EDP.ipynb

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  "metadata": {
    "colab": {
      "name": "EDP.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ouardavalue/EDP/blob/master/EDP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rO9mjmX2z1OY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqxrQiO9CaFe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly.graph_objects as go\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from plotly.subplots import make_subplots"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VShYslw2IxG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Electric cars chargings Points optimal location\n",
        "#Domain Energy & Environment\n",
        "\n",
        "#Identify the optimal points where to install the electric vehicule cchargings points\n",
        "#EDP is a company that leads the energy sector. in spain, EDP is present in generation, distribution and commercialization of electricity , gas and services.\n",
        "\n",
        "#the goal is to identify the optimal points where to install the electric vehicule chargings. EDP is now expandings its public charging infrastructure and it is of high importance where to place the nex chargings points.\n",
        "#the decision will be based on the location of existing points, the trafic information and matriculation of existing cars plus projection of electric cars in the future.\n",
        "\n",
        "#need spanish traffic information\n",
        "#EDP own public infrastructure data\n",
        "\n",
        "#the objective of this challenge is to develop a predictive Model which based on the inputs included in the datasets generates the charging points optimal location. the algorithm should be able of deciding where EDP should install its charging points.\n",
        "#the best is to have a dashbord that shows in the best possible way the result of the predictive model."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RAGVVaLIdV-2",
        "colab_type": "text"
      },
      "source": [
        "#File loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEYOYR0tyHOc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKB32h6REFm6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# code qui permet en cas de changement de chemin du repertoire root, pour eviter de retaper tout le chemin\n",
        "#fonction utilisée: concatenantion\n",
        "data_dir = './gdrive/My Drive/edp_data/'\n",
        "mat_file = data_dir + 'matricules.xlsx'\n",
        "points_file = data_dir + 'PuntosRecarga.xlsx'\n",
        "provincia_file = data_dir + 'provincia_listado.xlsx'\n",
        "impagos_file = data_dir + 'impagos.xlsx'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwhO1yfKdZlN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#matricules.xlxs\n",
        "mat_cols = ['Categoria_veh_elect', 'Año', 'Mercado', 'Marca', 'Modelo', 'Provincia', 'Canal']\n",
        "df_matricules = pd.read_excel(mat_file,\n",
        "                              usecols=mat_cols)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5kXDLqr890s",
        "colab_type": "text"
      },
      "source": [
        "\"Canal\" : Particular/Empressa/Alquilador"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "372y7HbydcoR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#provincia_listado.xlsx\n",
        "prov_cols = ['Autonomia', 'Superficie', 'PROVINCIA']\n",
        "df_provincia = pd.read_excel(provincia_file,\n",
        "                             usecols=prov_cols)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-blIb9vfdfkh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#impagos.xlsx\n",
        "impagos_cols = ['CONSUMO_ELECTRICO_ANUAL', 'PROVINCIA', 'TIPO_CLIENTE']\n",
        "df_impagos = pd.read_excel(impagos_file,\n",
        "                           usecols=impagos_cols)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WVJmmoNDbfx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#PuntosRecarga.xlsx\n",
        "df_points = pd.read_excel(points_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFsCdlb81VJC",
        "colab_type": "code",
        "outputId": "2dfbe013-c0ce-4bcf-acb2-10fc779673a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "df_impagos.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1051 entries, 0 to 1050\n",
            "Data columns (total 3 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   CONSUMO_ELECTRICO_ANUAL  1051 non-null   float64\n",
            " 1   PROVINCIA                1051 non-null   object \n",
            " 2   TIPO_CLIENTE             1051 non-null   object \n",
            "dtypes: float64(1), object(2)\n",
            "memory usage: 24.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gbutmO1ENzU",
        "colab_type": "code",
        "outputId": "e6704294-84c5-4aca-e01d-cfa38bc6de26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "df_matricules.info()\n",
        "   "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 130 entries, 0 to 129\n",
            "Data columns (total 7 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   Categoria_veh_elect  130 non-null    object\n",
            " 1   Año                  130 non-null    int64 \n",
            " 2   Mercado              130 non-null    object\n",
            " 3   Marca                130 non-null    object\n",
            " 4   Modelo               130 non-null    object\n",
            " 5   Provincia            130 non-null    object\n",
            " 6   Canal                130 non-null    object\n",
            "dtypes: int64(1), object(6)\n",
            "memory usage: 7.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tV4BM8P0NAvI",
        "colab_type": "code",
        "outputId": "936de5b4-06e9-4193-adf8-4b0262b7f4cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "df_provincia.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 52 entries, 0 to 51\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   PROVINCIA   52 non-null     object \n",
            " 1   Superficie  52 non-null     float64\n",
            " 2   Autonomia   52 non-null     object \n",
            "dtypes: float64(1), object(2)\n",
            "memory usage: 1.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0RP56wNZw6c",
        "colab_type": "code",
        "outputId": "6d19b855-8930-4282-9728-b06d785b378c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        }
      },
      "source": [
        "df_points.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 156 entries, 0 to 155\n",
            "Data columns (total 11 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   grupo               153 non-null    object \n",
            " 1   organizacion        153 non-null    object \n",
            " 2   SuperCPO            156 non-null    object \n",
            " 3   GrupoCargadoresCPO  156 non-null    object \n",
            " 4   region              156 non-null    object \n",
            " 5   zona                156 non-null    object \n",
            " 6   ubicacion           156 non-null    object \n",
            " 7   latitud             156 non-null    float64\n",
            " 8   longitud            156 non-null    float64\n",
            " 9   posteRecarga        156 non-null    object \n",
            " 10  toma                154 non-null    object \n",
            "dtypes: float64(2), object(9)\n",
            "memory usage: 13.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGrnEbvEdxfG",
        "colab_type": "text"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXox5LaBXS4a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def match_region_name(reg_src, list_reg):\n",
        "  \"\"\"\n",
        "  Matches composite names which appears in a different order\n",
        "  e.g. 'Principade de Asturias' and 'Asturias Principado de'\n",
        "  \"\"\"\n",
        "  l_src = reg_src.split()\n",
        "\n",
        "  if len(l_src) == 1:\n",
        "    return reg_src\n",
        "\n",
        "  l = list(map(str.split, list_reg))\n",
        "  l = [i for i in l if len(i) > 1]\n",
        "  return ' '.join(next((s for s in l if set(l_src) == set(s)), reg_src.split()))\n",
        "\n",
        "def replace_whole_str(sub, l):\n",
        "  return next((s for s in l if sub in s), sub)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLk1-tmVZ6mV",
        "colab_type": "code",
        "outputId": "3e4685f8-e368-42f6-f79d-4a1a86869d4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "#Normalisation des regions du fichier points\n",
        "df_points['region'] = df_points['region'].str.lower()\n",
        "df_points['region'] = df_points['region'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')\n",
        "df_points['region'].unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['principado de asturias', 'pais vasco', 'comunidad valenciana',\n",
              "       'cantabria', 'castilla y leon', 'region de murcia', 'andalucia'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgURYJPbNhRJ",
        "colab_type": "code",
        "outputId": "1ef8d94f-e52e-4d02-8667-1abe89702772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "#Normalisation des noms de provinces du fichier provincia_listado\n",
        "df_provincia['PROVINCIA'] = df_provincia['PROVINCIA'].str.lower()\n",
        "df_provincia['PROVINCIA'] = df_provincia['PROVINCIA'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')\n",
        "df_provincia['PROVINCIA'] = df_provincia['PROVINCIA'].str.replace('\\(|\\)', '', regex=True)\n",
        "\n",
        "df_provincia['Autonomia'] = df_provincia['Autonomia'].str.lower()\n",
        "df_provincia['Autonomia'] = df_provincia['Autonomia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')\n",
        "\n",
        "df_provincia['Autonomia'] = df_provincia['Autonomia'].str.replace('\\(|\\)', '', regex=True)\n",
        "df_provincia['Autonomia'] = df_provincia['Autonomia'].apply(lambda x : match_region_name(x, df_points['region'].unique()))\n",
        "df_provincia['Autonomia'].unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['pais vasco', 'castilla-la mancha', 'comunidad valenciana',\n",
              "       'andalucia', 'castilla y leon', 'extremadura', 'balears illes',\n",
              "       'cataluna', 'galicia', 'aragon', 'rioja la', 'madrid comunidad de',\n",
              "       'region de murcia', 'navarra comunidad foral de',\n",
              "       'principado de asturias', 'canarias', 'cantabria',\n",
              "       'ceuta ciudad de', 'melilla ciudad de'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAe8N8ugP-T2",
        "colab_type": "code",
        "outputId": "0af45d9f-978f-4338-a0e0-505cfc4df933",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "#Impagos normalisation\n",
        "df_impagos['PROVINCIA'] = df_impagos['PROVINCIA'].str.lower()\n",
        "df_impagos['PROVINCIA'] = df_impagos['PROVINCIA'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')\n",
        "#df_impagos['PROVINCIA'] = df_impagos['PROVINCIA'].apply(lambda x : x.replace(' / ', '/'))\n",
        "df_impagos['PROVINCIA'] = df_impagos['PROVINCIA'].str.replace(' / ', '/')\n",
        "df_impagos['PROVINCIA'] = df_impagos['PROVINCIA'].apply(lambda x : replace_whole_str(x, df_provincia['PROVINCIA'].unique()))\n",
        "df_impagos['PROVINCIA'] = df_impagos['PROVINCIA'].apply(lambda x : match_region_name(x, df_provincia['PROVINCIA'].unique()))\n",
        "df_impagos['PROVINCIA'].unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['asturias', 'leon', 'navarra', 'baleares', 'madrid', 'toledo',\n",
              "       'alicante/alacant', 'zaragoza', 'salamanca', 'valladolid',\n",
              "       'cantabria', 'valencia/valencia', 'araba/alava', 'murcia',\n",
              "       'gerona', 'bizkaia', 'barcelona', 'badajoz', 'gipuzkoa', 'jaen',\n",
              "       'palencia', 'rioja la', 'lerida', 'sevilla', 'albacete', 'granada',\n",
              "       'huelva', 'cordoba', 'guadalajara'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPRWi3IbYcH_",
        "colab_type": "code",
        "outputId": "eeaa23ca-e6b8-406e-90c0-3f4d1e8bd3e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "df_provincia['PROVINCIA'].unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['araba/alava', 'albacete', 'alicante/alacant', 'almeria', 'avila',\n",
              "       'badajoz', 'balears illes', 'barcelona', 'burgos', 'caceres',\n",
              "       'cadiz', 'castellon/castello', 'ciudad real', 'cordoba',\n",
              "       'coruna la', 'cuenca', 'girona', 'granada', 'guadalajara',\n",
              "       'gipuzkoa', 'huelva', 'huesca', 'jaen', 'leon', 'lleida',\n",
              "       'rioja la', 'lugo', 'madrid', 'malaga', 'murcia', 'navarra',\n",
              "       'ourense', 'asturias', 'palencia', 'palmas las', 'pontevedra',\n",
              "       'salamanca', 'santa cruz de tenerife', 'cantabria', 'segovia',\n",
              "       'sevilla', 'soria', 'tarragona', 'teruel', 'toledo',\n",
              "       'valencia/valencia', 'valladolid', 'bizkaia', 'zamora', 'zaragoza',\n",
              "       'ceuta', 'melilla'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzyF0viNEPvT",
        "colab_type": "code",
        "outputId": "8281a8e5-150f-40e9-c78c-968d3c74cac6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "#Normalisation des noms des provinces du fichier matricules\n",
        "df_matricules['Provincia'] = df_matricules['Provincia'].apply(lambda x : x.strip().lower())\n",
        "df_matricules['Provincia'] = df_matricules['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')\n",
        "df_matricules['Provincia'] = df_matricules['Provincia'].str.replace('\\(|\\)', '', regex=True)\n",
        "df_matricules['Provincia'] = df_matricules['Provincia'].apply(lambda x : replace_whole_str(x, df_provincia['PROVINCIA'].unique()))\n",
        "df_matricules['Provincia'].unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['araba/alava', 'sta. cruz tenerife', 'murcia', 'valladolid',\n",
              "       'huelva', 'alicante/alacant', 'castellon/castello', 'ciudad real',\n",
              "       'coruna la', 'gerona', 'guadalajara', 'huesca', 'madrid',\n",
              "       'navarra', 'palmas las', 'pontevedra', 'tarragona', 'zaragoza',\n",
              "       'albacete', 'almeria', 'badajoz', 'burgos', 'cantabria', 'cordoba',\n",
              "       'guipuzcoa', 'sevilla', 'barcelona', 'asturias', 'baleares',\n",
              "       'leon', 'salamanca', 'toledo', 'valencia/valencia', 'rioja la',\n",
              "       'malaga', 'vizcaya', 'granada', 'cadiz', 'lugo', 'jaen', 'lerida',\n",
              "       'orense'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXbab5vF28m3",
        "colab_type": "code",
        "outputId": "7d2937a7-a8be-47e3-b2fa-2ac0b651c00d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "#Sanity check\n",
        "try:\n",
        "  assert set(df_matricules['Provincia'].unique()).issubset(set(df_provincia['PROVINCIA'].unique()))\n",
        "  print('Provincia names from matricules.xlsx MATCH names from provincia_listado.xlsx')\n",
        "except AssertionError:\n",
        "  print('Provincia names from matricules.xlsx DO NOT MATCH names from provincia_listado.xlsx')\n",
        "\n",
        "print('#'*50)\n",
        "\n",
        "try:\n",
        "  assert(set(df_points['region'].unique()).issubset(set(df_provincia['Autonomia'].unique())))\n",
        "  print('Autonomia names from PuntosRecarga MATCH names from provincia_listado.xlsx')\n",
        "except AssertionError:\n",
        "  print('Autonomia names from PuntosRecarga.xlsx DO NOT MATCH names from provincia_listado.xlsx')\n",
        "\n",
        "print('#'*50)\n",
        "\n",
        "try:\n",
        "  assert(set(df_impagos['PROVINCIA'].unique()).issubset(set(df_provincia['PROVINCIA'].unique())))\n",
        "  print('Provincia names from impagos.xlsx MATCH names from  provincia_listado.xlsx')\n",
        "except AssertionError:\n",
        "  print('Provincia names from impagos.xlsx DO NOT MATCH names from  provincia_listado.xlsx')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Provincia names from matricules.xlsx DO NOT MATCH names from provincia_listado.xlsx\n",
            "##################################################\n",
            "Autonomia names from PuntosRecarga MATCH names from provincia_listado.xlsx\n",
            "##################################################\n",
            "Provincia names from impagos.xlsx DO NOT MATCH names from  provincia_listado.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IxicG8A47ON",
        "colab_type": "code",
        "outputId": "0e7a4cd4-fd72-4898-b3af-cfa75f74ef2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# cette celule est à enlever éventuellement\n",
        "# Get not matching elements\n",
        "print(set(df_matricules['Provincia'].unique()).difference(set(df_provincia['PROVINCIA'].unique())))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'sta. cruz tenerife', 'baleares', 'orense', 'guipuzcoa', 'vizcaya', 'lerida', 'gerona'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZOGrskXX0Ka",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Join matricules w/ provincia to add Autonomia\n",
        "df_merged_w_provincia= df_matricules.merge(df_provincia,\n",
        "                                left_on='Provincia',\n",
        "                                right_on='PROVINCIA')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iB4Csn9SYE1R",
        "colab_type": "code",
        "outputId": "00d50fda-c979-4dfe-a06b-3d81447bff87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df_merged_w_provincia.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Categoria_veh_elect</th>\n",
              "      <th>Año</th>\n",
              "      <th>Mercado</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Modelo</th>\n",
              "      <th>Provincia</th>\n",
              "      <th>Canal</th>\n",
              "      <th>PROVINCIA</th>\n",
              "      <th>Superficie</th>\n",
              "      <th>Autonomia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2018</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>Particular</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>3042.44</td>\n",
              "      <td>pais vasco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2019</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>Empresa</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>3042.44</td>\n",
              "      <td>pais vasco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PHEV</td>\n",
              "      <td>2019</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>GOLF 1.4 GTE</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>Empresa</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>3042.44</td>\n",
              "      <td>pais vasco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2018</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-UP! 3P</td>\n",
              "      <td>murcia</td>\n",
              "      <td>Particular</td>\n",
              "      <td>murcia</td>\n",
              "      <td>11313.88</td>\n",
              "      <td>region de murcia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2019</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>murcia</td>\n",
              "      <td>Empresa</td>\n",
              "      <td>murcia</td>\n",
              "      <td>11313.88</td>\n",
              "      <td>region de murcia</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Categoria_veh_elect   Año  ... Superficie         Autonomia\n",
              "0                 BEV  2018  ...    3042.44        pais vasco\n",
              "1                 BEV  2019  ...    3042.44        pais vasco\n",
              "2                PHEV  2019  ...    3042.44        pais vasco\n",
              "3                 BEV  2018  ...   11313.88  region de murcia\n",
              "4                 BEV  2019  ...   11313.88  region de murcia\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2V7CMaKtAu0e",
        "colab_type": "code",
        "outputId": "ed051513-83e9-425e-8877-721785bba635",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "df_merged_w_provincia.info()  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 108 entries, 0 to 107\n",
            "Data columns (total 10 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Categoria_veh_elect  108 non-null    object \n",
            " 1   Año                  108 non-null    int64  \n",
            " 2   Mercado              108 non-null    object \n",
            " 3   Marca                108 non-null    object \n",
            " 4   Modelo               108 non-null    object \n",
            " 5   Provincia            108 non-null    object \n",
            " 6   Canal                108 non-null    object \n",
            " 7   PROVINCIA            108 non-null    object \n",
            " 8   Superficie           108 non-null    float64\n",
            " 9   Autonomia            108 non-null    object \n",
            "dtypes: float64(1), int64(1), object(8)\n",
            "memory usage: 9.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iDGAonyZacm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Join points w/ matricules\n",
        "df_merged_w_points = df_merged_w_provincia.merge(df_points,\n",
        "                                                 how='left',\n",
        "                                                 left_on='Autonomia',\n",
        "                                                 right_on='region')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9105lFoo7Ie",
        "colab_type": "code",
        "outputId": "42e63ab0-7a62-42d9-c1ef-b1ac1e99397a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "#Df n'ayant pas de bornes dans la même regions que les voitures\n",
        "#df_merged_w_points[df_merged_w_points['zona'].isnull()].info() # 56 entries\n",
        "df_merged_w_points[df_merged_w_points['zona'].isnull()].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Categoria_veh_elect</th>\n",
              "      <th>Año</th>\n",
              "      <th>Mercado</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Modelo</th>\n",
              "      <th>Provincia</th>\n",
              "      <th>Canal</th>\n",
              "      <th>PROVINCIA</th>\n",
              "      <th>Superficie</th>\n",
              "      <th>Autonomia</th>\n",
              "      <th>grupo</th>\n",
              "      <th>organizacion</th>\n",
              "      <th>SuperCPO</th>\n",
              "      <th>GrupoCargadoresCPO</th>\n",
              "      <th>region</th>\n",
              "      <th>zona</th>\n",
              "      <th>ubicacion</th>\n",
              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "      <th>posteRecarga</th>\n",
              "      <th>toma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2019</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>ciudad real</td>\n",
              "      <td>Empresa</td>\n",
              "      <td>ciudad real</td>\n",
              "      <td>19662.78</td>\n",
              "      <td>castilla-la mancha</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2019</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>coruna la</td>\n",
              "      <td>Particular</td>\n",
              "      <td>coruna la</td>\n",
              "      <td>7910.76</td>\n",
              "      <td>galicia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>PHEV</td>\n",
              "      <td>2018</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>GOLF 1.4 GTE</td>\n",
              "      <td>coruna la</td>\n",
              "      <td>Empresa</td>\n",
              "      <td>coruna la</td>\n",
              "      <td>7910.76</td>\n",
              "      <td>galicia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>PHEV</td>\n",
              "      <td>2019</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>PASSAT 1.4 GTE 4P (B6)</td>\n",
              "      <td>coruna la</td>\n",
              "      <td>Empresa</td>\n",
              "      <td>coruna la</td>\n",
              "      <td>7910.76</td>\n",
              "      <td>galicia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2019</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>coruna la</td>\n",
              "      <td>Empresa</td>\n",
              "      <td>coruna la</td>\n",
              "      <td>7910.76</td>\n",
              "      <td>galicia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Categoria_veh_elect   Año      Mercado  ... longitud posteRecarga toma\n",
              "87                 BEV  2019  Automoviles  ...      NaN          NaN  NaN\n",
              "88                 BEV  2019  Automoviles  ...      NaN          NaN  NaN\n",
              "89                PHEV  2018  Automoviles  ...      NaN          NaN  NaN\n",
              "90                PHEV  2019  Automoviles  ...      NaN          NaN  NaN\n",
              "91                 BEV  2019  Automoviles  ...      NaN          NaN  NaN\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5i--E2ApsaJk",
        "colab_type": "code",
        "outputId": "b765e9aa-4044-4d8a-a1f6-b62dbd853eea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "#Df bornes présentes dans la même région que les voitures\n",
        "#df_merged_w_points[df_merged_w_points['zona'].notnull()].info() # 868 entries\n",
        "df_merged_w_points[df_merged_w_points['zona'].notnull()].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Categoria_veh_elect</th>\n",
              "      <th>Año</th>\n",
              "      <th>Mercado</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Modelo</th>\n",
              "      <th>Provincia</th>\n",
              "      <th>Canal</th>\n",
              "      <th>PROVINCIA</th>\n",
              "      <th>Superficie</th>\n",
              "      <th>Autonomia</th>\n",
              "      <th>grupo</th>\n",
              "      <th>organizacion</th>\n",
              "      <th>SuperCPO</th>\n",
              "      <th>GrupoCargadoresCPO</th>\n",
              "      <th>region</th>\n",
              "      <th>zona</th>\n",
              "      <th>ubicacion</th>\n",
              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
              "      <th>posteRecarga</th>\n",
              "      <th>toma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2018</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>Particular</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>3042.44</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>Edp Moveon</td>\n",
              "      <td>EDP</td>\n",
              "      <td>EDP moveon</td>\n",
              "      <td>EDP Moveon</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>Vitoria</td>\n",
              "      <td>País Vasco</td>\n",
              "      <td>42.860120</td>\n",
              "      <td>-2.703290</td>\n",
              "      <td>Vitorialanda</td>\n",
              "      <td>CCS Combo 2 Plug (Cable Attached)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2018</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>Particular</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>3042.44</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>Edp Moveon</td>\n",
              "      <td>EDP</td>\n",
              "      <td>EDP moveon</td>\n",
              "      <td>EDP Moveon</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>Vitoria</td>\n",
              "      <td>País Vasco</td>\n",
              "      <td>42.860120</td>\n",
              "      <td>-2.703290</td>\n",
              "      <td>Vitorialanda</td>\n",
              "      <td>CHAdeMO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2018</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>Particular</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>3042.44</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>Edp Moveon</td>\n",
              "      <td>EDP</td>\n",
              "      <td>EDP moveon</td>\n",
              "      <td>EDP Moveon</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>Vitoria</td>\n",
              "      <td>País Vasco</td>\n",
              "      <td>42.860120</td>\n",
              "      <td>-2.703290</td>\n",
              "      <td>Vitorialanda</td>\n",
              "      <td>Type 2 Connector (Cable Attached)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2018</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>Particular</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>3042.44</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>EDP Mantenimiento</td>\n",
              "      <td>Mantenimiento</td>\n",
              "      <td>EDP moveon</td>\n",
              "      <td>EDP Moveon</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>Bergara</td>\n",
              "      <td>País Vasco</td>\n",
              "      <td>43.107969</td>\n",
              "      <td>-2.406641</td>\n",
              "      <td>Avia - Bergara</td>\n",
              "      <td>CHAdeMO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BEV</td>\n",
              "      <td>2018</td>\n",
              "      <td>Automoviles</td>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>E-GOLF 3P/5P</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>Particular</td>\n",
              "      <td>araba/alava</td>\n",
              "      <td>3042.44</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>ISASTUR</td>\n",
              "      <td>Mantenimiento</td>\n",
              "      <td>EDP moveon</td>\n",
              "      <td>EDP Moveon</td>\n",
              "      <td>pais vasco</td>\n",
              "      <td>Murguía</td>\n",
              "      <td>País Vasco</td>\n",
              "      <td>42.961774</td>\n",
              "      <td>-2.827486</td>\n",
              "      <td>Murguía</td>\n",
              "      <td>CHAdeMO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Categoria_veh_elect   Año  ...    posteRecarga                               toma\n",
              "0                 BEV  2018  ...    Vitorialanda  CCS Combo 2 Plug (Cable Attached)\n",
              "1                 BEV  2018  ...    Vitorialanda                            CHAdeMO\n",
              "2                 BEV  2018  ...    Vitorialanda  Type 2 Connector (Cable Attached)\n",
              "3                 BEV  2018  ...  Avia - Bergara                            CHAdeMO\n",
              "4                 BEV  2018  ...         Murguía                            CHAdeMO\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itmku4Z_FQNx",
        "colab_type": "text"
      },
      "source": [
        "#Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0ZyGOUtcc2z",
        "colab_type": "text"
      },
      "source": [
        "##Data matricules/points"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1c7PJCk0pkv",
        "colab_type": "code",
        "outputId": "e788a5dc-169f-4b33-a183-bd3ea2f0792c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "#Nombre de bornes par region\n",
        "df_point_per_region = df_points['region'].value_counts().rename_axis('region').reset_index(name='count_point')\n",
        "df_point_per_region"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>region</th>\n",
              "      <th>count_point</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>principado de asturias</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>comunidad valenciana</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>pais vasco</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cantabria</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>castilla y leon</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>andalucia</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>region de murcia</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   region  count_point\n",
              "0  principado de asturias          124\n",
              "1    comunidad valenciana           10\n",
              "2              pais vasco            7\n",
              "3               cantabria            5\n",
              "4         castilla y leon            4\n",
              "5               andalucia            4\n",
              "6        region de murcia            2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcAx-n2R1rIY",
        "colab_type": "code",
        "outputId": "05f6eec0-dc82-4d5a-cbeb-8e7c3f0200ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        }
      },
      "source": [
        "#Nb matricule par region\n",
        "df_veh_per_region = df_merged_w_provincia.groupby('Autonomia').agg({'Categoria_veh_elect': 'count'}).reset_index()\n",
        "df_veh_per_region"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Autonomia</th>\n",
              "      <th>Categoria_veh_elect</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>andalucia</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>aragon</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>canarias</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cantabria</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>castilla y leon</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>castilla-la mancha</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>cataluna</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>comunidad valenciana</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>extremadura</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>galicia</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>madrid comunidad de</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>navarra comunidad foral de</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>pais vasco</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>principado de asturias</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>region de murcia</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>rioja la</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Autonomia  Categoria_veh_elect\n",
              "0                    andalucia                   21\n",
              "1                       aragon                    5\n",
              "2                     canarias                    6\n",
              "3                    cantabria                    1\n",
              "4              castilla y leon                    7\n",
              "5           castilla-la mancha                    6\n",
              "6                     cataluna                   13\n",
              "7         comunidad valenciana                   10\n",
              "8                  extremadura                    1\n",
              "9                      galicia                    8\n",
              "10         madrid comunidad de                   11\n",
              "11  navarra comunidad foral de                    3\n",
              "12                  pais vasco                    3\n",
              "13      principado de asturias                    5\n",
              "14            region de murcia                    5\n",
              "15                    rioja la                    3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99aro8nw9Pia",
        "colab_type": "code",
        "outputId": "6c321266-4465-44f2-8238-c7e2e5095587",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        }
      },
      "source": [
        "#Par region, nombre de bornes et voiture (Left Join)\n",
        "df_merged = df_provincia.merge(df_point_per_region, how='left', left_on='Autonomia', right_on='region')\\\n",
        "                        .merge(df_veh_per_region, how='left', on='Autonomia')\n",
        "df_merged = df_merged[['Autonomia', 'count_point', 'Categoria_veh_elect']].drop_duplicates().fillna(0)\n",
        "df_merged.rename(columns={'Categoria_veh_elect': 'count_veh'}, inplace=True)\n",
        "df_merged"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Autonomia</th>\n",
              "      <th>count_point</th>\n",
              "      <th>count_veh</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pais vasco</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>castilla-la mancha</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>comunidad valenciana</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>andalucia</td>\n",
              "      <td>4.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>castilla y leon</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>extremadura</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>balears illes</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>cataluna</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>galicia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>aragon</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>rioja la</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>madrid comunidad de</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>region de murcia</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>navarra comunidad foral de</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>principado de asturias</td>\n",
              "      <td>124.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>canarias</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>cantabria</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>ceuta ciudad de</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>melilla ciudad de</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Autonomia  count_point  count_veh\n",
              "0                   pais vasco          7.0        3.0\n",
              "1           castilla-la mancha          0.0        6.0\n",
              "2         comunidad valenciana         10.0       10.0\n",
              "3                    andalucia          4.0       21.0\n",
              "4              castilla y leon          4.0        7.0\n",
              "5                  extremadura          0.0        1.0\n",
              "6                balears illes          0.0        0.0\n",
              "7                     cataluna          0.0       13.0\n",
              "14                     galicia          0.0        8.0\n",
              "21                      aragon          0.0        5.0\n",
              "25                    rioja la          0.0        3.0\n",
              "27         madrid comunidad de          0.0       11.0\n",
              "29            region de murcia          2.0        5.0\n",
              "30  navarra comunidad foral de          0.0        3.0\n",
              "32      principado de asturias        124.0        5.0\n",
              "34                    canarias          0.0        6.0\n",
              "38                   cantabria          5.0        1.0\n",
              "50             ceuta ciudad de          0.0        0.0\n",
              "51           melilla ciudad de          0.0        0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zdN7JE0VcFU9",
        "colab_type": "code",
        "outputId": "c144dbb2-ec7a-4cff-f58b-b2378e92e0da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "df_merged.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 19 entries, 0 to 51\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Autonomia    19 non-null     object \n",
            " 1   count_point  19 non-null     float64\n",
            " 2   count_veh    19 non-null     float64\n",
            "dtypes: float64(2), object(1)\n",
            "memory usage: 608.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEQCEcxtNXiF",
        "colab_type": "code",
        "outputId": "e9955d26-599c-4aed-9058-1e2b1b45129c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "x = df_merged['Autonomia'].values.tolist()\n",
        "fig = make_subplots(rows=2, cols=1, \n",
        "                    subplot_titles=['Number of vehicle per autonomia', 'Number of points per autonomia'])\n",
        "\n",
        "#Number of vehicle per autonomia\n",
        "fig.add_trace(go.Bar(x=x, y=df_merged['count_veh'].values.tolist(), text=df_merged['count_veh'].values.tolist(), textposition='outside', name='nb_vehicle'),\n",
        "              row=1,\n",
        "              col=1)\n",
        "\n",
        "\n",
        "#Number of points per autonomia\n",
        "fig.add_trace(go.Bar(x=x, y=df_merged['count_point'].values.tolist(), text=df_merged['count_point'].values.tolist(), textposition='outside', name='nb_poins'),\n",
        "              row=2,\n",
        "              col=1)\n",
        "\n",
        "fig.update_layout(height=900, width=1300)\n",
        "fig.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      },
      "source": [
        "df_veh_per_marca = df_matricules.groupby('Marca').agg({'Categoria_veh_elect': 'count'}).reset_index()\n",
        "df_veh_per_marca"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Marca</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VOLKSWAGEN</td>\n",
              "      <td>130</td>\n",
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            "text/plain": [
              "        Marca  Categoria_veh_elect\n",
              "0  VOLKSWAGEN                  130"
            ]
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          "metadata": {
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      "cell_type": "markdown",
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      "source": [
        "The number of vehicle is low and contains only Volkswagen cars for some reason. </br>\n",
        "This file must be incomplete."
      ]
    },
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      "source": [
        "autonomias = df_merged['Autonomia'].unique()\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Bar(name='nb_points', x=autonomias, y=df_merged['count_point']),\n",
        "    go.Bar(name='nb_vehicles', x=autonomias, y=df_merged['count_veh'])\n",
        "])\n",
        "# Change the bar mode\n",
        "fig.update_layout(title='Number of points and vehicle per autonomia', barmode='group')\n",
        "fig.show()"
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      "execution_count": 0,
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "adQiDr6cW4fR",
        "colab_type": "text"
      },
      "source": [
        "We can see that some cities have electrical vehicles but don't have charging points. <br/>\n",
        "Notice that Asturias has too many charging points (120+ points for 10- vehicles)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tS1AC0xFcjb_",
        "colab_type": "text"
      },
      "source": [
        "## Data Impagos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcLo9yZ6Z1ip",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s_counts = df_impagos['PROVINCIA'].value_counts()\n",
        "data = {'PROVINCIA': s_counts.index.tolist(), \n",
        "        'count_veh': s_counts.values.tolist()}\n",
        "df_counts = pd.DataFrame(data, columns=['PROVINCIA', 'count_veh'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "outputId": "35ec1c41-ecd7-4c22-93e3-7290408022e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "y = df_counts['count_veh'].values.tolist()\n",
        "\n",
        "fig = go.Figure(go.Bar(x=df_counts['PROVINCIA'].values.tolist(),\n",
        "                       y=y,\n",
        "                       text=y,\n",
        "                       textposition='outside'))\n",
        "\n",
        "fig.update_layout(title='Number of vehicle per autonomia according impagos file')\n",
        "fig.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdDKW4Kcp9--",
        "colab_type": "text"
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      "source": [
        "This barplot show electric/hybrid car users. Most of the customers are from Asturias, which is expected. <br/>\n",
        "As shown above, Asturias has most of the charging points but in this file, we have more users coming from there. <br/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEF3ubIaX2QF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_impagos = df_impagos[df_impagos['TIPO_CLIENTE'].isin(['ELEC', 'DUAL'])]\n",
        "df_impagos_consumo = df_impagos.groupby('PROVINCIA').agg({'CONSUMO_ELECTRICO_ANUAL': 'sum'}).reset_index()\n",
        "df_impagos = df_impagos.merge(df_provincia, on='PROVINCIA')\n",
        "df_impagos = df_impagos.groupby('Autonomia').agg({'CONSUMO_ELECTRICO_ANUAL': 'sum'}).reset_index()\n",
        "df_impagos.sort_values('CONSUMO_ELECTRICO_ANUAL', inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g2Jc2NJGiuF-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Plot electrical consumption per autonomia\n",
        "y = df_impagos_consumo['CONSUMO_ELECTRICO_ANUAL'].values.tolist()\n",
        "\n",
        "fig = go.Figure(go.Bar(x=df_impagos_consumo['Autonomia'].values.tolist(),\n",
        "                       y=y,\n",
        "                       text=y,\n",
        "                       textposition='outside',\n",
        "                       texttemplate='%{y:2.2f}'))\n",
        "\n",
        "fig.update_layout(title='Electrical consumption per autonomia')\n",
        "fig.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QivjA0sC_u_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#labels = df_provincia.Autonomia.drop_duplicates().tolist()\n",
        "labels = df_merged.Autonomia.drop_duplicates().tolist()\n",
        "x = [0] * len(labels) \n",
        "for index_i, row_i in df_merged.iterrows():\n",
        "    if row_i[\"Autonomia\"] in labels:\n",
        "        x[labels.index(row_i[\"Autonomia\"])] == row_i[\"count_point\"]\n",
        "    else: \n",
        "        print(\"Autonomia not found : {}\".format(row_i))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "1cc99956-5e07-4ca6-f18c-abc4a4becd76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.pie(df_merged[[\"Autonomia\", \"count_veh\"]], values='count_veh', names='Autonomia', title='Distribution of cars', hole=.1)\n",
        "fig.update_traces(textposition='inside', textinfo='value+percent+label')\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
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        "colab_type": "code",
        "outputId": "30cb5bdd-5e7a-462f-b2ff-5766bb267801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.pie(df_merged[[\"Autonomia\", \"count_point\"]], values='count_point', names='Autonomia', title='Distribution of Points', hole=.1)\n",
        "fig.update_traces(textposition='inside', textinfo='value+percent+label')\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Y_SrlxCvZHC",
        "colab_type": "code",
        "outputId": "682cecbc-2e09-494c-b0f6-914938ae355a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "source": [
        "\n",
        "\n",
        "print(labels,x)    \n",
        "# Create subplots: use 'domain' type for Pie subplot\n",
        "fig = make_subplots(rows=1, cols=1)\n",
        "\n",
        "fig = make_subplots(rows=1, cols=1, subplot_titles=['Pie Chart'])\n",
        "fig.add_trace(go.Pie(values=x, labels=labels, name=\"Points Number\"),1, 1)\n",
        "#fig.add_trace(go.Bar(x=x, y=df_merged['count_point'].values.tolist(), text=df_merged['count_point'].values.tolist(), textposition='outside', name='nb_poins'), row=2,col=1)\n",
        "\n",
        "\n",
        "# Use `hole` to create a donut-like pie chart\n",
        "#fig.update_traces(hole=.5, hoverinfo=\"label+percent+name\")\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text=\"Pie Chart\",\n",
        "    # Add annotations in the center of the donut pies.\n",
        "    annotations=[dict(text='Pie Chart', x=0.18, y=0.5, font_size=20, showarrow=False)])\n",
        "\n",
        "#fig.update_layout(height=900, width=1300)\n",
        "fig.show()\n",
        "\n",
        "# pour les différents type de bornes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['pais vasco', 'castilla-la mancha', 'comunidad valenciana', 'andalucia', 'castilla y leon', 'extremadura', 'balears illes', 'cataluna', 'galicia', 'aragon', 'rioja la', 'madrid comunidad de', 'region de murcia', 'navarra comunidad foral de', 'principado de asturias', 'canarias', 'cantabria', 'ceuta ciudad de', 'melilla ciudad de'] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
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            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-339-3547216699fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplot_titles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Pie Chart'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Points Number\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;31m#fig.add_trace(go.Bar(x=x, y=df_merged['count_point'].values.tolist(), text=df_merged['count_point'].values.tolist(), textposition='outside', name='nb_poins'), row=2,col=1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36madd_trace\u001b[0;34m(self, trace, row, col, secondary_y)\u001b[0m\n\u001b[1;32m   1621\u001b[0m             \u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1622\u001b[0m             \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1623\u001b[0;31m             \u001b[0msecondary_ys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msecondary_y\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1624\u001b[0m         )\n\u001b[1;32m   1625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36madd_traces\u001b[0;34m(self, data, rows, cols, secondary_ys)\u001b[0m\n\u001b[1;32m   1721\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrows\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1722\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_ys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1723\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_trace_grid_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1725\u001b[0m         \u001b[0;31m# Make deep copy of trace data (Optimize later if needed)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m_set_trace_grid_position\u001b[0;34m(self, trace, row, col, secondary_y)\u001b[0m\n\u001b[1;32m   1802\u001b[0m         \u001b[0mgrid_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_get_grid_ref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1803\u001b[0m         return _set_trace_grid_reference(\n\u001b[0;32m-> 1804\u001b[0;31m             \u001b[0mtrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid_ref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1805\u001b[0m         )\n\u001b[1;32m   1806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotly/subplots.py\u001b[0m in \u001b[0;36m_set_trace_grid_reference\u001b[0;34m(trace, layout, grid_ref, row, col, secondary_y)\u001b[0m\n\u001b[1;32m   1358\u001b[0m                     \u001b[0msubplot_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplot_refs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1359\u001b[0m                     \u001b[0mrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1360\u001b[0;31m                     \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1361\u001b[0m                 )\n\u001b[1;32m   1362\u001b[0m             )\n",
            "\u001b[0;31mValueError\u001b[0m: Trace type 'pie' is not compatible with subplot type 'xy'\nat grid position (1, 1) \n\nSee the docstring for the specs argument to plotly.subplots.make_subplots \nfor more information on subplot types"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJAT8cuT8uEs",
        "colab_type": "text"
      },
      "source": [
        "## Exploring distances between points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1rSz277G5XX1",
        "colab_type": "text"
      },
      "source": [
        " 1 - Looking the distribution of distance between points"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72q0Z63k9XO2",
        "colab_type": "code",
        "outputId": "286bf246-331a-4f54-a83c-a9162bdce394",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "# Plot distances between pooint to see if it's something relevant \n",
        "# for each point compute distance with the closet other point\n",
        "df_points_dist = df_merged_w_points[[\"Autonomia\",\"zona\",\"latitud\", \"longitud\"]]\n",
        "df_points_dist = df_points_dist.dropna().drop_duplicates()\n",
        "df_points_dist.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 119 entries, 0 to 306\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Autonomia  119 non-null    object \n",
            " 1   zona       119 non-null    object \n",
            " 2   latitud    119 non-null    float64\n",
            " 3   longitud   119 non-null    float64\n",
            "dtypes: float64(2), object(2)\n",
            "memory usage: 4.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3aeSET5wMPR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import geopy.distance\n",
        "distance_list = []\n",
        "for index_i, row_i in df_points_dist.iterrows() :\n",
        "    list_dist = [geopy.distance.vincenty((row_i[\"latitud\"], row_i[\"longitud\"]), (row_j[\"latitud\"], row_j[\"longitud\"])).km \n",
        "                 for index_j, row_j in df_points_dist.iterrows() if index_i != index_j \n",
        "                 ]\n",
        "    distance_list.append(min(list_dist)) \n",
        "df_points_dist[\"distances\"] = distance_list\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzZpClffl9pN",
        "colab_type": "code",
        "outputId": "6b7a291f-2849-418e-ceb0-991c54528ecf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "fig, axs = plt.subplots(1,2, figsize=(25,8))\n",
        "axs[0].hist(x = df_points_dist.distances, bins=30, rwidth=0.9, color='#636efa')\n",
        "#df_points_dist.distances.plot.hist(grid=True, bins=10, rwidth=0.9, color='#636efa')\n",
        "axs[0].set_title('Distances between the nearest points')\n",
        "axs[0].set_xlabel('distance(km)')\n",
        "axs[0].set_ylabel('Point number')\n",
        "#axs[0].set_grid(axis='y', alpha=0.75)\n",
        "\n",
        "axs[1].boxplot(df_points_dist.distances)\n",
        "axs[1].set_title('Boxplot for nearest distances between points')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot for nearest distances between points')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iG_rdwXWBlki",
        "colab_type": "code",
        "outputId": "6a7476ef-8961-41c7-f596-fd11ada6ceaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(\"mean {} km\".format(np.mean(df_points_dist.distances)))\n",
        "print(\"variance {}\".format(np.var(df_points_dist.distances)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean 4.757529808529499 km\n",
            "variance 229.82434493262843\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOpzxw2TDxSp",
        "colab_type": "code",
        "outputId": "bf3df9b4-a158-41c8-94a7-833cbf98b836",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "import scipy.stats as scp\n",
        "df_points_dist_per_region = df_points_dist['Autonomia'].value_counts().rename_axis('Autonomia').reset_index(name='count_point')\n",
        "#df_points_dist_per_region[\"mean\"] = [df_points_dist[df_points_dist[\"Autonomia\"] == row_i[\"Autonomia\"]].distances.mean for index_i, row_i in df_points_dist_per_region.iterrows()]\n",
        "df_points_dist_per_region[\"mean\"] = [np.mean(df_points_dist[df_points_dist[\"Autonomia\"] == row_i[\"Autonomia\"]].distances.tolist()) \n",
        "                                      for index_i, row_i in df_points_dist_per_region.iterrows()\n",
        "                                    ]\n",
        "df_points_dist_per_region[\"sd_deviation\"] = [scp.sem(df_points_dist[df_points_dist[\"Autonomia\"] == row_i[\"Autonomia\"]].distances.tolist()) \n",
        "                                      for index_i, row_i in df_points_dist_per_region.iterrows()\n",
        "                                    ]\n",
        "\n",
        "#h = se * scp.t.ppf((1 + confidence) / 2., n-1)\n",
        "    #return m, m-h, m+h                         \n",
        "df_points_dist_per_region"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Autonomia</th>\n",
              "      <th>count_point</th>\n",
              "      <th>mean</th>\n",
              "      <th>sd_deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>principado de asturias</td>\n",
              "      <td>95</td>\n",
              "      <td>2.783426</td>\n",
              "      <td>4.736549e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>comunidad valenciana</td>\n",
              "      <td>10</td>\n",
              "      <td>0.001504</td>\n",
              "      <td>2.495209e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>andalucia</td>\n",
              "      <td>4</td>\n",
              "      <td>0.000753</td>\n",
              "      <td>7.673644e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>pais vasco</td>\n",
              "      <td>3</td>\n",
              "      <td>22.336990</td>\n",
              "      <td>7.158657e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>cantabria</td>\n",
              "      <td>3</td>\n",
              "      <td>3.041947</td>\n",
              "      <td>2.362897e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>castilla y leon</td>\n",
              "      <td>2</td>\n",
              "      <td>112.777540</td>\n",
              "      <td>2.203828e-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>region de murcia</td>\n",
              "      <td>2</td>\n",
              "      <td>0.005298</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Autonomia  count_point        mean  sd_deviation\n",
              "0  principado de asturias           95    2.783426  4.736549e-01\n",
              "1    comunidad valenciana           10    0.001504  2.495209e-04\n",
              "2               andalucia            4    0.000753  7.673644e-05\n",
              "3              pais vasco            3   22.336990  7.158657e+00\n",
              "4               cantabria            3    3.041947  2.362897e+00\n",
              "5         castilla y leon            2  112.777540  2.203828e-13\n",
              "6        region de murcia            2    0.005298  0.000000e+00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iF-Xxtfr0JBz",
        "colab_type": "code",
        "outputId": "69f5fac7-6af3-47a3-efa8-8d9f170c3c86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "nb_autonomia=0\n",
        "for index_i, row_i in df_points_dist_per_region.iterrows():\n",
        "  if row_i.count_point > 2:\n",
        "    nb_autonomia+=1\n",
        "nb_autonomia\n",
        "#plt.subplots(nb_autonomia, sharey = True)\n",
        "ncols = 2\n",
        "nrows = nb_autonomia//2 + nb_autonomia%2\n",
        "fig = plt.figure(figsize=(25,20))\n",
        "plt.gcf().subplots_adjust( wspace = 0.2, hspace = 0.2)\n",
        "#plt.rc_context({'xtick.color':'red', 'ytick.color':'green', 'figure.facecolor':'white'})\n",
        "plt.rc_context({'figure.facecolor':'white'})\n",
        "i = 1\n",
        "for index_i, row_i in df_points_dist_per_region.iterrows():\n",
        "  if row_i.count_point > 2:\n",
        "    axs = fig.add_subplot(nrows, ncols, i )\n",
        "    \n",
        "    axs.hist(x = df_points_dist[df_points_dist[\"Autonomia\"] == row_i[\"Autonomia\"]].distances.tolist(), bins=5, rwidth=0.9)\n",
        "    axs.set_title('Nearest distances in : {} ({} points mean = {:.4f})'.format(row_i[\"Autonomia\"], row_i[\"count_point\"], row_i[\"mean\"]))\n",
        "    axs.set_xlabel('distance(km)')\n",
        "    axs.set_ylabel('Points number')\n",
        "    i+= 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x1440 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2EQ7etb6I5D",
        "colab_type": "code",
        "outputId": "77aed8ad-79d4-48b3-c8cc-37b9180e0009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "df_points_dist_per_region"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Autonomia</th>\n",
              "      <th>count_point</th>\n",
              "      <th>mean</th>\n",
              "      <th>sd_deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>principado de asturias</td>\n",
              "      <td>95</td>\n",
              "      <td>2.783426</td>\n",
              "      <td>4.736549e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>comunidad valenciana</td>\n",
              "      <td>10</td>\n",
              "      <td>0.001504</td>\n",
              "      <td>2.495209e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>andalucia</td>\n",
              "      <td>4</td>\n",
              "      <td>0.000753</td>\n",
              "      <td>7.673644e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>pais vasco</td>\n",
              "      <td>3</td>\n",
              "      <td>22.336990</td>\n",
              "      <td>7.158657e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>cantabria</td>\n",
              "      <td>3</td>\n",
              "      <td>3.041947</td>\n",
              "      <td>2.362897e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>castilla y leon</td>\n",
              "      <td>2</td>\n",
              "      <td>112.777540</td>\n",
              "      <td>2.203828e-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>region de murcia</td>\n",
              "      <td>2</td>\n",
              "      <td>0.005298</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Autonomia  count_point        mean  sd_deviation\n",
              "0  principado de asturias           95    2.783426  4.736549e-01\n",
              "1    comunidad valenciana           10    0.001504  2.495209e-04\n",
              "2               andalucia            4    0.000753  7.673644e-05\n",
              "3              pais vasco            3   22.336990  7.158657e+00\n",
              "4               cantabria            3    3.041947  2.362897e+00\n",
              "5         castilla y leon            2  112.777540  2.203828e-13\n",
              "6        region de murcia            2    0.005298  0.000000e+00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCEiLAjV4psf",
        "colab_type": "text"
      },
      "source": [
        "It appears that in the autonomia:\n",
        "* Asturia: the ~100 points are ~3 km around each other\n",
        "* Valancia: the 10 points are almost at the same location \n",
        "* Adalucia: the 4 poitns\n",
        "* Cantabria: the 3 points\n",
        "* Pais Vasco: the 3 points\n",
        "* Castilla y leon: not enough points to talk\n",
        "* Murcia: not enough points to talk\n",
        "\n",
        "2 strategies could be relevant:\n",
        "* Put the new points in the biggets traffic are, like in Asturia, especially if we consider that the electric cars are used to commuting for short distances.\n",
        "* Put the new points to allow to travel throughout Spain."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYcQMiav84L3",
        "colab_type": "text"
      },
      "source": [
        "2 - explore the areas containing most of the points"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXED9DUmCWQx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_points.info()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZM_5hZc81ke",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_coord_points = np.array( [[row_i[\"latitud\"], row_i[\"longitud\"]] for index_i, row_i in df_points.iterrows()])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uF8DwcwtDSVK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import copy\n",
        "import matplotlib\n",
        "\n",
        "from matplotlib.colors import LogNorm\n",
        "%matplotlib inline\n",
        "#cmap = copy.copy(matplotlib.cm.jet)\n",
        "#cmap.set_bad((0,0,0)) \n",
        "center = [40.4165001, -3.7025599]  #  Madrid location\n",
        "radius=0.1\n",
        "hist_range = [\n",
        "    [center[1] - radius, center[1] + radius],\n",
        "    [center[0] - radius, center[0] + radius]\n",
        "]\n",
        "print(\"center : {}\".format(hist_range))\n",
        "plt.hist2d(list(list_coord_points[:,0]), list(list_coord_points[:,1]), \n",
        "                bins=200, \n",
        "                norm=LogNorm(),\n",
        "                cmap=cmap, \n",
        "                #range = hist_range\n",
        "           )\n",
        "plt.grid('off')\n",
        "plt.axis('off')\n",
        "#fig.axes[0].get_xaxis().set_visible(False)\n",
        "#fig.axes[0].get_yaxis().set_visible(False)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "# c'est tout pourris"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4T4qvDIZn7J",
        "colab_type": "text"
      },
      "source": [
        "Plot point on a map"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOdYjkeplBbV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install geopandas"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMDCBjRQCT33",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install topojson"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGj1R4u5bGE8",
        "colab_type": "code",
        "outputId": "3954c3f2-82d3-422c-da77-73c601240559",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# https://github.com/deldersveld/topojson/tree/master/countries/spain\n",
        "import geopandas as gpd\n",
        "import json\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (20, 10)\n",
        "plt.rc_context({'figure.facecolor':'white'})\n",
        "df = gpd.read_file('spain-provinces.json')\n",
        "df.plot(color='grey', ax=ax,  alpha = 0.8)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f88be1f9d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOLvfh_ptiZu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#with open('spain-comunidad.json',\"r\") as json_file:\n",
        "    #   with json.load(json_file) as json_file_load:\n",
        "#df = pd.read_json('spain-comunidad.json')\n",
        "crs = {'init': 'epsg:4326'}\n",
        "geo_df_com = gpd.read_file('spain-comunidad.json', crs = crs)\n",
        "geo_df_com.info()\n",
        "#geo_df_com.scale(xfact = 0.00038734388431869277, yfact = 0.00027674749512036695)\n",
        "#geo_df_com.affine_transform([0, 0, 0, 0, -9.298193999999967,35.280422000000044])\n",
        "# Remove Island\n",
        "geo_df_com = geo_df_com[geo_df_com[\"NAME_1\"] != \"Islas Baleares\"]\n",
        "geo_df_com = geo_df_com[geo_df_com[\"NAME_1\"] != \"Ceuta y Melilla\"]\n",
        "\n",
        "geo_df_com['coord_label'] = geo_df_com['geometry'].apply(lambda x: x.representative_point().coords[:])\n",
        "geo_df_com['coord_label'] = [coords[0] for coords in geo_df_com['coord_label']]\n",
        "\n",
        "\n",
        "#geo_df_com.head()\n",
        "fig, ax = plt.subplots(1,1)\n",
        "# plot background map\n",
        "geo_df_com.plot(color='grey', ax = ax, alpha = 0.8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ey2ecxZ86ASs",
        "colab_type": "code",
        "outputId": "508495cd-ac1c-42c7-cb5c-87062f83d7b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "# https://github.com/rylativity/identifying_wnv_locations-raw_repo-/blob/master/03-Geo_modeling.ipynb\n",
        "from shapely.geometry import Point, Polygon\n",
        "df_points_dist = pd.DataFrame()\n",
        "geometry = [Point(row_i[\"longitud\"], row_i[\"latitud\"]) for index_i, row_i in df_merged_w_points.drop_duplicates().dropna().iterrows()\n",
        "                                ]\n",
        "geo_points = gpd.GeoDataFrame(geometry = geometry, crs = crs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyproj/crs/crs.py:53: FutureWarning:\n",
            "\n",
            "'+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trjKzJIAQHn1",
        "colab_type": "code",
        "outputId": "fdb35d64-4ed5-4fc2-fdb8-2e5afd920074",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 912
        }
      },
      "source": [
        "fig, ax = plt.subplots(1,1,figsize = (25, 25))\n",
        "# plot background map\n",
        "geo_df_com.plot(color='grey', ax = ax, alpha = 0.5, edgecolor='yellow', legend=True)\n",
        "ax.set_title(\"Autonomia regions\", fontsize=20)\n",
        "# plot autonomia region name\n",
        "for idx, row in geo_df_com.iterrows():\n",
        "    plt.annotate(s=row['NAME_1'], xy=row['coord_label'],\n",
        "                 horizontalalignment='center',color='blue')\n",
        "# plot point\n",
        "geo_points.plot(marker = '+', color='red', ax=ax, label = \"Charging points\", markersize = 40, legend = True)\n",
        "ax.legend(title=\"legend\")\n",
        "ax.set_axis_off()\n",
        "ax.set_axis_off()\n",
        "# TODO : ajouter route et principales villes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUfyNFScmAqD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test if Madrid is in the map \n",
        "p_test = Point(40.4167754, -3.7037902)  # Madrid coords\n",
        "test = [ p_test.within(poly[\"geometry\"]) for key, poly in geo_df_com.iterrows()]\n",
        "bool_test = True\n",
        "for i in test : bool_test &= i\n",
        "print(\"Madrid is inside ? : {}\".format(bool_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-k-S9_QwRC9",
        "colab_type": "text"
      },
      "source": [
        "## Dashboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfi2Rx5lxa-G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install chart_studio\n",
        "!pip install dash"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFH7XA2e9vx6",
        "colab_type": "code",
        "outputId": "cdc93831-665f-4045-b368-9ef2795255ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import dash_core_components\n",
        "print(dash_core_components.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.10.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUzRZm3lxV1E",
        "colab_type": "code",
        "outputId": "c02e888b-11ca-44d7-d03e-bfddd39c7aaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "import chart_studio.dashboard_objs as dashboard\n",
        "import plotly.graph_objs as go\n",
        "import chart_studio.plotly as py\n",
        "import numpy as np\n",
        "\n",
        "colorscale = [[0, '#FAEE1C'], [0.33, '#F3558E'], [0.66, '#9C1DE7'], [1, '#581B98']]\n",
        "trace1 = go.Scatter(\n",
        "    y = np.random.randn(500),\n",
        "    mode='markers',\n",
        "    marker=dict(\n",
        "        size=16,\n",
        "        color = np.random.randn(500),\n",
        "        colorscale=colorscale,\n",
        "        showscale=True\n",
        "    )\n",
        ")\n",
        "data = [trace1]\n",
        "url_1 = py.plot(data, filename='scatter-for-dashboard', auto_open=False)\n",
        "py.iplot(data, filename='scatter-for-dashboard')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "PlotlyRequestError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mPlotlyRequestError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-6daa3043fa96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m )\n\u001b[1;32m     17\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtrace1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0murl_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'scatter-for-dashboard'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauto_open\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'scatter-for-dashboard'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/chart_studio/plotly/plotly.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[1;32m    278\u001b[0m             \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgrid_filename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0mworld_readable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpayload\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"world_readable\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mauto_open\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         )\n\u001b[1;32m    282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/chart_studio/plotly/plotly.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m(cls, grid, filename, world_readable, auto_open, meta)\u001b[0m\n\u001b[1;32m   1085\u001b[0m                 \u001b[0mpayload\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"parent_path\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparent_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1086\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1087\u001b[0;31m         \u001b[0mfile_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_create_or_overwrite_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpayload\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1088\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1089\u001b[0m         \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"cols\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/chart_studio/plotly/plotly.py\u001b[0m in \u001b[0;36m_create_or_overwrite_grid\u001b[0;34m(data, max_retries)\u001b[0m\n\u001b[1;32m   1548\u001b[0m     \u001b[0;31m# Create file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1549\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1550\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mapi_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1551\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmax_retries\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"already exists\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/chart_studio/api/v2/grids.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(body)\u001b[0m\n\u001b[1;32m     16\u001b[0m     \"\"\"\n\u001b[1;32m     17\u001b[0m     \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRESOURCE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/retrying.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;34m@\u001b[0m\u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mRetrying\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/retrying.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_reject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mattempt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_exception\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m             \u001b[0mdelay_since_first_attempt_ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/retrying.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, wrap_exception)\u001b[0m\n\u001b[1;32m    245\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m                 \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/retrying.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m                 \u001b[0mattempt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAttempt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattempt_number\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m                 \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/chart_studio/api/v2/utils.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"No content\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m     \u001b[0mvalidate_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/chart_studio/api/v2/utils.py\u001b[0m in \u001b[0;36mvalidate_response\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"No Content\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mPlotlyRequestError\u001b[0m: Authentication credentials were not provided."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdO2n5NX9Oso",
        "colab_type": "code",
        "outputId": "81c52841-5d46-40e4-ee69-e144599ba241",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "import plotly.graph_objects as go # or plotly.express as px\n",
        "fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)\n",
        "# fig.add_trace( ... )\n",
        "# fig.update_layout( ... )\n",
        "\n",
        "import dash\n",
        "import dash_core_components as dcc\n",
        "import dash_html_components as html\n",
        "\n",
        "app = dash.Dash()\n",
        "app.layout = html.Div([\n",
        "    dcc.Graph(figure=fig)\n",
        "])\n",
        "\n",
        "app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running on http://127.0.0.1:8050/\n",
            "Debugger PIN: 382-971-768\n",
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: on\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "joVNxJIh_pnk",
        "colab_type": "code",
        "outputId": "bddf8fec-ebad-4f84-b2c2-1d0e20715485",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "!python app.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running on http://127.0.0.1:8050/\n",
            "Debugger PIN: 266-612-150\n",
            " * Serving Flask app \"app\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: on\n",
            "Running on http://127.0.0.1:8050/\n",
            "Debugger PIN: 040-068-791\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49yqm1to8ZSx",
        "colab_type": "text"
      },
      "source": [
        "# Premier jet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bFjAsjwIqAe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#One-Hot Encoding\n",
        "df_merged_dummy = pd.concat([pd.get_dummies(df_merged['Autonomia']), df_merged[['count_point', 'count_veh']]], axis=1)\n",
        "df_merged_dummy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-ylZdzNKA5j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Entrainement d'un modele KMeans\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "n_clusters = 3\n",
        "X = df_merged_dummy.values\n",
        "kmeans = KMeans(n_clusters=n_clusters)\n",
        "kmeans.fit(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIABgDg3KtwW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Check des corrdonées des centroides\n",
        "kmeans.cluster_centers_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AnsAa2dLK5o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Dimensionality reduction\n",
        "from sklearn.decomposition import PCA\n",
        "# Check we can stop at Dim 2\n",
        "n_components = 4\n",
        "pca = PCA(n_components=n_components)\n",
        "pca.fit(X)\n",
        "print(\"\\n -----explained_variance_ratio_---\")\n",
        "print(pca.explained_variance_ratio_)\n",
        "\n",
        "n_components = 2\n",
        "pca = PCA(n_components=n_components)\n",
        "pca.fit(X)\n",
        "X_reduced = pca.fit_transform(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_krkAOgRMJLG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Points en \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(X_reduced[:, 0], X_reduced[:, 1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6S7nMlk3M5Xv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_components = 2\n",
        "pca_center = PCA(n_components=n_components)\n",
        "center_reduced = pca_center.fit_transform(kmeans.cluster_centers_)\n",
        "plt.scatter(center_reduced[:, 0], center_reduced[:, 1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wT7vD_kfW_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#It will be really helpful if we can present all the result we find in a dashbord\n",
        "#using this link we have all the tools\n",
        "#https://dash-gallery.plotly.host/dash-manufacture-spc-dashboard/\n",
        "#this exemple of dahboard is really interesting !!"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}