le_leman.ipynb

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   "source": [
    "%load_ext watermark\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from IPython.display import Markdown as md\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "import seaborn as sns\n",
    "import json\n",
    "\n",
    "# this defines the css rules for the note-book table displays\n",
    "header_row = {'selector': 'th:nth-child(1)', 'props': f'background-color: #FFF; text-align:right'}\n",
    "even_rows = {\"selector\": 'tr:nth-child(even)', 'props': f'background-color: rgba(139, 69, 19, 0.08);'}\n",
    "odd_rows = {'selector': 'tr:nth-child(odd)', 'props': 'background: #FFF;'}\n",
    "table_font = {'selector': 'tr', 'props': 'font-size: 10px;'}\n",
    "table_data = {'selector': 'td', 'props': 'padding: 6px;'}\n",
    "table_caption = {'selector': 'caption', 'props': 'font-size: 14px; font-style: italic; caption-side: bottom; text-align: left; margin-top: 10px'}\n",
    "table_css_styles = [even_rows, odd_rows, table_font, header_row, table_caption]\n",
    "\n",
    "table_large_data = {'selector': 'tr', 'props': 'font-size: 14px;'}\n",
    "table_large_font = [even_rows, odd_rows, table_large_data, header_row, table_caption]\n",
    "\n",
    "def rotateText(x):\n",
    "    return 'writing-mode: vertical-lr; transform: rotate(-180deg);  padding:10px; margins:0; vertical-align: baseline;'\n",
    "\n",
    "cantons = [\"Vaud\", \"Genève\", \"Valais\"]\n",
    "cantons_avoisinante = \"Valais, Vaud, Genève\"\n",
    "other_cantons = [\"Vaud\", \"Genève\", \"Valais\"]\n",
    "lake = \"Le Léman\"\n",
    "\n",
    "def save_json(filename, data):\n",
    "    with open(f\"resources/{filename}\", \"w\") as afile:\n",
    "        json.dump(data, afile)\n",
    "\n",
    "def open_json(filename):\n",
    "    with open(f\"resources/{filename}\", \"r\") as afile:\n",
    "        data = json.load(afile)\n",
    "        return data\n",
    "    \n",
    "\n",
    "display_names = open_json(\"french_display_names.json\")\n",
    "\n",
    "land_use_names = open_json(\"french_land_use_names.json\")\n",
    "\n",
    "material_names = open_json(\"french_material_names.json\")\n",
    "\n",
    "group_names = open_json(\"french_group_names.json\")\n",
    "\n",
    "french_names = open_json(\"french_code_translations.json\")\n",
    "\n",
    "# land use\n",
    "land_use = pd.read_csv(\"resources/work_land_use.csv\")\n",
    "lu_map = land_use[land_use.use == \"Siedl\"][[\"slug\", \"scaled\"]].set_index(\"slug\")\n",
    "\n",
    "all_data = pd.read_csv(\"resources/survey_work_data.csv\", low_memory=False)\n",
    "\n",
    "all_data = all_data[['loc_date', 'slug', 'city', 'water_name_slug', 'date', 'doy', 'Project', 'code', 'pcs/m', 'quantity', 'canton', 'zone']].copy()\n",
    "all_data[\"date\"] = pd.to_datetime(all_data[\"date\"])\n",
    "all_data = all_data[all_data.code != \"G216\"]\n",
    "\n",
    "gh = pd.read_csv(\"resources/lac_leman_regions.csv\")\n",
    "gh.set_index(\"slug\", inplace=True, drop=True)\n",
    "\n",
    "codes_of_interest = [\n",
    "    \"G95\",\n",
    "    \"G70\",\n",
    "    \"Gfrags\",\n",
    "    \"Gfoam\",\n",
    "    \"G112\",\n",
    "    \"G27\",\n",
    "    \"G30\",\n",
    "    \"G67\",\n",
    "    \"G10\",\n",
    "    \"Gcaps\",\n",
    "]\n",
    "\n",
    "all_data = all_data[all_data.code.isin(codes_of_interest)].copy()\n",
    "all_data = all_data[all_data.canton.isin(other_cantons)].copy()\n",
    "\n",
    "all_x = all_data[all_data.canton.isin(other_cantons)].drop_duplicates([\"code\", \"loc_date\"]).copy()\n",
    "all_data  = all_x[all_x.zone.isin([\"lac\"])].copy()\n",
    "all_data[\"region\"] = all_data.slug.apply(lambda x: gh.loc[x, \"alabel\"])\n",
    "lac_riv = all_data.copy()\n",
    "\n",
    "operations={\n",
    "    \"loc_date\":\"nunique\", \n",
    "    \"city\":\"nunique\", \n",
    "    \"quantity\":\"sum\",\n",
    "    \"slug\":\"nunique\"\n",
    "}\n",
    "summary_data = lac_riv.groupby([\"region\"]).agg(operations)\n",
    "\n",
    "summary_data = summary_data.astype(\"int\")"
   ]
  },
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   "source": [
    "# Lac Léman"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6604be36-7332-4ea7-820c-da3835ea9e59",
   "metadata": {},
   "source": [
    "A compléter"
   ]
  },
  {
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   "execution_count": 2,
   "id": "21f2dc2d-97f9-4284-b5e6-2f09104ff825",
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   "source": [
    "dims = pd.read_csv(\"resources/work_dimensions.csv\")\n",
    "\n",
    "land_use_of_interest = [\n",
    "    'Siedl', \n",
    "    'Wald',\n",
    "    'Stadtzentr',\n",
    "    'Reben', \n",
    "    'Obstanlage',\n",
    "    'undefined',\n",
    "    'infrastructure',\n",
    "    'recreation',\n",
    "    'Strasse'\n",
    "]\n",
    "\n",
    "\n",
    "def dimensional_summary_data(dims, survey_data, request, zones=[\"lac\", \"rivière\", \"montagne\"]):\n",
    "    dfs = []\n",
    "    for label in zones:\n",
    "        d_f = dims[dims.slug.isin(survey_data[survey_data[request] == label].slug.unique())].copy()\n",
    "        d_f[request] = label\n",
    "        dfs.append(d_f)\n",
    "    dimensions = pd.concat(dfs)\n",
    "    d_sum = dimensions.groupby(request).agg({\"area\":\"sum\", \"length\":\"sum\", \"time\":\"sum\"})\n",
    "    return dimensions, d_sum\n",
    "\n",
    "dimensional_summary, d_sum = dimensional_summary_data(dims, lac_riv, \"region\", zones=lac_riv[\"region\"].unique())\n",
    "\n",
    "summary_by_zone = pd.concat([summary_data, d_sum], axis=1, ignore_index=False)\n",
    "summary_by_zone[\"hours\"] = (summary_by_zone[\"time\"]/60).round(2)\n",
    "summary_by_zone.drop(\"time\", inplace=True, axis=1)\n",
    "summary_by_zone.loc[\"total\", : ] = summary_by_zone.sum()"
   ]
  },
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6fa13 tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_6fa13 tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_6fa13 tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_6fa13 th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_6fa13 caption {\n",
       "  font-size: 14px;\n",
       "  font-style: italic;\n",
       "  caption-side: bottom;\n",
       "  text-align: left;\n",
       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6fa13\">\n",
       "  <caption>Historique des échantillonnages Le Léman : Le nombre d'échantillons et l'ampleur de l'effort par contexte. Remarque : les heures correspondent au temps passé à compter et à collecter.</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6fa13_level0_col0\" class=\"col_heading level0 col0\" >échantillons</th>\n",
       "      <th id=\"T_6fa13_level0_col1\" class=\"col_heading level0 col1\" >municipalités</th>\n",
       "      <th id=\"T_6fa13_level0_col2\" class=\"col_heading level0 col2\" >quantité</th>\n",
       "      <th id=\"T_6fa13_level0_col3\" class=\"col_heading level0 col3\" >site</th>\n",
       "      <th id=\"T_6fa13_level0_col4\" class=\"col_heading level0 col4\" >m²</th>\n",
       "      <th id=\"T_6fa13_level0_col5\" class=\"col_heading level0 col5\" >mètres</th>\n",
       "      <th id=\"T_6fa13_level0_col6\" class=\"col_heading level0 col6\" >heures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6fa13_level0_row0\" class=\"row_heading level0 row0\" >Grand lac</th>\n",
       "      <td id=\"T_6fa13_row0_col0\" class=\"data row0 col0\" >83</td>\n",
       "      <td id=\"T_6fa13_row0_col1\" class=\"data row0 col1\" >10</td>\n",
       "      <td id=\"T_6fa13_row0_col2\" class=\"data row0 col2\" >16,810</td>\n",
       "      <td id=\"T_6fa13_row0_col3\" class=\"data row0 col3\" >21</td>\n",
       "      <td id=\"T_6fa13_row0_col4\" class=\"data row0 col4\" >37,526</td>\n",
       "      <td id=\"T_6fa13_row0_col5\" class=\"data row0 col5\" >8,222</td>\n",
       "      <td id=\"T_6fa13_row0_col6\" class=\"data row0 col6\" >343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6fa13_level0_row1\" class=\"row_heading level0 row1\" >Haut lac</th>\n",
       "      <td id=\"T_6fa13_row1_col0\" class=\"data row1 col0\" >157</td>\n",
       "      <td id=\"T_6fa13_row1_col1\" class=\"data row1 col1\" >9</td>\n",
       "      <td id=\"T_6fa13_row1_col2\" class=\"data row1 col2\" >42,162</td>\n",
       "      <td id=\"T_6fa13_row1_col3\" class=\"data row1 col3\" >20</td>\n",
       "      <td id=\"T_6fa13_row1_col4\" class=\"data row1 col4\" >13,796</td>\n",
       "      <td id=\"T_6fa13_row1_col5\" class=\"data row1 col5\" >4,314</td>\n",
       "      <td id=\"T_6fa13_row1_col6\" class=\"data row1 col6\" >162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6fa13_level0_row2\" class=\"row_heading level0 row2\" >Petit lac</th>\n",
       "      <td id=\"T_6fa13_row2_col0\" class=\"data row2 col0\" >79</td>\n",
       "      <td id=\"T_6fa13_row2_col1\" class=\"data row2 col1\" >6</td>\n",
       "      <td id=\"T_6fa13_row2_col2\" class=\"data row2 col2\" >9,829</td>\n",
       "      <td id=\"T_6fa13_row2_col3\" class=\"data row2 col3\" >15</td>\n",
       "      <td id=\"T_6fa13_row2_col4\" class=\"data row2 col4\" >16,669</td>\n",
       "      <td id=\"T_6fa13_row2_col5\" class=\"data row2 col5\" >4,568</td>\n",
       "      <td id=\"T_6fa13_row2_col6\" class=\"data row2 col6\" >111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6fa13_level0_row3\" class=\"row_heading level0 row3\" >total</th>\n",
       "      <td id=\"T_6fa13_row3_col0\" class=\"data row3 col0\" >319</td>\n",
       "      <td id=\"T_6fa13_row3_col1\" class=\"data row3 col1\" >25</td>\n",
       "      <td id=\"T_6fa13_row3_col2\" class=\"data row3 col2\" >68,801</td>\n",
       "      <td id=\"T_6fa13_row3_col3\" class=\"data row3 col3\" >56</td>\n",
       "      <td id=\"T_6fa13_row3_col4\" class=\"data row3 col4\" >67,992</td>\n",
       "      <td id=\"T_6fa13_row3_col5\" class=\"data row3 col5\" >17,104</td>\n",
       "      <td id=\"T_6fa13_row3_col6\" class=\"data row3 col6\" >617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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   "source": [
    "caption_french = f\"Historique des échantillonnages {lake} : Le nombre d'échantillons et l'ampleur de l'effort par contexte. Remarque : les heures correspondent au temps passé à compter et à collecter.\"\n",
    "\n",
    "summary_by_zone.index.name = None\n",
    "summary_by_zone.rename(columns=display_names, inplace=True)\n",
    "summary_by_zone = summary_by_zone.astype(\"int\")\n",
    "summary_by_zone.style.set_table_styles(table_large_font).format('{:,}' ).set_caption(caption_french)"
   ]
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     "remove-input"
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_53df8 tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_53df8 tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_53df8 tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_53df8 th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_53df8 caption {\n",
       "  font-size: 14px;\n",
       "  font-style: italic;\n",
       "  caption-side: bottom;\n",
       "  text-align: left;\n",
       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_53df8\">\n",
       "  <caption>Historique des échantillonnages Le Léman : Le nombre d'échantillons et l'ampleur de l'effort par canton. Remarque : les heures correspondent au temps passé à compter et à collecter.</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_53df8_level0_col0\" class=\"col_heading level0 col0\" >échantillons</th>\n",
       "      <th id=\"T_53df8_level0_col1\" class=\"col_heading level0 col1\" >municipalités</th>\n",
       "      <th id=\"T_53df8_level0_col2\" class=\"col_heading level0 col2\" >quantité</th>\n",
       "      <th id=\"T_53df8_level0_col3\" class=\"col_heading level0 col3\" >site</th>\n",
       "      <th id=\"T_53df8_level0_col4\" class=\"col_heading level0 col4\" >m²</th>\n",
       "      <th id=\"T_53df8_level0_col5\" class=\"col_heading level0 col5\" >mètres</th>\n",
       "      <th id=\"T_53df8_level0_col6\" class=\"col_heading level0 col6\" >heures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_53df8_level0_row0\" class=\"row_heading level0 row0\" >Genève</th>\n",
       "      <td id=\"T_53df8_row0_col0\" class=\"data row0 col0\" >50</td>\n",
       "      <td id=\"T_53df8_row0_col1\" class=\"data row0 col1\" >4</td>\n",
       "      <td id=\"T_53df8_row0_col2\" class=\"data row0 col2\" >8,692</td>\n",
       "      <td id=\"T_53df8_row0_col3\" class=\"data row0 col3\" >11</td>\n",
       "      <td id=\"T_53df8_row0_col4\" class=\"data row0 col4\" >16,119</td>\n",
       "      <td id=\"T_53df8_row0_col5\" class=\"data row0 col5\" >4,432</td>\n",
       "      <td id=\"T_53df8_row0_col6\" class=\"data row0 col6\" >108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53df8_level0_row1\" class=\"row_heading level0 row1\" >Valais</th>\n",
       "      <td id=\"T_53df8_row1_col0\" class=\"data row1 col0\" >17</td>\n",
       "      <td id=\"T_53df8_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_53df8_row1_col2\" class=\"data row1 col2\" >8,367</td>\n",
       "      <td id=\"T_53df8_row1_col3\" class=\"data row1 col3\" >2</td>\n",
       "      <td id=\"T_53df8_row1_col4\" class=\"data row1 col4\" >4,718</td>\n",
       "      <td id=\"T_53df8_row1_col5\" class=\"data row1 col5\" >1,794</td>\n",
       "      <td id=\"T_53df8_row1_col6\" class=\"data row1 col6\" >74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53df8_level0_row2\" class=\"row_heading level0 row2\" >Vaud</th>\n",
       "      <td id=\"T_53df8_row2_col0\" class=\"data row2 col0\" >252</td>\n",
       "      <td id=\"T_53df8_row2_col1\" class=\"data row2 col1\" >19</td>\n",
       "      <td id=\"T_53df8_row2_col2\" class=\"data row2 col2\" >51,742</td>\n",
       "      <td id=\"T_53df8_row2_col3\" class=\"data row2 col3\" >43</td>\n",
       "      <td id=\"T_53df8_row2_col4\" class=\"data row2 col4\" >47,155</td>\n",
       "      <td id=\"T_53df8_row2_col5\" class=\"data row2 col5\" >10,878</td>\n",
       "      <td id=\"T_53df8_row2_col6\" class=\"data row2 col6\" >435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53df8_level0_row3\" class=\"row_heading level0 row3\" >total</th>\n",
       "      <td id=\"T_53df8_row3_col0\" class=\"data row3 col0\" >319</td>\n",
       "      <td id=\"T_53df8_row3_col1\" class=\"data row3 col1\" >25</td>\n",
       "      <td id=\"T_53df8_row3_col2\" class=\"data row3 col2\" >68,801</td>\n",
       "      <td id=\"T_53df8_row3_col3\" class=\"data row3 col3\" >56</td>\n",
       "      <td id=\"T_53df8_row3_col4\" class=\"data row3 col4\" >67,992</td>\n",
       "      <td id=\"T_53df8_row3_col5\" class=\"data row3 col5\" >17,104</td>\n",
       "      <td id=\"T_53df8_row3_col6\" class=\"data row3 col6\" >617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_data_l = lac_riv.groupby([\"canton\"]).agg(operations)\n",
    "\n",
    "dimensional_summary_l, d_sum_l = dimensional_summary_data(dims, lac_riv, \"canton\", zones=lac_riv[\"canton\"].unique())\n",
    "\n",
    "summary_by_canton = pd.concat([summary_data_l, d_sum_l], axis=1, ignore_index=False)\n",
    "summary_by_canton[\"hours\"] = (summary_by_canton[\"time\"]/60).round(2)\n",
    "summary_by_canton.drop(\"time\", inplace=True, axis=1)\n",
    "summary_by_canton.loc[\"total\", : ] = summary_by_canton.sum()\n",
    "\n",
    "caption_french = f\"Historique des échantillonnages {lake} : Le nombre d'échantillons et l'ampleur de l'effort par canton. Remarque : les heures correspondent au temps passé à compter et à collecter.\"\n",
    "\n",
    "summary_by_canton.index.name = None\n",
    "summary_by_canton.rename(columns=display_names, inplace=True)\n",
    "summary_by_canton = summary_by_canton.astype(\"int\")\n",
    "summary_by_canton.style.set_table_styles(table_large_font).format('{:,}' ).set_caption(caption_french)"
   ]
  },
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   "cell_type": "markdown",
   "id": "49b10687-f469-45ec-9019-345d4c213754",
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    "tags": []
   },
   "source": [
    "## Objets d'intérêt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0daae271-da27-4b7a-afaf-b23ec1f0f381",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4f9dd tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_4f9dd tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_4f9dd tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_4f9dd th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_4f9dd caption {\n",
       "  font-size: 14px;\n",
       "  font-style: italic;\n",
       "  caption-side: bottom;\n",
       "  text-align: left;\n",
       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4f9dd\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4f9dd_level0_col0\" class=\"col_heading level0 col0\" >quantité</th>\n",
       "      <th id=\"T_4f9dd_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n",
       "      <th id=\"T_4f9dd_level0_col2\" class=\"col_heading level0 col2\" >% de totale</th>\n",
       "      <th id=\"T_4f9dd_level0_col3\" class=\"col_heading level0 col3\" >taux d'échec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row0\" class=\"row_heading level0 row0\" >Plastiques fragmentés</th>\n",
       "      <td id=\"T_4f9dd_row0_col0\" class=\"data row0 col0\" >17,235</td>\n",
       "      <td id=\"T_4f9dd_row0_col1\" class=\"data row0 col1\" >0.56</td>\n",
       "      <td id=\"T_4f9dd_row0_col2\" class=\"data row0 col2\" >0.25</td>\n",
       "      <td id=\"T_4f9dd_row0_col3\" class=\"data row0 col3\" >0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row1\" class=\"row_heading level0 row1\" >Mégots et filtres à cigarettes</th>\n",
       "      <td id=\"T_4f9dd_row1_col0\" class=\"data row1 col0\" >16,342</td>\n",
       "      <td id=\"T_4f9dd_row1_col1\" class=\"data row1 col1\" >0.51</td>\n",
       "      <td id=\"T_4f9dd_row1_col2\" class=\"data row1 col2\" >0.24</td>\n",
       "      <td id=\"T_4f9dd_row1_col3\" class=\"data row1 col3\" >0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row2\" class=\"row_heading level0 row2\" >Polystyrène expansé fragmenté</th>\n",
       "      <td id=\"T_4f9dd_row2_col0\" class=\"data row2 col0\" >12,296</td>\n",
       "      <td id=\"T_4f9dd_row2_col1\" class=\"data row2 col1\" >0.24</td>\n",
       "      <td id=\"T_4f9dd_row2_col2\" class=\"data row2 col2\" >0.18</td>\n",
       "      <td id=\"T_4f9dd_row2_col3\" class=\"data row2 col3\" >0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row3\" class=\"row_heading level0 row3\" >Emballages de bonbons, de snacks</th>\n",
       "      <td id=\"T_4f9dd_row3_col0\" class=\"data row3 col0\" >6,686</td>\n",
       "      <td id=\"T_4f9dd_row3_col1\" class=\"data row3 col1\" >0.21</td>\n",
       "      <td id=\"T_4f9dd_row3_col2\" class=\"data row3 col2\" >0.1</td>\n",
       "      <td id=\"T_4f9dd_row3_col3\" class=\"data row3 col3\" >0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row4\" class=\"row_heading level0 row4\" >Coton-tige</th>\n",
       "      <td id=\"T_4f9dd_row4_col0\" class=\"data row4 col0\" >4,774</td>\n",
       "      <td id=\"T_4f9dd_row4_col1\" class=\"data row4 col1\" >0.14</td>\n",
       "      <td id=\"T_4f9dd_row4_col2\" class=\"data row4 col2\" >0.07</td>\n",
       "      <td id=\"T_4f9dd_row4_col3\" class=\"data row4 col3\" >0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row5\" class=\"row_heading level0 row5\" >Couvercles de bouteilles en plastique</th>\n",
       "      <td id=\"T_4f9dd_row5_col0\" class=\"data row5 col0\" >3,904</td>\n",
       "      <td id=\"T_4f9dd_row5_col1\" class=\"data row5 col1\" >0.13</td>\n",
       "      <td id=\"T_4f9dd_row5_col2\" class=\"data row5 col2\" >0.06</td>\n",
       "      <td id=\"T_4f9dd_row5_col3\" class=\"data row5 col3\" >0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row6\" class=\"row_heading level0 row6\" >Bâche, feuille plastique industrielle</th>\n",
       "      <td id=\"T_4f9dd_row6_col0\" class=\"data row6 col0\" >3,227</td>\n",
       "      <td id=\"T_4f9dd_row6_col1\" class=\"data row6 col1\" >0.06</td>\n",
       "      <td id=\"T_4f9dd_row6_col2\" class=\"data row6 col2\" >0.05</td>\n",
       "      <td id=\"T_4f9dd_row6_col3\" class=\"data row6 col3\" >0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row7\" class=\"row_heading level0 row7\" >Pellets industriels (GPI)</th>\n",
       "      <td id=\"T_4f9dd_row7_col0\" class=\"data row7 col0\" >2,686</td>\n",
       "      <td id=\"T_4f9dd_row7_col1\" class=\"data row7 col1\" >0.0</td>\n",
       "      <td id=\"T_4f9dd_row7_col2\" class=\"data row7 col2\" >0.04</td>\n",
       "      <td id=\"T_4f9dd_row7_col3\" class=\"data row7 col3\" >0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row8\" class=\"row_heading level0 row8\" >Cartouches de fusil de chasse</th>\n",
       "      <td id=\"T_4f9dd_row8_col0\" class=\"data row8 col0\" >1,028</td>\n",
       "      <td id=\"T_4f9dd_row8_col1\" class=\"data row8 col1\" >0.01</td>\n",
       "      <td id=\"T_4f9dd_row8_col2\" class=\"data row8 col2\" >0.01</td>\n",
       "      <td id=\"T_4f9dd_row8_col3\" class=\"data row8 col3\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f9dd_level0_row9\" class=\"row_heading level0 row9\" >Emballage fast food</th>\n",
       "      <td id=\"T_4f9dd_row9_col0\" class=\"data row9 col0\" >623</td>\n",
       "      <td id=\"T_4f9dd_row9_col1\" class=\"data row9 col1\" >0.0</td>\n",
       "      <td id=\"T_4f9dd_row9_col2\" class=\"data row9 col2\" >0.01</td>\n",
       "      <td id=\"T_4f9dd_row9_col3\" class=\"data row9 col3\" >0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f64ab3e4eb0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lac_riv[\"fail\"] = lac_riv['pcs/m'] > 0\n",
    "fail_rates = lac_riv.groupby(\"code\", as_index=False).fail.sum()\n",
    "n_samps = lac_riv.loc_date.nunique()\n",
    "fail_rates[\"rate\"]= fail_rates.fail/n_samps\n",
    "fail_rates = fail_rates.sort_values(by=\"rate\", ascending=False)\n",
    "failed = fail_rates[fail_rates.rate >= .5].code.unique()\n",
    "\n",
    "abundant = lac_riv.groupby(\"code\", as_index=False).quantity.sum()\n",
    "abundant_c = abundant.sort_values(by=\"quantity\", ascending=False)[:10].code.unique()\n",
    "\n",
    "most_common_codes = list(set([*abundant, *failed]))\n",
    "\n",
    "mc_data = lac_riv[lac_riv.code.isin(codes_of_interest)].groupby(\"code\", as_index=False).agg({\"quantity\":\"sum\", \"pcs/m\":\"median\", \"fail\":\"sum\"})\n",
    "mc_data[\"fail_rate\"] = (mc_data.fail/n_samps).round(2)\n",
    "mc_data[\"q_weight\"] = (mc_data.quantity/abundant.quantity.sum()).round(2)\n",
    "mc_data[\"object\"] = mc_data.code.apply(lambda x: french_names[x])\n",
    "mc_data = mc_data[[\"object\", \"quantity\", \"pcs/m\", \"q_weight\", \"fail_rate\"]]\n",
    "\n",
    "mc_data = mc_data.sort_values(by=\"quantity\", ascending=False)\n",
    "mc_data[\"quantity\"] = mc_data.quantity.astype(\"int\")\n",
    "mc_data[\"pcs/m\"] = mc_data[\"pcs/m\"].round(2)\n",
    "mc_data.set_index(\"object\", drop=True, inplace=True)\n",
    "mc_data.index.name = None\n",
    "mc_data.rename(columns=display_names).style.set_table_styles(table_large_font).format('{:,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504cb022-4091-462a-b3f4-ac2a4b7e11fa",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Communes et zones d'intérêt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f6ca782-d64a-430f-b9cc-6aae5b021746",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "__Municipalités:__ Aubonne, Genève, Bouveret, Clarens, Crans, Cully, Gland, Grangettes, Hermance, Lutry, La Tour-de-Peilz, Saint-Sulpice (VD), Vevey, Versoix, Préverenges, Rolle, Savonière, Tolochenaz, Vidy, Montreux, Bourg-en-Lavaux, Saint-Gingolph, Morges, Allaman, Lausanne"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_in_canton = ', '.join(lac_riv.city.unique())\n",
    "\n",
    "munix = f'__Municipalités:__ {cities_in_canton}'\n",
    "md(munix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2fba0b0b-12d8-48b1-98df-40fe6ca55888",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "__Rivières:__ arve, rhone, ognonnaz"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rivers = ', '.join([x for x in all_x[all_x.zone == 'rivière'].water_name_slug.unique() if x != 'la-thiele'])\n",
    "riverx = f'__Rivières:__ {rivers}'\n",
    "md(riverx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8565c8-03f3-40b8-bb37-80afa76212db",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Utilisation des sols: (m² utilisé)/(m² disponible)\n",
    "\n",
    "L'utilisation du sol des sites étudiés en par rapport à l'utilisation du sol d'autres cantons sur le lac. \n",
    "\n",
    "Chaque lieu d'inventaire est géolocalisé au centre d'un hexagone de 3 000 mètres. L'ampleur des attributs de l'occupation et de l'utilisation des sols à l'intérieur de cet hexagone est calculée. Cette procédure peut améliorer le choix des lieux d'échantillonnage. Les résultats ont déjà été utilisés pour identifier les caractéristiques d'un lieu d'inventaire qui peuvent conduire à l'accumulation de certains déchets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7317f264-93ba-42ef-8562-58ce651da694",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 5, figsize=(7,6), sharey=True)\n",
    "\n",
    "valais_locs = lac_riv.slug.unique()\n",
    "# region_locs = not_valais.slug.unique()\n",
    "\n",
    "index_order = [\n",
    "    'Siedl',\n",
    "    'infrastructure', \n",
    "    'Wald',\n",
    "    'Obstanlage',\n",
    "    'Undefined',\n",
    "    'recreation',\n",
    "    'Reben',\n",
    "    'Stadtzentr',\n",
    "  \n",
    "]\n",
    "\n",
    "ax_s = {\n",
    "    'Siedl':[0,0],\n",
    "    'infrastructure':[0,1], \n",
    "    'Wald':[0,2],\n",
    "    'Obstanlage':[0,3],\n",
    "    'undefined':[0,4],\n",
    "    'recreation':[1,0],\n",
    "    'Reben':[1,1],\n",
    "    'Stadtzentr':[1,2],\n",
    "}\n",
    "\n",
    "l_used = land_use[land_use.use.isin(land_use_of_interest)]\n",
    "v_land_use = land_use[land_use.slug.isin(valais_locs)].copy()\n",
    "# r_land_use = land_use[land_use.slug.isin(region_locs)].copy()\n",
    "\n",
    "no_streets = [x for x in land_use_of_interest if x != \"Strasse\"]\n",
    "\n",
    "for i, name in enumerate(ax_s.keys()):\n",
    "    \n",
    "    row = ax_s[name][0]\n",
    "    column = ax_s[name][1]\n",
    "    ax = axs[row, column]\n",
    "    # sns.stripplot(data = v_land_use[v_land_use.use == name], y = \"scaled\", color=\"magenta\", zorder=20, size=6, ax=ax)\n",
    "    sns.boxplot(data= v_land_use[v_land_use.use == name], y=\"scaled\",  ax=ax, color='slategray')\n",
    "    ax.set_xticks([0], [land_use_names[name]])\n",
    "    ax.set_ylabel(\"\")\n",
    "\n",
    "axs[1, 4].axis('off')\n",
    "a_space = axs[1, 3]\n",
    "a_space.axis('off')\n",
    "h = [Patch(facecolor='slategray', edgecolor='black', label='Color Patch')]\n",
    "l = [cantons_avoisinante ]\n",
    "a_space.legend(h,l, bbox_to_anchor=(0,1), loc=\"upper left\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf84ded-25e9-4cd5-9265-f75bd0a2d831",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Résultats des inventaires: pcs/m\n",
    "\n",
    "La densité est calculée en additionnant tous les objets trouvés pour chaque jour et lieu d'échantillonnage. La somme pour chaque échantillon est ensuite divisée par la longueur du rivage, de la rive ou du tronçon de sentier. Pour un exemple, voir le rapport fédéral sur le calcul des lignes de base. [Baselines](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/baselines.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70cae240-e3f1-4da4-9081-7212867684e5",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # make the survey total from the list of available codes\n",
    "# # survey_totals from valais\n",
    "vs_jt = lac_riv.groupby([\"loc_date\", \"date\", \"region\"], as_index=False)[\"pcs/m\"].sum()\n",
    "\n",
    "french_caption = \"La densité totale par échantillon pour les 74 objets d'intérêt. \\\n",
    "Le premier prélèvement en Valais date de 2016. Les prélèvements en rivière \\\n",
    "et en montagne ont été réalisés lors du rapport fédéral.\"\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.scatterplot(data=vs_jt, x=\"date\", y=\"pcs/m\", hue=\"region\", zorder=20, palette={'Haut lac':'royalblue', 'Grand lac':'saddlebrown', 'Petit lac': 'black'}, ax=ax)\n",
    "\n",
    "ax.set_ylim(-1, 60)\n",
    "ax.legend(bbox_to_anchor=(0, 1), loc=\"upper left\", ncol=2)\n",
    "ax.set_xlabel(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0efd6c1f-43a3-4c0e-a822-dc523513303f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Répartition de la densité des déchets\n",
    "\n",
    "__a définir__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dff034e-a94f-4abd-af22-af0f45ca98cb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "__Répartition des objets selon matériel.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c592827-ec8a-45c6-b646-530dbb177b78",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_92488 tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_92488 tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_92488 tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_92488 th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_92488 caption {\n",
       "  font-size: 14px;\n",
       "  font-style: italic;\n",
       "  caption-side: bottom;\n",
       "  text-align: left;\n",
       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_92488\">\n",
       "  <caption>Total des objets par type de matériau. Tous les échantillons.</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_92488_level0_col0\" class=\"col_heading level0 col0\" >quantité</th>\n",
       "      <th id=\"T_92488_level0_col1\" class=\"col_heading level0 col1\" >% de totale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_92488_level0_row0\" class=\"row_heading level0 row0\" >Plastique</th>\n",
       "      <td id=\"T_92488_row0_col0\" class=\"data row0 col0\" >68,801</td>\n",
       "      <td id=\"T_92488_row0_col1\" class=\"data row0 col1\" >1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f64ab145670>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCodes = pd.read_csv(\"resources/codes.csv\")\n",
    "dfCodes.set_index(\"code\", drop=True, inplace=True)\n",
    "dfCodes.loc[\"Gcaps\", [\"material\", \"description\", \"parent_code\", \"groupname\"]] = \"Plastic\", \"Plastic bottle tops\", \"Parent code\", 'food and drink'\n",
    "\n",
    "code_d = dfCodes[\"description\"]\n",
    "code_m = dfCodes[\"material\"]\n",
    "code_g = dfCodes[\"groupname\"]\n",
    "\n",
    "caption_material_fr = \"Total des objets par type de matériau. Tous les échantillons.\"\n",
    "\n",
    "lac_riv[\"material\"] = lac_riv.code.apply(lambda x: code_m.loc[x])\n",
    "material_totals = lac_riv.groupby(\"material\", as_index=False).quantity.sum()\n",
    "material_totals[\"quantity\"] = material_totals.quantity.astype(int)\n",
    "material_totals[\"material\"] = material_totals.material.apply(lambda x: material_names[x])\n",
    "material_totals[\"q_weight\"] = (material_totals.quantity/material_totals.quantity.sum()).round(3)\n",
    "material_totals = material_totals.set_index(\"material\", drop=True)\n",
    "material_totals.sort_values(by=\"quantity\", ascending=False, inplace=True)\n",
    "material_totals.index.name = None\n",
    "material_totals.rename(columns=display_names).style.set_table_styles(table_large_font).format('{:,}').set_caption(caption_material_fr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004331c7-5926-4791-95b7-20a996b35003",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Objets d'intérêt résultats municipaux: pcs/m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1bc5978f-01ab-487f-a016-33953670a1c7",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  background-color: #fffbcf;\n",
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       "  background-color: #fffaca;\n",
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       "  background-color: #fed26d;\n",
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       "#T_69c3d_row15_col6 {\n",
       "  background-color: #feab39;\n",
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       "#T_69c3d_row16_col6 {\n",
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       "#T_69c3d_row16_col8 {\n",
       "  background-color: #feeba2;\n",
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       "  background-color: #b03f03;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #fee697;\n",
       "  color: #000000;\n",
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       "#T_69c3d_row19_col8 {\n",
       "  background-color: #e2650f;\n",
       "  color: #f1f1f1;\n",
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       "#T_69c3d_row19_col9 {\n",
       "  background-color: #fc9427;\n",
       "  color: #000000;\n",
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       "  background-color: #fede86;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_69c3d_row23_col5 {\n",
       "  background-color: #fff8c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_69c3d_row23_col6 {\n",
       "  background-color: #fec14c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_69c3d_row24_col8 {\n",
       "  background-color: #fee493;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_69c3d_level0_col0, #T_69c3d_level0_col1, #T_69c3d_level0_col2, #T_69c3d_level0_col3, #T_69c3d_level0_col4, #T_69c3d_level0_col5, #T_69c3d_level0_col6, #T_69c3d_level0_col7, #T_69c3d_level0_col8, #T_69c3d_level0_col9 {\n",
       "  writing-mode: vertical-lr;\n",
       "  transform: rotate(-180deg);\n",
       "  padding: 10px;\n",
       "  margins: 0;\n",
       "  vertical-align: baseline;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_69c3d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_69c3d_level0_col0\" class=\"col_heading level0 col0\" >Bâche, feuille plastique industrielle</th>\n",
       "      <th id=\"T_69c3d_level0_col1\" class=\"col_heading level0 col1\" >Cartouches de fusil de chasse</th>\n",
       "      <th id=\"T_69c3d_level0_col2\" class=\"col_heading level0 col2\" >Coton-tige</th>\n",
       "      <th id=\"T_69c3d_level0_col3\" class=\"col_heading level0 col3\" >Couvercles de bouteilles en plastique</th>\n",
       "      <th id=\"T_69c3d_level0_col4\" class=\"col_heading level0 col4\" >Emballage fast food</th>\n",
       "      <th id=\"T_69c3d_level0_col5\" class=\"col_heading level0 col5\" >Emballages de bonbons, de snacks</th>\n",
       "      <th id=\"T_69c3d_level0_col6\" class=\"col_heading level0 col6\" >Mégots et filtres à cigarettes</th>\n",
       "      <th id=\"T_69c3d_level0_col7\" class=\"col_heading level0 col7\" >Pellets industriels (GPI)</th>\n",
       "      <th id=\"T_69c3d_level0_col8\" class=\"col_heading level0 col8\" >Plastiques fragmentés</th>\n",
       "      <th id=\"T_69c3d_level0_col9\" class=\"col_heading level0 col9\" >Polystyrène expansé fragmenté</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row0\" class=\"row_heading level0 row0\" >Allaman</th>\n",
       "      <td id=\"T_69c3d_row0_col0\" class=\"data row0 col0\" >0.35</td>\n",
       "      <td id=\"T_69c3d_row0_col1\" class=\"data row0 col1\" >0.14</td>\n",
       "      <td id=\"T_69c3d_row0_col2\" class=\"data row0 col2\" >0.68</td>\n",
       "      <td id=\"T_69c3d_row0_col3\" class=\"data row0 col3\" >0.34</td>\n",
       "      <td id=\"T_69c3d_row0_col4\" class=\"data row0 col4\" >0.04</td>\n",
       "      <td id=\"T_69c3d_row0_col5\" class=\"data row0 col5\" >0.53</td>\n",
       "      <td id=\"T_69c3d_row0_col6\" class=\"data row0 col6\" >0.37</td>\n",
       "      <td id=\"T_69c3d_row0_col7\" class=\"data row0 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row0_col8\" class=\"data row0 col8\" >1.27</td>\n",
       "      <td id=\"T_69c3d_row0_col9\" class=\"data row0 col9\" >0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row1\" class=\"row_heading level0 row1\" >Aubonne</th>\n",
       "      <td id=\"T_69c3d_row1_col0\" class=\"data row1 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row1_col1\" class=\"data row1 col1\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row1_col2\" class=\"data row1 col2\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
       "      <td id=\"T_69c3d_row1_col4\" class=\"data row1 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row1_col5\" class=\"data row1 col5\" >0.11</td>\n",
       "      <td id=\"T_69c3d_row1_col6\" class=\"data row1 col6\" >0.2</td>\n",
       "      <td id=\"T_69c3d_row1_col7\" class=\"data row1 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row1_col8\" class=\"data row1 col8\" >0.24</td>\n",
       "      <td id=\"T_69c3d_row1_col9\" class=\"data row1 col9\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row2\" class=\"row_heading level0 row2\" >Bourg-en-Lavaux</th>\n",
       "      <td id=\"T_69c3d_row2_col0\" class=\"data row2 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row2_col1\" class=\"data row2 col1\" >0.12</td>\n",
       "      <td id=\"T_69c3d_row2_col2\" class=\"data row2 col2\" >0.45</td>\n",
       "      <td id=\"T_69c3d_row2_col3\" class=\"data row2 col3\" >0.29</td>\n",
       "      <td id=\"T_69c3d_row2_col4\" class=\"data row2 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row2_col5\" class=\"data row2 col5\" >0.08</td>\n",
       "      <td id=\"T_69c3d_row2_col6\" class=\"data row2 col6\" >0.34</td>\n",
       "      <td id=\"T_69c3d_row2_col7\" class=\"data row2 col7\" >0.04</td>\n",
       "      <td id=\"T_69c3d_row2_col8\" class=\"data row2 col8\" >1.25</td>\n",
       "      <td id=\"T_69c3d_row2_col9\" class=\"data row2 col9\" >0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row3\" class=\"row_heading level0 row3\" >Bouveret</th>\n",
       "      <td id=\"T_69c3d_row3_col0\" class=\"data row3 col0\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row3_col1\" class=\"data row3 col1\" >0.1</td>\n",
       "      <td id=\"T_69c3d_row3_col2\" class=\"data row3 col2\" >0.23</td>\n",
       "      <td id=\"T_69c3d_row3_col3\" class=\"data row3 col3\" >0.42</td>\n",
       "      <td id=\"T_69c3d_row3_col4\" class=\"data row3 col4\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row3_col5\" class=\"data row3 col5\" >0.53</td>\n",
       "      <td id=\"T_69c3d_row3_col6\" class=\"data row3 col6\" >0.45</td>\n",
       "      <td id=\"T_69c3d_row3_col7\" class=\"data row3 col7\" >0.63</td>\n",
       "      <td id=\"T_69c3d_row3_col8\" class=\"data row3 col8\" >2.23</td>\n",
       "      <td id=\"T_69c3d_row3_col9\" class=\"data row3 col9\" >0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row4\" class=\"row_heading level0 row4\" >Clarens</th>\n",
       "      <td id=\"T_69c3d_row4_col0\" class=\"data row4 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row4_col1\" class=\"data row4 col1\" >0.03</td>\n",
       "      <td id=\"T_69c3d_row4_col2\" class=\"data row4 col2\" >0.26</td>\n",
       "      <td id=\"T_69c3d_row4_col3\" class=\"data row4 col3\" >0.06</td>\n",
       "      <td id=\"T_69c3d_row4_col4\" class=\"data row4 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row4_col5\" class=\"data row4 col5\" >0.38</td>\n",
       "      <td id=\"T_69c3d_row4_col6\" class=\"data row4 col6\" >2.08</td>\n",
       "      <td id=\"T_69c3d_row4_col7\" class=\"data row4 col7\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row4_col8\" class=\"data row4 col8\" >1.57</td>\n",
       "      <td id=\"T_69c3d_row4_col9\" class=\"data row4 col9\" >0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row5\" class=\"row_heading level0 row5\" >Crans</th>\n",
       "      <td id=\"T_69c3d_row5_col0\" class=\"data row5 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row5_col1\" class=\"data row5 col1\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row5_col2\" class=\"data row5 col2\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row5_col3\" class=\"data row5 col3\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row5_col4\" class=\"data row5 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row5_col5\" class=\"data row5 col5\" >0.05</td>\n",
       "      <td id=\"T_69c3d_row5_col6\" class=\"data row5 col6\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row5_col7\" class=\"data row5 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row5_col8\" class=\"data row5 col8\" >0.17</td>\n",
       "      <td id=\"T_69c3d_row5_col9\" class=\"data row5 col9\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row6\" class=\"row_heading level0 row6\" >Cully</th>\n",
       "      <td id=\"T_69c3d_row6_col0\" class=\"data row6 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row6_col1\" class=\"data row6 col1\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row6_col2\" class=\"data row6 col2\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row6_col3\" class=\"data row6 col3\" >0.08</td>\n",
       "      <td id=\"T_69c3d_row6_col4\" class=\"data row6 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row6_col5\" class=\"data row6 col5\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row6_col6\" class=\"data row6 col6\" >0.18</td>\n",
       "      <td id=\"T_69c3d_row6_col7\" class=\"data row6 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row6_col8\" class=\"data row6 col8\" >0.15</td>\n",
       "      <td id=\"T_69c3d_row6_col9\" class=\"data row6 col9\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row7\" class=\"row_heading level0 row7\" >Genève</th>\n",
       "      <td id=\"T_69c3d_row7_col0\" class=\"data row7 col0\" >0.25</td>\n",
       "      <td id=\"T_69c3d_row7_col1\" class=\"data row7 col1\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row7_col2\" class=\"data row7 col2\" >0.14</td>\n",
       "      <td id=\"T_69c3d_row7_col3\" class=\"data row7 col3\" >0.17</td>\n",
       "      <td id=\"T_69c3d_row7_col4\" class=\"data row7 col4\" >0.03</td>\n",
       "      <td id=\"T_69c3d_row7_col5\" class=\"data row7 col5\" >0.46</td>\n",
       "      <td id=\"T_69c3d_row7_col6\" class=\"data row7 col6\" >0.78</td>\n",
       "      <td id=\"T_69c3d_row7_col7\" class=\"data row7 col7\" >0.21</td>\n",
       "      <td id=\"T_69c3d_row7_col8\" class=\"data row7 col8\" >1.1</td>\n",
       "      <td id=\"T_69c3d_row7_col9\" class=\"data row7 col9\" >0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row8\" class=\"row_heading level0 row8\" >Gland</th>\n",
       "      <td id=\"T_69c3d_row8_col0\" class=\"data row8 col0\" >0.11</td>\n",
       "      <td id=\"T_69c3d_row8_col1\" class=\"data row8 col1\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row8_col2\" class=\"data row8 col2\" >0.2</td>\n",
       "      <td id=\"T_69c3d_row8_col3\" class=\"data row8 col3\" >0.21</td>\n",
       "      <td id=\"T_69c3d_row8_col4\" class=\"data row8 col4\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row8_col5\" class=\"data row8 col5\" >0.82</td>\n",
       "      <td id=\"T_69c3d_row8_col6\" class=\"data row8 col6\" >0.12</td>\n",
       "      <td id=\"T_69c3d_row8_col7\" class=\"data row8 col7\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row8_col8\" class=\"data row8 col8\" >0.58</td>\n",
       "      <td id=\"T_69c3d_row8_col9\" class=\"data row8 col9\" >0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row9\" class=\"row_heading level0 row9\" >Grangettes</th>\n",
       "      <td id=\"T_69c3d_row9_col0\" class=\"data row9 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row9_col1\" class=\"data row9 col1\" >0.03</td>\n",
       "      <td id=\"T_69c3d_row9_col2\" class=\"data row9 col2\" >0.58</td>\n",
       "      <td id=\"T_69c3d_row9_col3\" class=\"data row9 col3\" >0.39</td>\n",
       "      <td id=\"T_69c3d_row9_col4\" class=\"data row9 col4\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row9_col5\" class=\"data row9 col5\" >1.38</td>\n",
       "      <td id=\"T_69c3d_row9_col6\" class=\"data row9 col6\" >0.44</td>\n",
       "      <td id=\"T_69c3d_row9_col7\" class=\"data row9 col7\" >0.47</td>\n",
       "      <td id=\"T_69c3d_row9_col8\" class=\"data row9 col8\" >5.86</td>\n",
       "      <td id=\"T_69c3d_row9_col9\" class=\"data row9 col9\" >1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row10\" class=\"row_heading level0 row10\" >Hermance</th>\n",
       "      <td id=\"T_69c3d_row10_col0\" class=\"data row10 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row10_col1\" class=\"data row10 col1\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row10_col2\" class=\"data row10 col2\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row10_col3\" class=\"data row10 col3\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row10_col4\" class=\"data row10 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row10_col5\" class=\"data row10 col5\" >0.03</td>\n",
       "      <td id=\"T_69c3d_row10_col6\" class=\"data row10 col6\" >0.29</td>\n",
       "      <td id=\"T_69c3d_row10_col7\" class=\"data row10 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row10_col8\" class=\"data row10 col8\" >0.34</td>\n",
       "      <td id=\"T_69c3d_row10_col9\" class=\"data row10 col9\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row11\" class=\"row_heading level0 row11\" >La Tour-de-Peilz</th>\n",
       "      <td id=\"T_69c3d_row11_col0\" class=\"data row11 col0\" >0.55</td>\n",
       "      <td id=\"T_69c3d_row11_col1\" class=\"data row11 col1\" >0.13</td>\n",
       "      <td id=\"T_69c3d_row11_col2\" class=\"data row11 col2\" >0.81</td>\n",
       "      <td id=\"T_69c3d_row11_col3\" class=\"data row11 col3\" >0.5</td>\n",
       "      <td id=\"T_69c3d_row11_col4\" class=\"data row11 col4\" >0.07</td>\n",
       "      <td id=\"T_69c3d_row11_col5\" class=\"data row11 col5\" >0.53</td>\n",
       "      <td id=\"T_69c3d_row11_col6\" class=\"data row11 col6\" >1.26</td>\n",
       "      <td id=\"T_69c3d_row11_col7\" class=\"data row11 col7\" >0.01</td>\n",
       "      <td id=\"T_69c3d_row11_col8\" class=\"data row11 col8\" >1.86</td>\n",
       "      <td id=\"T_69c3d_row11_col9\" class=\"data row11 col9\" >1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row12\" class=\"row_heading level0 row12\" >Lausanne</th>\n",
       "      <td id=\"T_69c3d_row12_col0\" class=\"data row12 col0\" >0.61</td>\n",
       "      <td id=\"T_69c3d_row12_col1\" class=\"data row12 col1\" >0.01</td>\n",
       "      <td id=\"T_69c3d_row12_col2\" class=\"data row12 col2\" >0.94</td>\n",
       "      <td id=\"T_69c3d_row12_col3\" class=\"data row12 col3\" >0.23</td>\n",
       "      <td id=\"T_69c3d_row12_col4\" class=\"data row12 col4\" >0.01</td>\n",
       "      <td id=\"T_69c3d_row12_col5\" class=\"data row12 col5\" >0.52</td>\n",
       "      <td id=\"T_69c3d_row12_col6\" class=\"data row12 col6\" >1.62</td>\n",
       "      <td id=\"T_69c3d_row12_col7\" class=\"data row12 col7\" >0.7</td>\n",
       "      <td id=\"T_69c3d_row12_col8\" class=\"data row12 col8\" >2.79</td>\n",
       "      <td id=\"T_69c3d_row12_col9\" class=\"data row12 col9\" >0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row13\" class=\"row_heading level0 row13\" >Lutry</th>\n",
       "      <td id=\"T_69c3d_row13_col0\" class=\"data row13 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row13_col1\" class=\"data row13 col1\" >0.05</td>\n",
       "      <td id=\"T_69c3d_row13_col2\" class=\"data row13 col2\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row13_col3\" class=\"data row13 col3\" >0.05</td>\n",
       "      <td id=\"T_69c3d_row13_col4\" class=\"data row13 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row13_col5\" class=\"data row13 col5\" >0.05</td>\n",
       "      <td id=\"T_69c3d_row13_col6\" class=\"data row13 col6\" >0.08</td>\n",
       "      <td id=\"T_69c3d_row13_col7\" class=\"data row13 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row13_col8\" class=\"data row13 col8\" >1.73</td>\n",
       "      <td id=\"T_69c3d_row13_col9\" class=\"data row13 col9\" >0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row14\" class=\"row_heading level0 row14\" >Montreux</th>\n",
       "      <td id=\"T_69c3d_row14_col0\" class=\"data row14 col0\" >0.25</td>\n",
       "      <td id=\"T_69c3d_row14_col1\" class=\"data row14 col1\" >0.11</td>\n",
       "      <td id=\"T_69c3d_row14_col2\" class=\"data row14 col2\" >0.38</td>\n",
       "      <td id=\"T_69c3d_row14_col3\" class=\"data row14 col3\" >0.27</td>\n",
       "      <td id=\"T_69c3d_row14_col4\" class=\"data row14 col4\" >0.04</td>\n",
       "      <td id=\"T_69c3d_row14_col5\" class=\"data row14 col5\" >0.31</td>\n",
       "      <td id=\"T_69c3d_row14_col6\" class=\"data row14 col6\" >1.66</td>\n",
       "      <td id=\"T_69c3d_row14_col7\" class=\"data row14 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row14_col8\" class=\"data row14 col8\" >0.69</td>\n",
       "      <td id=\"T_69c3d_row14_col9\" class=\"data row14 col9\" >1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row15\" class=\"row_heading level0 row15\" >Morges</th>\n",
       "      <td id=\"T_69c3d_row15_col0\" class=\"data row15 col0\" >0.2</td>\n",
       "      <td id=\"T_69c3d_row15_col1\" class=\"data row15 col1\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row15_col2\" class=\"data row15 col2\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row15_col3\" class=\"data row15 col3\" >0.07</td>\n",
       "      <td id=\"T_69c3d_row15_col4\" class=\"data row15 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row15_col5\" class=\"data row15 col5\" >0.63</td>\n",
       "      <td id=\"T_69c3d_row15_col6\" class=\"data row15 col6\" >2.63</td>\n",
       "      <td id=\"T_69c3d_row15_col7\" class=\"data row15 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row15_col8\" class=\"data row15 col8\" >0.27</td>\n",
       "      <td id=\"T_69c3d_row15_col9\" class=\"data row15 col9\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row16\" class=\"row_heading level0 row16\" >Préverenges</th>\n",
       "      <td id=\"T_69c3d_row16_col0\" class=\"data row16 col0\" >0.14</td>\n",
       "      <td id=\"T_69c3d_row16_col1\" class=\"data row16 col1\" >0.01</td>\n",
       "      <td id=\"T_69c3d_row16_col2\" class=\"data row16 col2\" >0.18</td>\n",
       "      <td id=\"T_69c3d_row16_col3\" class=\"data row16 col3\" >0.18</td>\n",
       "      <td id=\"T_69c3d_row16_col4\" class=\"data row16 col4\" >0.01</td>\n",
       "      <td id=\"T_69c3d_row16_col5\" class=\"data row16 col5\" >0.39</td>\n",
       "      <td id=\"T_69c3d_row16_col6\" class=\"data row16 col6\" >0.97</td>\n",
       "      <td id=\"T_69c3d_row16_col7\" class=\"data row16 col7\" >1.07</td>\n",
       "      <td id=\"T_69c3d_row16_col8\" class=\"data row16 col8\" >1.17</td>\n",
       "      <td id=\"T_69c3d_row16_col9\" class=\"data row16 col9\" >0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row17\" class=\"row_heading level0 row17\" >Rolle</th>\n",
       "      <td id=\"T_69c3d_row17_col0\" class=\"data row17 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row17_col1\" class=\"data row17 col1\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row17_col2\" class=\"data row17 col2\" >0.07</td>\n",
       "      <td id=\"T_69c3d_row17_col3\" class=\"data row17 col3\" >0.11</td>\n",
       "      <td id=\"T_69c3d_row17_col4\" class=\"data row17 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row17_col5\" class=\"data row17 col5\" >0.3</td>\n",
       "      <td id=\"T_69c3d_row17_col6\" class=\"data row17 col6\" >0.9</td>\n",
       "      <td id=\"T_69c3d_row17_col7\" class=\"data row17 col7\" >0.03</td>\n",
       "      <td id=\"T_69c3d_row17_col8\" class=\"data row17 col8\" >1.73</td>\n",
       "      <td id=\"T_69c3d_row17_col9\" class=\"data row17 col9\" >0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row18\" class=\"row_heading level0 row18\" >Saint-Gingolph</th>\n",
       "      <td id=\"T_69c3d_row18_col0\" class=\"data row18 col0\" >1.75</td>\n",
       "      <td id=\"T_69c3d_row18_col1\" class=\"data row18 col1\" >0.68</td>\n",
       "      <td id=\"T_69c3d_row18_col2\" class=\"data row18 col2\" >1.4</td>\n",
       "      <td id=\"T_69c3d_row18_col3\" class=\"data row18 col3\" >1.49</td>\n",
       "      <td id=\"T_69c3d_row18_col4\" class=\"data row18 col4\" >0.46</td>\n",
       "      <td id=\"T_69c3d_row18_col5\" class=\"data row18 col5\" >1.67</td>\n",
       "      <td id=\"T_69c3d_row18_col6\" class=\"data row18 col6\" >0.31</td>\n",
       "      <td id=\"T_69c3d_row18_col7\" class=\"data row18 col7\" >0.12</td>\n",
       "      <td id=\"T_69c3d_row18_col8\" class=\"data row18 col8\" >3.62</td>\n",
       "      <td id=\"T_69c3d_row18_col9\" class=\"data row18 col9\" >4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row19\" class=\"row_heading level0 row19\" >Saint-Sulpice (VD)</th>\n",
       "      <td id=\"T_69c3d_row19_col0\" class=\"data row19 col0\" >0.36</td>\n",
       "      <td id=\"T_69c3d_row19_col1\" class=\"data row19 col1\" >0.05</td>\n",
       "      <td id=\"T_69c3d_row19_col2\" class=\"data row19 col2\" >0.81</td>\n",
       "      <td id=\"T_69c3d_row19_col3\" class=\"data row19 col3\" >0.52</td>\n",
       "      <td id=\"T_69c3d_row19_col4\" class=\"data row19 col4\" >0.03</td>\n",
       "      <td id=\"T_69c3d_row19_col5\" class=\"data row19 col5\" >1.1</td>\n",
       "      <td id=\"T_69c3d_row19_col6\" class=\"data row19 col6\" >1.36</td>\n",
       "      <td id=\"T_69c3d_row19_col7\" class=\"data row19 col7\" >0.29</td>\n",
       "      <td id=\"T_69c3d_row19_col8\" class=\"data row19 col8\" >3.89</td>\n",
       "      <td id=\"T_69c3d_row19_col9\" class=\"data row19 col9\" >3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row20\" class=\"row_heading level0 row20\" >Savonière</th>\n",
       "      <td id=\"T_69c3d_row20_col0\" class=\"data row20 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row20_col1\" class=\"data row20 col1\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row20_col2\" class=\"data row20 col2\" >0.15</td>\n",
       "      <td id=\"T_69c3d_row20_col3\" class=\"data row20 col3\" >0.13</td>\n",
       "      <td id=\"T_69c3d_row20_col4\" class=\"data row20 col4\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row20_col5\" class=\"data row20 col5\" >0.35</td>\n",
       "      <td id=\"T_69c3d_row20_col6\" class=\"data row20 col6\" >0.48</td>\n",
       "      <td id=\"T_69c3d_row20_col7\" class=\"data row20 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row20_col8\" class=\"data row20 col8\" >0.95</td>\n",
       "      <td id=\"T_69c3d_row20_col9\" class=\"data row20 col9\" >0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row21\" class=\"row_heading level0 row21\" >Tolochenaz</th>\n",
       "      <td id=\"T_69c3d_row21_col0\" class=\"data row21 col0\" >0.06</td>\n",
       "      <td id=\"T_69c3d_row21_col1\" class=\"data row21 col1\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row21_col2\" class=\"data row21 col2\" >0.06</td>\n",
       "      <td id=\"T_69c3d_row21_col3\" class=\"data row21 col3\" >0.04</td>\n",
       "      <td id=\"T_69c3d_row21_col4\" class=\"data row21 col4\" >0.01</td>\n",
       "      <td id=\"T_69c3d_row21_col5\" class=\"data row21 col5\" >0.08</td>\n",
       "      <td id=\"T_69c3d_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
       "      <td id=\"T_69c3d_row21_col7\" class=\"data row21 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row21_col8\" class=\"data row21 col8\" >0.41</td>\n",
       "      <td id=\"T_69c3d_row21_col9\" class=\"data row21 col9\" >0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row22\" class=\"row_heading level0 row22\" >Versoix</th>\n",
       "      <td id=\"T_69c3d_row22_col0\" class=\"data row22 col0\" >0.1</td>\n",
       "      <td id=\"T_69c3d_row22_col1\" class=\"data row22 col1\" >0.03</td>\n",
       "      <td id=\"T_69c3d_row22_col2\" class=\"data row22 col2\" >0.35</td>\n",
       "      <td id=\"T_69c3d_row22_col3\" class=\"data row22 col3\" >0.26</td>\n",
       "      <td id=\"T_69c3d_row22_col4\" class=\"data row22 col4\" >0.01</td>\n",
       "      <td id=\"T_69c3d_row22_col5\" class=\"data row22 col5\" >0.41</td>\n",
       "      <td id=\"T_69c3d_row22_col6\" class=\"data row22 col6\" >0.34</td>\n",
       "      <td id=\"T_69c3d_row22_col7\" class=\"data row22 col7\" >0.08</td>\n",
       "      <td id=\"T_69c3d_row22_col8\" class=\"data row22 col8\" >1.58</td>\n",
       "      <td id=\"T_69c3d_row22_col9\" class=\"data row22 col9\" >0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row23\" class=\"row_heading level0 row23\" >Vevey</th>\n",
       "      <td id=\"T_69c3d_row23_col0\" class=\"data row23 col0\" >0.33</td>\n",
       "      <td id=\"T_69c3d_row23_col1\" class=\"data row23 col1\" >0.1</td>\n",
       "      <td id=\"T_69c3d_row23_col2\" class=\"data row23 col2\" >0.32</td>\n",
       "      <td id=\"T_69c3d_row23_col3\" class=\"data row23 col3\" >0.39</td>\n",
       "      <td id=\"T_69c3d_row23_col4\" class=\"data row23 col4\" >0.09</td>\n",
       "      <td id=\"T_69c3d_row23_col5\" class=\"data row23 col5\" >0.66</td>\n",
       "      <td id=\"T_69c3d_row23_col6\" class=\"data row23 col6\" >2.25</td>\n",
       "      <td id=\"T_69c3d_row23_col7\" class=\"data row23 col7\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row23_col8\" class=\"data row23 col8\" >0.68</td>\n",
       "      <td id=\"T_69c3d_row23_col9\" class=\"data row23 col9\" >0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_69c3d_level0_row24\" class=\"row_heading level0 row24\" >Vidy</th>\n",
       "      <td id=\"T_69c3d_row24_col0\" class=\"data row24 col0\" >0.0</td>\n",
       "      <td id=\"T_69c3d_row24_col1\" class=\"data row24 col1\" >0.02</td>\n",
       "      <td id=\"T_69c3d_row24_col2\" class=\"data row24 col2\" >0.29</td>\n",
       "      <td id=\"T_69c3d_row24_col3\" class=\"data row24 col3\" >0.19</td>\n",
       "      <td id=\"T_69c3d_row24_col4\" class=\"data row24 col4\" >0.01</td>\n",
       "      <td id=\"T_69c3d_row24_col5\" class=\"data row24 col5\" >0.62</td>\n",
       "      <td id=\"T_69c3d_row24_col6\" class=\"data row24 col6\" >1.76</td>\n",
       "      <td id=\"T_69c3d_row24_col7\" class=\"data row24 col7\" >0.15</td>\n",
       "      <td id=\"T_69c3d_row24_col8\" class=\"data row24 col8\" >1.44</td>\n",
       "      <td id=\"T_69c3d_row24_col9\" class=\"data row24 col9\" >0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f64a2e1ef40>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objects_city = lac_riv[lac_riv.code.isin(codes_of_interest)].groupby([\"city\",\"code\"], as_index=False)[\"pcs/m\"].mean()\n",
    "objects_city[\"object\"] = objects_city.code.apply(lambda x: french_names[x])\n",
    "municipal_results = objects_city.pivot(columns=\"city\", index=\"object\", values=\"pcs/m\").fillna(0)\n",
    "municipal_results = municipal_results.round(2)\n",
    "municipal_results.index.name = None\n",
    "municipal_results.columns.name = None\n",
    "municipal_results = municipal_results.T\n",
    "dist = municipal_results.style.set_table_styles(table_large_font).format('{:,}').applymap_index(rotateText, axis=1)\n",
    "dist.background_gradient(axis=None, vmin= municipal_results.min().min(), vmax= municipal_results.max().max(), cmap=\"YlOrBr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "488e32ba-dc11-4bc4-8ad1-136a240a4a4e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Résultats par utilité d'objet: pcs/m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4383ec10-a952-4a2d-83aa-9c3eccf96255",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "L'utilité d'un objet est basée sur l'utilisation de l'objet avant qu'il ne soit jeté ou sur la description de l'objet si l'utilisation d'origine n'est pas déterminée. \n",
    "\n",
    "* __eaux usées :__ objets rejetés par les stations d'épuration des eaux, y compris les objets susceptibles d'être jetés dans les toilettes\n",
    "* __microplastiques (< 5 mm) :__ plastiques fragmentés et résines plastiques de préproduction\n",
    "* __infrastructures :__ éléments liés à la construction et à l'entretien des bâtiments, des routes et de l'approvisionnement en eau et en électricité\n",
    "* __alimentation et boissons :__ tous les matériaux liés à la consommation d'aliments et de boissons\n",
    "* __agriculture :__ principalement les feuilles industrielles, c'est-à-dire le paillage et les couvertures de rangs, les serres, la fumigation des sols, l'enrubannage des balles. Comprend les plastiques durs pour les clôtures agricoles, les pots de fleurs, etc.\n",
    "* __tabac :__ principalement les filtres de cigarettes, y compris tout le matériel lié au tabagisme\n",
    "* __Loisirs :__ objets liés aux sports et aux loisirs, tels que la pêche, la chasse, la randonnée, etc.\n",
    "* __Emballages non alimentaires et boissons :__ matériaux d'emballage non identifiés comme étant liés à l'alimentation, aux boissons ou au tabac.\n",
    "* __fragments de plastique :__ morceaux de plastique d'origine ou d'utilisation indéterminée\n",
    "* __articles personnels :__ accessoires, hygiène et vêtements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f321bbd-8c1b-4b02-93e0-8e3352758288",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
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       "  background-color: #fff8c4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row18_col0 {\n",
       "  background-color: #fff2b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row18_col1 {\n",
       "  background-color: #fec857;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row18_col3 {\n",
       "  background-color: #fead3a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row18_col4 {\n",
       "  background-color: #f3801c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9402a_row19_col1 {\n",
       "  background-color: #fff1b0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row19_col3 {\n",
       "  background-color: #febe4a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row19_col4 {\n",
       "  background-color: #feeda6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row22_col3 {\n",
       "  background-color: #fedd84;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row24_col3 {\n",
       "  background-color: #fedb80;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_row24_col7 {\n",
       "  background-color: #fede86;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9402a_level0_col0, #T_9402a_level0_col1, #T_9402a_level0_col2, #T_9402a_level0_col3, #T_9402a_level0_col4, #T_9402a_level0_col5, #T_9402a_level0_col6, #T_9402a_level0_col7 {\n",
       "  writing-mode: vertical-lr;\n",
       "  transform: rotate(-180deg);\n",
       "  padding: 10px;\n",
       "  margins: 0;\n",
       "  vertical-align: baseline;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9402a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9402a_level0_col0\" class=\"col_heading level0 col0\" >agriculture</th>\n",
       "      <th id=\"T_9402a_level0_col1\" class=\"col_heading level0 col1\" >alimentation et boissons</th>\n",
       "      <th id=\"T_9402a_level0_col2\" class=\"col_heading level0 col2\" >eaux usées</th>\n",
       "      <th id=\"T_9402a_level0_col3\" class=\"col_heading level0 col3\" >fragments de plastique</th>\n",
       "      <th id=\"T_9402a_level0_col4\" class=\"col_heading level0 col4\" >infrastructures</th>\n",
       "      <th id=\"T_9402a_level0_col5\" class=\"col_heading level0 col5\" >loisirs</th>\n",
       "      <th id=\"T_9402a_level0_col6\" class=\"col_heading level0 col6\" >microplastiques (< 5 mm)</th>\n",
       "      <th id=\"T_9402a_level0_col7\" class=\"col_heading level0 col7\" >tabac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row0\" class=\"row_heading level0 row0\" >Allaman</th>\n",
       "      <td id=\"T_9402a_row0_col0\" class=\"data row0 col0\" >0.33</td>\n",
       "      <td id=\"T_9402a_row0_col1\" class=\"data row0 col1\" >0.85</td>\n",
       "      <td id=\"T_9402a_row0_col2\" class=\"data row0 col2\" >0.81</td>\n",
       "      <td id=\"T_9402a_row0_col3\" class=\"data row0 col3\" >1.51</td>\n",
       "      <td id=\"T_9402a_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
       "      <td id=\"T_9402a_row0_col5\" class=\"data row0 col5\" >0.17</td>\n",
       "      <td id=\"T_9402a_row0_col6\" class=\"data row0 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row0_col7\" class=\"data row0 col7\" >0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row1\" class=\"row_heading level0 row1\" >Aubonne</th>\n",
       "      <td id=\"T_9402a_row1_col0\" class=\"data row1 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row1_col1\" class=\"data row1 col1\" >0.15</td>\n",
       "      <td id=\"T_9402a_row1_col2\" class=\"data row1 col2\" >0.0</td>\n",
       "      <td id=\"T_9402a_row1_col3\" class=\"data row1 col3\" >0.24</td>\n",
       "      <td id=\"T_9402a_row1_col4\" class=\"data row1 col4\" >0.03</td>\n",
       "      <td id=\"T_9402a_row1_col5\" class=\"data row1 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row1_col6\" class=\"data row1 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row1_col7\" class=\"data row1 col7\" >0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row2\" class=\"row_heading level0 row2\" >Bourg-en-Lavaux</th>\n",
       "      <td id=\"T_9402a_row2_col0\" class=\"data row2 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row2_col1\" class=\"data row2 col1\" >0.37</td>\n",
       "      <td id=\"T_9402a_row2_col2\" class=\"data row2 col2\" >0.45</td>\n",
       "      <td id=\"T_9402a_row2_col3\" class=\"data row2 col3\" >1.25</td>\n",
       "      <td id=\"T_9402a_row2_col4\" class=\"data row2 col4\" >0.3</td>\n",
       "      <td id=\"T_9402a_row2_col5\" class=\"data row2 col5\" >0.12</td>\n",
       "      <td id=\"T_9402a_row2_col6\" class=\"data row2 col6\" >0.04</td>\n",
       "      <td id=\"T_9402a_row2_col7\" class=\"data row2 col7\" >0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row3\" class=\"row_heading level0 row3\" >Bouveret</th>\n",
       "      <td id=\"T_9402a_row3_col0\" class=\"data row3 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row3_col1\" class=\"data row3 col1\" >0.87</td>\n",
       "      <td id=\"T_9402a_row3_col2\" class=\"data row3 col2\" >0.19</td>\n",
       "      <td id=\"T_9402a_row3_col3\" class=\"data row3 col3\" >2.18</td>\n",
       "      <td id=\"T_9402a_row3_col4\" class=\"data row3 col4\" >0.3</td>\n",
       "      <td id=\"T_9402a_row3_col5\" class=\"data row3 col5\" >0.04</td>\n",
       "      <td id=\"T_9402a_row3_col6\" class=\"data row3 col6\" >0.42</td>\n",
       "      <td id=\"T_9402a_row3_col7\" class=\"data row3 col7\" >0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row4\" class=\"row_heading level0 row4\" >Clarens</th>\n",
       "      <td id=\"T_9402a_row4_col0\" class=\"data row4 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row4_col1\" class=\"data row4 col1\" >0.29</td>\n",
       "      <td id=\"T_9402a_row4_col2\" class=\"data row4 col2\" >0.27</td>\n",
       "      <td id=\"T_9402a_row4_col3\" class=\"data row4 col3\" >1.34</td>\n",
       "      <td id=\"T_9402a_row4_col4\" class=\"data row4 col4\" >0.21</td>\n",
       "      <td id=\"T_9402a_row4_col5\" class=\"data row4 col5\" >0.02</td>\n",
       "      <td id=\"T_9402a_row4_col6\" class=\"data row4 col6\" >0.02</td>\n",
       "      <td id=\"T_9402a_row4_col7\" class=\"data row4 col7\" >1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row5\" class=\"row_heading level0 row5\" >Crans</th>\n",
       "      <td id=\"T_9402a_row5_col0\" class=\"data row5 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row5_col1\" class=\"data row5 col1\" >0.05</td>\n",
       "      <td id=\"T_9402a_row5_col2\" class=\"data row5 col2\" >0.0</td>\n",
       "      <td id=\"T_9402a_row5_col3\" class=\"data row5 col3\" >0.14</td>\n",
       "      <td id=\"T_9402a_row5_col4\" class=\"data row5 col4\" >0.0</td>\n",
       "      <td id=\"T_9402a_row5_col5\" class=\"data row5 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row5_col6\" class=\"data row5 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row5_col7\" class=\"data row5 col7\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row6\" class=\"row_heading level0 row6\" >Cully</th>\n",
       "      <td id=\"T_9402a_row6_col0\" class=\"data row6 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
       "      <td id=\"T_9402a_row6_col2\" class=\"data row6 col2\" >0.0</td>\n",
       "      <td id=\"T_9402a_row6_col3\" class=\"data row6 col3\" >0.14</td>\n",
       "      <td id=\"T_9402a_row6_col4\" class=\"data row6 col4\" >0.0</td>\n",
       "      <td id=\"T_9402a_row6_col5\" class=\"data row6 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row6_col6\" class=\"data row6 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row6_col7\" class=\"data row6 col7\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row7\" class=\"row_heading level0 row7\" >Genève</th>\n",
       "      <td id=\"T_9402a_row7_col0\" class=\"data row7 col0\" >0.02</td>\n",
       "      <td id=\"T_9402a_row7_col1\" class=\"data row7 col1\" >0.27</td>\n",
       "      <td id=\"T_9402a_row7_col2\" class=\"data row7 col2\" >0.05</td>\n",
       "      <td id=\"T_9402a_row7_col3\" class=\"data row7 col3\" >0.79</td>\n",
       "      <td id=\"T_9402a_row7_col4\" class=\"data row7 col4\" >0.1</td>\n",
       "      <td id=\"T_9402a_row7_col5\" class=\"data row7 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row7_col6\" class=\"data row7 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row7_col7\" class=\"data row7 col7\" >0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row8\" class=\"row_heading level0 row8\" >Gland</th>\n",
       "      <td id=\"T_9402a_row8_col0\" class=\"data row8 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row8_col1\" class=\"data row8 col1\" >0.2</td>\n",
       "      <td id=\"T_9402a_row8_col2\" class=\"data row8 col2\" >0.06</td>\n",
       "      <td id=\"T_9402a_row8_col3\" class=\"data row8 col3\" >0.25</td>\n",
       "      <td id=\"T_9402a_row8_col4\" class=\"data row8 col4\" >0.25</td>\n",
       "      <td id=\"T_9402a_row8_col5\" class=\"data row8 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row8_col6\" class=\"data row8 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row8_col7\" class=\"data row8 col7\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row9\" class=\"row_heading level0 row9\" >Grangettes</th>\n",
       "      <td id=\"T_9402a_row9_col0\" class=\"data row9 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row9_col1\" class=\"data row9 col1\" >2.06</td>\n",
       "      <td id=\"T_9402a_row9_col2\" class=\"data row9 col2\" >0.55</td>\n",
       "      <td id=\"T_9402a_row9_col3\" class=\"data row9 col3\" >5.79</td>\n",
       "      <td id=\"T_9402a_row9_col4\" class=\"data row9 col4\" >1.31</td>\n",
       "      <td id=\"T_9402a_row9_col5\" class=\"data row9 col5\" >0.02</td>\n",
       "      <td id=\"T_9402a_row9_col6\" class=\"data row9 col6\" >0.27</td>\n",
       "      <td id=\"T_9402a_row9_col7\" class=\"data row9 col7\" >0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row10\" class=\"row_heading level0 row10\" >Hermance</th>\n",
       "      <td id=\"T_9402a_row10_col0\" class=\"data row10 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row10_col1\" class=\"data row10 col1\" >0.03</td>\n",
       "      <td id=\"T_9402a_row10_col2\" class=\"data row10 col2\" >0.0</td>\n",
       "      <td id=\"T_9402a_row10_col3\" class=\"data row10 col3\" >0.25</td>\n",
       "      <td id=\"T_9402a_row10_col4\" class=\"data row10 col4\" >0.0</td>\n",
       "      <td id=\"T_9402a_row10_col5\" class=\"data row10 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row10_col6\" class=\"data row10 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row10_col7\" class=\"data row10 col7\" >0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row11\" class=\"row_heading level0 row11\" >La Tour-de-Peilz</th>\n",
       "      <td id=\"T_9402a_row11_col0\" class=\"data row11 col0\" >0.21</td>\n",
       "      <td id=\"T_9402a_row11_col1\" class=\"data row11 col1\" >0.69</td>\n",
       "      <td id=\"T_9402a_row11_col2\" class=\"data row11 col2\" >0.24</td>\n",
       "      <td id=\"T_9402a_row11_col3\" class=\"data row11 col3\" >0.69</td>\n",
       "      <td id=\"T_9402a_row11_col4\" class=\"data row11 col4\" >0.38</td>\n",
       "      <td id=\"T_9402a_row11_col5\" class=\"data row11 col5\" >0.05</td>\n",
       "      <td id=\"T_9402a_row11_col6\" class=\"data row11 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row11_col7\" class=\"data row11 col7\" >0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row12\" class=\"row_heading level0 row12\" >Lausanne</th>\n",
       "      <td id=\"T_9402a_row12_col0\" class=\"data row12 col0\" >0.35</td>\n",
       "      <td id=\"T_9402a_row12_col1\" class=\"data row12 col1\" >0.54</td>\n",
       "      <td id=\"T_9402a_row12_col2\" class=\"data row12 col2\" >0.55</td>\n",
       "      <td id=\"T_9402a_row12_col3\" class=\"data row12 col3\" >0.9</td>\n",
       "      <td id=\"T_9402a_row12_col4\" class=\"data row12 col4\" >0.15</td>\n",
       "      <td id=\"T_9402a_row12_col5\" class=\"data row12 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row12_col6\" class=\"data row12 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row12_col7\" class=\"data row12 col7\" >1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row13\" class=\"row_heading level0 row13\" >Lutry</th>\n",
       "      <td id=\"T_9402a_row13_col0\" class=\"data row13 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row13_col1\" class=\"data row13 col1\" >0.1</td>\n",
       "      <td id=\"T_9402a_row13_col2\" class=\"data row13 col2\" >0.0</td>\n",
       "      <td id=\"T_9402a_row13_col3\" class=\"data row13 col3\" >1.72</td>\n",
       "      <td id=\"T_9402a_row13_col4\" class=\"data row13 col4\" >0.09</td>\n",
       "      <td id=\"T_9402a_row13_col5\" class=\"data row13 col5\" >0.06</td>\n",
       "      <td id=\"T_9402a_row13_col6\" class=\"data row13 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row13_col7\" class=\"data row13 col7\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row14\" class=\"row_heading level0 row14\" >Montreux</th>\n",
       "      <td id=\"T_9402a_row14_col0\" class=\"data row14 col0\" >0.07</td>\n",
       "      <td id=\"T_9402a_row14_col1\" class=\"data row14 col1\" >0.37</td>\n",
       "      <td id=\"T_9402a_row14_col2\" class=\"data row14 col2\" >0.13</td>\n",
       "      <td id=\"T_9402a_row14_col3\" class=\"data row14 col3\" >0.28</td>\n",
       "      <td id=\"T_9402a_row14_col4\" class=\"data row14 col4\" >0.39</td>\n",
       "      <td id=\"T_9402a_row14_col5\" class=\"data row14 col5\" >0.03</td>\n",
       "      <td id=\"T_9402a_row14_col6\" class=\"data row14 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row14_col7\" class=\"data row14 col7\" >1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row15\" class=\"row_heading level0 row15\" >Morges</th>\n",
       "      <td id=\"T_9402a_row15_col0\" class=\"data row15 col0\" >0.2</td>\n",
       "      <td id=\"T_9402a_row15_col1\" class=\"data row15 col1\" >0.7</td>\n",
       "      <td id=\"T_9402a_row15_col2\" class=\"data row15 col2\" >0.0</td>\n",
       "      <td id=\"T_9402a_row15_col3\" class=\"data row15 col3\" >0.27</td>\n",
       "      <td id=\"T_9402a_row15_col4\" class=\"data row15 col4\" >0.5</td>\n",
       "      <td id=\"T_9402a_row15_col5\" class=\"data row15 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row15_col6\" class=\"data row15 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row15_col7\" class=\"data row15 col7\" >2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row16\" class=\"row_heading level0 row16\" >Préverenges</th>\n",
       "      <td id=\"T_9402a_row16_col0\" class=\"data row16 col0\" >0.09</td>\n",
       "      <td id=\"T_9402a_row16_col1\" class=\"data row16 col1\" >0.26</td>\n",
       "      <td id=\"T_9402a_row16_col2\" class=\"data row16 col2\" >0.1</td>\n",
       "      <td id=\"T_9402a_row16_col3\" class=\"data row16 col3\" >0.5</td>\n",
       "      <td id=\"T_9402a_row16_col4\" class=\"data row16 col4\" >0.24</td>\n",
       "      <td id=\"T_9402a_row16_col5\" class=\"data row16 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row16_col6\" class=\"data row16 col6\" >0.58</td>\n",
       "      <td id=\"T_9402a_row16_col7\" class=\"data row16 col7\" >0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row17\" class=\"row_heading level0 row17\" >Rolle</th>\n",
       "      <td id=\"T_9402a_row17_col0\" class=\"data row17 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row17_col1\" class=\"data row17 col1\" >0.39</td>\n",
       "      <td id=\"T_9402a_row17_col2\" class=\"data row17 col2\" >0.06</td>\n",
       "      <td id=\"T_9402a_row17_col3\" class=\"data row17 col3\" >1.31</td>\n",
       "      <td id=\"T_9402a_row17_col4\" class=\"data row17 col4\" >0.16</td>\n",
       "      <td id=\"T_9402a_row17_col5\" class=\"data row17 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row17_col6\" class=\"data row17 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row17_col7\" class=\"data row17 col7\" >0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row18\" class=\"row_heading level0 row18\" >Saint-Gingolph</th>\n",
       "      <td id=\"T_9402a_row18_col0\" class=\"data row18 col0\" >0.92</td>\n",
       "      <td id=\"T_9402a_row18_col1\" class=\"data row18 col1\" >2.1</td>\n",
       "      <td id=\"T_9402a_row18_col2\" class=\"data row18 col2\" >1.25</td>\n",
       "      <td id=\"T_9402a_row18_col3\" class=\"data row18 col3\" >2.56</td>\n",
       "      <td id=\"T_9402a_row18_col4\" class=\"data row18 col4\" >3.34</td>\n",
       "      <td id=\"T_9402a_row18_col5\" class=\"data row18 col5\" >0.61</td>\n",
       "      <td id=\"T_9402a_row18_col6\" class=\"data row18 col6\" >0.02</td>\n",
       "      <td id=\"T_9402a_row18_col7\" class=\"data row18 col7\" >0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row19\" class=\"row_heading level0 row19\" >Saint-Sulpice (VD)</th>\n",
       "      <td id=\"T_9402a_row19_col0\" class=\"data row19 col0\" >0.23</td>\n",
       "      <td id=\"T_9402a_row19_col1\" class=\"data row19 col1\" >0.94</td>\n",
       "      <td id=\"T_9402a_row19_col2\" class=\"data row19 col2\" >0.57</td>\n",
       "      <td id=\"T_9402a_row19_col3\" class=\"data row19 col3\" >2.28</td>\n",
       "      <td id=\"T_9402a_row19_col4\" class=\"data row19 col4\" >1.1</td>\n",
       "      <td id=\"T_9402a_row19_col5\" class=\"data row19 col5\" >0.04</td>\n",
       "      <td id=\"T_9402a_row19_col6\" class=\"data row19 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row19_col7\" class=\"data row19 col7\" >0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row20\" class=\"row_heading level0 row20\" >Savonière</th>\n",
       "      <td id=\"T_9402a_row20_col0\" class=\"data row20 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row20_col1\" class=\"data row20 col1\" >0.45</td>\n",
       "      <td id=\"T_9402a_row20_col2\" class=\"data row20 col2\" >0.15</td>\n",
       "      <td id=\"T_9402a_row20_col3\" class=\"data row20 col3\" >0.78</td>\n",
       "      <td id=\"T_9402a_row20_col4\" class=\"data row20 col4\" >0.02</td>\n",
       "      <td id=\"T_9402a_row20_col5\" class=\"data row20 col5\" >0.01</td>\n",
       "      <td id=\"T_9402a_row20_col6\" class=\"data row20 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row20_col7\" class=\"data row20 col7\" >0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row21\" class=\"row_heading level0 row21\" >Tolochenaz</th>\n",
       "      <td id=\"T_9402a_row21_col0\" class=\"data row21 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row21_col1\" class=\"data row21 col1\" >0.1</td>\n",
       "      <td id=\"T_9402a_row21_col2\" class=\"data row21 col2\" >0.0</td>\n",
       "      <td id=\"T_9402a_row21_col3\" class=\"data row21 col3\" >0.31</td>\n",
       "      <td id=\"T_9402a_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
       "      <td id=\"T_9402a_row21_col5\" class=\"data row21 col5\" >0.0</td>\n",
       "      <td id=\"T_9402a_row21_col6\" class=\"data row21 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row21_col7\" class=\"data row21 col7\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row22\" class=\"row_heading level0 row22\" >Versoix</th>\n",
       "      <td id=\"T_9402a_row22_col0\" class=\"data row22 col0\" >0.03</td>\n",
       "      <td id=\"T_9402a_row22_col1\" class=\"data row22 col1\" >0.45</td>\n",
       "      <td id=\"T_9402a_row22_col2\" class=\"data row22 col2\" >0.33</td>\n",
       "      <td id=\"T_9402a_row22_col3\" class=\"data row22 col3\" >1.6</td>\n",
       "      <td id=\"T_9402a_row22_col4\" class=\"data row22 col4\" >0.02</td>\n",
       "      <td id=\"T_9402a_row22_col5\" class=\"data row22 col5\" >0.02</td>\n",
       "      <td id=\"T_9402a_row22_col6\" class=\"data row22 col6\" >0.02</td>\n",
       "      <td id=\"T_9402a_row22_col7\" class=\"data row22 col7\" >0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row23\" class=\"row_heading level0 row23\" >Vevey</th>\n",
       "      <td id=\"T_9402a_row23_col0\" class=\"data row23 col0\" >0.08</td>\n",
       "      <td id=\"T_9402a_row23_col1\" class=\"data row23 col1\" >0.54</td>\n",
       "      <td id=\"T_9402a_row23_col2\" class=\"data row23 col2\" >0.08</td>\n",
       "      <td id=\"T_9402a_row23_col3\" class=\"data row23 col3\" >0.22</td>\n",
       "      <td id=\"T_9402a_row23_col4\" class=\"data row23 col4\" >0.5</td>\n",
       "      <td id=\"T_9402a_row23_col5\" class=\"data row23 col5\" >0.02</td>\n",
       "      <td id=\"T_9402a_row23_col6\" class=\"data row23 col6\" >0.0</td>\n",
       "      <td id=\"T_9402a_row23_col7\" class=\"data row23 col7\" >0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9402a_level0_row24\" class=\"row_heading level0 row24\" >Vidy</th>\n",
       "      <td id=\"T_9402a_row24_col0\" class=\"data row24 col0\" >0.0</td>\n",
       "      <td id=\"T_9402a_row24_col1\" class=\"data row24 col1\" >0.87</td>\n",
       "      <td id=\"T_9402a_row24_col2\" class=\"data row24 col2\" >0.3</td>\n",
       "      <td id=\"T_9402a_row24_col3\" class=\"data row24 col3\" >1.63</td>\n",
       "      <td id=\"T_9402a_row24_col4\" class=\"data row24 col4\" >0.01</td>\n",
       "      <td id=\"T_9402a_row24_col5\" class=\"data row24 col5\" >0.02</td>\n",
       "      <td id=\"T_9402a_row24_col6\" class=\"data row24 col6\" >0.15</td>\n",
       "      <td id=\"T_9402a_row24_col7\" class=\"data row24 col7\" >1.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f64a2fe9370>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lac_riv[\"groupname\"] = lac_riv.code.apply(lambda x: code_g.loc[x])\n",
    "\n",
    "cg_agg = lac_riv.groupby([\"loc_date\", \"city\", \"groupname\"], as_index=False)[\"pcs/m\"].sum()\n",
    "cg_agg[\"groupname\"] = cg_agg.groupname.apply(lambda x: group_names[x])\n",
    "cg_agg_group = cg_agg.groupby([\"city\",\"groupname\"], as_index=False)[\"pcs/m\"].median()\n",
    "\n",
    "cg_display = cg_agg_group.pivot(columns=\"city\", index=\"groupname\", values=[\"pcs/m\"])\n",
    "cg_display=cg_display.round(2)\n",
    "cg_display.index.name = None\n",
    "cg_display.columns = cg_display.columns.droplevel()\n",
    "cg_display.fillna(0, inplace=True)\n",
    "cg_display.columns.name = None\n",
    "cg_display = cg_display.T\n",
    "\n",
    "dist = cg_display.style.set_table_styles(table_large_font).format('{:,}').applymap_index(rotateText, axis=1)\n",
    "dist.background_gradient(axis=None, vmin=cg_display.min().min(), vmax= cg_display.max().max(), cmap=\"YlOrBr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b980747-c52a-4f2d-b936-d7bc8c2e1a55",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Lieux d'enquêtes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "873c291a-19f7-4654-8e3a-fbfbfd809b47",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_94e6a tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_94e6a tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_94e6a tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_94e6a th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_94e6a caption {\n",
       "  font-size: 14px;\n",
       "  font-style: italic;\n",
       "  caption-side: bottom;\n",
       "  text-align: left;\n",
       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_94e6a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_94e6a_level0_col0\" class=\"col_heading level0 col0\" >city</th>\n",
       "      <th id=\"T_94e6a_level0_col1\" class=\"col_heading level0 col1\" >zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row0\" class=\"row_heading level0 row0\" >aubonne</th>\n",
       "      <td id=\"T_94e6a_row0_col0\" class=\"data row0 col0\" >Aubonne</td>\n",
       "      <td id=\"T_94e6a_row0_col1\" class=\"data row0 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row1\" class=\"row_heading level0 row1\" >baby-plage</th>\n",
       "      <td id=\"T_94e6a_row1_col0\" class=\"data row1 col0\" >Genève</td>\n",
       "      <td id=\"T_94e6a_row1_col1\" class=\"data row1 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row2\" class=\"row_heading level0 row2\" >bouveret</th>\n",
       "      <td id=\"T_94e6a_row2_col0\" class=\"data row2 col0\" >Bouveret</td>\n",
       "      <td id=\"T_94e6a_row2_col1\" class=\"data row2 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row3\" class=\"row_heading level0 row3\" >clarens</th>\n",
       "      <td id=\"T_94e6a_row3_col0\" class=\"data row3 col0\" >Clarens</td>\n",
       "      <td id=\"T_94e6a_row3_col1\" class=\"data row3 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row4\" class=\"row_heading level0 row4\" >crans</th>\n",
       "      <td id=\"T_94e6a_row4_col0\" class=\"data row4 col0\" >Crans</td>\n",
       "      <td id=\"T_94e6a_row4_col1\" class=\"data row4 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row5\" class=\"row_heading level0 row5\" >cully-p</th>\n",
       "      <td id=\"T_94e6a_row5_col0\" class=\"data row5 col0\" >Cully</td>\n",
       "      <td id=\"T_94e6a_row5_col1\" class=\"data row5 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row6\" class=\"row_heading level0 row6\" >gland</th>\n",
       "      <td id=\"T_94e6a_row6_col0\" class=\"data row6 col0\" >Gland</td>\n",
       "      <td id=\"T_94e6a_row6_col1\" class=\"data row6 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row7\" class=\"row_heading level0 row7\" >grangettes</th>\n",
       "      <td id=\"T_94e6a_row7_col0\" class=\"data row7 col0\" >Grangettes</td>\n",
       "      <td id=\"T_94e6a_row7_col1\" class=\"data row7 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row8\" class=\"row_heading level0 row8\" >hermance</th>\n",
       "      <td id=\"T_94e6a_row8_col0\" class=\"data row8 col0\" >Hermance</td>\n",
       "      <td id=\"T_94e6a_row8_col1\" class=\"data row8 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row9\" class=\"row_heading level0 row9\" >lutry</th>\n",
       "      <td id=\"T_94e6a_row9_col0\" class=\"data row9 col0\" >Lutry</td>\n",
       "      <td id=\"T_94e6a_row9_col1\" class=\"data row9 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row10\" class=\"row_heading level0 row10\" >maladaire</th>\n",
       "      <td id=\"T_94e6a_row10_col0\" class=\"data row10 col0\" >La Tour-de-Peilz</td>\n",
       "      <td id=\"T_94e6a_row10_col1\" class=\"data row10 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row11\" class=\"row_heading level0 row11\" >parc-des-pierrettes</th>\n",
       "      <td id=\"T_94e6a_row11_col0\" class=\"data row11 col0\" >Saint-Sulpice (VD)</td>\n",
       "      <td id=\"T_94e6a_row11_col1\" class=\"data row11 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row12\" class=\"row_heading level0 row12\" >pichette</th>\n",
       "      <td id=\"T_94e6a_row12_col0\" class=\"data row12 col0\" >Vevey</td>\n",
       "      <td id=\"T_94e6a_row12_col1\" class=\"data row12 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row13\" class=\"row_heading level0 row13\" >plage-de-st-sulpice</th>\n",
       "      <td id=\"T_94e6a_row13_col0\" class=\"data row13 col0\" >Saint-Sulpice (VD)</td>\n",
       "      <td id=\"T_94e6a_row13_col1\" class=\"data row13 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row14\" class=\"row_heading level0 row14\" >port-choiseul</th>\n",
       "      <td id=\"T_94e6a_row14_col0\" class=\"data row14 col0\" >Versoix</td>\n",
       "      <td id=\"T_94e6a_row14_col1\" class=\"data row14 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row15\" class=\"row_heading level0 row15\" >preverenges-p</th>\n",
       "      <td id=\"T_94e6a_row15_col0\" class=\"data row15 col0\" >Préverenges</td>\n",
       "      <td id=\"T_94e6a_row15_col1\" class=\"data row15 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row16\" class=\"row_heading level0 row16\" >rolle</th>\n",
       "      <td id=\"T_94e6a_row16_col0\" class=\"data row16 col0\" >Rolle</td>\n",
       "      <td id=\"T_94e6a_row16_col1\" class=\"data row16 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row17\" class=\"row_heading level0 row17\" >savoniere</th>\n",
       "      <td id=\"T_94e6a_row17_col0\" class=\"data row17 col0\" >Savonière</td>\n",
       "      <td id=\"T_94e6a_row17_col1\" class=\"data row17 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row18\" class=\"row_heading level0 row18\" >tiger-duck-beach</th>\n",
       "      <td id=\"T_94e6a_row18_col0\" class=\"data row18 col0\" >Saint-Sulpice (VD)</td>\n",
       "      <td id=\"T_94e6a_row18_col1\" class=\"data row18 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row19\" class=\"row_heading level0 row19\" >tolochenaz-p</th>\n",
       "      <td id=\"T_94e6a_row19_col0\" class=\"data row19 col0\" >Tolochenaz</td>\n",
       "      <td id=\"T_94e6a_row19_col1\" class=\"data row19 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row20\" class=\"row_heading level0 row20\" >versoix-p</th>\n",
       "      <td id=\"T_94e6a_row20_col0\" class=\"data row20 col0\" >Versoix</td>\n",
       "      <td id=\"T_94e6a_row20_col1\" class=\"data row20 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row21\" class=\"row_heading level0 row21\" >vidy-p</th>\n",
       "      <td id=\"T_94e6a_row21_col0\" class=\"data row21 col0\" >Vidy</td>\n",
       "      <td id=\"T_94e6a_row21_col1\" class=\"data row21 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row22\" class=\"row_heading level0 row22\" >villa-barton</th>\n",
       "      <td id=\"T_94e6a_row22_col0\" class=\"data row22 col0\" >Genève</td>\n",
       "      <td id=\"T_94e6a_row22_col1\" class=\"data row22 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row23\" class=\"row_heading level0 row23\" >anarchy-beach</th>\n",
       "      <td id=\"T_94e6a_row23_col0\" class=\"data row23 col0\" >La Tour-de-Peilz</td>\n",
       "      <td id=\"T_94e6a_row23_col1\" class=\"data row23 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row24\" class=\"row_heading level0 row24\" >arabie</th>\n",
       "      <td id=\"T_94e6a_row24_col0\" class=\"data row24 col0\" >Vevey</td>\n",
       "      <td id=\"T_94e6a_row24_col1\" class=\"data row24 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row25\" class=\"row_heading level0 row25\" >baby-plage-geneva</th>\n",
       "      <td id=\"T_94e6a_row25_col0\" class=\"data row25 col0\" >Genève</td>\n",
       "      <td id=\"T_94e6a_row25_col1\" class=\"data row25 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row26\" class=\"row_heading level0 row26\" >baby-plage-ii-geneve</th>\n",
       "      <td id=\"T_94e6a_row26_col0\" class=\"data row26 col0\" >Genève</td>\n",
       "      <td id=\"T_94e6a_row26_col1\" class=\"data row26 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row27\" class=\"row_heading level0 row27\" >bain-des-dames</th>\n",
       "      <td id=\"T_94e6a_row27_col0\" class=\"data row27 col0\" >La Tour-de-Peilz</td>\n",
       "      <td id=\"T_94e6a_row27_col1\" class=\"data row27 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row28\" class=\"row_heading level0 row28\" >baye-de-clarens</th>\n",
       "      <td id=\"T_94e6a_row28_col0\" class=\"data row28 col0\" >Montreux</td>\n",
       "      <td id=\"T_94e6a_row28_col1\" class=\"data row28 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row29\" class=\"row_heading level0 row29\" >baye-de-montreux-d</th>\n",
       "      <td id=\"T_94e6a_row29_col0\" class=\"data row29 col0\" >Montreux</td>\n",
       "      <td id=\"T_94e6a_row29_col1\" class=\"data row29 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row30\" class=\"row_heading level0 row30\" >baye-de-montreux-g</th>\n",
       "      <td id=\"T_94e6a_row30_col0\" class=\"data row30 col0\" >Montreux</td>\n",
       "      <td id=\"T_94e6a_row30_col1\" class=\"data row30 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row31\" class=\"row_heading level0 row31\" >boiron</th>\n",
       "      <td id=\"T_94e6a_row31_col0\" class=\"data row31 col0\" >Tolochenaz</td>\n",
       "      <td id=\"T_94e6a_row31_col1\" class=\"data row31 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row32\" class=\"row_heading level0 row32\" >cully-plage</th>\n",
       "      <td id=\"T_94e6a_row32_col0\" class=\"data row32 col0\" >Bourg-en-Lavaux</td>\n",
       "      <td id=\"T_94e6a_row32_col1\" class=\"data row32 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row33\" class=\"row_heading level0 row33\" >grand-clos</th>\n",
       "      <td id=\"T_94e6a_row33_col0\" class=\"data row33 col0\" >Saint-Gingolph</td>\n",
       "      <td id=\"T_94e6a_row33_col1\" class=\"data row33 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row34\" class=\"row_heading level0 row34\" >jardin-botanique</th>\n",
       "      <td id=\"T_94e6a_row34_col0\" class=\"data row34 col0\" >Genève</td>\n",
       "      <td id=\"T_94e6a_row34_col1\" class=\"data row34 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row35\" class=\"row_heading level0 row35\" >la-morges</th>\n",
       "      <td id=\"T_94e6a_row35_col0\" class=\"data row35 col0\" >Morges</td>\n",
       "      <td id=\"T_94e6a_row35_col1\" class=\"data row35 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row36\" class=\"row_heading level0 row36\" >la-pecherie</th>\n",
       "      <td id=\"T_94e6a_row36_col0\" class=\"data row36 col0\" >Allaman</td>\n",
       "      <td id=\"T_94e6a_row36_col1\" class=\"data row36 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row37\" class=\"row_heading level0 row37\" >lacleman_gland_kubela</th>\n",
       "      <td id=\"T_94e6a_row37_col0\" class=\"data row37 col0\" >Gland</td>\n",
       "      <td id=\"T_94e6a_row37_col1\" class=\"data row37 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row38\" class=\"row_heading level0 row38\" >lacleman_gland_lecoanets</th>\n",
       "      <td id=\"T_94e6a_row38_col0\" class=\"data row38 col0\" >Gland</td>\n",
       "      <td id=\"T_94e6a_row38_col1\" class=\"data row38 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row39\" class=\"row_heading level0 row39\" >lacleman_vidy_santie</th>\n",
       "      <td id=\"T_94e6a_row39_col0\" class=\"data row39 col0\" >Lausanne</td>\n",
       "      <td id=\"T_94e6a_row39_col1\" class=\"data row39 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row40\" class=\"row_heading level0 row40\" >le-pierrier</th>\n",
       "      <td id=\"T_94e6a_row40_col0\" class=\"data row40 col0\" >Montreux</td>\n",
       "      <td id=\"T_94e6a_row40_col1\" class=\"data row40 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row41\" class=\"row_heading level0 row41\" >le-port</th>\n",
       "      <td id=\"T_94e6a_row41_col0\" class=\"data row41 col0\" >La Tour-de-Peilz</td>\n",
       "      <td id=\"T_94e6a_row41_col1\" class=\"data row41 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row42\" class=\"row_heading level0 row42\" >oyonne</th>\n",
       "      <td id=\"T_94e6a_row42_col0\" class=\"data row42 col0\" >La Tour-de-Peilz</td>\n",
       "      <td id=\"T_94e6a_row42_col1\" class=\"data row42 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row43\" class=\"row_heading level0 row43\" >pierrier-sud</th>\n",
       "      <td id=\"T_94e6a_row43_col0\" class=\"data row43 col0\" >Montreux</td>\n",
       "      <td id=\"T_94e6a_row43_col1\" class=\"data row43 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row44\" class=\"row_heading level0 row44\" >plage-de-dorigny</th>\n",
       "      <td id=\"T_94e6a_row44_col0\" class=\"data row44 col0\" >Saint-Sulpice (VD)</td>\n",
       "      <td id=\"T_94e6a_row44_col1\" class=\"data row44 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row45\" class=\"row_heading level0 row45\" >preverenges</th>\n",
       "      <td id=\"T_94e6a_row45_col0\" class=\"data row45 col0\" >Préverenges</td>\n",
       "      <td id=\"T_94e6a_row45_col1\" class=\"data row45 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row46\" class=\"row_heading level0 row46\" >preverenges-le-sout</th>\n",
       "      <td id=\"T_94e6a_row46_col0\" class=\"data row46 col0\" >Préverenges</td>\n",
       "      <td id=\"T_94e6a_row46_col1\" class=\"data row46 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row47\" class=\"row_heading level0 row47\" >quai-maria-belgia</th>\n",
       "      <td id=\"T_94e6a_row47_col0\" class=\"data row47 col0\" >Vevey</td>\n",
       "      <td id=\"T_94e6a_row47_col1\" class=\"data row47 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row48\" class=\"row_heading level0 row48\" >rocky-plage</th>\n",
       "      <td id=\"T_94e6a_row48_col0\" class=\"data row48 col0\" >Genève</td>\n",
       "      <td id=\"T_94e6a_row48_col1\" class=\"data row48 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row49\" class=\"row_heading level0 row49\" >rolle-plage</th>\n",
       "      <td id=\"T_94e6a_row49_col0\" class=\"data row49 col0\" >Rolle</td>\n",
       "      <td id=\"T_94e6a_row49_col1\" class=\"data row49 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row50\" class=\"row_heading level0 row50\" >saint-sulpice</th>\n",
       "      <td id=\"T_94e6a_row50_col0\" class=\"data row50 col0\" >Saint-Sulpice (VD)</td>\n",
       "      <td id=\"T_94e6a_row50_col1\" class=\"data row50 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row51\" class=\"row_heading level0 row51\" >tolochenaz</th>\n",
       "      <td id=\"T_94e6a_row51_col0\" class=\"data row51 col0\" >Tolochenaz</td>\n",
       "      <td id=\"T_94e6a_row51_col1\" class=\"data row51 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row52\" class=\"row_heading level0 row52\" >versoix</th>\n",
       "      <td id=\"T_94e6a_row52_col0\" class=\"data row52 col0\" >Versoix</td>\n",
       "      <td id=\"T_94e6a_row52_col1\" class=\"data row52 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row53\" class=\"row_heading level0 row53\" >veveyse</th>\n",
       "      <td id=\"T_94e6a_row53_col0\" class=\"data row53 col0\" >Vevey</td>\n",
       "      <td id=\"T_94e6a_row53_col1\" class=\"data row53 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row54\" class=\"row_heading level0 row54\" >vidy</th>\n",
       "      <td id=\"T_94e6a_row54_col0\" class=\"data row54 col0\" >Lausanne</td>\n",
       "      <td id=\"T_94e6a_row54_col1\" class=\"data row54 col1\" >lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94e6a_level0_row55\" class=\"row_heading level0 row55\" >vidy-ruines</th>\n",
       "      <td id=\"T_94e6a_row55_col0\" class=\"data row55 col0\" >Lausanne</td>\n",
       "      <td id=\"T_94e6a_row55_col1\" class=\"data row55 col1\" >lac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f64f81f99d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_locations = lac_riv[[\"slug\", \"city\", \"zone\"]].drop_duplicates()\n",
    "survey_locations.set_index(\"slug\", inplace=True)\n",
    "survey_locations.index.name = None\n",
    "survey_locations.style.set_table_styles(table_large_font)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708769b8-fa03-46c0-89c4-6b006ccde2e3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Inventaire complet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2bc71e72-cb2c-43c8-a38c-8ce27acf937f",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "operators = {\n",
    "    \"quantity\":\"sum\",\n",
    "    \"pcs/m\": \"median\",\n",
    "}\n",
    "\n",
    "lac_riv[\"fail\"] = lac_riv[\"quantity\"] > 0\n",
    "code_fails = lac_riv.groupby(\"code\").fail.sum()\n",
    "code_fail_rate = code_fails/lac_riv.loc_date.nunique()\n",
    "\n",
    "\n",
    "inventory = lac_riv.groupby(\"code\", as_index=False).agg(operators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a5de6413-e813-4f08-8e66-34b27f24f3e3",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "\n",
    "inventory[\"fail_rate\"] = inventory.code.apply(lambda x: code_fail_rate.loc[x])\n",
    "inventory = inventory[inventory.quantity > 0]\n",
    "inventory[\"object\"] =inventory.code.apply(lambda x: french_names[x])\n",
    "inventory = inventory[[\"code\", \"object\", \"quantity\", \"pcs/m\", \"fail_rate\"]]\n",
    "inventory.set_index(\"code\", inplace=True)\n",
    "inventory[\"quantity\"] = inventory.quantity.astype(\"int\")\n",
    "\n",
    "inventory.sort_values(by=\"quantity\", inplace=True, ascending=False)\n",
    "inventory.rename(columns=display_names, inplace=True)\n",
    "inventory.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "343e6ef2-11d1-4680-8ae0-686b7af276d2",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6dbf5 tr:nth-child(even) {\n",
       "  background-color: rgba(139, 69, 19, 0.08);\n",
       "}\n",
       "#T_6dbf5 tr:nth-child(odd) {\n",
       "  background: #FFF;\n",
       "}\n",
       "#T_6dbf5 tr {\n",
       "  font-size: 14px;\n",
       "}\n",
       "#T_6dbf5 th:nth-child(1) {\n",
       "  background-color: #FFF;\n",
       "  text-align: right;\n",
       "}\n",
       "#T_6dbf5 caption {\n",
       "  font-size: 14px;\n",
       "  font-style: italic;\n",
       "  caption-side: bottom;\n",
       "  text-align: left;\n",
       "  margin-top: 10px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6dbf5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6dbf5_level0_col0\" class=\"col_heading level0 col0\" >object</th>\n",
       "      <th id=\"T_6dbf5_level0_col1\" class=\"col_heading level0 col1\" >quantité</th>\n",
       "      <th id=\"T_6dbf5_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n",
       "      <th id=\"T_6dbf5_level0_col3\" class=\"col_heading level0 col3\" >taux d'échec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row0\" class=\"row_heading level0 row0\" >Gfrags</th>\n",
       "      <td id=\"T_6dbf5_row0_col0\" class=\"data row0 col0\" >Plastiques fragmentés</td>\n",
       "      <td id=\"T_6dbf5_row0_col1\" class=\"data row0 col1\" >17235</td>\n",
       "      <td id=\"T_6dbf5_row0_col2\" class=\"data row0 col2\" >0.560</td>\n",
       "      <td id=\"T_6dbf5_row0_col3\" class=\"data row0 col3\" >0.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row1\" class=\"row_heading level0 row1\" >G27</th>\n",
       "      <td id=\"T_6dbf5_row1_col0\" class=\"data row1 col0\" >Mégots et filtres à cigarettes</td>\n",
       "      <td id=\"T_6dbf5_row1_col1\" class=\"data row1 col1\" >16342</td>\n",
       "      <td id=\"T_6dbf5_row1_col2\" class=\"data row1 col2\" >0.510</td>\n",
       "      <td id=\"T_6dbf5_row1_col3\" class=\"data row1 col3\" >0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row2\" class=\"row_heading level0 row2\" >Gfoam</th>\n",
       "      <td id=\"T_6dbf5_row2_col0\" class=\"data row2 col0\" >Polystyrène expansé fragmenté</td>\n",
       "      <td id=\"T_6dbf5_row2_col1\" class=\"data row2 col1\" >12296</td>\n",
       "      <td id=\"T_6dbf5_row2_col2\" class=\"data row2 col2\" >0.240</td>\n",
       "      <td id=\"T_6dbf5_row2_col3\" class=\"data row2 col3\" >0.818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row3\" class=\"row_heading level0 row3\" >G30</th>\n",
       "      <td id=\"T_6dbf5_row3_col0\" class=\"data row3 col0\" >Emballages de bonbons, de snacks</td>\n",
       "      <td id=\"T_6dbf5_row3_col1\" class=\"data row3 col1\" >6686</td>\n",
       "      <td id=\"T_6dbf5_row3_col2\" class=\"data row3 col2\" >0.214</td>\n",
       "      <td id=\"T_6dbf5_row3_col3\" class=\"data row3 col3\" >0.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row4\" class=\"row_heading level0 row4\" >G95</th>\n",
       "      <td id=\"T_6dbf5_row4_col0\" class=\"data row4 col0\" >Coton-tige</td>\n",
       "      <td id=\"T_6dbf5_row4_col1\" class=\"data row4 col1\" >4774</td>\n",
       "      <td id=\"T_6dbf5_row4_col2\" class=\"data row4 col2\" >0.140</td>\n",
       "      <td id=\"T_6dbf5_row4_col3\" class=\"data row4 col3\" >0.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row5\" class=\"row_heading level0 row5\" >Gcaps</th>\n",
       "      <td id=\"T_6dbf5_row5_col0\" class=\"data row5 col0\" >Couvercles de bouteilles en plastique</td>\n",
       "      <td id=\"T_6dbf5_row5_col1\" class=\"data row5 col1\" >3904</td>\n",
       "      <td id=\"T_6dbf5_row5_col2\" class=\"data row5 col2\" >0.130</td>\n",
       "      <td id=\"T_6dbf5_row5_col3\" class=\"data row5 col3\" >0.853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row6\" class=\"row_heading level0 row6\" >G67</th>\n",
       "      <td id=\"T_6dbf5_row6_col0\" class=\"data row6 col0\" >Bâche, feuille plastique industrielle</td>\n",
       "      <td id=\"T_6dbf5_row6_col1\" class=\"data row6 col1\" >3227</td>\n",
       "      <td id=\"T_6dbf5_row6_col2\" class=\"data row6 col2\" >0.060</td>\n",
       "      <td id=\"T_6dbf5_row6_col3\" class=\"data row6 col3\" >0.583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row7\" class=\"row_heading level0 row7\" >G112</th>\n",
       "      <td id=\"T_6dbf5_row7_col0\" class=\"data row7 col0\" >Pellets industriels (GPI)</td>\n",
       "      <td id=\"T_6dbf5_row7_col1\" class=\"data row7 col1\" >2686</td>\n",
       "      <td id=\"T_6dbf5_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
       "      <td id=\"T_6dbf5_row7_col3\" class=\"data row7 col3\" >0.232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row8\" class=\"row_heading level0 row8\" >G70</th>\n",
       "      <td id=\"T_6dbf5_row8_col0\" class=\"data row8 col0\" >Cartouches de fusil de chasse</td>\n",
       "      <td id=\"T_6dbf5_row8_col1\" class=\"data row8 col1\" >1028</td>\n",
       "      <td id=\"T_6dbf5_row8_col2\" class=\"data row8 col2\" >0.010</td>\n",
       "      <td id=\"T_6dbf5_row8_col3\" class=\"data row8 col3\" >0.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6dbf5_level0_row9\" class=\"row_heading level0 row9\" >G10</th>\n",
       "      <td id=\"T_6dbf5_row9_col0\" class=\"data row9 col0\" >Emballage fast food</td>\n",
       "      <td id=\"T_6dbf5_row9_col1\" class=\"data row9 col1\" >623</td>\n",
       "      <td id=\"T_6dbf5_row9_col2\" class=\"data row9 col2\" >0.000</td>\n",
       "      <td id=\"T_6dbf5_row9_col3\" class=\"data row9 col3\" >0.373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f64a2bf95b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inventory.style.set_table_styles(table_large_font).format(precision=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5bec42d8-ed97-4004-828b-409022b85d7c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "<br />\n",
       "<br />\n",
       "Ceci est un ébauche : le script a été mis à jour le 22/09/2023 in Biel, CH\n",
       "\n",
       "Contactez analyst@ pour plus d'informations\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "today = dt.datetime.now().date().strftime(\"%d/%m/%Y\")\n",
    "where = \"Biel, CH\"\n",
    "\n",
    "my_block = f\"\"\"\n",
    "<br />\n",
    "<br />\n",
    "Ceci est un ébauche : le script a été mis à jour le {today} in {where}\n",
    "\n",
    "Contactez analyst@ pour plus d'informations\n",
    "\"\"\"\n",
    "\n",
    "md(my_block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "11142d5a-9945-4ed7-8ed6-1aae1d07a1af",
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Git repo: https://github.com/hammerdirt-analyst/cantonal_reports.git\n",
      "\n",
      "Git branch: main\n",
      "\n",
      "numpy     : 1.25.2\n",
      "seaborn   : 0.12.2\n",
      "pandas    : 2.0.3\n",
      "json      : 2.0.9\n",
      "matplotlib: 3.7.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark --iversions -b -r"
   ]
  }
 ],
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