SSE_ABHISHEK_ANGNE.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhishekangne7/Ignite/blob/main/SSE_ABHISHEK_ANGNE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "2almShJ43fDg"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKyW5HuW3hmt",
        "outputId": "40d779c1-d0cc-41e8-a198-3535cc1e2438"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "jv8TG7WR3cex"
      },
      "outputs": [],
      "source": [
        "cork_ev_chargers = pd.read_csv('/content/drive/MyDrive/SSE/Cork_EV_chargers.csv')\n",
        "south_dublin_ev_chargers = pd.read_csv('/content/drive/MyDrive/SSE/SouthDublin_EV_chargers.csv')\n",
        "fingal_ev_chargers = pd.read_csv('/content/drive/MyDrive/SSE/FingalCounty_EV_chargers.csv')\n",
        "population_irl = pd.read_csv('/content/drive/MyDrive/SSE/Population_IRL.csv')\n",
        "ev_ownership_sex = pd.read_csv('/content/drive/MyDrive/SSE/EV_Ownership.csv')\n",
        "ev_ownership_county = pd.read_csv('/content/drive/MyDrive/SSE/EV_ownership_county.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "X_yyPO616R5V"
      },
      "outputs": [],
      "source": [
        "# Add a new column 'county' and populate with appropriate values\n",
        "cork_ev_chargers['county'] = 'Cork'\n",
        "fingal_ev_chargers['county'] = 'Dublin'\n",
        "south_dublin_ev_chargers['county'] = 'Dublin'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87g9wsFm3uBl",
        "outputId": "e4797ab2-bb86-4fd5-d99c-11d43b6a4dfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['OBJECTID', 'Name', 'Details', 'Longitude', 'Latitude', 'Type',\n",
            "       'county'],\n",
            "      dtype='object')\n",
            "Index(['X', 'Y', 'Location', 'Address', 'EV_Charging__', 'Lat', 'Long',\n",
            "       'ObjectId', 'county'],\n",
            "      dtype='object')\n",
            "Index(['LEA', 'Location', 'Operator', 'Number_of_chargers', 'Type', 'Rating',\n",
            "       'ObjectId', 'county'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# Inspect column names for each dataset\n",
        "print(cork_ev_chargers.columns)\n",
        "print(fingal_ev_chargers.columns)\n",
        "print(south_dublin_ev_chargers.columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OIGSE7MV560d",
        "outputId": "95a1aac7-5f41-4bf2-d5ec-f2aa8b7d80da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['objectid', 'name', 'details', 'longitude', 'latitude', 'type',\n",
            "       'county'],\n",
            "      dtype='object')\n",
            "Index(['location', 'address', 'ev_charging__', 'objectid', 'county',\n",
            "       'longitude', 'latitude'],\n",
            "      dtype='object')\n",
            "Index(['lea', 'location', 'operator', 'number_of_chargers', 'type', 'rating',\n",
            "       'objectid', 'county'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# Standardize column names for consistency\n",
        "cork_ev_chargers.columns = cork_ev_chargers.columns.str.lower().str.replace(' ', '_')\n",
        "fingal_ev_chargers.columns = fingal_ev_chargers.columns.str.lower().str.replace(' ', '_')\n",
        "south_dublin_ev_chargers.columns = south_dublin_ev_chargers.columns.str.lower().str.replace(' ', '_')\n",
        "\n",
        "# Combine longitude and latitude columns, ensuring they exist\n",
        "if 'long' in cork_ev_chargers.columns and 'lat' in cork_ev_chargers.columns:\n",
        "    cork_ev_chargers['longitude'] = cork_ev_chargers['long']\n",
        "    cork_ev_chargers['latitude'] = cork_ev_chargers['lat']\n",
        "elif 'longitude' in cork_ev_chargers.columns and 'latitude' in cork_ev_chargers.columns:\n",
        "    cork_ev_chargers['longitude'] = cork_ev_chargers['longitude']\n",
        "    cork_ev_chargers['latitude'] = cork_ev_chargers['latitude']\n",
        "\n",
        "if 'long' in fingal_ev_chargers.columns and 'lat' in fingal_ev_chargers.columns:\n",
        "    fingal_ev_chargers['longitude'] = fingal_ev_chargers['long']\n",
        "    fingal_ev_chargers['latitude'] = fingal_ev_chargers['lat']\n",
        "elif 'longitude' in fingal_ev_chargers.columns and 'latitude' in fingal_ev_chargers.columns:\n",
        "    fingal_ev_chargers['longitude'] = fingal_ev_chargers['longitude']\n",
        "    fingal_ev_chargers['latitude'] = fingal_ev_chargers['latitude']\n",
        "\n",
        "if 'long' in south_dublin_ev_chargers.columns and 'lat' in south_dublin_ev_chargers.columns:\n",
        "    south_dublin_ev_chargers['longitude'] = south_dublin_ev_chargers['long']\n",
        "    south_dublin_ev_chargers['latitude'] = south_dublin_ev_chargers['lat']\n",
        "elif 'longitude' in south_dublin_ev_chargers.columns and 'latitude' in south_dublin_ev_chargers.columns:\n",
        "    south_dublin_ev_chargers['longitude'] = south_dublin_ev_chargers['longitude']\n",
        "    south_dublin_ev_chargers['latitude'] = south_dublin_ev_chargers['latitude']\n",
        "\n",
        "# Drop redundant columns\n",
        "cork_ev_chargers.drop(columns=['long', 'lat', 'x', 'y'], inplace=True, errors='ignore')\n",
        "fingal_ev_chargers.drop(columns=['long', 'lat', 'x', 'y'], inplace=True, errors='ignore')\n",
        "south_dublin_ev_chargers.drop(columns=['long', 'lat', 'x', 'y'], inplace=True, errors='ignore')\n",
        "\n",
        "# Verify the modified datasets\n",
        "print(cork_ev_chargers.columns)\n",
        "print(fingal_ev_chargers.columns)\n",
        "print(south_dublin_ev_chargers.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bD4EU-hh3u03",
        "outputId": "c6db10f5-c596-4808-9dde-7635d04248bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                 name  \\\n",
            "0                                 Copley Street, Cork   \n",
            "1   Copley Street, Opposite Keohans Funeral Home, ...   \n",
            "2                         95 Glanmire Road, Cork City   \n",
            "3                   South Mall (opp no 41), Cork City   \n",
            "4    Black Ash Park & Ride, Off South Link Road, Cork   \n",
            "5                        Cork Institute of Technology   \n",
            "6                                    Cork County Hall   \n",
            "7                                        Kent Station   \n",
            "8                   Paul Street Multi Storey Car Park   \n",
            "9             North Main Street Multi Storey Car Park   \n",
            "10            City Hall Q-park Multi Storey Car Park    \n",
            "11         Grand Parade Q-park Multi Storey Car Park    \n",
            "12        St. Finbarr’s Q-park Multi Storey Car Park    \n",
            "13      Carrolls’s Quay Q-park Multi Storey Car Park    \n",
            "14                     Musgrave Group HQ, Ballycureen   \n",
            "15       Deasey’s Topaz Service Station, Commons Road   \n",
            "16                      Esso/Centra , Rochestown Road   \n",
            "17                   Topaz Frankfield Service Station   \n",
            "18                           Nissan Fast Charge Point   \n",
            "19                                The River Lee Hotel   \n",
            "\n",
            "                                              details  longitude   latitude  \\\n",
            "0   Type-2 AC Socket 22kW (x2, 1 Occupied, 1 Avail...  -8.467683  51.895178   \n",
            "1   Type-2 AC Socket 22kW (x2, ALL Occupied) CP:C80JH  -8.467683  51.895178   \n",
            "2           Type-2 AC Socket 22kW (x2, ALL Available)  -8.456496  51.902475   \n",
            "3   Type-2 AC Socket 22kW (x2, 1 Occupied, 1 Avail...  -8.473353  51.896344   \n",
            "4       1 charge point with facility to charge 2 cars  -8.466503  51.878588   \n",
            "5       1 charge point with facility to charge 2 cars  -8.534618  51.885974   \n",
            "6   \\r\\n Type-2 AC Socket 22kW (x2, 1 Occupied, 1 ...  -8.507728  51.892898   \n",
            "7            Type-2 AC Socket 22kW (x2, ALL Occupied)  -8.456643  51.902168   \n",
            "8     Type-2 AC Socket 3.7kW Slow Charger (x2 spaces)  -8.474829  51.899790   \n",
            "9     Type-2 AC Socket 3.7kW Slow Charger (x2 spaces)  -8.477943  51.900810   \n",
            "10  Type-2 AC Socket 3.7kW Slow Charger (x2 spaces...  -8.464365  51.896450   \n",
            "11    Type-2 AC Socket 3.7kW Slow Charger (x2 spaces)  -8.474345  51.896597   \n",
            "12  Type-2 AC Socket 3.7kW Slow Charger (x2 spaces...  -8.481072  51.896371   \n",
            "13    Type-2 AC Socket 3.7kW Slow Charger (x2 spaces)  -8.472286  51.901742   \n",
            "14    Type-2 AC Socket 3.7kW Slow Charger (x2 spaces)  -8.474922  51.867446   \n",
            "15  CHAdeMO DC 45kW (1 space), Combo DC 45kW (1 sp...  -8.482522  51.919775   \n",
            "16  CHAdeMO DC 45kW (1 space), Combo DC 45kW (1 sp...  -8.395404  51.877000   \n",
            "17  CHAdeMO DC 45kW (1 space), AC (Type-2) 43kW (1...  -8.470828  51.872098   \n",
            "18  CHAdeMO DC 50kW (Available) Business hours onl...  -8.514566  51.893482   \n",
            "19  Type-2 AC Socket 3.7kW (Available) 24 hour acc...  -8.484846  51.895955   \n",
            "\n",
            "           type county  number_of_chargers  \n",
            "0      Standard   Cork                   2  \n",
            "1      Standard   Cork                   2  \n",
            "2      Standard   Cork                   2  \n",
            "3      Standard   Cork                   2  \n",
            "4      Standard   Cork                   2  \n",
            "5      Standard   Cork                   2  \n",
            "6      Standard   Cork                   2  \n",
            "7      Standard   Cork                   2  \n",
            "8      Standard   Cork                   2  \n",
            "9      Standard   Cork                   2  \n",
            "10     Standard   Cork                   2  \n",
            "11     Standard   Cork                   2  \n",
            "12     Standard   Cork                   2  \n",
            "13     Standard   Cork                   2  \n",
            "14     Standard   Cork                   2  \n",
            "15  Fast Charge   Cork                   1  \n",
            "16  Fast Charge   Cork                   1  \n",
            "17  Fast Charge   Cork                   1  \n",
            "18  Fast Charge   Cork                   1  \n",
            "19     Standard   Cork                   1  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23 entries, 0 to 22\n",
            "Data columns (total 7 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   name                23 non-null     object \n",
            " 1   details             23 non-null     object \n",
            " 2   longitude           23 non-null     float64\n",
            " 3   latitude            23 non-null     float64\n",
            " 4   type                21 non-null     object \n",
            " 5   county              23 non-null     object \n",
            " 6   number_of_chargers  23 non-null     int64  \n",
            "dtypes: float64(2), int64(1), object(4)\n",
            "memory usage: 1.4+ KB\n"
          ]
        }
      ],
      "source": [
        "#Some more cleaning and transformation\n",
        "\n",
        "# Drop the columns that we are not using for our analysis\n",
        "cork_ev_chargers.drop(columns=['objectid'], inplace=True)\n",
        "\n",
        "# Extract charger count from 'details' and rename it to 'number_of_chargers'\n",
        "def extract_chargers(details):\n",
        "    if \"x2\" in details or \"2 cars\" in details:\n",
        "        return 2\n",
        "    else:\n",
        "        return 1\n",
        "\n",
        "cork_ev_chargers['number_of_chargers'] = cork_ev_chargers['details'].apply(extract_chargers)\n",
        "\n",
        "# Display the transformed DataFrame\n",
        "print(cork_ev_chargers.head(20))\n",
        "\n",
        "cork_ev_chargers.info()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9MaMJSf_q0f",
        "outputId": "5bde65d4-4b98-4d22-9e9f-b3572236e183"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 location                             address  ev_charging__  \\\n",
            "0  Malahide Train Station  Station Road, Malahide, Co  Dublin              1   \n",
            "1  Malahihe Garda Station  1 - 2 Saint James Terrace Malahide              1   \n",
            "\n",
            "   objectid  county  longitude   latitude  \n",
            "0         1  Dublin  -6.160727  53.444902  \n",
            "1         2  Dublin  -6.152040  53.451086  \n",
            "                 location                             address  ev_charging__  \\\n",
            "0  Malahide Train Station  Station Road, Malahide, Co  Dublin              1   \n",
            "1  Malahihe Garda Station  1 - 2 Saint James Terrace Malahide              1   \n",
            "\n",
            "   county  longitude   latitude  \n",
            "0  Dublin  -6.160727  53.444902  \n",
            "1  Dublin  -6.152040  53.451086  \n",
            "                 location                             address  \\\n",
            "0  Malahide Train Station  Station Road, Malahide, Co  Dublin   \n",
            "1  Malahihe Garda Station  1 - 2 Saint James Terrace Malahide   \n",
            "\n",
            "   number_of_chargers  county  longitude   latitude  \n",
            "0                   1  Dublin  -6.160727  53.444902  \n",
            "1                   1  Dublin  -6.152040  53.451086  \n"
          ]
        }
      ],
      "source": [
        "#The Fingal_ev_chargers.csv is the next one we will look at\n",
        "print(fingal_ev_chargers.head(2))\n",
        "\n",
        "# Drop the columns that are no longer needed\n",
        "fingal_ev_chargers.drop(columns=['objectid'], inplace=True)\n",
        "print(fingal_ev_chargers.head(2))\n",
        "\n",
        "# Rename the ev_charging__ column to chargers to maintain consistency\n",
        "fingal_ev_chargers.rename(columns={'ev_charging__': 'number_of_chargers'}, inplace=True)\n",
        "\n",
        "print(fingal_ev_chargers.head(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVinex-cAg91",
        "outputId": "961c219d-1836-457e-c390-02624235448a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        lea  \\\n",
            "0                Clondalkin   \n",
            "1                Clondalkin   \n",
            "2                Clondalkin   \n",
            "3                Clondalkin   \n",
            "4                Clondalkin   \n",
            "5                Clondalkin   \n",
            "6                     Lucan   \n",
            "7                     Lucan   \n",
            "8                     Lucan   \n",
            "9                     Lucan   \n",
            "10                    Lucan   \n",
            "11               Clondalkin   \n",
            "12               Clondalkin   \n",
            "13               Clondalkin   \n",
            "14     Palmerstown Fonthill   \n",
            "15     Palmerstown Fonthill   \n",
            "16   Rathfarnham_Templeogue   \n",
            "17   Rathfarnham_Templeogue   \n",
            "18   Rathfarnham_Templeogue   \n",
            "19         Tallaght Central   \n",
            "20         Tallaght Central   \n",
            "21         Tallaght Central   \n",
            "22           Tallaght South   \n",
            "23           Tallaght South   \n",
            "24                 Jobstown   \n",
            "25  Rathfarnham -Templeogue   \n",
            "26  Rathfarnham -Templeogue   \n",
            "27  Rathfarnham -Templeogue   \n",
            "28  Rathfarnham -Templeogue   \n",
            "29  Rathfarnham -Templeogue   \n",
            "30  Rathfarnham -Templeogue   \n",
            "31   Firhouse-Bohernabreena   \n",
            "32   Firhouse-Bohernabreena   \n",
            "\n",
            "                                             location       operator  \\\n",
            "0                           Clondalkin, Maldron Hotel         EasyGo   \n",
            "1                       Clondalkin, Circle K Newlands            ESB   \n",
            "2                       Clondalkin, Circle K Newlands            ESB   \n",
            "3                       Clondalkin, Circle K Newlands            ESB   \n",
            "4                   Clondalkin, Main Street Rathcoole            ESB   \n",
            "5                       Clondalkin, Tesco, Colndalkin            ESB   \n",
            "6                               Lucan, Lidl Adamstown         EasyGo   \n",
            "7                  Lucan, Starbucks Lucan Retail Park         EasyGo   \n",
            "8                                 Lucan, Tesco, Lucan            ESB   \n",
            "9                        Lucan, Lucan Shopping Centre            ESB   \n",
            "10                       Lucan, Lucan Shopping Centre            ESB   \n",
            "11                   Clondalkin, Pfizer, Grangecastle         EasyGo   \n",
            "12               Clondalkin, Park West Road The Plaza            ESB   \n",
            "13                    Clondalkin, Supervalu Newcastle            ESB   \n",
            "14                Palmerstown Fonthill, Liffey Valley         EasyGo   \n",
            "15                Palmerstown Fonthill, Liffey Valley            ESB   \n",
            "16            Rathfarnham_Templeogue, Greenhills Road            ESB   \n",
            "17  Rathfarnham_Templeogue, Circle K Templeville Road            ESB   \n",
            "18      Rathfarnham_Templeogue, Moran’s Red Cow Hotel            ESB   \n",
            "19                 Tallaght Central, Windsor, Belgard         Nissan   \n",
            "20                    Tallaght Central, Lidl Tallaght  Maldron Hotel   \n",
            "21             Tallaght Central, The Square, Tallaght            ESB   \n",
            "22                  Tallaght South, Lidl Fortunestown         EasyGo   \n",
            "23           Tallaght South, Citywest Shopping Centre            ESB   \n",
            "24                    Jobstown, Bon Secours, Citywest         EasyGo   \n",
            "25       Rathfarnham -Templeogue, Supervalu Knocklyon            ESB   \n",
            "26       Rathfarnham -Templeogue, Supervalu Knocklyon            ESB   \n",
            "27       Rathfarnham -Templeogue, Supervalu Knocklyon            ESB   \n",
            "28       Rathfarnham -Templeogue, Supervalu Knocklyon            ESB   \n",
            "29       Rathfarnham -Templeogue, Supervalu Knocklyon            ESB   \n",
            "30           Rathfarnham -Templeogue, Faughs GAA Club         EasyGo   \n",
            "31  Firhouse-Bohernabreena, Stocking Avenue Tesco ...            ESB   \n",
            "32       Firhouse-Bohernabreena, Edmonstown Golf Club         EasyGo   \n",
            "\n",
            "    number_of_chargers     type rating  objectid  county  \n",
            "0                    2   Type 2   22kw         1  Dublin  \n",
            "1                    1  CHAdeMO   50kW         2  Dublin  \n",
            "2                    1   Type 2   43kw         3  Dublin  \n",
            "3                    1    Combi   50kW         4  Dublin  \n",
            "4                    2   Type 2   22kW         5  Dublin  \n",
            "5                    2   Type 2   22kW         6  Dublin  \n",
            "6                    2   Type 2   22kW         7  Dublin  \n",
            "7                    2      CCS   50kw         8  Dublin  \n",
            "8                    2   Type 2   22kW         9  Dublin  \n",
            "9                    1      CCS  100kw        10  Dublin  \n",
            "10                   1    Combi   50kW        11  Dublin  \n",
            "11                  28   Type 2    7kW        12  Dublin  \n",
            "12                   2   Type 2   22kW        13  Dublin  \n",
            "13                   4      CCS  100kW        14  Dublin  \n",
            "14                   2   Type 2    7kW        15  Dublin  \n",
            "15                   4   Type 2   22kW        16  Dublin  \n",
            "16                   2   Type 2   22kW        17  Dublin  \n",
            "17                   1    Combi   50kW        18  Dublin  \n",
            "18                   1   Type 2    7kW        19  Dublin  \n",
            "19                   1  CHAdeMO   50kW        20  Dublin  \n",
            "20                   2   Type 2   22kW        21  Dublin  \n",
            "21                   4   Type 2   22kW        22  Dublin  \n",
            "22                   2   Type 2    7kW        23  Dublin  \n",
            "23                   2   Type 2   22kW        24  Dublin  \n",
            "24                   2   Type 2   22kW        25  Dublin  \n",
            "25                   1   Type 2   22kw        26  Dublin  \n",
            "26                   2      CCS  200kw        27  Dublin  \n",
            "27                   1      CCS   50kw        28  Dublin  \n",
            "28                   1  CHAdeMO   50kw        29  Dublin  \n",
            "29                   2  CHAdeMO   70kw        30  Dublin  \n",
            "30                   2   Type 2    7kW        31  Dublin  \n",
            "31                   2   Type 2   22kW        32  Dublin  \n",
            "32                   2   Type 2   22kW        33  Dublin  \n",
            "                            location  number_of_chargers     type  county\n",
            "0          Clondalkin, Maldron Hotel                   2   Type 2  Dublin\n",
            "1      Clondalkin, Circle K Newlands                   1  CHAdeMO  Dublin\n",
            "2      Clondalkin, Circle K Newlands                   1   Type 2  Dublin\n",
            "3      Clondalkin, Circle K Newlands                   1    Combi  Dublin\n",
            "4  Clondalkin, Main Street Rathcoole                   2   Type 2  Dublin\n"
          ]
        }
      ],
      "source": [
        "#south_dublin_ev_chargers\n",
        "\n",
        "south_dublin_ev_chargers.head()\n",
        "\n",
        "# Merge 'lea' and 'location' into one column named 'location'\n",
        "south_dublin_ev_chargers['location'] = south_dublin_ev_chargers['lea'] + ', ' + south_dublin_ev_chargers['location']\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(south_dublin_ev_chargers)\n",
        "\n",
        "# Drop the 'lea' and other columns that are no longer needed\n",
        "south_dublin_ev_chargers.drop(columns=['operator', 'objectid','rating','lea'], inplace=True)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "\n",
        "print(south_dublin_ev_chargers.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WPUrqND6vD5",
        "outputId": "dff84cda-41a3-4f8b-cdf4-6454f2ba4244"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['name', 'details', 'longitude', 'latitude', 'type', 'county',\n",
            "       'number_of_chargers', 'location', 'address'],\n",
            "      dtype='object')\n",
            "  county location  longitude   latitude\n",
            "0   Cork      NaN  -8.467683  51.895178\n",
            "1   Cork      NaN  -8.467683  51.895178\n",
            "2   Cork      NaN  -8.456496  51.902475\n",
            "3   Cork      NaN  -8.473353  51.896344\n",
            "4   Cork      NaN  -8.466503  51.878588\n"
          ]
        }
      ],
      "source": [
        "# Combine datasets\n",
        "ev_chargers_combined = pd.concat([cork_ev_chargers, fingal_ev_chargers, south_dublin_ev_chargers], ignore_index=True)\n",
        "\n",
        "# Verify the combined dataset\n",
        "print(ev_chargers_combined.columns)\n",
        "print(ev_chargers_combined[['county', 'location', 'longitude', 'latitude']].head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rezr3nNc60_r",
        "outputId": "3039d6b0-44d5-4498-9351-90f44f6e94f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [county, location, longitude, latitude, number_of_chargers]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "# Group and summarize combined EV charger data by location and type\n",
        "ev_chargers_summary = ev_chargers_combined.groupby(['county', 'location', 'type', 'longitude', 'latitude']).agg({\n",
        "    'number_of_chargers': 'sum'\n",
        "}).reset_index()\n",
        "\n",
        "# Identify locations with limited charging infrastructure\n",
        "limited_infrastructure = ev_chargers_summary[ev_chargers_summary['number_of_chargers'] < 2]\n",
        "\n",
        "# Verify limited infrastructure dataset\n",
        "print(limited_infrastructure[['county', 'location', 'longitude', 'latitude', 'number_of_chargers']])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "m4ych-rqC0Ig",
        "outputId": "3e40d725-d3fa-4cd1-daae-df313fdf42a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [county, location, longitude, latitude]\n",
              "Index: []"
            ],
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              "type": "dataframe",
              "variable_name": "limited_infrastructure_locations",
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          },
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      ],
      "source": [
        "# Combine longitude and latitude columns, ensuring they exist\n",
        "def combine_lat_long(df):\n",
        "    if 'long' in df.columns and 'lat' in df.columns:\n",
        "        df['longitude'] = df['long']\n",
        "        df['latitude'] = df['lat']\n",
        "    elif 'longitude' in df.columns and 'latitude' in df.columns:\n",
        "        df['longitude'] = df['longitude']\n",
        "        df['latitude'] = df['latitude']\n",
        "    return df\n",
        "\n",
        "# Apply the function to each dataset\n",
        "cork_ev_chargers = combine_lat_long(cork_ev_chargers)\n",
        "fingal_ev_chargers = combine_lat_long(fingal_ev_chargers)\n",
        "south_dublin_ev_chargers = combine_lat_long(south_dublin_ev_chargers)\n",
        "\n",
        "# Drop redundant columns\n",
        "cork_ev_chargers.drop(columns=['long', 'lat', 'x', 'y'], inplace=True, errors='ignore')\n",
        "fingal_ev_chargers.drop(columns=['long', 'lat', 'x', 'y'], inplace=True, errors='ignore')\n",
        "south_dublin_ev_chargers.drop(columns=['long', 'lat', 'x', 'y'], inplace=True, errors='ignore')\n",
        "\n",
        "# Combine datasets\n",
        "ev_chargers_combined = pd.concat([cork_ev_chargers, fingal_ev_chargers, south_dublin_ev_chargers], ignore_index=True)\n",
        "\n",
        "# Group and summarize combined EV charger data by location and type\n",
        "ev_chargers_summary = ev_chargers_combined.groupby(['county', 'location', 'type', 'longitude', 'latitude']).agg({\n",
        "    'number_of_chargers': 'sum'\n",
        "}).reset_index()\n",
        "\n",
        "# Identify locations with limited charging infrastructure\n",
        "limited_infrastructure = ev_chargers_summary[ev_chargers_summary['number_of_chargers'] < 2]\n",
        "\n",
        "# Return the locations with need for additional EV chargers\n",
        "limited_infrastructure_locations = limited_infrastructure[['county', 'location', 'longitude', 'latitude']]\n",
        "limited_infrastructure_locations\n"
      ]
    },
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        },
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        "outputId": "ee0727bf-e8ab-4c2b-ca4f-607bb8f5161a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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Mufo7d+6UYRhq2rRpifdg+/btTuffsWNHbd68WSdOnNCaNWvUsGFDtW7dWldccYVj+P4333zj+PLjbPz9/ZWYmFjiUXx7vWLFi+3Nnz9fkrR//36tWbNGAwcOPOtw/KCgIEkq9xZ9panI5/zTTz+pb9++Cg4OVlBQkMLCwhyLC5752V1yySXy8vJy2rZ37141bNiwxPSMM/tzZdpZ3Nbidh4+fFgnTpwotc7KvM6gQYO0Zs0a/fnnn/rqq680ePBgfffdd7r++uuVn59/1uP37dun4cOHKzQ0VAEBAQoLC1Pnzp0llXyvKuKPP/5QXl6e4uLiSuxr3ry5ioqKnNaYkKr+u5uVlaUlS5aoc+fO2rVrl+NxzTXXaNOmTfr1119LHBMdHe30vDiMDxs2rMTv2RtvvKGCggLH+1DRvwmTJk1SVlaWmjVrplatWunBBx/UDz/8UO65AMCFgjn6AGBxV199tWPV/bKcbUGx8hQVFal79+566KGHSt3frFkzp+dlBUvDMKr0+vfcc49mz56t++67T+3bt1dwcLBsNpsGDhzotBhgVTVp0kRDhw7Vf//731Jvz1ZUVCSbzaYvvvii1HMLCAhw/NyhQwfZ7XatW7dOa9ascQT6jh07as2aNfrll1/0xx9/VDjoV1SbNm0UHx+v9957T48++qjee+89GYZx1tX2JTm+NNi2bZv69OlT4dc82+eclZWlzp07KygoSJMmTVJMTIx8fHy0ZcsWPfzwwyU+u/LWETgX1d0fzyYoKEjdu3dX9+7d5enpqblz52rDhg2O0F6awsJCde/eXZmZmXr44YcVHx8vf39/HThwQMOHD6+Wfl4RVX2vPvjgAxUUFGj69OmaPn16if3z5s1TcnKy07YzP+/ic3z22WfLvPVg8e9aRf8mdOrUSbt379aiRYv01Vdf6Y033tBzzz2nV199Vbfffnu55wQAro6gDwAXsUaNGqmoqEg7d+5U8+bNHdsPHTqkrKwsNWrUyLGtrC8DYmJilJub67iCXx0q88XDhx9+qGHDhjkFiPz8fKfV7c/VY489pnfffVf/+c9/SuyLiYmRYRiKjo4u8aXGma6++mp5eXlpzZo1WrNmjR588EFJZuB4/fXXtXz5csfz6jZkyBA9/vjj+uGHHzR//nw1bdrUscp7eTp06KA6deo4viSozIJ85Vm1apWOHj2qjz/+2Ol8i+8GURGNGjXSypUrS9xqb9euXdXSRslcTM/Hx6fUOs/1ddq2bau5c+c6poWU1e+3bdumX3/9VXPnznW6TWJpq8NX9HcnLCxMfn5+2rFjR4l9v/zyi9zc3BQZGVmhus5m3rx5atmypZ544okS+1577TXNnz+/RNA/U/H0iqCgoLP+ranM34TQ0FCNGDFCI0aMcCyCOXHiRII+gAseQ/cB4CLWu3dvSSqxMv6MGTMkSf/4xz8c2/z9/Uv9H+UBAwZo3bp1Wrp0aYl9WVlZOnXqVKXbVbxifEXCuru7e4krii+88EKJW7Sdi5iYGA0dOlSvvfaaMjIynPb169dP7u7uSk5OLtEOwzB09OhRx3MfHx/97W9/03vvvad9+/Y5XdE/ceKEZs2apZiYGDVs2LDa2l6s+Or9hAkT9P3331foar4k+fn56eGHH9b27dv18MMPl3r19t1339W3335bqfYUf2Fwen0nT57Uyy+/XOE6evbsKbvdrtdff92xraioyHGLuerg7u6uxMREffLJJzp48KBj+65du/TFF1+c9fi8vDzH+gtnKj6+ePh8Wf2+tPfKMAw9//zzJeqs6O+Ou7u7evTooUWLFjmt3XDo0CHNnz9fHTp0cEzbOBe///67vv76aw0YMEA33XRTiceIESO0a9cubdiwodx62rRpo5iYGE2bNk25ubkl9p9+m7+K/k04/XdTMkcExMbGOt1aFAAuVFzRB4CL2BVXXKFhw4bpv//9r2Mo9bfffqu5c+eqT58+uvbaax1l27Rpo1deeUWTJ09WbGys6tevr65du+rBBx/U4sWLdd1112n48OFq06aNjh8/rm3btunDDz9UWlqa6tWrV6l2tWnTRpJ07733qmfPno4F5Upz3XXX6Z133lFwcLAuu+wyrVu3TsuWLXPcGq26/Pvf/9Y777yjHTt2qEWLFo7tMTExmjx5ssaPH6+0tDT16dNHgYGB2rNnjxYuXKhRo0Zp3LhxjvIdO3bU008/reDgYLVq1UqSedU4Li5OO3bs0PDhwyvcpuzsbL377rul7iue614sOjpaCQkJWrRokSRVOOhL0oMPPqiffvpJ06dP18qVK3XTTTcpPDxcGRkZ+uSTT/Ttt98qNTW1wvVJ5m3j6tSpo2HDhunee++VzWbTO++8U6kh83369NHVV1+tBx54QLt27VJ8fLwWL16szMxMSec2JeV0EydO1FdffaVrrrlGd955pwoLC/Xiiy+qZcuW+v7778s9Ni8vTwkJCfr73/+upKQkRUZGKisrS5988onWrFmjPn366KqrrpJk9qWQkBC9+uqrCgwMlL+/v9q1a6f4+HjFxMRo3LhxOnDggIKCgvTRRx+VOje+Mr87kydPVkpKijp06KC77rpLHh4eeu2111RQUKBnnnnm3N60/2/+/PkyDEM33HBDqft79+4tDw8PzZs3r8Tt/E7n5uamN954Q7169VKLFi00YsQIXXLJJTpw4IBWrlypoKAgffrpp5Iq/jfhsssuU5cuXdSmTRuFhoZq06ZN+vDDD51uQwkAF6zzvMo/AOA8Of02ceWx2+1GcnKyER0dbXh6ehqRkZHG+PHjnW5vZhiGkZGRYfzjH/8wAgMDDUlOt9o7duyYMX78eCM2Ntbw8vIy6tWrZyQkJBjTpk0zTp48aRjGX7fXe/bZZ0u0QWfc9uvUqVPGPffcY4SFhRk2m83pdmFnlv3zzz+NESNGGPXq1TMCAgKMnj17Gr/88ovRqFEjY9iwYY5yVbm93pmGDRtmSHK6vV6xjz76yOjQoYPh7+9v+Pv7G/Hx8cbdd99t7Nixw6nc559/bkgyevXq5bT99ttvNyQZb775ZrntK1be7fXK+s/7Sy+9ZEgyrr766gq9xpk+/PBDo0ePHkZoaKjh4eFhNGzY0LjllluMVatWOcqU9f6V9v6vXbvW+Pvf/274+voaERERxkMPPWQsXbq0RLkzb2l4uj/++MMYPHiwERgYaAQHBxvDhw831q5da0gyFixY4ChX1u317r777hJ1ntl3DMMwli9fblx11VWGl5eXERMTY7zxxhvGAw88YPj4+JT7ntntduP11183+vTpYzRq1Mjw9vY2/Pz8jKuuusp49tlnjYKCAqfyixYtMi677DLHrfuKb7X3888/G4mJiUZAQIBRr14944477nDcCvD02/FV5nfHMAxjy5YtRs+ePY2AgADDz8/PuPbaa43U1FSnMpX5TM/UqlUrIyoqqtz3qEuXLkb9+vUNu93uqPODDz4otex3331n9OvXz6hbt67h7e1tNGrUyBgwYICxfPlyR5mK/k2YPHmycfXVVxshISGGr6+vER8fb0yZMsXxNwsALmQ2w6ih1WYAAABqwSeffKK+ffvqm2++0TXXXFNjr9OnTx9uzwYAcEnM0QcAABesEydOOD0vLCzUCy+8oKCgILVu3brGXmfnzp1asmSJunTpUm2vAQBAdWGOPgAAuGDdc889OnHihNq3b6+CggJ9/PHHSk1N1VNPPVWtt+Rr0qSJhg8friZNmmjv3r165ZVX5OXlVeZtJQEAqE0M3QcAABes+fPna/r06dq1a5fy8/MVGxurO++8s9oXVBsxYoRWrlypjIwMeXt7q3379nrqqaeqddQAAADVhaAPAAAAAICFMEcfAAAAAAALIegDAAAAAGAhLMZXRUVFRTp48KACAwNls9lquzkAAAAAAIszDEPHjh1TRESE3NzKvm5P0K+igwcPKjIysrabAQAAAAC4yPz++++69NJLy9xP0K+iwMBASeYbHBQUVMutOf/sdru++uor9ejRQ56enrXdHKAE+ihcHX0Uro4+CldHH4Wrq4k+mpOTo8jISEceLQtBv4qKh+sHBQVdtEHfz89PQUFB/GGFS6KPwtXRR+Hq6KNwdfRRuLqa7KNnmz7OYnwAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCHM0a9BhYWFstvttd2MGmG32+Xh4aH8/HwVFhbWdnNwgXN3d5eHhwe3qgQAAACqAUG/huTm5mr//v0yDKO2m1IjDMNQeHi4fv/9d8IZqoWfn58aNmwoLy+v2m4KAAAAcEEj6NeAwsJC7d+/X35+fgoLC7NkEC4qKlJubq4CAgLk5sYMEFSdYRg6efKk/vjjD+3Zs0dNmzalTwEAAADngKBfA+x2uwzDUFhYmHx9fWu7OTWiqKhIJ0+elI+PD6EM58zX11eenp7au3evo18BAAAAqBoSWg2y4pV8oKbwhREAAABQPfg/awAAAAAALISgDwAAAACAhRD0Ua26dOmi++67r8rHr1q1SjabTVlZWdXWJldls9n0ySef1HYzAAAAAFgMQd/F2e1Sbq75b00bPny4bDZbiUdSUlKF6/j444/15JNPVqhsaV8KJCQkKD09XcHBwZVpeqUVf6FQ2iMjI0P33HOPmjdvXuqx+/btk7u7uxYvXlxm/cV1NGnSRN7e3oqMjNT111+v5cuX19QpAQAAAIAkVt13Wfv2Samp5iM/X/LxkRISzEdUVM29blJSkmbPnu20zdvbu8LHh4aGntPre3l5KTw8/JzqqIwdO3YoKCjIaVv9+vU1cuRIvfjii0pNTVVCQoLT/jlz5qh+/frq3bt3qXWmpaXpmmuuUUhIiJ599lm1atVKdrtdS5cu1d13361ffvmlxs7n5MmTNXYf+pqsGwAAAED14Yq+C9q4UZo6VXr/fSkvT/LyMv99/31z+6ZNNffa3t7eCg8Pd3rUqVNHknkV3MvLS2vWrHGUf/bZZ1W/fn0dOnRIUsmr9C+//LKaNm0qHx8fNWjQQDfddJMkc/TA6tWr9fzzzzuupKelpZUYuj9nzhyFhIRo6dKlat68uQICApSUlKT09HTHa5w6dUr33nuvQkJCVLduXT388MMaNmyY+vTpc9bzrV+/fonzdXNz05VXXqnWrVvrrbfecipvGIbmzJmjYcOGycOj9O/J7rrrLtlsNn377bfq37+/mjVrphYtWuj+++/X+vXrncoeOXJEffv2lZ+fn5o2beo0SqCwsFAjR45UdHS0fH19FRcXp+eff97p+OHDh6tPnz6aMmWKIiIiFBcXJ0lKTU3VlVdeKR8fH7Vt21affPKJbDabvv/+e8exP/74o3r16qWAgAA1aNBAt956q44cOeLY36VLF40ZM0b33Xef6tWrp549e8owDE2cOFFRUVHy9vZWRESE7r333rO+zwAAAADOH4K+i9m3T3rrLSknR2rVSrr0UqlePfPfVq3M7W++aZY734pD/K233qrs7Gz98MMPmjBhgt544w01aNCgRPlNmzbp3nvv1aRJk7Rjxw59+eWX6tSpkyTp+eefV/v27XXHHXcoPT1d6enpioyMLPV18/LyNG3aNL3zzjv6+uuvtW/fPo0bN86x/z//+Y/mzZun2bNna+3atcrJyamWue8jR47U+++/r+PHjzu2rVq1Snv27NE///nPUo/JzMzUl19+qbvvvlv+/v4l9oeEhDg9T05O1oABA/TDDz+od+/eGjJkiDIzMyVJRUVFuvTSS/XBBx/o559/1oQJE/Too4/q/fffd6pj+fLl2rFjh1JSUvTZZ58pJydH119/vVq1aqUtW7boySef1MMPP+x0TFZWlrp27aqrrrpKmzZt0pdffqlDhw5pwIABTuXmzp0rLy8vrV27Vq+++qo++ugjPffcc3rttde0c+dOffLJJ2rVqlWF31MAAAAANY+h+y4mNVU6dMgM9Tab8z6bTYqNlbZtM8vVxBD+zz77TAEBAU7bHn30UT366KOSpMmTJyslJUWjR4/Wtm3bdNttt+mGG24ota59+/bJ399f1113nQIDA9WoUSNdddVVkqTg4GB5eXnJz8/vrEP17Xa7Xn31VcXExEiSxowZo0mTJjn2v/DCCxo/frz69u0rSXrxxRe1ZMmSCp3vpZde6vS8UaNG+umnnyRJgwcP1gMPPKAPPvhAw4cPlyTNnj1bHTp0ULNmzUqtb9euXTIMQ/Hx8RV6/eHDh2vQoEGSpKeeekqzZs3St99+q6SkJHl6eio5OdlRNjo6WuvWrdP777/vFMj9/f31xhtvOIbVv/rqq7LZbHr99dfl4+Ojyy67TAcOHNAdd9zhOObFF1/UVVddpaeeesqx7a233lJkZKR+/fVXx/k1bdpUzzzzjKPM559/rvDwcCUmJsrT01NRUVG6+uqrK3SuAAAAAM4Pgr4LsdvNAB8aWjLkF7PZzP2pqVL//pKnZ/W24dprr9Urr7zitO30efdeXl6aN2+eLr/8ckVGRmrGjBll1tW9e3c1atRITZo0UVJSkpKSkhzD1CvDz8/PEfIlqWHDhjp8+LAkKTs7W4cOHXIKm+7u7mrTpo2KiorOWveaNWsUGBjoeO552hsaEhKifv366a233tLw4cOVk5Ojjz76SC+99FKZ9RmGUalzu/zyyx0/+/v7KygoyHFukvTSSy/prbfe0r59+3TixAmdPHlSV155pVMdrVq1cpo7v2PHDl1++eXy8fFxbDszjG/dulUrV64s8aWOJO3evdsR9Nu0aeO07+abb9bMmTMdn2nv3r11/fXXlzmNAQAAAMD5x/+du5CCgr8W3iuPj49ZtqCg+oO+v7+/YmNjyy2TmpoqSfrzzz+VmZnpFJRPFxgYqC1btmjVqlX66quvNGHCBE2cOFEbN24sMYS9PJ5nnKTNZqt0oC5LdHR0uW0ZOXKkunXrpl27dmnlypVyd3fXzTffXGb5pk2bymazVXjBvdLOrfgLigULFmjcuHGaPn262rdvr8DAQD377LPasGGD0zGlTRE4m9zcXF1//fX6z3/+U2Jfw4YNy6w7MjJSO3bs0LJly5SSkqK77rpLzz77rFavXl3iXAAAAADUDubouxBvbzPE5+eXXy4/3yxbicXwq83u3bs1duxYvfbaa2rbtq1GjBhR7pVzDw8PJSYm6plnntEPP/ygtLQ0rVixQpI5OqCwsPCc2hMcHKwGDRpo48aNjm2FhYXasmXLOdVb7Nprr1V0dLRmz56t2bNna+DAgeUG69DQUPXs2VMvvfSS09z+YsWLDFbE2rVrlZCQoLvuuktXXXWVYmNjtXv37rMeFxcXp23btqmgoMCx7fT3R5Jat26tn376SY0bN1ZsbKzT42xfHPj6+ur666/XrFmztGrVKq1bt07btm2r8HkBAAAAqFkEfRfi6WnePi8zUyrrgrVhmPsTEqr/ar4kFRQUKCMjw+lRvBJ7YWGhhg4dqp49e2rEiBF68cUX9cMPP2j69Oml1vXZZ59p1qxZ+v7777V37169/fbbKioqcqwM37hxY23YsEFpaWk6cuRIhYbal+aee+7R1KlTtWjRIu3YsUP/93//pz///FO2suY/nObw4cMlztdutzv222w2/fOf/9Qrr7yidevWaeTIkWet86WXXlJhYaGuvvpqffTRR9q5c6e2b9+uWbNmqX379hU+r6ZNm2rTpk1aunSpfv31Vz3++OMlAntpBg8erKKiIo0aNUrbt2/X0qVLNW3aNMf5SNLdd9+tzMxMDRo0SBs3btTu3bu1dOlSjRgxotwvX+bMmaM333xTP/74o3777Te9++678vX1VaNGjSp8XgAAAEBNsdul3Fzz34sZQd/FJCRIDRpIu3aVDPuGYW5v0MAsVxO+/PJLNWzY0OnRoUMHSdKUKVO0d+9evfbaa5Kk8PBwvfrqq3rssce0devWEnWFhITo448/VteuXdW8eXO9+uqreu+999SiRQtJ0rhx4+Tu7q7LLrtMYWFh2lfFWwk8/PDDGjRokG677Ta1b99eAQEB6tmzp9Mc9bLExcWVON/Nmzc7lRk+fLiys7PVokULtWvX7qx1NmnSRFu2bNG1116rBx54QC1btlT37t21fPnyEusflGf06NHq16+fbrnlFrVr105Hjx7VXXfdddbjgoKC9Omnn+r777/XlVdeqX//+9+aMGGCJDnek4iICK1du1aFhYXq0aOHWrVqpfvuu08hISFycyv7z0JISIhef/11XXPNNbr88su1bNkyffrpp6pbt26FzwsAAACobvv2SQsWSA88IN1/v/nvggW1c7cyV2Azqmuy80UmJydHwcHBys7OVlBQkNO+/Px87dmzR9HR0RUKm2fatMm8hd6hQ+bCe8XD+TMzzZA/cqTUtm11nUnVFBUVKScnR0FBQeUGw9pQVFSk5s2ba8CAAXryySdruzkuYd68eRoxYoSys7Pl6+tb280p1bn+3pzJbrdryZIl6t27N+sHwCXRR+Hq6KNwdfRRFNu40bxFuavlp5roo+Xl0NOxGJ8LattWql/fXFk/NdVcdM/PT0pMNK/k18Rt9S5ke/fu1VdffaXOnTuroKBAL774ovbs2aPBgwfXdtNqzdtvv60mTZrokksu0datW/Xwww9rwIABLhvyAQAAgKrYt88M+Tk5JW9Rfskl5ojoN98089XFlKMI+i4qKsp89O9vBn1v75qZk28Fbm5umjNnjsaNGyfDMNSyZUstW7ZMzZs3r+2m1ZqMjAxNmDBBGRkZatiwoW6++WZNmTKltpsFAAAAVKvUVPNK/pkhXzKfx8ZK27aZ5Qj6cBmengT8s4mMjNTatWtruxku5aGHHtJDDz1U280AAAAAaozdbgb40NCSIb+YzWbuT001L6JeLNnKtSZXAwAAAABQAQUF5lz8sy3v5ONjlj3t7tOWR9AHAAAAAFxwvL3/WnivPPn5Zllv7/PTLldA0AcAAAAAXHA8Pc3FyjMzS96avJhhmPsTEi6eYfsSQR8AAAAAcIFKSDBvobdrV8mwbxjm9gYNzHIXE4I+AAAAAOCCFBUljRwpBQWZq+vv3y8dOWL+u22buX3kyItrxX2JVfddm2FIx479tcJEYGDZy0kCAAAAwEWobVupfn1zZf3UVHPRPT8/KTHRvJJ/sYV8iaDvmo4fl5Yvlz7+WNqxQyoslNzdpbg4qV8/qVs3yd+/tltZ69LS0hQdHa3vvvtOV155ZYWOGT58uLKysvTJJ5/UaNsuRHPmzNF9992nrKys2m4KAAAAUClRUeajf38z6Ht7X1xz8s/E0H1Xs2WLdPPN0iOPSBs3Sm5u5tV8Nzfz+SOPmPu3bKn2lx4+fLhsNptsNpu8vLwUGxurSZMm6dSpUxU6fs6cOQoJCan2dhW3rU+fPk7bIiMjlZ6erpYtW1bb66xatUo2m00tWrRQYWGh076QkBDNmTOn2l7rbP744w/deeedioqKkre3t8LDw9WzZ0+tXbvWUcZms1XpS4vGjRtr5syZTttuueUW/frrr+fYagAAAKD2eHpKAQEXd8iXuKLvWrZske67z5xUEhkpeXk57w8NlU6elNLSpLFjpeeek1q3rtYmJCUlafbs2SooKNCSJUt09913y9PTU+PHj6/W16kO7u7uCg8Pr5G6f/vtN7399tsaMWJEjdRfEf3799fJkyc1d+5cNWnSRIcOHdLy5ct19OjRGnk9X19f+fr61kjdAAAAAM4frui7iuPHpcceM0N+kyYlQ34xLy9z/x9/mOWPH6/WZhRfOW7UqJHuvPNOJSYmavHixZKkP//8U7fddpvq1KmjgIAA3XTTTdq5c6ck80r4iBEjlJ2d7RgVMHHiRElSQUGBxo0bp0suuUT+/v5q166dVq1a5XjN4pEAS5cuVfPmzRUQEKCkpCSlp6dLkiZOnKi5c+dq0aJFjrpXrVqltLQ02Ww2ff/995KkwsJCjRw5UtHR0fL19VVcXJyef/75Kr0P99xzj5544gkVFBSUWSYrK0u33367wsLCFBQUpK5du2rr1q2SpOzsbLm7u2vTpk2SpKKiIoWGhurvf/+74/h3331XkZGRZda9Zs0a/ec//9G1116rRo0a6eqrr9b48eN1ww03SDKvyktS3759ZbPZHM93796tG2+8UQ0aNFBAQID+9re/admyZY66u3Tpor1792rs2LGO91MqfUTGK6+8opiYGHl5eSkuLk7vvPOO036bzaY33nhDffv2lZ+fn5o2beroL5LZZ4YMGaKwsDD5+vqqadOmmj17dpnvKQAAAIBzR9B3FcuXm1fqIyPPvuCezWaWS0uTVqyo0Wb5+vrq5MmTkszh85s2bdLixYsdw8evu+462e12JSQkaObMmQoKClJ6errS09M1btw4SdKYMWO0bt06LViwQD/88INuvvlmJSUlOb4kkKS8vDxNmzZN77zzjr7++mvt27fPcfy4ceM0YMAAR/hPT09XQin3xygqKtKll16qDz74QD///LMmTJigRx99VO+//36lz/u+++7TqVOn9MILL5RZ5uabb9bhw4f1xRdfaPPmzWrdurW6deumzMxMBQcH68orr3R8obFt2zbZbDZ99913ys3NlSStXr1anTt3LrXugIAABQQE6JNPPinzy4aNGzdKkmbPnq309HTH89zcXPXu3VvLly/Xd999p6SkJF1//fXat2+fJOnjjz/WpZdeqkmTJjnez9IsXLhQ//d//6cHHnhAP/74o0aPHq0RI0Zo5cqVTuWSk5M1YMAA/fDDD+rdu7eGDBmizMxMSdLjjz+un3/+WV988YW2b9+uV155RfXq1SvzPQUAAABw7gj6rsAwzIX3pLKv5J/Jy8sM/B99VPKGkdXSJEPLli3T0qVL1bVrV+3cuVOLFy/WG2+8oY4dO+qKK67Qf//7Xx04cECffPKJvLy8FBwcLJvNpvDwcIWHhysgIED79u3T7Nmz9cEHH6hjx46KiYnRuHHj1KFDB6cru3a7Xa+++qratm2r1q1ba8yYMVq+fLkkM/T6+vo6RhuEh4fLq5T3ydPTU8nJyWrbtq2io6M1ZMgQjRgxokpB38/PT0888YSmTp2q7OzsEvu/+eYbffvtt/rggw/Utm1bNW3aVNOmTVNISIg+/PBDSeaV8+Kgv2rVKnXv3l3NmzfXN99849hWVtD38PDQnDlzNHfuXIWEhOiaa67Ro48+qh9++MFRJiwsTJK5dkB4eLjj+RVXXKHRo0erZcuWatq0qZ588knFxMQ4rrSHhobK3d1dgYGBjvezNNOmTdPw4cN11113qVmzZrr//vvVr18/TZs2zanc8OHDNWjQIMXGxuqpp55Sbm6uvv32W0nSvn37dNVVV6lt27Zq3LixEhMTdf3111foMwAAAABQNQR9V3DsmLm6fp06lTsuJMQ87v9fIa4On332mQICAuTj46NevXrplltu0cSJE7V9+3Z5eHioXbt2jrKhoaGKi4vT9u3by6xv27ZtKiwsVLNmzRxXqQMCArR69Wrt3r3bUc7Pz08xMTGO5w0bNtThw4cr3f6XXnpJbdq0UVhYmAICAvTf//7XcSW7skaOHKm6devqP//5T4l9W7duVW5ururWret0Xnv27HGcV+fOnfXNN9+osLBQq1evVpcuXRzh/+DBg9q1a5e6dOlS5uv3799fBw8e1OLFi5WUlKRVq1apdevWZ10QMDc3V+PGjVPz5s0VEhKigIAAbd++vdLvw/bt23XNNdc4bbvmmmtKfN6XX36542d/f38FBQU5Prs777xTCxYs0JVXXqmHHnpIqamplWoDAAAAgMpjMT5XkJ9v3kKvsktDurtLdrt04oQUGFgtTbn22mv1yiuvyMvLSxEREfLwOLcukpubK3d3d23evFnu7u5O+wICAhw/e55x7jabTUYlRyosWLBA48aN0/Tp09W+fXsFBgbq2Wef1YYNG6rUdg8PD02ZMkXDhw/XmDFjnPbl5uaqYcOGTmsNFCue596pUycdO3ZMW7Zs0ddff62nnnpK4eHhevrpp3XFFVcoIiJCTZs2LbcNPj4+6t69u7p3767HH39ct99+u5544gkNHz68zGPGjRunlJQUTZs2TbGxsfL19dVNN93kmIJR3Ur77IqKiiRJvXr10t69e7VkyRKlpKSoW7duuvvuu0uMCgAAAABQfQj6rsDHxwztZ9zO7awKC83jqnGldH9/f8XGxpbY3rx5c506dUobNmxwzI/PzMzUjh07dNlll0mSvLy8StyS7qqrrlJhYaEOHz6sjh07VrldpdV9prVr1yohIUF33XWXY9vpowaq4uabb9azzz6r5ORkp+2tW7dWRkaGPDw8HIvgnSkkJESXX365XnzxRXl6eio+Pl7169fXLbfcos8++6zMYfvlueyyy5xup+fp6VnifVm7dq2GDx+uvn37SjK/lEhLS3MqU5H3s3nz5lq7dq2GDRvmVHfx511RYWFhGjZsmIYNG6aOHTvqwQcfJOgDAAAANYih+64gMFCKi5Oysip3XFaWedxpV8ZrStOmTXXjjTfqjjvu0DfffKOtW7dq1KhRuuSSS3TjjTdKMleBz83N1fLly3XkyBHl5eWpWbNmGjJkiG677TZ9/PHH2rNnj7799ltNnTpVn3/+eYVfv3Hjxvrhhx+0Y8cOHTlyRHa7vdQ2btq0SUuXLtWvv/6qxx9/3LFA3bl4+umn9dZbb+n4aXc4SExMVPv27dWnTx999dVXSktLU2pqqv797387VtqXzHn68+bNc4T60NBQNW/eXP/73//KDfpHjx5V165d9e677+qHH37Qnj179MEHH+iZZ55xvN/F78vy5cuVkZGhP//80/E+fPzxx/r++++1detWDR482HGF/fTjvv76ax04cEBHjhwptQ0PPvig5syZo1deeUU7d+7UjBkz9PHHHzsWSayICRMmaNGiRdq1a5d++uknffbZZ2revHmFjwcAAABQeQR9V2CzSf36mYvqVXR49cmTZvn+/c++Sn81mT17ttq0aaPrrrvOMXf7s88+cwzdTkhI0L/+9S/dcsstCgsL0zPPPOM47rbbbtMDDzyguLg49enTRxs3blRUVFSFX/uOO+5QXFyc2rZtq7CwMMeq/6cbPXq0+vXrp1tuuUXt2rXT0aNHna7uV1XXrl3VtWtXnTp1yrHNZrNpyZIl6tSpk0aMGKFmzZpp4MCB2rt3rxo0aOAo17lzZxUWFjrNxe/SpUuJbWcKCAhQu3bt9Nxzz6lTp05q2bKlHn/8cd1xxx168cUXHeWmT5+ulJQURUZG6qqrrpIkzZgxQ3Xq1FFCQoKuv/569ezZU61bt3aqf9KkSUpLS1NMTIxjEb8z9enTR88//7ymTZumFi1a6LXXXtPs2bPLbfeZvLy8NH78eF1++eXq1KmT3N3dtWDBggofDwAAAKDybEZlJ0JDkpSTk6Pg4GBlZ2crKCjIaV9+fr727Nmj6Oho+fj4VKzC48elm282b5nXpEn54d0wpN9+kxo3lj74QPL3r/J5VFVRUZFycnIUFBQkNze+L8K5q9LvTTnsdruWLFmi3r17l1hHAHAF9FG4OvooXB19FK6uJvpoeTn0dCQ0V+HvL02eLIWFmSG+rCv7J0+a+8PCpClTaiXkAwAAAABcF4vxuZLWraXnnpMee8y8sm+zmbfQK16oLyvLvJrfuLEZ8v//UG0AAAAAAIoR9F1N69bmcPwVK6SPPpJ27DBvoefuLv3tb+ac/K5duZIPAAAAACgVQd8V+ftL118vXXedlJsrnThh3kIvIOC8LbwHAAAAALgw1eoc/YkTJ8pmszk94uPjHftHjx6tmJgY+fr6KiwsTDfeeKN++eWXcuscPnx4iTqTkpKcymRmZmrIkCEKCgpSSEiIRo4cqdzc3Go/v3Ne59BmM2+9V7+++S8hHxbGuqAAAABA9aj1xfhatGih9PR0x+Obb75x7GvTpo1mz56t7du3a+nSpTIMQz169FBhYWG5dSYlJTnV+d577zntHzJkiH766SelpKTos88+09dff61Ro0ZV2zm5u7tLkk5W9FZ5AJSXlydJrJoLAAAAnKNaH7rv4eGh8PDwUvedHr4bN26syZMn64orrnDc/7ss3t7eZda5fft2ffnll9q4caPatm0rSXrhhRfUu3dvTZs2TREREedwNiYPDw/5+fnpjz/+kKenpyVvP1dUVKSTJ08qPz/fkueH88cwDOXl5enw4cMKCQlxfFEGAAAAoGpqPejv3LlTERER8vHxUfv27TV16lRFRUWVKHf8+HHNnj1b0dHRioyMLLfOVatWqX79+qpTp466du2qyZMnq27dupKkdevWKSQkxBHyJSkxMVFubm7asGGD+vbtW2qdBQUFKigocDzPycmRZN4b0W63lygfFhamffv2KS0t7azvwYXIMAzl5+fLx8dHNqYUoBoEBQWpbt26pf4+VUVxPdVVH1Dd6KNwdfRRuDr6KFxdTfTRitZVq0G/Xbt2mjNnjuLi4pSenq7k5GR17NhRP/74owIDAyVJL7/8sh566CEdP35ccXFxSklJkZeXV5l1JiUlqV+/foqOjtbu3bv16KOPqlevXlq3bp3c3d2VkZGh+vXrOx3j4eGh0NBQZWRklFnv1KlTlZycXGL7V199JT8/vzKPc3d3JwgDZ1FYWFhjc/RTUlJqpF6gutBH4eroo3B19FG4uurso8XTXc/GZrjQClhZWVlq1KiRZsyYoZEjR0qSsrOzdfjwYaWnp2vatGk6cOCA1q5dKx8fnwrV+dtvvykmJkbLli1Tt27d9NRTT2nu3LnasWOHU7n69esrOTlZd955Z6n1lHZFPzIyUkeOHFFQUFAVz/jCZbfblZKSou7duzOnGi6JPgpXRx+Fq6OPwtXRR+HqaqKP5uTkqF69esrOzi43h9b60P3ThYSEqFmzZtq1a5djW3BwsIKDg9W0aVP9/e9/V506dbRw4UINGjSoQnU2adJE9erV065du9StWzeFh4fr8OHDTmVOnTqlzMzMMuf1S+a8f29v7xLbPT09L+o/LBf7+cP10Ufh6uijcHX0Ubg6+ihcXXX20YrW41KrqOXm5mr37t1q2LBhqfsNw5BhGE5X1s9m//79Onr0qKPO9u3bKysrS5s3b3aUWbFihYqKitSuXbtzOwEAAAAAAGpZrQb9cePGafXq1UpLS1Nqaqr69u0rd3d3DRo0SL/99pumTp2qzZs3a9++fUpNTdXNN98sX19f9e7d21FHfHy8Fi5cKMn8ouDBBx/U+vXrlZaWpuXLl+vGG29UbGysevbsKUlq3ry5kpKSdMcdd+jbb7/V2rVrNWbMGA0cOLBaVtwHAAAAAKA21erQ/f3792vQoEE6evSowsLC1KFDB61fv15hYWGy2+1as2aNZs6cqT///FMNGjRQp06dlJqa6rSY3o4dO5SdnS3JXPjuhx9+0Ny5c5WVlaWIiAj16NFDTz75pNOw+3nz5mnMmDHq1q2b3Nzc1L9/f82aNeu8nz8AAAAAANWtVoP+ggULytwXERGhJUuWnLWO09cS9PX11dKlS896TGhoqObPn1+xRgIAAAAAcAFxqTn6AAAAAADXZrdLubnmv3BNLrXqPgAAAADANe3bJ6Wmmo/8fMnHR0pIMB9RUbXdOpyOoA8AAAAAKNfGjdJbb0mHDkmhoWbIz8uT3n9fWr1aGjlSatu2tluJYgR9AAAAAECZ9u0zQ35OjtSqlWSz/bXvkkukXbukN9+U6tc/9yv7drtUUCB5e0vVdOv5ixJBHwAAAABQptRU80r+mSFfMp/Hxkrbtpnlqhr0mRZQvViMDwAAAABQKrvdDN+hoSVDfjGbzdyfmlq1BfrWrZMmTZIWLDCnA3h5/TUtYOpUadOmczuHixFX9AEAAAAApSoo+OsKe3l8fMyyBQUVH3K/b5+0eLH02mvSiRPmlwUeHlJkpFSvXvVPC7iYcEUfAAAAAFAqb28zxOfnl18uP98s6+1dsXo3bjSv1r/9tnTsmFSnjnTqlPTTT9KaNdLBg39NCzh0yBwtgIoj6AMAAAAASuXpac6Tz8yUDKP0MoZh7k9IqNjV/OLF/bKy/hr27+8vBQWZV+4LCqQtW6Ts7HOfFnCxIugDAAAAAMqUkCA1aGAOoz8z7BuGub1BA7NcRRQv7te4sXkV3+O0CeXFwf74cen3381tp08LQMUQ9AEAAAAAZYqKkkaONK+4b9sm7d8vHTli/rttm7l95MiKzaE/fXE/Dw/zceqUcxmbTfL1NYN+YWHlpwWAxfgAAAAAAGfRtq05rL74FngFBZKfn5SYWLlb4J2+uJ+7u3TppdLPP0uBgc6r+hd/AXDqlDktIDGx4ov8gaAPAAAAAKiAqCjz0b+/Gdi9vSsfvosX98vL+6vOvXvNMH/6LfyKh/SnpVVuWgBMDN0HAAAAAFSYp6cUEFC1K+xnLu4XHCy1bm1+AXD4sJSTY87PL94fElLxaQH4C1f0AQAAAADnTUKCtHq1uYhfbKwUEWGuuv/77+bj6FFzKP+wYdINNxDyq4KgDwAAAAA4b4oX93vzTXMxv9BQczh/SIg5ZL9VK+n226W//722W3rhIugDAAAAAM6rshb3Gziwcov7oXQEfQAAAADAeVcdi/uhdAR9AAAAAECt8fQk4Fc3Vt0HAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAAA1xG6XcnPNf+GarPgZcXs9AAAAAKhm+/ZJqanmIz9f8vGREhLMR1RUbbcOkrU/I4I+AAAAAFSjjRult96SDh2SQkPNAJmXJ73/vrR6tTRypNS2bW238uJm9c+IoA8AAAAA1WTfPjNA5uRIrVpJNttf+y65RNq1S3rzTal+/Qv/qvGF6mL4jJijDwAAAADVJDXVvEocG+scICXzeWysuT81tXbah4vjMyLoAwAAAEA1sNvNcBgaWjJAFrPZzP2pqdZa/O1CcbF8RgR9AAAAAKgGBQV/LepWHh8fs2xBwflpF/5ysXxGBH0AAAAAqAbe3mZAzM8vv1x+vlnW2/v8tAt/uVg+I4I+AAAAAFQDT0/z1myZmZJhlF7GMMz9CQlmeZxfF8tnRNAHAAAAgGqSkCA1aGCu3H5mkDQMc3uDBmY51I6L4TMi6AMAAABANYmKMu/BHhQkbdsm7d8vHTli/rttm7l95MgL97ZtVnAxfEYetd0AAAAAALCStm3Ne7CnppqPggLJz09KTDSvEl/IAdIqrP4ZEfQBAAAAoJpFRZmP/v3NEOntfeHO97YqK39GBH0AAAAAqCGentYJj1Zlxc+IOfoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFhIrQb9iRMnymazOT3i4+Md+0ePHq2YmBj5+voqLCxMN954o3755Zcy67Pb7Xr44YfVqlUr+fv7KyIiQrfddpsOHjzoVK5x48YlXvfpp5+usfMEAAAAAOB8qfUr+i1atFB6errj8c033zj2tWnTRrNnz9b27du1dOlSGYahHj16qLCwsNS68vLytGXLFj3++OPasmWLPv74Y+3YsUM33HBDibKTJk1yet177rmnxs4RAAAAAIDzxaPWG+DhofDw8FL3jRo1yvFz48aNNXnyZF1xxRVKS0tTTExMifLBwcFKSUlx2vbiiy/q6quv1r59+xQVFeXYHhgYWObrlqagoEAFBQWO5zk5OZLMUQR2u73C9VhF8TlfjOeOCwN9FK6OPgpXRx+Fq6OPwtXVRB+taF02wzCManvVSpo4caKeffZZBQcHy8fHR+3bt9fUqVOdAnmx48eP67HHHtOiRYv0yy+/yMvLq0KvsWzZMvXo0UNZWVkKCgqSZH5pkJ+fL7vdrqioKA0ePFhjx46Vh0fZ33tMnDhRycnJJbbPnz9ffn5+FTxjAAAAAACqJi8vT4MHD1Z2drYj35amVoP+F198odzcXMXFxSk9PV3Jyck6cOCAfvzxRwUGBkqSXn75ZT300EM6fvy44uLi9Pnnn5d6Nb80+fn5uuaaaxQfH6958+Y5ts+YMUOtW7dWaGioUlNTNX78eI0YMUIzZswos67SruhHRkbqyJEj5b7BVmW325WSkqLu3bvL09OztpsDlEAfhaujj8LV0Ufh6uijcHU10UdzcnJUr169swb9Wh2636tXL8fPl19+udq1a6dGjRrp/fff18iRIyVJQ4YMUffu3ZWenq5p06ZpwIABWrt2rXx8fMqt2263a8CAATIMQ6+88orTvvvvv9/pdb28vDR69GhNnTpV3t7epdbn7e1d6j5PT8+L+g/LxX7+cH30Ubg6+ihcHX0Uro4+CldXnX20ovXU+mJ8pwsJCVGzZs20a9cux7bg4GA1bdpUnTp10ocffqhffvlFCxcuLLee4pC/d+9epaSknPWKe7t27XTq1CmlpaVVx2kAAAAAAFBrXCro5+bmavfu3WrYsGGp+w3DkGEYTkPoz1Qc8nfu3Klly5apbt26Z33d77//Xm5ubqpfv36V2w4AAAAAgCuo1aA/btw4rV69WmlpaUpNTVXfvn3l7u6uQYMG6bffftPUqVO1efNm7du3T6mpqbr55pvl6+ur3r17O+qIj493XOG32+266aabtGnTJs2bN0+FhYXKyMhQRkaGTp48KUlat26dZs6cqa1bt+q3337TvHnzNHbsWA0dOlR16tSplfcBAAAAAIDqUqtz9Pfv369Bgwbp6NGjCgsLU4cOHbR+/XqFhYXJbrdrzZo1mjlzpv788081aNBAnTp1UmpqqtOV9x07dig7O1uSdODAAS1evFiSdOWVVzq91sqVK9WlSxd5e3trwYIFmjhxogoKChQdHa2xY8c6zdsHAAAAAOBCVatBf8GCBWXui4iI0JIlS85ax+k3DWjcuLHOdhOB1q1ba/369RVvJAAAAAAAFxCXmqMPAAAAAADODUEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsBCCPgAAAAAAFkLQBwAAAADAQgj6AAAAAABYCEEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsBCCPgAAAAAAFkLQBwAAAADAQgj6AAAAAABYCEEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsBCCPgAAAAAAFkLQBwAAAADAQgj6AAAAAABYCEEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsBCCPgAAAAAAFkLQBwAAAADAQgj6AAAAAABYCEEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsBCCPgAAAAAAFkLQBwAAAADAQgj6AAAAAABYCEEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsBCCPgAAAAAAFkLQBwAAAADAQgj6AAAAAABYCEEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsBCCPgAAAAAAFkLQBwAAAADAQgj6AAAAAABYCEEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsBCCPgAAAAAAFkLQBwAAAADAQgj6AAAAAABYCEEfAAAAAAALIegDAAAAAGAhBH0AAAAAACyEoA8AAAAAgIUQ9AEAAAAAsJBaDfoTJ06UzWZzesTHxzv2jx49WjExMfL19VVYWJhuvPFG/fLLL+XWaRiGJkyYoIYNG8rX11eJiYnauXOnU5nMzEwNGTJEQUFBCgkJ0ciRI5Wbm1sj5wgAAAAAwPlU61f0W7RoofT0dMfjm2++cexr06aNZs+ere3bt2vp0qUyDEM9evRQYWFhmfU988wzmjVrll599VVt2LBB/v7+6tmzp/Lz8x1lhgwZop9++kkpKSn67LPP9PXXX2vUqFE1ep4AAAAAAJwPHrXeAA8PhYeHl7rv9PDduHFjTZ48WVdccYXS0tIUExNTorxhGJo5c6Yee+wx3XjjjZKkt99+Ww0aNNAnn3yigQMHavv27fryyy+1ceNGtW3bVpL0wgsvqHfv3po2bZoiIiJq4CwBAAAAADg/aj3o79y5UxEREfLx8VH79u01depURUVFlSh3/PhxzZ49W9HR0YqMjCy1rj179igjI0OJiYmObcHBwWrXrp3WrVungQMHat26dQoJCXGEfElKTEyUm5ubNmzYoL59+5Zad0FBgQoKChzPc3JyJEl2u112u71K534hKz7ni/HccWGgj8LV0Ufh6uijcHX0Ubi6muijFa2rVoN+u3btNGfOHMXFxSk9PV3Jycnq2LGjfvzxRwUGBkqSXn75ZT300EM6fvy44uLilJKSIi8vr1Lry8jIkCQ1aNDAaXuDBg0c+zIyMlS/fn2n/R4eHgoNDXWUKc3UqVOVnJxcYvtXX30lPz+/ip+0xaSkpNR2E4By0Ufh6uijcHX0Ubg6+ihcXXX20by8vAqVq9Wg36tXL8fPl19+udq1a6dGjRrp/fff18iRIyWZ8+m7d++u9PR0TZs2TQMGDNDatWvl4+NzXts6fvx43X///Y7nOTk5ioyMVI8ePRQUFHRe2+IK7Ha7UlJS1L17d3l6etZ2c4AS6KNwdfRRuDr6KFwdfRSurib6aPHI8rOp9aH7pwsJCVGzZs20a9cux7bg4GAFBweradOm+vvf/646depo4cKFGjRoUInji+f6Hzp0SA0bNnRsP3TokK688kpHmcOHDzsdd+rUKWVmZpa5VoAkeXt7y9vbu8R2T0/Pi/oPy8V+/nB99FG4OvooXB19FK6OPgpXV519tKL11Pqq+6fLzc3V7t27nUL66QzDkGEYTnPlTxcdHa3w8HAtX77csS0nJ0cbNmxQ+/btJUnt27dXVlaWNm/e7CizYsUKFRUVqV27dtV4NgAAAAAAnH+1GvTHjRun1atXKy0tTampqerbt6/c3d01aNAg/fbbb5o6dao2b96sffv2KTU1VTfffLN8fX3Vu3dvRx3x8fFauHChJMlms+m+++7T5MmTtXjxYm3btk233XabIiIi1KdPH0lS8+bNlZSUpDvuuEPffvut1q5dqzFjxmjgwIGsuA8AAAAAuODV6tD9/fv3a9CgQTp69KjCwsLUoUMHrV+/XmFhYbLb7VqzZo1mzpypP//8Uw0aNFCnTp2UmprqtJjejh07lJ2d7XhevHDfqFGjlJWVpQ4dOujLL790mtM/b948jRkzRt26dZObm5v69++vWbNmnddzBwAAAACgJtRq0F+wYEGZ+yIiIrRkyZKz1mEYhtNzm82mSZMmadKkSWUeExoaqvnz51e8oQAAAAAAXCBcao4+AAAAAAA4NwR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFVDno7969W4899pgGDRqkw4cPS5K++OIL/fTTT9XWOAAAAAAAUDlVCvqrV69Wq1attGHDBn388cfKzc2VJG3dulVPPPFEtTYQAAAAAABUXJWC/iOPPKLJkycrJSVFXl5eju1du3bV+vXrq61xAAAAAACgcqoU9Ldt26a+ffuW2F6/fn0dOXLknBsFAAAAAACqpkpBPyQkROnp6SW2f/fdd7rkkkvOuVEAAAAAAKBqqhT0Bw4cqIcfflgZGRmy2WwqKirS2rVrNW7cON12223V3UYAAAAAAFBBVQr6Tz31lOLj4xUZGanc3Fxddtll6tSpkxISEvTYY49VdxsBAAAAAEAFeVTlIC8vL73++ut6/PHH9eOPPyo3N1dXXXWVmjZtWt3tAwAAAAAAlVCloF8sKipKUVFR1dUWAAAAAABwjioc9O+///4KVzpjxowqNQYAAAAAAJybCgf97777zun5li1bdOrUKcXFxUmSfv31V7m7u6tNmzbV20IAAAAAAFBhFQ76K1eudPw8Y8YMBQYGau7cuapTp44k6c8//9SIESPUsWPH6m8lAAAAAACokCqtuj99+nRNnTrVEfIlqU6dOpo8ebKmT59e4XomTpwom83m9IiPj5ckZWZm6p577lFcXJx8fX0VFRWle++9V9nZ2eXWeWZ9xY9nn33WUaZx48Yl9j/99NOVfBcAAAAAAHA9VVqMLycnR3/88UeJ7X/88YeOHTtWqbpatGihZcuW/dUgD7NJBw8e1MGDBzVt2jRddtll2rt3r/71r3/p4MGD+vDDD8usLz093en5F198oZEjR6p///5O2ydNmqQ77rjD8TwwMLBS7QYAAAAAwBVVKej37dtXI0aM0PTp03X11VdLkjZs2KAHH3xQ/fr1q1wDPDwUHh5eYnvLli310UcfOZ7HxMRoypQpGjp0qE6dOuX4QuBMZ9a1aNEiXXvttWrSpInT9sDAwFJfFwAAAACAC1mVgv6rr76qcePGafDgwbLb7WZFHh4aOXKk0xD5iti5c6ciIiLk4+Oj9u3ba+rUqWXesi87O1tBQUFlhvwzHTp0SJ9//rnmzp1bYt/TTz+tJ598UlFRURo8eLDGjh1bbr0FBQUqKChwPM/JyZEk2e12x3twMSk+54vx3HFhoI/C1dFH4eroo3B19FG4uprooxWty2YYhlHVFzl+/Lh2794tybzi7u/vX6njv/jiC+Xm5iouLk7p6elKTk7WgQMH9OOPP5YYSn/kyBG1adNGQ4cO1ZQpUypU/zPPPKOnn35aBw8elI+Pj2P7jBkz1Lp1a4WGhio1NVXjx4/XiBEjyr0t4MSJE5WcnFxi+/z58+Xn51fBMwYAAAAAoGry8vI0ePBgx0XwspxT0K9uWVlZatSokWbMmKGRI0c6tufk5Kh79+4KDQ3V4sWL5enpWaH64uPj1b17d73wwgvllnvrrbc0evRo5ebmytvbu9QypV3Rj4yM1JEjR8p9g63KbrcrJSVF3bt3r/DnAZxP9FG4OvooXB19FK6OPgpXVxN9NCcnR/Xq1Ttr0K/S0P1rr71WNputzP0rVqyoSrUKCQlRs2bNtGvXLse2Y8eOKSkpSYGBgVq4cGGF36A1a9Zox44d+t///nfWsu3atdOpU6eUlpamuLi4Ust4e3uX+iWAp6fnRf2H5WI/f7g++ihcHX0Uro4+CldHH4Wrq84+WtF6qhT0r7zySqfndrtd33//vX788UcNGzasKlVKknJzc7V7927deuutksxvK3r27Clvb28tXrzYafj92bz55ptq06aNrrjiirOW/f777+Xm5qb69etXue0AAAAAALiCKgX95557rtTtEydOVG5uboXrGTdunK6//no1atRIBw8e1BNPPCF3d3cNGjRIOTk56tGjh/Ly8vTuu+8qJyfHsQBeWFiY3N3dJZnD86dOnaq+ffs66s3JydEHH3yg6dOnl3jNdevWacOGDbr22msVGBiodevWaezYsRo6dKjq1KlTmbcBAAAAAACXU6WgX5ahQ4fq6quv1rRp0ypUfv/+/Ro0aJCOHj2qsLAwdejQQevXr1dYWJhWrVqlDRs2SJJiY2OdjtuzZ48aN24sSdqxY4eys7Od9i9YsECGYWjQoEElXtPb21sLFizQxIkTVVBQoOjoaI0dO1b3339/Fc4YAAAAAADXUq1Bf926dZUaXr9gwYIy93Xp0kUVWSewtDKjRo3SqFGjSi3funVrrV+/vsJtBAAAAADgQlKloN+vXz+n54ZhKD09XZs2bdLjjz9eLQ0DAAAAAACVV6WgHxQU5LTqvpubm+Li4jRp0iT16NGj2hoHAAAAAAAqp0pBf86cOdXcDAAAAAAAUB3cqnJQkyZNdPTo0RLbs7Ky1KRJk3NuFAAAAAAAqJoqBf20tDQVFhaW2F5QUKADBw6cc6MAAAAAAEDVVGro/uLFix0/L126VMHBwY7nhYWFWr58ueO2dwAAAAAA4PyrVNDv06ePJMlms2nYsGFO+zw9PdW4cWNNnz692hoHAAAAAAAqp1JBv6ioSJIUHR2tjRs3ql69ejXSKAAAAAAAUDVVWnV/z5491d0OAAAAAABQDSoc9GfNmqVRo0bJx8dHs2bNKrfsvffee84NAwAAAAAAlVfhoP/cc89pyJAh8vHx0XPPPVdmOZvNRtAHAAAAAKCWVDjonz5cn6H7AAAAAAC4JreqHDRp0iTl5eWV2H7ixAlNmjTpnBsFAAAAAACqpkpBPzk5Wbm5uSW25+XlKTk5+ZwbBQAAAAAAqqZKQd8wDNlsthLbt27dqtDQ0HNuFAAAAAAAqJpK3V6vTp06stlsstlsatasmVPYLywsVG5urv71r39VeyMBAAAAAEDFVCroz5w5U4Zh6J///KeSk5MVHBzs2Ofl5aXGjRurffv21d5IAAAAAABQMZUK+sOGDZMkRUdHKyEhQZ6enjXSKAAAAAAAUDWVCvrFOnfu7Pg5Pz9fJ0+edNofFBR0bq0CAAAAAABVUqXF+PLy8jRmzBjVr19f/v7+qlOnjtMDAAAAAADUjioF/QcffFArVqzQK6+8Im9vb73xxhtKTk5WRESE3n777epuIwAAAAAAqKAqDd3/9NNP9fbbb6tLly4aMWKEOnbsqNjYWDVq1Ejz5s3TkCFDqrudAAAAAACgAqp0RT8zM1NNmjSRZM7Hz8zMlCR16NBBX3/9dfW1DgAAAAAAVEqVgn6TJk20Z88eSVJ8fLzef/99SeaV/tNvuQcAAAAAAM6vKgX9ESNGaOvWrZKkRx55RC+99JJ8fHw0duxYPfTQQ9XaQAAAAAAAUHFVmqM/duxYx8+JiYn65ZdftHnzZtWrV0/vvvtutTUOAAAAAABUTpWu6J+pUaNG6tevn4KDg/Xmm29WR5UAAAAAAKAKqiXoAwAAAAAA10DQBwAAAADAQgj6AAAAAABYSKUW4+vXr1+5+7Oyss6lLQAAAAAA4BxVKugHBwefdf9tt912Tg0CAAAAAABVV6mgP3v27JpqBwAAAAAAqAbM0QcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICF1GrQnzhxomw2m9MjPj5ekpSZmal77rlHcXFx8vX1VVRUlO69915lZ2eXW+fw4cNL1JmUlORUJjMzU0OGDFFQUJBCQkI0cuRI5ebm1th5AgAAAABwvnjUdgNatGihZcuWOZ57eJhNOnjwoA4ePKhp06bpsssu0969e/Wvf/1LBw8e1IcfflhunUlJSZo9e7bjube3t9P+IUOGKD09XSkpKbLb7RoxYoRGjRql+fPnV+OZAQAAAABw/tV60Pfw8FB4eHiJ7S1bttRHH33keB4TE6MpU6Zo6NChOnXqlOMLgdJ4e3uXWqckbd++XV9++aU2btyotm3bSpJeeOEF9e7dW9OmTVNERMQ5nhEAAAAAALWn1oP+zp07FRERIR8fH7Vv315Tp05VVFRUqWWzs7MVFBRUbsiXpFWrVql+/fqqU6eOunbtqsmTJ6tu3bqSpHXr1ikkJMQR8iUpMTFRbm5u2rBhg/r27VtqnQUFBSooKHA8z8nJkSTZ7XbZ7fZKnbMVFJ/zxXjuuDDQR+Hq6KNwdfRRuDr6KFxdTfTRitZlMwzDqLZXraQvvvhCubm5iouLU3p6upKTk3XgwAH9+OOPCgwMdCp75MgRtWnTRkOHDtWUKVPKrHPBggXy8/NTdHS0du/erUcffVQBAQFat26d3N3d9dRTT2nu3LnasWOH03H169dXcnKy7rzzzlLrnThxopKTk0tsnz9/vvz8/Kpw9gAAAAAAVFxeXp4GDx7suAhelloN+mfKyspSo0aNNGPGDI0cOdKxPScnR927d1doaKgWL14sT0/PCtf522+/KSYmRsuWLVO3bt2qHPRLu6IfGRmpI0eOlPsGW5XdbldKSoq6d+9eqc8DOF/oo3B19FG4OvooXB19FK6uJvpoTk6O6tWrd9agX+tD908XEhKiZs2aadeuXY5tx44dU1JSkgIDA7Vw4cJKv0FNmjRRvXr1tGvXLnXr1k3h4eE6fPiwU5lTp04pMzOzzHn9kjnv/8xF/STJ09Pzov7DcrGfP1wffRSujj4KV0cfhaujj8LVVWcfrWg9tXp7vTPl5uZq9+7datiwoSTz24oePXrIy8tLixcvlo+PT6Xr3L9/v44ePeqos3379srKytLmzZsdZVasWKGioiK1a9euek4EAAAAAIBaUqtBf9y4cVq9erXS0tKUmpqqvn37yt3dXYMGDXKE/OPHj+vNN99UTk6OMjIylJGRocLCQkcd8fHxWrhwoSTzi4IHH3xQ69evV1pampYvX64bb7xRsbGx6tmzpySpefPmSkpK0h133KFvv/1Wa9eu1ZgxYzRw4EBW3AcAAAAAXPBqdej+/v37NWjQIB09elRhYWHq0KGD1q9fr7CwMK1atUobNmyQJMXGxjodt2fPHjVu3FiStGPHDmVnZ0uS3N3d9cMPP2ju3LnKyspSRESEevTooSeffNJp2P28efM0ZswYdevWTW5uburfv79mzZp1fk4aAAAAAIAaVKtBf8GCBWXu69KliyqyTuDpZXx9fbV06dKzHhMaGqr58+dXrJEAAAAAAFxAXGqOPgAAAAAAODcEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFhIrQb9iRMnymazOT3i4+MlSZmZmbrnnnsUFxcnX19fRUVF6d5771V2dnaZ9dntdj388MNq1aqV/P39FRERodtuu00HDx50Kte4ceMSr/v000/X6LkCAAAAAHA+eNR2A1q0aKFly5Y5nnt4mE06ePCgDh48qGnTpumyyy7T3r179a9//UsHDx7Uhx9+WGpdeXl52rJlix5//HFdccUV+vPPP/V///d/uuGGG7Rp0yanspMmTdIdd9zheB4YGFgDZwcAAAAAwPlV60Hfw8ND4eHhJba3bNlSH330keN5TEyMpkyZoqFDh+rUqVOOLwROFxwcrJSUFKdtL774oq6++mrt27dPUVFRju2BgYGlvi4AAAAAABeyWg/6O3fuVEREhHx8fNS+fXtNnTrVKZCfLjs7W0FBQaWG/LJkZ2fLZrMpJCTEafvTTz+tJ598UlFRURo8eLDGjh1bbr0FBQUqKChwPM/JyZFkThew2+0Vbo9VFJ/zxXjuuDDQR+Hq6KNwdfRRuDr6KFxdTfTRitZlMwzDqLZXraQvvvhCubm5iouLU3p6upKTk3XgwAH9+OOPJYbSHzlyRG3atNHQoUM1ZcqUCtWfn5+va665RvHx8Zo3b55j+4wZM9S6dWuFhoYqNTVV48eP14gRIzRjxowy65o4caKSk5NLbJ8/f778/PwqeMYAAAAAAFRNXl6eBg8e7LgIXpZaDfpnysrKUqNGjTRjxgyNHDnSsT0nJ0fdu3dXaGioFi9eLE9Pz7PWZbfb1b9/f+3fv1+rVq0q90146623NHr0aOXm5srb27vUMqVd0Y+MjNSRI0fKrduq7Ha7UlJS1L179wp9HsD5Rh+Fq6OPwtXRR+Hq6KNwdTXRR3NyclSvXr2zBv1aH7p/upCQEDVr1ky7du1ybDt27JiSkpIUGBiohQsXVjjkDxgwQHv37tWKFSvOGsTbtWunU6dOKS0tTXFxcaWW8fb2LvVLAE9Pz4v6D8vFfv5wffRRuDr6KFwdfRSujj4KV1edfbSi9dTq7fXOlJubq927d6thw4aSzG8revToIS8vLy1evFg+Pj5nraM45O/cuVPLli1T3bp1z3rM999/Lzc3N9WvX/+czwEAAAAAgNpUq0F/3LhxWr16tdLS0pSamqq+ffvK3d1dgwYNcoT848eP680331ROTo4yMjKUkZGhwsJCRx3x8fFauHChJDPk33TTTdq0aZPmzZunwsJCxzEnT56UJK1bt04zZ87U1q1b9dtvv2nevHkaO3ashg4dqjp16tTK+wAAAAAAQHWp1aH7+/fv16BBg3T06FGFhYWpQ4cOWr9+vcLCwrRq1Spt2LBBkhQbG+t03J49e9S4cWNJ0o4dO5SdnS1JOnDggBYvXixJuvLKK52OWblypbp06SJvb28tWLBAEydOVEFBgaKjozV27Fjdf//9NXuyAAAAAACcB7Ua9BcsWFDmvi5duqgi6wSeXqZx48ZnPaZ169Zav359xRsJAAAAAMAFxKXm6AMAAAAAgHND0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9AAAAAAAspFaD/sSJE2Wz2Zwe8fHxkqTMzEzdc889iouLk6+vr6KionTvvfcqOzu73DoNw9CECRPUsGFD+fr6KjExUTt37nQqk5mZqSFDhigoKEghISEaOXKkcnNza+w8AQAAAAA4X2r9in6LFi2Unp7ueHzzzTeSpIMHD+rgwYOaNm2afvzxR82ZM0dffvmlRo4cWW59zzzzjGbNmqVXX31VGzZskL+/v3r27Kn8/HxHmSFDhuinn35SSkqKPvvsM3399dcaNWpUjZ4nAAAAAADng0etN8DDQ+Hh4SW2t2zZUh999JHjeUxMjKZMmaKhQ4fq1KlT8vAo2XTDMDRz5kw99thjuvHGGyVJb7/9tho0aKBPPvlEAwcO1Pbt2/Xll19q48aNatu2rSTphRdeUO/evTVt2jRFRETU0JkCAAAAAFDzaj3o79y5UxEREfLx8VH79u01depURUVFlVo2OztbQUFBpYZ8SdqzZ48yMjKUmJjo2BYcHKx27dpp3bp1GjhwoNatW6eQkBBHyJekxMREubm5acOGDerbt2+pdRcUFKigoMDxPCcnR5Jkt9tlt9srfd4XuuJzvhjPHRcG+ihcHX0Uro4+CldHH4Wrq4k+WtG6ajXot2vXTnPmzFFcXJzS09OVnJysjh076scff1RgYKBT2SNHjujJJ58sd4h9RkaGJKlBgwZO2xs0aODYl5GRofr16zvt9/DwUGhoqKNMaaZOnark5OQS27/66iv5+fmVf6IWlpKSUttNAMpFH4Wro4/C1dFH4eroo3B11dlH8/LyKlSuVoN+r169HD9ffvnlateunRo1aqT333/faS5+Tk6O/vGPf+iyyy7TxIkTa6Gl0vjx43X//fc7tSkyMlI9evRQUFBQrbSpNtntdqWkpKh79+7y9PSs7eYAJdBH4eroo3B19FG4OvooXF1N9NHikeVnU+tD908XEhKiZs2aadeuXY5tx44dU1JSkgIDA7Vw4cJy36Diuf6HDh1Sw4YNHdsPHTqkK6+80lHm8OHDTsedOnVKmZmZpa4VUMzb21ve3t4ltnt6el7Uf1gu9vOH66OPwtXRR+Hq6KNwdfRRuLrq7KMVrafWV90/XW5urnbv3u0I6Tk5OerRo4e8vLy0ePFi+fj4lHt8dHS0wsPDtXz5cse2nJwcbdiwQe3bt5cktW/fXllZWdq8ebOjzIoVK1RUVKR27drVwFkBAAAAAHD+1GrQHzdunFavXq20tDSlpqaqb9++cnd316BBgxwh//jx43rzzTeVk5OjjIwMZWRkqLCw0FFHfHy8Fi5cKEmy2Wy67777NHnyZC1evFjbtm3TbbfdpoiICPXp00eS1Lx5cyUlJemOO+7Qt99+q7Vr12rMmDEaOHAgK+4DAAAAAC54tTp0f//+/Ro0aJCOHj2qsLAwdejQQevXr1dYWJhWrVqlDRs2SJJiY2OdjtuzZ48aN24sSdqxY4eys7Md+x566CEdP35co0aNUlZWljp06KAvv/zSaTTAvHnzNGbMGHXr1k1ubm7q37+/Zs2aVfMnDAAAAABADavVoL9gwYIy93Xp0kWGYZy1jjPL2Gw2TZo0SZMmTSrzmNDQUM2fP7/iDQUAAAAA4ALhUnP0AQAAAADAuSHoAwAAAABgIQR9AAAAAAAshKAPAAAAAICFEPQBAAAAALAQgj4AAAAAABZC0AcAAAAAwEII+gAAAAAAWAhBHwAAAAAACyHoAwAAAABgIQR9i7Pbpdxc818AAAAAgPV51HYDUDP27ZNSU81Hfr7k4yMlJJiPqKjabh0AAAAAoKYQ9C1o40bprbekQ4ek0FAz5OflSe+/L61eLY0cKbVtW9utBAAAAADUBIK+xezbZ4b8nBypVSvJZvtr3yWXSLt2SW++KdWvz5V9AAAAALAi5uhbTGqqeSU/NtY55Evm89hYc39qas28PmsCAAAAAEDt4oq+hdjtZoAPDS0Z8ovZbOb+1FSpf3/J07N6Xps1AQAAAADANRD0LaSg4K+QXR4fH7NsQUH1BH3WBAAAAAAA10HQtxBv779Cdnny8yU/P7P8udq/nzUBAAAAAMCVMEffQjw9zaHymZmSYZRexjDM/QkJ1XM1f8OG2l0TAAAAAADgjKBvMQkJUoMG5pX0M8O+YZjbGzQwy1WHDRsqviZA8QJ9eXlSRsbZRx5ILO4HAAAAAJXF0H2LiYoy58S/+aa0bdtfc+bz880r+Q0amPuraxh9QUHJNQEKC81HUZF06pTk4WGW+/pradEic95+QYE5daBzZ2nQIKl9e+c6WNwPAAAAAKqGoG9Bbduac+KLg3JBgTknPzGx+oOyt7d07Jj5c2amlJYm7dhh/nz8uOTmJrm7S4GB0hdfmFfmg4IkLy8zwH/4obR8ufTww9Jtt5n1sLgfAAAAAFQdQd+ioqLMR//+f109r+qcfLvdrMPNzbxKf/oifu3aSXPmSD/9JP38sxnuCwtL1pGTY/4bHCzVqSP5+0t165ojDPbtk/7zH6lpU3MBPxb3AwAAAICqI+hbnKdn1QN+8fD5pUvNn48cMcN5o0ZSz56Sr685UuDnn6X0dHMNgNJC/umys83h/EFBUsOG5pX+qCgzwL/3njni4NChkiFf+mtxv23bzHYR9AEAAACgJII+SlU8fP6XX8wQf/Kk+YVBWpr5/OBB6a67pDfeMK/iS2Wv9H+m4iH96elmnT4+ZuBftcr8EqCii/v17189dw4AAAAAACsh6EPSX8Pzvb3NAP7WW+a/OTlmmG7QwAzZxbfnK56X/9135rHBwdLRoxV/vdxcM+zn5JhB38vLfP2cHCkkpPxjfXzMsgUFBH0AAAAAOBNB/yJX2ur2krRnjzmPPi/PnA9ffIW9+Ip6Vpb5PD/fnLfv7l651zUMc5RAdrZUr575s7e3OaQ/P7/8Y/PzzSkDp68VAAAAAAAwEfQvYqWtbp+ba65s7+lp3hbP17f0ufLFIdvd/a9b6VWGzWaG++Jb8R07Zs77T0gwV9e/5JLSh+8XjyhITORqPgAAAACUxq22G4DasW+f8+r2l15qXlkPDzeH4Z86ZX4BUNbieh7//ysim+2vsF/WvPrSuJ3W8/bvN4fr33ij9Le/mdMEdu0qOeffMMztDRqYXwgUs9vNLyjs9oq/PgAAAABYFVf0L1KpqaWvbu/ubl4pDwgw59xnZ5u3wztT8RV8d3cztBffvu/kyYq9fvEXA3l55pcGl14q/e9/5qiCSy+Vtm83V9cvHmmQn29eyW/QQBo50lxxv7RpBwkJ5oMV+QEAAABcrAj6FyG73QzHpa1u7+5uBu2ffzbny2dnm6H+9CvwhvHXPHpfX/Pqv5+fGfL//LPiq++7u5u32AsPN4O5l5cZ/DdvNutr08b8MqKgwHyemPhXiC9t2kFenjnsf/Vq88uAtm2r5/0CAAAAgAsJQf8iVFDgvPDemaKipL17zTLu7uaV/Xr1nFfdDww0y7ZpY34ZsGOHGfqLiv5aqK80xV8Y2GxSTIx5fGys8xcOl1xiDtHfv18aN868il88YkAqOe2gtGPffNNcRJAr+wAAAAAuNszRvwh5e/81HL40wcFS69ZmGR8fc5j977+bt9v7/XfzCn5x0L//fum118x/W7Y059qXtwK/YZhh/5JLzJEF0dGlL/YXG2terd+40ZxGcPrCe8XTDs78guDMY1NTK/3WAAAAAMAFj6B/EfL0NIfAZ2aWPcy+YUOpUSOpXz+pfXszkPv4mP/+/e/SiBFmuauuMq+ajxkjPfGEOezf09McTl88pN7NzQz/3t7m1fmGDc3X9fQse7X+4tv4paY6L7JX3rSDsx0LAAAAABcDhu5fpBISzLnsu3aVvDJevLp9dLQ5dL5hQ3O4v5ubGcyLb623ZMlfx+zbJ82ebV5JDwgwF9jz9jav/BcVmfPnPT3NLw9OnDDLBweXf/Xfx8d83YKCv67on23aQXnHAgAAAMDFgCv6F6moKHPBuqAgc3X7/fulI0fMf7dtM7cXr25fvAq/n1/JYfTFUlOljAxzf1CQeSW9eLSAm5vk729uy8kxvwQoLDRX8y8v6Ofnm18WFH+xIJ192kF5xwIAAADAxYAr+hextm3NBeuKb1FX2ur2FVE8nL5ePfOKvo+PedX+xAlzgT6bzXx4epoL9+Xnm1fzfXzMLwNKG4JfvOhfYqLzFwvF0w7ef9+c51+ZYwEAAADgYkDQv8hFRZmP/v3NoH/66vYVVTyc3s/vr1vzhYebV/iPHzfrc3MzF/E7edJ8PmiQdOxY+VMHGjQwQ/2ZKjLtoKxjAQAAAMDqCPqQZIbvql79Lh5On5f31635CgqkyEgzzGdnm/P0DcP8MiAhwVy87/Bh8zZ427b9tXBffr55Nb5Bg7+mDpypeNpBVY4FAAAAAKsj6OOcnTmcvnVracsWcz6+r68Z7u12M9i3aGHeiq94JEFVpw5U17QDAAAAALAagj6qxZnD6Tt2lH7/3XycOmVebW/ZUpo0yQzpxc5l6kB1TDsAAAAAAKsh6KNalDacPjzcXIH/yBHz51GjnEP+6c5l6sC5HAsAAAAAVkPQR7UpbTh9QIDUowfD6QEAAADgfCHoo1oxnB4AAAAAahdBHzWC4fQAAAAAUDvcarsBAAAAAACg+hD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6AMAAAAAYCEEfQAAAAAALISgDwAAAACAhRD0AQAAAACwEII+AAAAAAAW4lHbDbhQGYYhScrJyanlltQOu92uvLw85eTkyNPTs7abA5RAH4Wro4/C1dFH4eroo3B1NdFHi/NncR4tC0G/io4dOyZJioyMrOWWAAAAAAAuJseOHVNwcHCZ+23G2b4KQKmKiop08OBBBQYGymaz1XZzzrucnBxFRkbq999/V1BQUG03ByiBPgpXRx+Fq6OPwtXRR+HqaqKPGoahY8eOKSIiQm5uZc/E54p+Fbm5uenSSy+t7WbUuqCgIP6wwqXRR+Hq6KNwdfRRuDr6KFxddffR8q7kF2MxPgAAAAAALISgDwAAAACAhRD0USXe3t564okn5O3tXdtNAUpFH4Wro4/C1dFH4eroo3B1tdlHWYwPAAAAAAAL4Yo+AAAAAAAWQtAHAAAAAMBCCPoAAAAAAFgIQR8AAAAAAAsh6KPCfv31V914442qV6+egoKC1KFDB61cubLcY4YPHy6bzeb0SEpKOk8txsWmKn3UMAxNmDBBDRs2lK+vrxITE7Vz587z1GJcTFatWlXi72HxY+PGjWUe16VLlxLl//Wvf53HluNiUdU+mp+fr7vvvlt169ZVQECA+vfvr0OHDp3HluNi8/nnn6tdu3by9fVVnTp11KdPn3LL8/+jON8q20dr4v9HCfqosOuuu06nTp3SihUrtHnzZl1xxRW67rrrlJGRUe5xSUlJSk9Pdzzee++989RiXGyq0kefeeYZzZo1S6+++qo2bNggf39/9ezZU/n5+eex5bgYJCQkOP0tTE9P1+23367o6Gi1bdu23GPvuOMOp+OeeeaZ89RqXEyq2kfHjh2rTz/9VB988IFWr16tgwcPql+/fuex5biYfPTRR7r11ls1YsQIbd26VWvXrtXgwYPPehz/P4rzpSp9tEb+f9QAKuCPP/4wJBlff/21Y1tOTo4hyUhJSSnzuGHDhhk33njjeWghLnZV6aNFRUVGeHi48eyzzzq2ZWVlGd7e3sZ77/2/du4+pqnr/wP4+7byECeuQCktWBAIA53Oh0ZIN6LOgcgepIFkWWY2WDBEs02SzQgOMx3OZdni5hLDHiIhuJhvjFFkIQuEQrp1hKFuAbaEdiuDQF1lhq0qDEYd5/eHsaE/pxawRdv3K+kf3J57zuc0n9ycD+fe+z+fx0zBbXJyUsTExIiqqqo7ttuwYYMoKyvzT1BE03iTo06nU4SEhIhTp065j/X29goAoqOjwx9hUhBxuVwiPj5eHDt2bEbncT1K/jKbHPXVepQ7+uSV6OhopKWl4fjx4xgbG8P169fx+eefQ6VSQafT3fFck8kElUqFtLQ07Ny5EyMjI36KmoLJbHK0v78fly5dQnZ2tvvYww8/jMzMTHR0dPgrdApSX331FUZGRvDKK6/cte2JEyegVCqxYsUK7N27F3///bcfIqRg502O/vDDD3C5XB7X0fT0dCQkJPA6Svfcjz/+iIsXL0Imk2HNmjXQaDTIy8vDzz//fNdzuR4lf5hNjvpqPbpg1mdSUJEkCUajEQaDAREREZDJZFCpVGhqakJkZORtz9uyZQsKCgqQlJSEvr4+vPXWW8jLy0NHRwfkcrkfZ0CBbjY5evOW/tjYWI/jsbGxd30khWiuampqkJubiyVLltyx3YsvvojExETExcWhp6cH5eXlsFqtOHPmjJ8ipWDlTY5eunQJoaGhUCgUHsd5HSVf+O233wAABw4cwEcffYSlS5fi8OHD2LhxI3755RdERUX953lcj5K/zCZHfbUe5Y5+kKuoqLjti3dufiwWC4QQePXVV6FSqWA2m3Hu3DkYDAY899xzcDgct+3/hRdewNatW7Fy5UoYDAY0Njbi/PnzMJlM/pskPdB8naNEc+Vtjk5nt9vR3NyMkpKSu/ZfWlqK3NxcrFy5Etu2bcPx48dRX1+Pvr4+X02JAoyvc5RorrzN0ampKQBAZWUlCgsLodPpUFtbC0mScOrUqdv2z/UozZWvc9QXuKMf5N58800UFxffsU1ycjLa2trQ2NiIv/76C4sXLwYAVFdXo6WlBXV1daioqPBqvOTkZCiVSthsNjz11FNzDZ+CgC9zVK1WAwCGh4eh0Wjcx4eHh7F69ep7NgcKbN7m6HS1tbWIjo7G1q1bZzxeZmYmAMBmsyElJWXG51Pw8WWOqtVqTE5Owul0euzqDw8Pu6+xRHfjbY7e/Mf98uXL3cfDwsKQnJyMwcFBr8fjepRmypc56qv1KAv9IBcTE4OYmJi7trv5PKhM5nkTiEwmc//nyht2ux0jIyMeSUx0J77M0aSkJKjVarS2trovpFevXkVnZyd27tw5t8ApaHibozcJIVBbW4uXX34ZISEhMx6vq6sLAHgdJa/5Mkd1Oh1CQkLQ2tqKwsJCAIDVasXg4CD0ev2c4qbg4W2O6nQ6hIWFwWq1IisrCwDgcrkwMDCAxMREr8fjepRmypc56rP16Kxf40dB5fLlyyI6OloUFBSIrq4uYbVaxe7du0VISIjo6upyt0tLSxNnzpwRQghx7do1sXv3btHR0SH6+/uF0WgUa9euFampqWJiYmK+pkIBajY5KoQQ77//vlAoFKKhoUH09PSI/Px8kZSUJMbHx+djGhQEjEajACB6e3tv+c5ut4u0tDTR2dkphBDCZrOJqqoqceHCBdHf3y8aGhpEcnKyWL9+vb/DpiAykxwVQogdO3aIhIQE0dbWJi5cuCD0er3Q6/X+DJmCSFlZmYiPjxfNzc3CYrGIkpISoVKpxJ9//uluw/UozaeZ5qgQvlmPckefvKJUKtHU1ITKykps2rQJLpcLjz76KBoaGrBq1Sp3O6vViitXrgAA5HI5enp6UFdXB6fTibi4OGzevBkHDx5EWFjYfE2FAtRschQA9uzZg7GxMZSWlsLpdCIrKwtNTU0IDw+fj2lQEKipqcHjjz+O9PT0W75zuVywWq3uO1RCQ0NhNBpx5MgRjI2NQavVorCwEPv27fN32BREZpKjAPDxxx9DJpOhsLAQ//zzD3Jzc1FdXe3PkCmIfPjhh1iwYAFeeukljI+PIzMzE21tbR4v3uV6lObTTHMU8M16VBJCiDnNhIiIiIiIiIjuG3zrPhEREREREVEAYaFPREREREREFEBY6BMREREREREFEBb6RERERERERAGEhT4RERERERFRAGGhT0RERERERBRAWOgTERERERERBRAW+kREREREREQBhIU+ERER+c3AwAAkSUJXV5dP+pckCWfPnvVJ30RERA8KFvpERERBpLi4GAaDYd7G12q1cDgcWLFiBQDAZDJBkiQ4nc55i4mIiCjQLJjvAIiIiCh4yOVyqNXq+Q6DiIgooHFHn4iIiAAA33zzDTIyMhAWFgaNRoOKigpcv37d/f3GjRuxa9cu7NmzB1FRUVCr1Thw4IBHHxaLBVlZWQgPD8fy5cthNBo9bqeffuv+wMAAnnzySQBAZGQkJElCcXExAGDp0qU4cuSIR9+rV6/2GO/XX3/F+vXr3WO1tLTcMqehoSE8//zzUCgUiIqKQn5+PgYGBub6UxEREd3XWOgTERERLl68iKeffhrr1q1Dd3c3Pv30U9TU1ODdd9/1aFdXV4eHHnoInZ2d+OCDD1BVVeUusP/9918YDAYsXLgQnZ2d+OKLL1BZWXnbMbVaLU6fPg0AsFqtcDgc+OSTT7yKd2pqCgUFBQgNDUVnZyc+++wzlJeXe7RxuVzIzc1FREQEzGYz2tvbsWjRImzZsgWTk5Mz+XmIiIgeKLx1n4iIiFBdXQ2tVoujR49CkiSkp6fj999/R3l5Od5++23IZDf2Bh577DHs378fAJCamoqjR4+itbUVOTk5aGlpQV9fH0wmk/v2/EOHDiEnJ+c/x5TL5YiKigIAqFQqKBQKr+M1Go2wWCxobm5GXFwcAOC9995DXl6eu83JkycxNTWFY8eOQZIkAEBtbS0UCgVMJhM2b948sx+JiIjoAcFCn4iIiNDb2wu9Xu8uiAHgiSeewOjoKOx2OxISEgDcKPSn02g0+OOPPwDc2JXXarUez+BnZGT4LF6tVusu8gFAr9d7tOnu7obNZkNERITH8YmJCfT19fkkLiIiovsBC30iIiLyWkhIiMffkiRhamrqno8jk8kghPA45nK5ZtTH6OgodDodTpw4cct3MTExc4qPiIjofsZCn4iIiLBs2TKcPn0aQgj3rn57ezsiIiKwZMkSr/pIS0vD0NAQhoeHERsbCwA4f/78Hc8JDQ0FcOP5/uliYmLgcDjcf1+9ehX9/f0e8Q4NDcHhcECj0QAAvv/+e48+1q5di5MnT0KlUmHx4sVezYGIiCgQ8GV8REREQebKlSvo6ury+JSWlmJoaAivv/46LBYLGhoasH//frzxxhvu5/PvJicnBykpKSgqKkJPTw/a29uxb98+APB4JGC6xMRESJKExsZGXL58GaOjowCATZs24csvv4TZbMZPP/2EoqIiyOVy93nZ2dl45JFHUFRUhO7ubpjN5lte/Ldt2zYolUrk5+fDbDajv78fJpMJu3btgt1un81PR0RE9EBgoU9ERBRkTCYT1qxZ4/E5ePAgvv76a5w7dw6rVq3Cjh07UFJS4i7UvSGXy3H27FmMjo5i3bp12L59u7v4Dg8P/89z4uPj8c4776CiogKxsbF47bXXAAB79+7Fhg0b8Oyzz+KZZ56BwWBASkqK+zyZTIb6+nqMj48jIyMD27dvx6FDhzz6XrhwIb799lskJCSgoKAAy5YtQ0lJCSYmJrjDT0REAU0S//8BOCIiIqJ7pL29HVlZWbDZbB6FOhEREfkOC30iIiK6Z+rr67Fo0SKkpqbCZrOhrKwMkZGR+O677+Y7NCIioqDBl/ERERHRPXPt2jWUl5djcHAQSqUS2dnZOHz48HyHRUREFFS4o09EREREREQUQPgyPiIiIiIiIqIAwkKfiIiIiIiIKICw0CciIiIiIiIKICz0iYiIiIiIiAIIC30iIiIiIiKiAMJCn4iIiIiIiCiAsNAnIiIiIiIiCiAs9ImIiIiIiIgCyP8Bj2Q8yXLjzBcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the existing EV chargers\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "# Plot existing EV chargers\n",
        "plt.scatter(ev_chargers_combined['longitude'], ev_chargers_combined['latitude'],\n",
        "            c='blue', alpha=0.5, s=50, label='Existing EV Chargers')\n",
        "\n",
        "# Highlight potential new charging stations (limited infrastructure)\n",
        "plt.scatter(limited_infrastructure['longitude'], limited_infrastructure['latitude'],\n",
        "            c='red', alpha=0.7, s=100, label='Potential New Stations')\n",
        "\n",
        "# Customize the plot\n",
        "plt.title('Potential New EV Charging Station Areas')\n",
        "plt.xlabel('Longitude')\n",
        "plt.ylabel('Latitude')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "population_irl = pd.read_csv('/content/drive/MyDrive/SSE/Population_IRL.csv')\n",
        "ev_ownership_sex = pd.read_csv('/content/drive/MyDrive/SSE/EV_Ownership.csv')\n",
        "ev_ownership_county = pd.read_csv('/content/drive/MyDrive/SSE/EV_ownership_county.csv')\n",
        "\n",
        "# Standardize column names\n",
        "ev_ownership_county.columns = ev_ownership_county.columns.str.lower().str.replace(' ', '_')\n",
        "ev_ownership_sex.columns = ev_ownership_sex.columns.str.lower().str.replace(' ', '_')\n",
        "population_irl.columns = population_irl.columns.str.lower().str.replace(' ', '_')\n",
        "\n",
        "# Rename tlist(a1) to year for clarity\n",
        "ev_ownership_county.rename(columns={'tlist(a1)': 'year'}, inplace=True)\n",
        "\n",
        "# Check for and remove duplicates in EV ownership data\n",
        "ev_ownership_county = ev_ownership_county.drop_duplicates()\n",
        "\n",
        "# Check for and remove duplicates in EV ownership data\n",
        "ev_ownership_sex = ev_ownership_sex.drop_duplicates()\n",
        "\n"
      ],
      "metadata": {
        "id": "X9w0IskDPTa3"
      },
      "execution_count": 181,
      "outputs": []
    },
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              "                 statistic_label  year   sex      age_group unit  value\n",
              "0  Owns an Electric Vehicle (EV)  2021  Male  18 - 29 years    %    1.2\n",
              "1  Owns an Electric Vehicle (EV)  2021  Male  30 - 44 years    %    4.1\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ev_ownership_sex",
              "summary": "{\n  \"name\": \"ev_ownership_sex\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"statistic_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Does not own an Electric Vehicle (EV)\",\n          \"Owns an Electric Vehicle (EV)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2021,\n        \"max\": 2021,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"30 - 44 years\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.14269360316704,\n        \"min\": 1.2,\n        \"max\": 98.8,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
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      "source": [
        "ev_ownership_county.head(3)"
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      "metadata": {
        "id": "MWX7QPBEPTSW",
        "outputId": "e1fae1b1-d720-4545-c23c-a1e544363961",
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      "execution_count": 183,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  statistic                statistic_label  year  year  c03657v04399  \\\n",
              "0  NTA44C01  Owns an Electric Vehicle (EV)  2019  2019            10   \n",
              "1  NTA44C01  Owns an Electric Vehicle (EV)  2019  2019            20   \n",
              "2  NTA44C01  Owns an Electric Vehicle (EV)  2019  2019            30   \n",
              "\n",
              "                         region unit  value  \n",
              "0                         State    %    1.5  \n",
              "1                        Dublin    %    1.6  \n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  .colab-df-quickchart {\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"ev_ownership_county\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"statistic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NTA44C02\",\n          \"NTA44C01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"statistic_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Does not own an Electric Vehicle (EV)\",\n          \"Owns an Electric Vehicle (EV)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2021,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2021,\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2021,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2021,\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c03657v04399\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 10,\n        \"max\": 30,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"State\",\n          \"Dublin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.61171049441672,\n        \"min\": 1.4,\n        \"max\": 98.6,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          95.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "population_irl.head()"
      ],
      "metadata": {
        "id": "UyxGrSNDPTP_",
        "outputId": "4c5a9a4b-41af-4ba5-e742-77a197d5df20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             statistic_label  censusyear  county         sex    unit    value\n",
              "0  Population at Each Census        1841   State  Both sexes  Number  6528799\n",
              "1  Population at Each Census        1841   State        Male  Number  3222485\n",
              "2  Population at Each Census        1841   State      Female  Number  3306314\n",
              "3  Population at Each Census        1841  Carlow  Both sexes  Number    86228\n",
              "4  Population at Each Census        1841  Carlow        Male  Number    42428"
            ],
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              "      <th>3</th>\n",
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              "      <td>1841</td>\n",
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              "      <td>Population at Each Census</td>\n",
              "      <td>1841</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "type": "dataframe",
              "variable_name": "population_irl",
              "summary": "{\n  \"name\": \"population_irl\",\n  \"rows\": 2106,\n  \"fields\": [\n    {\n      \"column\": \"statistic_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Population at Each Census\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"censusyear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53,\n        \"min\": 1841,\n        \"max\": 2022,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          1926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"county\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Meath\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Both sexes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Number\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 514012,\n        \"min\": 12013,\n        \"max\": 6528799,\n        \"num_unique_values\": 2095,\n        \"samples\": [\n          217277\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the DataFrame and create a copy to avoid SettingWithCopyWarning\n",
        "population_irl = population_irl[population_irl['censusyear'] == 2022].copy()\n",
        "\n",
        "#Drop unnecessary columns\n",
        "population_irl.drop(columns=['unit', 'statistic_label', 'censusyear'], inplace=True)\n",
        "\n",
        "#Rename the 'value' column to 'population'\n",
        "population_irl.rename(columns={'value': 'population'}, inplace=True)\n",
        "\n",
        "population_irl.head(2)"
      ],
      "metadata": {
        "id": "Vnhu4AcHSYP8",
        "outputId": "ecf59853-42cf-46f3-e1a7-5debe3407cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     county         sex  population\n",
              "2025  State  Both sexes     5149139\n",
              "2026  State        Male     2544549"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "population_irl",
              "summary": "{\n  \"name\": \"population_irl\",\n  \"rows\": 81,\n  \"fields\": [\n    {\n      \"column\": \"county\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Meath\",\n          \"Clare\",\n          \"Offaly\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Both sexes\",\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 698060,\n        \"min\": 17553,\n        \"max\": 5149139,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          96221,\n          5149139,\n          68907\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ev_ownership_question(ev_ownership):\n",
        "    ev_ownership['statistic_label'] = ev_ownership['statistic_label'].apply(\n",
        "        lambda x: '1' if x == 'Owns an Electric Vehicle (EV)' else '0' if x == 'Does not own an Electric Vehicle (EV)' else x\n",
        "    )\n",
        "    return ev_ownership"
      ],
      "metadata": {
        "id": "0E2EcXv1SYNc"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ev_ownership_county = ev_ownership_question(ev_ownership_county)"
      ],
      "metadata": {
        "id": "SWvhpK58SYLE"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ev_ownership_sex = ev_ownership_question(ev_ownership_sex)"
      ],
      "metadata": {
        "id": "13gxfdp0SYIm"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ev_ownership_county.head(3)"
      ],
      "metadata": {
        "id": "4mOZYB4hSYCp",
        "outputId": "8868a65f-b388-4e96-f4ca-9731c3e28816",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  statistic statistic_label  year  year  c03657v04399  \\\n",
              "0  NTA44C01               1  2019  2019            10   \n",
              "1  NTA44C01               1  2019  2019            20   \n",
              "2  NTA44C01               1  2019  2019            30   \n",
              "\n",
              "                         region unit  value  \n",
              "0                         State    %    1.5  \n",
              "1                        Dublin    %    1.6  \n",
              "2  All regions excluding Dublin    %    1.4  "
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-ba32be93-1f4f-406a-a127-2b0bbdb83e35\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba32be93-1f4f-406a-a127-2b0bbdb83e35')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ba32be93-1f4f-406a-a127-2b0bbdb83e35 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ev_ownership_county",
              "summary": "{\n  \"name\": \"ev_ownership_county\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"statistic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NTA44C02\",\n          \"NTA44C01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"statistic_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2021,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2021,\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2021,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2021,\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c03657v04399\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 10,\n        \"max\": 30,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"State\",\n          \"Dublin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.61171049441672,\n        \"min\": 1.4,\n        \"max\": 98.6,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          95.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ev_ownership_sex.head(3)"
      ],
      "metadata": {
        "id": "KnUMdy9bTDJd",
        "outputId": "e5c58587-45b8-46f5-c8f3-21293ddbea70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  statistic_label  year   sex      age_group unit  value\n",
              "0               1  2021  Male  18 - 29 years    %    1.2\n",
              "1               1  2021  Male  30 - 44 years    %    4.1\n",
              "2               1  2021  Male  45 - 59 years    %    4.5"
            ],
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              "      <th>statistic_label</th>\n",
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              "      <td>1</td>\n",
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              "      <td>18 - 29 years</td>\n",
              "      <td>%</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "      <td>Male</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>%</td>\n",
              "      <td>4.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "      <td>Male</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>%</td>\n",
              "      <td>4.5</td>\n",
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              "  </tbody>\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ccc318a4-07b3-4911-b00e-6230d569c346 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ccc318a4-07b3-4911-b00e-6230d569c346');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
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              "</div>\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ev_ownership_sex",
              "summary": "{\n  \"name\": \"ev_ownership_sex\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"statistic_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2021,\n        \"max\": 2021,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"30 - 44 years\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.14269360316704,\n        \"min\": 1.2,\n        \"max\": 98.8,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rename the column to appropriate names\n",
        "ev_ownership_sex.rename(columns={'statistic_label': 'owns_an_ev', 'value': 'percent_ownership'}, inplace=True)\n",
        "\n",
        "# Drop the columns that are no longer needed\n",
        "ev_ownership_sex.drop(columns=['unit'], inplace=True)\n",
        "\n",
        "ev_ownership_sex.head(2)\n"
      ],
      "metadata": {
        "id": "sgefq_BvTDG_",
        "outputId": "a534fb8e-7d63-46f1-d1d5-594b49d02d4f",
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          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  owns_an_ev  year   sex      age_group  percent_ownership\n",
              "0          1  2021  Male  18 - 29 years                1.2\n",
              "1          1  2021  Male  30 - 44 years                4.1"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ev_ownership_sex",
              "summary": "{\n  \"name\": \"ev_ownership_sex\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"owns_an_ev\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2021,\n        \"max\": 2021,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"30 - 44 years\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_ownership\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.14269360316704,\n        \"min\": 1.2,\n        \"max\": 98.8,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rename the column to appropriate names\n",
        "ev_ownership_county.rename(columns={'statistic_label': 'owns_an_ev', 'value': 'percent_ownership', 'region': 'county'}, inplace=True)\n",
        "\n",
        "# Drop the columns that are no longer needed\n",
        "ev_ownership_county.drop(columns=['unit','statistic','c03657v04399'], inplace=True)\n",
        "\n",
        "ev_ownership_county.head(2)"
      ],
      "metadata": {
        "id": "IytjBEBlTQGo",
        "outputId": "33e82d26-8aba-4c1e-d1bf-522d4d05e8c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  owns_an_ev  year  year  county  percent_ownership\n",
              "0          1  2019  2019   State                1.5\n",
              "1          1  2019  2019  Dublin                1.6"
            ],
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              "        const element = document.querySelector('#df-9bcc08bf-09f1-4eea-a50a-4f6abadd8d0e');\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ba869ac8-3777-4c04-a3fe-929773ccd3ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ev_ownership_county",
              "summary": "{\n  \"name\": \"ev_ownership_county\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"owns_an_ev\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2021,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2021,\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2021,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2021,\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"county\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"State\",\n          \"Dublin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_ownership\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.61171049441672,\n        \"min\": 1.4,\n        \"max\": 98.6,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          95.1,\n          96.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicate 'year' column in ev_ownership_county\n",
        "ev_ownership_county = ev_ownership_county.loc[:, ~ev_ownership_county.columns.duplicated()]\n",
        "\n",
        "# Merging the ev_ownership dataframes\n",
        "ev_ownership_merged = pd.merge(\n",
        "    ev_ownership_sex,\n",
        "    ev_ownership_county,\n",
        "    on=['owns_an_ev', 'year'],\n",
        "    suffixes=('_individual', '_county')\n",
        ")\n",
        "\n",
        "# Display the merged DataFrame\n",
        "ev_ownership_merged.head(10)"
      ],
      "metadata": {
        "id": "q9XTZvuzTQA-",
        "outputId": "013e5687-ae7f-407d-86b4-7d813ebaa41a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  owns_an_ev  year   sex      age_group  percent_ownership_individual  \\\n",
              "0          1  2021  Male  18 - 29 years                           1.2   \n",
              "1          1  2021  Male  18 - 29 years                           1.2   \n",
              "2          1  2021  Male  18 - 29 years                           1.2   \n",
              "3          1  2021  Male  30 - 44 years                           4.1   \n",
              "4          1  2021  Male  30 - 44 years                           4.1   \n",
              "5          1  2021  Male  30 - 44 years                           4.1   \n",
              "6          1  2021  Male  45 - 59 years                           4.5   \n",
              "7          1  2021  Male  45 - 59 years                           4.5   \n",
              "8          1  2021  Male  45 - 59 years                           4.5   \n",
              "9          1  2021  Male  60 - 74 years                           4.9   \n",
              "\n",
              "                         county  percent_ownership_county  \n",
              "0                         State                       3.2  \n",
              "1                        Dublin                       4.9  \n",
              "2  All regions excluding Dublin                       2.5  \n",
              "3                         State                       3.2  \n",
              "4                        Dublin                       4.9  \n",
              "5  All regions excluding Dublin                       2.5  \n",
              "6                         State                       3.2  \n",
              "7                        Dublin                       4.9  \n",
              "8  All regions excluding Dublin                       2.5  \n",
              "9                         State                       3.2  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>owns_an_ev</th>\n",
              "      <th>year</th>\n",
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              "      <td>18 - 29 years</td>\n",
              "      <td>1.2</td>\n",
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              "      <td>2.5</td>\n",
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              "      <td>2021</td>\n",
              "      <td>Male</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>4.5</td>\n",
              "      <td>All regions excluding Dublin</td>\n",
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              "      <td>3.2</td>\n",
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              "\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      buttonEl.style.display =\n",
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              "        const dataTable =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-22e3b1a7-5d27-401c-a55f-48f0b9ca9b7d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ev_ownership_merged",
              "summary": "{\n  \"name\": \"ev_ownership_merged\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"owns_an_ev\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2021,\n        \"max\": 2021,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"30 - 44 years\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_ownership_individual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47.31968093076949,\n        \"min\": 1.2,\n        \"max\": 98.8,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"county\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"State\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_ownership_county\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.86981609753711,\n        \"min\": 2.5,\n        \"max\": 97.5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create fact table for EV chargers\n",
        "fact_ev_chargers = ev_chargers_combined[['location', 'number_of_chargers', 'county']]"
      ],
      "metadata": {
        "id": "mTl9UmTNTDAy"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create dimension table for EV ownership and add county column\n",
        "dim_ev_ownership = ev_ownership_merged.copy()"
      ],
      "metadata": {
        "id": "yWYnPySOYqYz"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Aggregate ownership data by county\n",
        "ev_ownership_aggregated = dim_ev_ownership.groupby(['county','sex','age_group','owns_an_ev'])['percent_ownership_individual'].mean().reset_index()\n"
      ],
      "metadata": {
        "id": "A1Iw99PiYt0k"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ev_ownership_aggregated.rename(columns={'percent_ownership_individual': 'percent_ownership_county'}, inplace=True)"
      ],
      "metadata": {
        "id": "obZtHq0bYzUo"
      },
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ev_ownership_aggregated.head(100)"
      ],
      "metadata": {
        "id": "YA58U8b4Y7Ul",
        "outputId": "dace2050-0099-4374-d1f5-3891cfdcc0e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          county     sex          age_group owns_an_ev  \\\n",
              "0   All regions excluding Dublin  Female      18 - 29 years          0   \n",
              "1   All regions excluding Dublin  Female      18 - 29 years          1   \n",
              "2   All regions excluding Dublin  Female      30 - 44 years          0   \n",
              "3   All regions excluding Dublin  Female      30 - 44 years          1   \n",
              "4   All regions excluding Dublin  Female      45 - 59 years          0   \n",
              "5   All regions excluding Dublin  Female      45 - 59 years          1   \n",
              "6   All regions excluding Dublin  Female      60 - 74 years          0   \n",
              "7   All regions excluding Dublin  Female      60 - 74 years          1   \n",
              "8   All regions excluding Dublin  Female  75 years and over          0   \n",
              "9   All regions excluding Dublin  Female  75 years and over          1   \n",
              "10  All regions excluding Dublin    Male      18 - 29 years          0   \n",
              "11  All regions excluding Dublin    Male      18 - 29 years          1   \n",
              "12  All regions excluding Dublin    Male      30 - 44 years          0   \n",
              "13  All regions excluding Dublin    Male      30 - 44 years          1   \n",
              "14  All regions excluding Dublin    Male      45 - 59 years          0   \n",
              "15  All regions excluding Dublin    Male      45 - 59 years          1   \n",
              "16  All regions excluding Dublin    Male      60 - 74 years          0   \n",
              "17  All regions excluding Dublin    Male      60 - 74 years          1   \n",
              "18  All regions excluding Dublin    Male  75 years and over          0   \n",
              "19  All regions excluding Dublin    Male  75 years and over          1   \n",
              "20                        Dublin  Female      18 - 29 years          0   \n",
              "21                        Dublin  Female      18 - 29 years          1   \n",
              "22                        Dublin  Female      30 - 44 years          0   \n",
              "23                        Dublin  Female      30 - 44 years          1   \n",
              "24                        Dublin  Female      45 - 59 years          0   \n",
              "25                        Dublin  Female      45 - 59 years          1   \n",
              "26                        Dublin  Female      60 - 74 years          0   \n",
              "27                        Dublin  Female      60 - 74 years          1   \n",
              "28                        Dublin  Female  75 years and over          0   \n",
              "29                        Dublin  Female  75 years and over          1   \n",
              "30                        Dublin    Male      18 - 29 years          0   \n",
              "31                        Dublin    Male      18 - 29 years          1   \n",
              "32                        Dublin    Male      30 - 44 years          0   \n",
              "33                        Dublin    Male      30 - 44 years          1   \n",
              "34                        Dublin    Male      45 - 59 years          0   \n",
              "35                        Dublin    Male      45 - 59 years          1   \n",
              "36                        Dublin    Male      60 - 74 years          0   \n",
              "37                        Dublin    Male      60 - 74 years          1   \n",
              "38                        Dublin    Male  75 years and over          0   \n",
              "39                        Dublin    Male  75 years and over          1   \n",
              "40                         State  Female      18 - 29 years          0   \n",
              "41                         State  Female      18 - 29 years          1   \n",
              "42                         State  Female      30 - 44 years          0   \n",
              "43                         State  Female      30 - 44 years          1   \n",
              "44                         State  Female      45 - 59 years          0   \n",
              "45                         State  Female      45 - 59 years          1   \n",
              "46                         State  Female      60 - 74 years          0   \n",
              "47                         State  Female      60 - 74 years          1   \n",
              "48                         State  Female  75 years and over          0   \n",
              "49                         State  Female  75 years and over          1   \n",
              "50                         State    Male      18 - 29 years          0   \n",
              "51                         State    Male      18 - 29 years          1   \n",
              "52                         State    Male      30 - 44 years          0   \n",
              "53                         State    Male      30 - 44 years          1   \n",
              "54                         State    Male      45 - 59 years          0   \n",
              "55                         State    Male      45 - 59 years          1   \n",
              "56                         State    Male      60 - 74 years          0   \n",
              "57                         State    Male      60 - 74 years          1   \n",
              "58                         State    Male  75 years and over          0   \n",
              "59                         State    Male  75 years and over          1   \n",
              "\n",
              "    percent_ownership_county  \n",
              "0                       98.2  \n",
              "1                        1.8  \n",
              "2                       96.8  \n",
              "3                        3.2  \n",
              "4                       97.2  \n",
              "5                        2.8  \n",
              "6                       96.7  \n",
              "7                        3.3  \n",
              "8                       98.0  \n",
              "9                        2.0  \n",
              "10                      98.8  \n",
              "11                       1.2  \n",
              "12                      95.9  \n",
              "13                       4.1  \n",
              "14                      95.5  \n",
              "15                       4.5  \n",
              "16                      95.1  \n",
              "17                       4.9  \n",
              "18                      96.9  \n",
              "19                       3.1  \n",
              "20                      98.2  \n",
              "21                       1.8  \n",
              "22                      96.8  \n",
              "23                       3.2  \n",
              "24                      97.2  \n",
              "25                       2.8  \n",
              "26                      96.7  \n",
              "27                       3.3  \n",
              "28                      98.0  \n",
              "29                       2.0  \n",
              "30                      98.8  \n",
              "31                       1.2  \n",
              "32                      95.9  \n",
              "33                       4.1  \n",
              "34                      95.5  \n",
              "35                       4.5  \n",
              "36                      95.1  \n",
              "37                       4.9  \n",
              "38                      96.9  \n",
              "39                       3.1  \n",
              "40                      98.2  \n",
              "41                       1.8  \n",
              "42                      96.8  \n",
              "43                       3.2  \n",
              "44                      97.2  \n",
              "45                       2.8  \n",
              "46                      96.7  \n",
              "47                       3.3  \n",
              "48                      98.0  \n",
              "49                       2.0  \n",
              "50                      98.8  \n",
              "51                       1.2  \n",
              "52                      95.9  \n",
              "53                       4.1  \n",
              "54                      95.5  \n",
              "55                       4.5  \n",
              "56                      95.1  \n",
              "57                       4.9  \n",
              "58                      96.9  \n",
              "59                       3.1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1dfac34d-0e21-45a8-a31e-717f96ecd744\" class=\"colab-df-container\">\n",
              "    <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>county</th>\n",
              "      <th>sex</th>\n",
              "      <th>age_group</th>\n",
              "      <th>owns_an_ev</th>\n",
              "      <th>percent_ownership_county</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>0</td>\n",
              "      <td>98.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>0</td>\n",
              "      <td>96.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>1</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>0</td>\n",
              "      <td>97.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>1</td>\n",
              "      <td>2.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>0</td>\n",
              "      <td>96.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>1</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>0</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>0</td>\n",
              "      <td>98.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>0</td>\n",
              "      <td>96.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>All regions excluding Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>1</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>0</td>\n",
              "      <td>98.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>0</td>\n",
              "      <td>96.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>1</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>0</td>\n",
              "      <td>97.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>1</td>\n",
              "      <td>2.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>0</td>\n",
              "      <td>96.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>1</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>0</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Female</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>0</td>\n",
              "      <td>98.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>0</td>\n",
              "      <td>96.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Dublin</td>\n",
              "      <td>Male</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>1</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>0</td>\n",
              "      <td>98.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>0</td>\n",
              "      <td>96.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>1</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>0</td>\n",
              "      <td>97.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>1</td>\n",
              "      <td>2.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>0</td>\n",
              "      <td>96.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>1</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>0</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>State</td>\n",
              "      <td>Female</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>0</td>\n",
              "      <td>98.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>18 - 29 years</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>30 - 44 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>45 - 59 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>0</td>\n",
              "      <td>95.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>60 - 74 years</td>\n",
              "      <td>1</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>0</td>\n",
              "      <td>96.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>75 years and over</td>\n",
              "      <td>1</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1dfac34d-0e21-45a8-a31e-717f96ecd744');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-bab037a3-628e-4e69-a43b-a3861c1fa78f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ev_ownership_aggregated",
              "summary": "{\n  \"name\": \"ev_ownership_aggregated\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"county\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"All regions excluding Dublin\",\n          \"Dublin\",\n          \"State\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"30 - 44 years\",\n          \"75 years and over\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"owns_an_ev\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_ownership_county\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47.31968093076949,\n        \"min\": 1.2,\n        \"max\": 98.8,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          98.2,\n          4.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "source": [
        "#Ownership by county\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "ev_ownership_aggregated.groupby('county').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "id": "BtsF1OVLZIlB",
        "outputId": "3c035196-a038-45fe-b069-643e936cf09d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 211,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "7FAdfoViDv9R",
        "outputId": "9a523c6f-1630-43e8-c081-cbf67d48fdc0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     county         sex  population\n",
              "2025  State  Both sexes     5149139\n",
              "2026  State        Male     2544549"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8d8e057d-a5ee-444f-a2c0-d1fb35394ef6\" class=\"colab-df-container\">\n",
              "    <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>county</th>\n",
              "      <th>sex</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2025</th>\n",
              "      <td>State</td>\n",
              "      <td>Both sexes</td>\n",
              "      <td>5149139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2026</th>\n",
              "      <td>State</td>\n",
              "      <td>Male</td>\n",
              "      <td>2544549</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d8e057d-a5ee-444f-a2c0-d1fb35394ef6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8d8e057d-a5ee-444f-a2c0-d1fb35394ef6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8d8e057d-a5ee-444f-a2c0-d1fb35394ef6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-438b74f0-5eef-4a29-bc89-676f68ba25e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "population_irl",
              "summary": "{\n  \"name\": \"population_irl\",\n  \"rows\": 81,\n  \"fields\": [\n    {\n      \"column\": \"county\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Meath\",\n          \"Clare\",\n          \"Offaly\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Both sexes\",\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 698060,\n        \"min\": 17553,\n        \"max\": 5149139,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          96221,\n          5149139,\n          68907\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 211
        }
      ],
      "source": [
        "#Working with the population data\n",
        "\n",
        "population_irl.head(2)\n",
        "\n",
        "# Filter data to only include relevant columns and rows (e.g., total population for each county and year)\n",
        "population_filtered = population_irl[(population_irl['population'] == 'Population at Each Census') &\n",
        "                                     (population_irl['sex'] == 'Both sexes')]\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bmqzE9WfaFjn"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNunWtG+gHu87W4UT8K/cgy",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}