Longueur Maximale totale exprimée en mm</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1569172</th>\n      <td>3676992</td>\n      <td>2071</td>\n      <td>86.0</td>\n      <td>Longueur Maximale totale exprimée en mm</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1569183</th>\n      <td>3647224</td>\n      <td>2131</td>\n      <td>25.0</td>\n      <td>Longueur Minimale totale exprimée en mm</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1569185</th>\n      <td>3666127</td>\n      <td>2104</td>\n      <td>71.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1569189</th>\n      <td>3676992</td>\n      <td>2096</td>\n      <td>258.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1569193</th>\n      <td>3644508</td>\n      <td>2129</td>\n      <td>56.0</td>\n      <td>Longueur Maximale totale exprimée en mm</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n<p>90552 rows × 5 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "On garde que les critères de chaque taxons pour lesquelles on a assez de valeurs pour étudier l'évolution"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T13:37:48.528118Z",
     "iopub.execute_input": "2022-12-19T13:37:48.528430Z",
     "iopub.status.idle": "2022-12-19T13:37:48.535312Z",
     "shell.execute_reply.started": "2022-12-19T13:37:48.528408Z",
     "shell.execute_reply": "2022-12-19T13:37:48.533511Z"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "a = length_taxons\\\n",
    "    .drop(columns=[\"critere_taxon\"])\\\n",
    "    .groupby([\"taxon_id\", \"critere_taxon_id\"])\\\n",
    "    [\"liste_ff_id\"]\\\n",
    "    .count()\n",
    "\n",
    "to_keep = a[a > 2500].sort_values(ascending=False)\n",
    "to_keep"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T14:08:50.992106Z",
     "iopub.execute_input": "2022-12-19T14:08:50.992417Z",
     "iopub.status.idle": "2022-12-19T14:08:51.011103Z",
     "shell.execute_reply.started": "2022-12-19T14:08:50.992394Z",
     "shell.execute_reply": "2022-12-19T14:08:51.009381Z"
    },
    "trusted": true
   },
   "execution_count": 90,
   "outputs": [
    {
     "execution_count": 90,
     "output_type": "execute_result",
     "data": {
      "text/plain": "taxon_id  critere_taxon_id\n2071      4                   4355\n31041     4                   3190\n2113      4                   3119\n2038      4                   3086\n2080      4                   2868\n2125      4                   2699\nName: liste_ff_id, dtype: int64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "stn_lf = station.merge(list_faune, left_on='id', right_on='id')\n",
    "stn_taxon = stn_lf.merge(taxon_denom, left_on='liste_faunistique_floristique', right_on='liste_ff_id')\n",
    "stn_taxon"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T15:17:32.820489Z",
     "iopub.execute_input": "2022-12-19T15:17:32.820854Z",
     "iopub.status.idle": "2022-12-19T15:17:35.231984Z",
     "shell.execute_reply.started": "2022-12-19T15:17:32.820826Z",
     "shell.execute_reply": "2022-12-19T15:17:35.230481Z"
    },
    "trusted": true
   },
   "execution_count": 180,
   "outputs": [
    {
     "execution_count": 180,
     "output_type": "execute_result",
     "data": {
      "text/plain": "              id  date_year_x  \\\n0         398943         2007   \n1         398943         2007   \n2         398943         2007   \n3         398943         2007   \n4         398943         2007   \n...          ...          ...   \n10864784  421014         2013   \n10864785  421014         2013   \n10864786  421014         2013   \n10864787  421014         2013   \n10864788  421014         2013   \n\n                                            nom_masse_eau  her           st_x  \\\n0                                                  SAMBRE   20  751467.817242   \n1                                                  SAMBRE   20  751467.817242   \n2                                                  SAMBRE   20  751467.817242   \n3                                                  SAMBRE   20  751467.817242   \n4                                                  SAMBRE   20  751467.817242   \n...                                                   ...  ...            ...   \n10864784  Allondon et Lion de leur confluence a la Suisse    5  932199.000000   \n10864785  Allondon et Lion de leur confluence a la Suisse    5  932199.000000   \n10864786  Allondon et Lion de leur confluence a la Suisse    5  932199.000000   \n10864787  Allondon et Lion de leur confluence a la Suisse    5  932199.000000   \n10864788  Allondon et Lion de leur confluence a la Suisse    5  932199.000000   \n\n                  st_y  date_year_y                date_prel  \\\n0         7.004902e+06         2010  2010-08-11 00:00:00.000   \n1         7.004902e+06         2010  2010-08-11 00:00:00.000   \n2         7.004902e+06         2010  2010-08-11 00:00:00.000   \n3         7.004902e+06         2010  2010-08-11 00:00:00.000   \n4         7.004902e+06         2010  2010-08-11 00:00:00.000   \n...                ...          ...                      ...   \n10864784  6.574390e+06         2008  2008-05-30 00:00:00.000   \n10864785  6.574390e+06         2008  2008-05-30 00:00:00.000   \n10864786  6.574390e+06         2008  2008-05-30 00:00:00.000   \n10864787  6.574390e+06         2008  2008-05-30 00:00:00.000   \n10864788  6.574390e+06         2008  2008-05-30 00:00:00.000   \n\n          liste_faunistique_floristique  facies_substrat    facies_vitesse  \\\n0                               3541596  Pierres, galets  Vitesse < 5 cm/s   \n1                               3541596  Pierres, galets  Vitesse < 5 cm/s   \n2                               3541596  Pierres, galets  Vitesse < 5 cm/s   \n3                               3541596  Pierres, galets  Vitesse < 5 cm/s   \n4                               3541596  Pierres, galets  Vitesse < 5 cm/s   \n...                                 ...              ...               ...   \n10864784                        3672844              NaN               NaN   \n10864785                        3672844              NaN               NaN   \n10864786                        3672844              NaN               NaN   \n10864787                        3672844              NaN               NaN   \n10864788                        3672844              NaN               NaN   \n\n         facies_profondeur  liste_ff_id  taxon_id  valeur  \\\n0                      NaN      3541596      7843     4.0   \n1                      NaN      3541596      7881    66.0   \n2                      NaN      3541596      9034     1.0   \n3                      NaN      3541596      8992     1.0   \n4                      NaN      3541596      8719     1.0   \n...                    ...          ...       ...     ...   \n10864784               NaN      3672844      2080   678.0   \n10864785               NaN      3672844      2165     8.0   \n10864786               NaN      3672844      2080    63.0   \n10864787               NaN      3672844      2221    31.0   \n10864788               NaN      3672844      2165    16.0   \n\n                                              critere_taxon  critere_taxon_id  \n0                                  Nombre exact d'individus                 1  \n1                                  Nombre exact d'individus                 1  \n2                                  Nombre exact d'individus                 1  \n3                                  Nombre exact d'individus                 1  \n4                                  Nombre exact d'individus                 1  \n...                                                     ...               ...  \n10864784  Poids total de l'ensemble des individus appart...                 5  \n10864785                           Nombre exact d'individus                 1  \n10864786                           Nombre exact d'individus                 1  \n10864787  Poids total de l'ensemble des individus appart...                 5  \n10864788  Poids total de l'ensemble des individus appart...                 5  \n\n[10864789 rows x 17 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>date_year_x</th>\n      <th>nom_masse_eau</th>\n      <th>her</th>\n      <th>st_x</th>\n      <th>st_y</th>\n      <th>date_year_y</th>\n      <th>date_prel</th>\n      <th>liste_faunistique_floristique</th>\n      <th>facies_substrat</th>\n      <th>facies_vitesse</th>\n      <th>facies_profondeur</th>\n      <th>liste_ff_id</th>\n      <th>taxon_id</th>\n      <th>valeur</th>\n      <th>critere_taxon</th>\n      <th>critere_taxon_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>398943</td>\n      <td>2007</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n      <td>2010</td>\n      <td>2010-08-11 00:00:00.000</td>\n      <td>3541596</td>\n      <td>Pierres, galets</td>\n      <td>Vitesse &lt; 5 cm/s</td>\n      <td>NaN</td>\n      <td>3541596</td>\n      <td>7843</td>\n      <td>4.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>398943</td>\n      <td>2007</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n      <td>2010</td>\n      <td>2010-08-11 00:00:00.000</td>\n      <td>3541596</td>\n      <td>Pierres, galets</td>\n      <td>Vitesse &lt; 5 cm/s</td>\n      <td>NaN</td>\n      <td>3541596</td>\n      <td>7881</td>\n      <td>66.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>398943</td>\n      <td>2007</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n      <td>2010</td>\n      <td>2010-08-11 00:00:00.000</td>\n      <td>3541596</td>\n      <td>Pierres, galets</td>\n      <td>Vitesse &lt; 5 cm/s</td>\n      <td>NaN</td>\n      <td>3541596</td>\n      <td>9034</td>\n      <td>1.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>398943</td>\n      <td>2007</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n      <td>2010</td>\n      <td>2010-08-11 00:00:00.000</td>\n      <td>3541596</td>\n      <td>Pierres, galets</td>\n      <td>Vitesse &lt; 5 cm/s</td>\n      <td>NaN</td>\n      <td>3541596</td>\n      <td>8992</td>\n      <td>1.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>398943</td>\n      <td>2007</td>\n      <td>SAMBRE</td>\n      <td>20</td>\n      <td>751467.817242</td>\n      <td>7.004902e+06</td>\n      <td>2010</td>\n      <td>2010-08-11 00:00:00.000</td>\n      <td>3541596</td>\n      <td>Pierres, galets</td>\n      <td>Vitesse &lt; 5 cm/s</td>\n      <td>NaN</td>\n      <td>3541596</td>\n      <td>8719</td>\n      <td>1.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>10864784</th>\n      <td>421014</td>\n      <td>2013</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n      <td>2008</td>\n      <td>2008-05-30 00:00:00.000</td>\n      <td>3672844</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3672844</td>\n      <td>2080</td>\n      <td>678.0</td>\n      <td>Poids total de l'ensemble des individus appart...</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>10864785</th>\n      <td>421014</td>\n      <td>2013</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n      <td>2008</td>\n      <td>2008-05-30 00:00:00.000</td>\n      <td>3672844</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3672844</td>\n      <td>2165</td>\n      <td>8.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10864786</th>\n      <td>421014</td>\n      <td>2013</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n      <td>2008</td>\n      <td>2008-05-30 00:00:00.000</td>\n      <td>3672844</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3672844</td>\n      <td>2080</td>\n      <td>63.0</td>\n      <td>Nombre exact d'individus</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10864787</th>\n      <td>421014</td>\n      <td>2013</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n      <td>2008</td>\n      <td>2008-05-30 00:00:00.000</td>\n      <td>3672844</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3672844</td>\n      <td>2221</td>\n      <td>31.0</td>\n      <td>Poids total de l'ensemble des individus appart...</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>10864788</th>\n      <td>421014</td>\n      <td>2013</td>\n      <td>Allondon et Lion de leur confluence a la Suisse</td>\n      <td>5</td>\n      <td>932199.000000</td>\n      <td>6.574390e+06</td>\n      <td>2008</td>\n      <td>2008-05-30 00:00:00.000</td>\n      <td>3672844</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3672844</td>\n      <td>2165</td>\n      <td>16.0</td>\n      <td>Poids total de l'ensemble des individus appart...</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n<p>10864789 rows × 17 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "stn_taxon_taille = stn_taxon[(stn_taxon['taxon_id'] == 2080) & (stn_taxon['critere_taxon_id'] == 4)]"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T16:07:39.949022Z",
     "iopub.execute_input": "2022-12-19T16:07:39.950163Z",
     "iopub.status.idle": "2022-12-19T16:07:39.992564Z",
     "shell.execute_reply.started": "2022-12-19T16:07:39.950114Z",
     "shell.execute_reply": "2022-12-19T16:07:39.991277Z"
    },
    "trusted": true
   },
   "execution_count": 263,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "test = stn_taxon_taille.groupby(['id', 'taxon_id']).nunique().sort_values('liste_faunistique_floristique', ascending=False).head(5)\\\n",
    ".groupby('id').count().index.tolist()\n"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T16:08:49.346131Z",
     "iopub.execute_input": "2022-12-19T16:08:49.346451Z",
     "iopub.status.idle": "2022-12-19T16:08:49.383557Z",
     "shell.execute_reply.started": "2022-12-19T16:08:49.346427Z",
     "shell.execute_reply": "2022-12-19T16:08:49.382208Z"
    },
    "trusted": true
   },
   "execution_count": 266,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "taxon_to_keep = stn_taxon_taille.groupby(['id', 'taxon_id']).count().groupby('id').head(5).groupby('taxon_id').count()\\\n",
    ".sort_values('valeur', ascending = False).head(5).index.tolist()\n",
    "taxon_to_keep"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T15:55:44.668354Z",
     "iopub.execute_input": "2022-12-19T15:55:44.668870Z",
     "iopub.status.idle": "2022-12-19T15:55:44.809992Z",
     "shell.execute_reply.started": "2022-12-19T15:55:44.668842Z",
     "shell.execute_reply": "2022-12-19T15:55:44.809062Z"
    },
    "trusted": true
   },
   "execution_count": 240,
   "outputs": [
    {
     "execution_count": 240,
     "output_type": "execute_result",
     "data": {
      "text/plain": "[2071, 2038, 2080, 871, 2050]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "stn_to_keep = stn_taxon_taille[stn_taxon_taille['taxon_id'].isin(taxon_to_keep)].groupby('id').count().sort_values('valeur', ascending=False).head(5)\\\n",
    ".index.tolist()\n",
    "stn_to_keep"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T15:55:45.486174Z",
     "iopub.execute_input": "2022-12-19T15:55:45.486494Z",
     "iopub.status.idle": "2022-12-19T15:55:45.545576Z",
     "shell.execute_reply.started": "2022-12-19T15:55:45.486469Z",
     "shell.execute_reply": "2022-12-19T15:55:45.544213Z"
    },
    "trusted": true
   },
   "execution_count": 241,
   "outputs": [
    {
     "execution_count": 241,
     "output_type": "execute_result",
     "data": {
      "text/plain": "[399225, 404039, 402817, 404651, 413256]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "stn_taxon_red = stn_taxon_taille[stn_taxon_taille['id'].isin(test) & stn_taxon_taille['taxon_id'].isin(taxon_to_keep)]\n",
    "stn_taxon_red"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T15:55:57.199596Z",
     "iopub.execute_input": "2022-12-19T15:55:57.199952Z",
     "iopub.status.idle": "2022-12-19T15:55:57.236559Z",
     "shell.execute_reply.started": "2022-12-19T15:55:57.199923Z",
     "shell.execute_reply": "2022-12-19T15:55:57.234859Z"
    },
    "trusted": true
   },
   "execution_count": 242,
   "outputs": [
    {
     "execution_count": 242,
     "output_type": "execute_result",
     "data": {
      "text/plain": "             id  date_year_x  \\\n455368   399225         2007   \n455372   399225         2007   \n455386   399225         2007   \n455422   399225         2007   \n455428   399225         2008   \n...         ...          ...   \n5158611  403587         2011   \n5158636  403587         2012   \n5158652  403587         2012   \n5158677  403587         2013   \n5158693  403587         2013   \n\n                                             nom_masse_eau  her          st_x  \\\n455368   La Boutonne de sa source au confluent de la Belle    9  451708.46875   \n455372   La Boutonne de sa source au confluent de la Belle    9  451708.46875   \n455386   La Boutonne de sa source au confluent de la Belle    9  451708.46875   \n455422   La Boutonne de sa source au confluent de la Belle    9  451708.46875   \n455428   La Boutonne de sa source au confluent de la Belle    9  451708.46875   \n...                                                    ...  ...           ...   \n5158611  LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...    9  462403.00000   \n5158636  LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...    9  462403.00000   \n5158652  LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...    9  462403.00000   \n5158677  LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...    9  462403.00000   \n5158693  LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...    9  462403.00000   \n\n              st_y  date_year_y                date_prel  \\\n455368   6565540.5         2012  2012-09-07 00:00:00.000   \n455372   6565540.5         2012  2012-09-07 00:00:00.000   \n455386   6565540.5         2012  2012-09-07 00:00:00.000   \n455422   6565540.5         2012  2012-09-07 00:00:00.000   \n455428   6565540.5         2012  2012-09-07 00:00:00.000   \n...            ...          ...                      ...   \n5158611  6587740.0         2012  2012-09-06 00:00:00.000   \n5158636  6587740.0         2012  2012-09-06 00:00:00.000   \n5158652  6587740.0         2012  2012-09-06 00:00:00.000   \n5158677  6587740.0         2012  2012-09-06 00:00:00.000   \n5158693  6587740.0         2012  2012-09-06 00:00:00.000   \n\n         liste_faunistique_floristique facies_substrat facies_vitesse  \\\n455368                         3644456             NaN            NaN   \n455372                         3644456             NaN            NaN   \n455386                         3644456             NaN            NaN   \n455422                         3644456             NaN            NaN   \n455428                         3644456             NaN            NaN   \n...                                ...             ...            ...   \n5158611                        3679642             NaN            NaN   \n5158636                        3679642             NaN            NaN   \n5158652                        3679642             NaN            NaN   \n5158677                        3679642             NaN            NaN   \n5158693                        3679642             NaN            NaN   \n\n        facies_profondeur  liste_ff_id  taxon_id  valeur  \\\n455368                NaN      3644456      2080    61.0   \n455372                NaN      3644456      2071    84.0   \n455386                NaN      3644456       871    68.0   \n455422                NaN      3644456      2038   249.0   \n455428                NaN      3644456      2080    61.0   \n...                   ...          ...       ...     ...   \n5158611               NaN      3679642      2038   379.0   \n5158636               NaN      3679642      2071    77.0   \n5158652               NaN      3679642      2038   379.0   \n5158677               NaN      3679642      2071    77.0   \n5158693               NaN      3679642      2038   379.0   \n\n                                       critere_taxon  critere_taxon_id  \n455368   Longueur Individuelle totale exprimée en mm                 4  \n455372   Longueur Individuelle totale exprimée en mm                 4  \n455386   Longueur Individuelle totale exprimée en mm                 4  \n455422   Longueur Individuelle totale exprimée en mm                 4  \n455428   Longueur Individuelle totale exprimée en mm                 4  \n...                                              ...               ...  \n5158611  Longueur Individuelle totale exprimée en mm                 4  \n5158636  Longueur Individuelle totale exprimée en mm                 4  \n5158652  Longueur Individuelle totale exprimée en mm                 4  \n5158677  Longueur Individuelle totale exprimée en mm                 4  \n5158693  Longueur Individuelle totale exprimée en mm                 4  \n\n[1120 rows x 17 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>date_year_x</th>\n      <th>nom_masse_eau</th>\n      <th>her</th>\n      <th>st_x</th>\n      <th>st_y</th>\n      <th>date_year_y</th>\n      <th>date_prel</th>\n      <th>liste_faunistique_floristique</th>\n      <th>facies_substrat</th>\n      <th>facies_vitesse</th>\n      <th>facies_profondeur</th>\n      <th>liste_ff_id</th>\n      <th>taxon_id</th>\n      <th>valeur</th>\n      <th>critere_taxon</th>\n      <th>critere_taxon_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>455368</th>\n      <td>399225</td>\n      <td>2007</td>\n      <td>La Boutonne de sa source au confluent de la Belle</td>\n      <td>9</td>\n      <td>451708.46875</td>\n      <td>6565540.5</td>\n      <td>2012</td>\n      <td>2012-09-07 00:00:00.000</td>\n      <td>3644456</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3644456</td>\n      <td>2080</td>\n      <td>61.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>455372</th>\n      <td>399225</td>\n      <td>2007</td>\n      <td>La Boutonne de sa source au confluent de la Belle</td>\n      <td>9</td>\n      <td>451708.46875</td>\n      <td>6565540.5</td>\n      <td>2012</td>\n      <td>2012-09-07 00:00:00.000</td>\n      <td>3644456</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3644456</td>\n      <td>2071</td>\n      <td>84.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>455386</th>\n      <td>399225</td>\n      <td>2007</td>\n      <td>La Boutonne de sa source au confluent de la Belle</td>\n      <td>9</td>\n      <td>451708.46875</td>\n      <td>6565540.5</td>\n      <td>2012</td>\n      <td>2012-09-07 00:00:00.000</td>\n      <td>3644456</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3644456</td>\n      <td>871</td>\n      <td>68.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>455422</th>\n      <td>399225</td>\n      <td>2007</td>\n      <td>La Boutonne de sa source au confluent de la Belle</td>\n      <td>9</td>\n      <td>451708.46875</td>\n      <td>6565540.5</td>\n      <td>2012</td>\n      <td>2012-09-07 00:00:00.000</td>\n      <td>3644456</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3644456</td>\n      <td>2038</td>\n      <td>249.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>455428</th>\n      <td>399225</td>\n      <td>2008</td>\n      <td>La Boutonne de sa source au confluent de la Belle</td>\n      <td>9</td>\n      <td>451708.46875</td>\n      <td>6565540.5</td>\n      <td>2012</td>\n      <td>2012-09-07 00:00:00.000</td>\n      <td>3644456</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3644456</td>\n      <td>2080</td>\n      <td>61.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5158611</th>\n      <td>403587</td>\n      <td>2011</td>\n      <td>LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...</td>\n      <td>9</td>\n      <td>462403.00000</td>\n      <td>6587740.0</td>\n      <td>2012</td>\n      <td>2012-09-06 00:00:00.000</td>\n      <td>3679642</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3679642</td>\n      <td>2038</td>\n      <td>379.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5158636</th>\n      <td>403587</td>\n      <td>2012</td>\n      <td>LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...</td>\n      <td>9</td>\n      <td>462403.00000</td>\n      <td>6587740.0</td>\n      <td>2012</td>\n      <td>2012-09-06 00:00:00.000</td>\n      <td>3679642</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3679642</td>\n      <td>2071</td>\n      <td>77.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5158652</th>\n      <td>403587</td>\n      <td>2012</td>\n      <td>LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...</td>\n      <td>9</td>\n      <td>462403.00000</td>\n      <td>6587740.0</td>\n      <td>2012</td>\n      <td>2012-09-06 00:00:00.000</td>\n      <td>3679642</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3679642</td>\n      <td>2038</td>\n      <td>379.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5158677</th>\n      <td>403587</td>\n      <td>2013</td>\n      <td>LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...</td>\n      <td>9</td>\n      <td>462403.00000</td>\n      <td>6587740.0</td>\n      <td>2012</td>\n      <td>2012-09-06 00:00:00.000</td>\n      <td>3679642</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3679642</td>\n      <td>2071</td>\n      <td>77.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5158693</th>\n      <td>403587</td>\n      <td>2013</td>\n      <td>LA SEVRE NIORTAISE ET SES AFFLUENTS DEPUIS LA ...</td>\n      <td>9</td>\n      <td>462403.00000</td>\n      <td>6587740.0</td>\n      <td>2012</td>\n      <td>2012-09-06 00:00:00.000</td>\n      <td>3679642</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3679642</td>\n      <td>2038</td>\n      <td>379.0</td>\n      <td>Longueur Individuelle totale exprimée en mm</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>1120 rows × 17 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(figsize = (12,6))\n",
    "sns.lineplot(data=stn_taxon_taille[(stn_taxon_taille['taxon_id'] == 2080) & (stn_taxon_taille['id'].isin(test))], x=\"date_year_y\", y=\"valeur\", hue=\"id\", ci=None, ax=ax)\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-12-19T16:10:10.432864Z",
     "iopub.execute_input": "2022-12-19T16:10:10.433249Z",
     "iopub.status.idle": "2022-12-19T16:10:10.692576Z",
     "shell.execute_reply.started": "2022-12-19T16:10:10.433223Z",
     "shell.execute_reply": "2022-12-19T16:10:10.691192Z"
    },
    "trusted": true
   },
   "execution_count": 270,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  }
 ]
}