Stage 3 - Semi_Finals - Safety Index.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Is1th3sXsLqf"
      },
      "source": [
        "# World Data League 2022"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JeP6xkRIBJco"
      },
      "source": [
        "## Notebook Submission Template\n",
        "\n",
        "This notebook is one of the mandatory deliverables when you submit your solution. Its structure follows the WDL evaluation criteria and it has dedicated cells where you should add information. Make sure your code is readable as it will be the only technical support the jury will have to evaluate your work. Make sure to list all the datasets used besides the ones provided.\n",
        "\n",
        "Instructions:\n",
        "1. 🧱 Create a separate copy of this template and **do not change** the predefined structure [x]\n",
        "2. 👥 Fill in the Authors section with the name of each team member [x]\n",
        "3. 💻 Develop your code - make sure to add comments and save all the output you want the jury to see. Your code **must be** runnable!\n",
        "4. 📄 Fill in all the text sections\n",
        "5. 🗑️ Remove this section (‘Notebook Submission Template’) and any instructions inside other sections\n",
        "6. 📥 Export as HTML and make sure all the visualisations are visible.\n",
        "7. ⬆️ Upload the .ipynb file to the submission platform and make sure that all the visualisations are visible and everything (text, images, ..) in all deliverables renders correctly.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94NlB4NxwLra"
      },
      "source": [
        "## 🎯 Challenge\n",
        "*Predicting a safety score for women in Costa Rica*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVBl_rp5gAdK"
      },
      "source": [
        "## Team: Data Crocodiles\n",
        "## 👥 Authors\n",
        "* Diogo Pessoa\n",
        "* Fábio Lopes\n",
        "* Francisco Valente\n",
        "* Júlio Medeiros"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8rCpNajszur"
      },
      "source": [
        "## 💻 Development\n",
        "Start coding here! 🐱‍🏍\n",
        "\n",
        "Create the necessary subsections (e.g. EDA, different experiments, etc..) and markdown cells to include descriptions of your work where you see fit. Comment your code. \n",
        "\n",
        "All new subsections must start with three hash characters.\n",
        "\n",
        "Pro-tip 1: Don't forget to make the jury's life easier. Remove any unnecessary prints before submitting the work. Hide any long output cells (from training a model for example). For each subsection, have a quick introduction (justifying what you are about to do) and conclusion (results you got from what you did). \n",
        "\n",
        "Pro-tip 2: Have many similiar graphs which all tell the same story? Add them to the appendix and show only a couple of examples, with the mention that all the others are in the appendix."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Requirements\n",
        "\n",
        "Installing packages required to develop the project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "afB4W0KnutpV"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: matplotlib==3.4.3 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (3.4.3)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.4.3) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.4.3) (0.10.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.4.3) (7.2.0)\n",
            "Requirement already satisfied: numpy>=1.16 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.4.3) (1.18.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.4.3) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.4.3) (2.8.1)\n",
            "Requirement already satisfied: six in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib==3.4.3) (1.15.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: shap in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (0.40.0)\n",
            "Requirement already satisfied: numba in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from shap) (0.50.1)\n",
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            "Requirement already satisfied: numpy in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from shap) (1.18.5)\n",
            "Requirement already satisfied: pandas in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from shap) (1.0.5)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from shap) (4.47.0)\n",
            "Requirement already satisfied: scipy in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from shap) (1.5.0)\n",
            "Requirement already satisfied: cloudpickle in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from shap) (1.5.0)\n",
            "Requirement already satisfied: scikit-learn in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from shap) (0.23.1)\n",
            "Requirement already satisfied: llvmlite<0.34,>=0.33.0.dev0 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from numba->shap) (0.33.0+1.g022ab0f)\n",
            "Requirement already satisfied: setuptools in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from numba->shap) (49.2.0.post20200714)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from packaging>20.9->shap) (2.4.7)\n",
            "Requirement already satisfied: pytz>=2017.2 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from pandas->shap) (2020.1)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from pandas->shap) (2.8.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from scikit-learn->shap) (2.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from scikit-learn->shap) (0.16.0)\n",
            "Requirement already satisfied: six>=1.5 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.6.1->pandas->shap) (1.15.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: holidays in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (0.13)\n",
            "Requirement already satisfied: python-dateutil in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from holidays) (2.8.1)\n",
            "Requirement already satisfied: hijri-converter in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from holidays) (2.2.3)\n",
            "Requirement already satisfied: korean-lunar-calendar in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from holidays) (0.2.1)\n",
            "Requirement already satisfied: convertdate>=2.3.0 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from holidays) (2.4.0)\n",
            "Requirement already satisfied: six>=1.5 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from python-dateutil->holidays) (1.15.0)\n",
            "Requirement already satisfied: pymeeus<=1,>=0.3.13 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from convertdate>=2.3.0->holidays) (0.5.11)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: suntime in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (1.2.5)\n",
            "Requirement already satisfied: python-dateutil in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from suntime) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /Users/fabiolopes/opt/anaconda3/lib/python3.8/site-packages (from python-dateutil->suntime) (1.15.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install matplotlib==3.4.3\n",
        "%pip install shap\n",
        "%pip install holidays\n",
        "%pip install suntime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Import Packages\n",
        "\n",
        "Importing packages required to develop the project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import io\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import requests\n",
        "import datetime\n",
        "import zipfile\n",
        "from suntime import Sun\n",
        "import holidays\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Custom Functions\n",
        "\n",
        "Functions developed by us to perform our analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "LQ90XveiBN6A"
      },
      "outputs": [],
      "source": [
        "def download_data(url,folder_name):\n",
        "    '''\n",
        "    Verify whether data is available and if not, download it.\n",
        "    Input: \n",
        "        url: Dataset URL\n",
        "        folder_name: Directory to store data\n",
        "    '''\n",
        "    if os.path.isdir(folder_name)==False:\n",
        "        print(\"Downloading Data...\")\n",
        "        r = requests.get(url, stream=True)\n",
        "        z = zipfile.ZipFile(io.BytesIO(r.content))\n",
        "        z.extractall()\n",
        "        print(\"Data successfully downloaded!\")\n",
        "    else:\n",
        "        print(\"Data was already downloaded.\")\n",
        "    \n",
        "    return None\n",
        "\n",
        "def get_arcgis_data(arcgis_folder):\n",
        "    '''\n",
        "    Obtain arcGIS data\n",
        "    Input:\n",
        "        arcgis_folder: Folder where arcGIS data are stored\n",
        "    Output:\n",
        "        arcgis_data: Dictionary with all arcGIS data\n",
        "    '''\n",
        "\n",
        "    # Python dictionary to save all arcGIS data\n",
        "    arcgis_data = {}\n",
        "\n",
        "    arcgis_data['Districts'] = pd.read_csv(f'{arcgis_folder}/arcGIS_districts.csv')\n",
        "    arcgis_data['Road Centers'] = pd.read_csv(f'{arcgis_folder}/arcGIS_centro_vias.csv')\n",
        "    arcgis_data['Water'] = pd.read_csv(f'{arcgis_folder}/arcGIS_agua.csv')\n",
        "    arcgis_data['City Center'] = pd.read_csv(f'{arcgis_folder}/arcGIS_catasto_central.csv')\n",
        "    arcgis_data['Condos Boundaries'] = pd.read_csv(f'{arcgis_folder}/arcGIS_catasto_codominios.csv')\n",
        "    arcgis_data['Cadastral Roads'] = pd.read_csv(f'{arcgis_folder}/arcGIS_catasto_vias.csv')\n",
        "    arcgis_data['Blocks Boundaries'] = pd.read_csv(f'{arcgis_folder}/arcGIS_catasto_bloque.csv')\n",
        "    arcgis_data['Estates Boundaries'] = pd.read_csv(f'{arcgis_folder}/arcGIS_catasto_predios.csv')\n",
        "    arcgis_data['Roads'] = pd.read_csv(f'{arcgis_folder}/arcGIS_vialidad.csv')\n",
        "    arcgis_data['Setbacks'] = pd.read_csv(f'{arcgis_folder}/arcGIS_retiros.csv')\n",
        "    arcgis_data['Land Use Zones'] = pd.read_csv(f'{arcgis_folder}/arcGIS_zonas.csv')\n",
        "    arcgis_data['Susceptible Zones'] = pd.read_csv(f'{arcgis_folder}/arcGIS_susceptibilidad.csv')\n",
        "    arcgis_data['Heritage'] = pd.read_csv(f'{arcgis_folder}/arcGIS_patrimonio.csv')\n",
        "    arcgis_data['Public Areas'] = pd.read_csv(f'{arcgis_folder}/arcGIS_areas_public_use.csv')\n",
        "    arcgis_data['Green Areas'] = pd.read_csv(f'{arcgis_folder}/arcGIS_green_areas.csv')\n",
        "    arcgis_data['Security'] = pd.read_csv(f'{arcgis_folder}/arcGIS_security.csv')\n",
        "    arcgis_data['Churches'] = pd.read_csv(f'{arcgis_folder}/arcGIS_churches.csv')\n",
        "    arcgis_data['Schools'] = pd.read_csv(f'{arcgis_folder}/arcGIS_education.csv')\n",
        "    arcgis_data['Culture'] = pd.read_csv(f'{arcgis_folder}/arcGIS_entertainment_culture.csv')\n",
        "    arcgis_data['Governmental'] = pd.read_csv(f'{arcgis_folder}/arcGIS_governmental.csv')\n",
        "    arcgis_data['Communal Areas'] = pd.read_csv(f'{arcgis_folder}/arcGIS_communal_areas.csv')\n",
        "    arcgis_data['Commercial Centers'] = pd.read_csv(f'{arcgis_folder}/arcGIS_commercial_centers.csv')\n",
        "    arcgis_data['Housing Complexes'] = pd.read_csv(f'{arcgis_folder}/arcGIS_housing_complexes.csv')\n",
        "    arcgis_data['Healthcare'] = pd.read_csv(f'{arcgis_folder}/arcGIS_healthcare.csv')\n",
        "\n",
        "    return arcgis_data\n",
        "\n",
        "def merge_date_time(dataframe):\n",
        "    '''\n",
        "    Merge Date column with Time column\n",
        "    Input:\n",
        "        dataframe: Dataset with Date column and Time column in string format\n",
        "    Output:\n",
        "        dataframe: Dataset with Start Datetime and End Datetime columns in datetime format\n",
        "    '''\n",
        "    # Convert date to datetime\n",
        "    dataframe['Date'] = pd.to_datetime(dataframe['Date'],format=\"%d.%m.%y\")\n",
        "\n",
        "    # Create year, month, and day columns in crime records data\n",
        "    dataframe['Year'] = dataframe['Date'].dt.year\n",
        "    dataframe['Month'] = dataframe['Date'].dt.month\n",
        "    dataframe['Day'] = dataframe['Date'].dt.day\n",
        "\n",
        "    # Get start crime time and end crime time\n",
        "    dataframe[['Start Time','End Time']] = dataframe['Time'].str.split(' - ',expand=True)\n",
        "    dataframe[['Start Hour','Start Minute','Start Second']] = dataframe['Start Time'].str.split(':',expand=True)\n",
        "    dataframe[['End Hour','End Minute','End Second']] = dataframe['End Time'].str.split(':',expand=True)\n",
        "\n",
        "    # Convert columns from string to numeric\n",
        "    dataframe[\"Start Hour\"] = pd.to_numeric(dataframe[\"Start Hour\"])\n",
        "    dataframe[\"Start Minute\"] = pd.to_numeric(dataframe[\"Start Minute\"])\n",
        "    dataframe[\"Start Second\"] = pd.to_numeric(dataframe[\"Start Second\"])\n",
        "    dataframe[\"End Hour\"] = pd.to_numeric(dataframe[\"End Hour\"])\n",
        "    dataframe[\"End Minute\"] = pd.to_numeric(dataframe[\"End Minute\"])\n",
        "    dataframe[\"End Second\"] = pd.to_numeric(dataframe[\"End Second\"])\n",
        "\n",
        "    # Obtain start and end datetimes dict(year=df.Y, month=df.M, day=df.D)\n",
        "    dataframe['Start Datetime'] = pd.to_datetime(dict(year = dataframe['Year'], month = dataframe['Month'],\n",
        "                                                                day = dataframe['Day'], hour = dataframe['Start Hour'],\n",
        "                                                                minute = dataframe['Start Minute'], second = dataframe['Start Second']))\n",
        "\n",
        "    dataframe['End Datetime'] = pd.to_datetime(dict(year = dataframe['Year'], month = dataframe['Month'],\n",
        "                                                            day = dataframe['Day'], hour = dataframe['End Hour'],\n",
        "                                                            minute = dataframe['End Minute'], second = dataframe['End Second']))\n",
        "\n",
        "    # Remove Date column\n",
        "    #dataframe.drop(['Date', 'Year', 'Month', 'Day', 'Start Hour', 'Start Minute', 'Start Second',\n",
        "    #                        'End Hour', 'End Minute', 'End Second', 'Time', 'Start Time', 'End Time'], inplace=True, axis=1)\n",
        "    \n",
        "    return dataframe\n",
        "\n",
        "def get_date_features(dataframe):\n",
        "    '''\n",
        "    Extract features from the datetimes\n",
        "    Input:\n",
        "        dataframe: Initial Dataset\n",
        "    Output:\n",
        "        dataframe: Dataset with features extracted from datetimes\n",
        "    '''\n",
        "    # Get number of samples\n",
        "    number_samples = dataframe.shape[0]\n",
        "    \n",
        "    # Get weekday name for each index returned by weekday function provided by pandas library\n",
        "    dataframe_weekdays = []\n",
        "    dataframe_weekdays_binary = np.zeros((number_samples,7))\n",
        "    \n",
        "    # Get day stages (morning, afternoon, night)\n",
        "    dataframe_day_stage = []\n",
        "    dataframe_day_stage_binary = np.zeros((number_samples,3))\n",
        "    sun = Sun(9.934739,-84.087502)\n",
        "    \n",
        "    # Get season (winter, spring, summer, autumn)\n",
        "    Y = 2000 # dummy leap year to allow input X-02-29 (leap day)\n",
        "    seasons = [(0, (datetime.date(Y,  1,  1),  datetime.date(Y,  3, 20))),\n",
        "               (1, (datetime.date(Y,  3, 21),  datetime.date(Y,  6, 20))),\n",
        "               (2, (datetime.date(Y,  6, 21),  datetime.date(Y,  9, 22))),\n",
        "               (3, (datetime.date(Y,  9, 23),  datetime.date(Y, 12, 20))),\n",
        "               (0, (datetime.date(Y, 12, 21),  datetime.date(Y, 12, 31)))]\n",
        "    \n",
        "    dataframe_season = []\n",
        "    dataframe_season_binary = np.zeros((number_samples,4))\n",
        "\n",
        "    for index,date in enumerate(dataframe['Start Datetime'].values):\n",
        "        \n",
        "        # Get the day of the week\n",
        "        date = pd.to_datetime(date)\n",
        "        date_weekday = date.weekday()\n",
        "        dataframe_weekdays.append(date_weekday)\n",
        "        dataframe_weekdays_binary[index,date_weekday] = 1\n",
        "        \n",
        "        # Get sunrise and sunset times (These values come with UTC time zone. No correction needed.)\n",
        "        sunrise_time = sun.get_sunrise_time(date)\n",
        "        sunrise_hour = sunrise_time.hour\n",
        "        sunrise_minute = sunrise_time.minute\n",
        "        sunset_time = sun.get_sunset_time(date)\n",
        "        sunset_hour = sunset_time.hour\n",
        "        sunset_minute = sunset_time.minute\n",
        "        \n",
        "        # Get date hours\n",
        "        date_hour = date.hour\n",
        "        \n",
        "        # 11 UTC is 12 CET\n",
        "        if date_hour<11 and date_hour>sunrise_hour:\n",
        "            day_stage = 0 # Morning\n",
        "                \n",
        "        elif date_hour>=11 and date_hour<=sunset_hour:\n",
        "            day_stage = 1 # Afternoon\n",
        "                \n",
        "        else:\n",
        "            day_stage = 2 # Night\n",
        "            \n",
        "        dataframe_day_stage.append(day_stage)\n",
        "        dataframe_day_stage_binary[index,day_stage] = 1\n",
        "            \n",
        "        date = date.replace(year=Y)\n",
        "        season =  next(season for season, (start, end) in seasons if start <= date <= end)\n",
        "        \n",
        "        dataframe_season.append(season)\n",
        "        dataframe_season_binary[index,season] = 1\n",
        "        \n",
        "\n",
        "    # Store both information in the main dataset\n",
        "    dataframe['Weekday'] = dataframe_weekdays\n",
        "    dataframe['Monday'] = dataframe_weekdays_binary[:,0]\n",
        "    dataframe['Tuesday'] = dataframe_weekdays_binary[:,1]\n",
        "    dataframe['Wednesday'] = dataframe_weekdays_binary[:,2]\n",
        "    dataframe['Thursday'] = dataframe_weekdays_binary[:,3]\n",
        "    dataframe['Friday'] = dataframe_weekdays_binary[:,4]\n",
        "    dataframe['Saturday'] = dataframe_weekdays_binary[:,5]\n",
        "    dataframe['Sunday'] = dataframe_weekdays_binary[:,6]\n",
        "    dataframe['Day Stage'] = dataframe_day_stage\n",
        "    dataframe['Morning'] = dataframe_day_stage_binary[:,0]\n",
        "    dataframe['Afternoon'] = dataframe_day_stage_binary[:,1]\n",
        "    dataframe['Night'] = dataframe_day_stage_binary[:,2]\n",
        "    dataframe['Season'] = dataframe_season\n",
        "    dataframe['Winter'] = dataframe_season_binary[:,0]\n",
        "    dataframe['Spring'] = dataframe_season_binary[:,1]\n",
        "    dataframe['Summer'] = dataframe_season_binary[:,2]\n",
        "    dataframe['Autumn'] = dataframe_season_binary[:,3]\n",
        "    \n",
        "    return dataframe        \n",
        "\n",
        "def get_district_crime_count_by_trimester(dataframe, district_names):\n",
        "    '''\n",
        "    Compute number of crimes per district by trimester\n",
        "    \n",
        "    Input:\n",
        "        dataframe: Dataset\n",
        "        district_names: District names\n",
        "    Output:\n",
        "        district_crime_count_by_trimester: Dictionary with crime count by trimester for each district\n",
        "    '''\n",
        "\n",
        "    district_crime_count_by_trimester = {district_name:[] for district_name in district_names}\n",
        "    for district_name in district_names:\n",
        "        data = dataframe[dataframe['District']==district_name]\n",
        "        for year in range(2010,2023):\n",
        "            trimester_values = []\n",
        "            for month in range(1,13):\n",
        "                month_crime_count = data[((data['Year']==year) & (data['Month']==month))]['Crime'].count()\n",
        "                trimester_values.append(month_crime_count)\n",
        "                if len(trimester_values) == 3:\n",
        "                    trimester_count = sum(trimester_values)\n",
        "                    district_crime_count_by_trimester[district_name].append(trimester_count)\n",
        "                    trimester_values = []\n",
        "    \n",
        "    return district_crime_count_by_trimester\n",
        "\n",
        "\n",
        "def get_district_crime_count_by_year(dataframe, district_names):\n",
        "    '''\n",
        "    Compute number of crimes per district by year\n",
        "    \n",
        "    Input:\n",
        "        dataframe: Dataset\n",
        "        district_names: District names\n",
        "    Output:\n",
        "        district_crime_count_by_year: Dictionary with crime count by year for each district\n",
        "    '''\n",
        "\n",
        "    district_crime_count_by_year = {district_name:[] for district_name in district_names}\n",
        "    for district_name in district_names:\n",
        "        data = dataframe[dataframe['District']==district_name]\n",
        "        for year in range(2010,2023):\n",
        "            year_crime_count = data[data['Year']==year]['Crime'].count()\n",
        "            district_crime_count_by_year[district_name].append(year_crime_count)\n",
        "    \n",
        "    return district_crime_count_by_year                                                               \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data Loading\n",
        "\n",
        "Download datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data was already downloaded.\n"
          ]
        }
      ],
      "source": [
        "# Datasets URL\n",
        "url = \"https://wdl-data.fra1.digitaloceanspaces.com/urbanalytica/urbanalytica_datasets.zip\"\n",
        "\n",
        "# Dataset folder\n",
        "folder_name = 'datasets'\n",
        "\n",
        "# Download datasets if necessary\n",
        "download_data(url,folder_name)\n",
        "\n",
        "# Street harassement dataset  (Street Harassement reported in 2021 in Costa Rica)\n",
        "street_harassement_dataset = pd.read_csv('datasets/costa_rica_street_harassement_english.csv',index_col=False)\n",
        "\n",
        "# Crime data (Crime records for Costa Rica from 2010 to 2022)\n",
        "crime_data_dataset = pd.read_csv('datasets/costa_rica_crime_data_english/costa_rica_crimes_english.csv',index_col=False)\n",
        "crime_data_dataset = crime_data_dataset.iloc[:,1:]\n",
        "\n",
        "# District data (Demographic, crime-rate data and Social Development Index 2017 at the district level)\n",
        "district_dataset = pd.read_csv('datasets/district_data.csv',index_col=False)\n",
        "district_dataset = district_dataset.iloc[:,1:]\n",
        "\n",
        "# arcGIS data (Costa Rica - San Jose (Capital City))\n",
        "arcgis_data = get_arcgis_data('datasets/arcGIS_zoning_data_san_jose')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data Preprocessing\n",
        "\n",
        "Prepare datasets for exploratory data analysis (EDA). We only selected data from San Jose canton since it contains the capital city of Costa Rica and urban districts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Split {Provincia, Canton, Distrito} column into Province, Canton, and District columns\n",
        "district_dataset[['Province','Canton','District']] = district_dataset['{Provincia, Canton, Distrito}'].str.extract('{\"(?P<Province>[\\s\\S]+)\", \"(?P<Canton>[\\s\\S]+)\", \"(?P<District>[\\s\\S]+)\"}',expand=True)\n",
        "district_dataset.drop(['{Provincia, Canton, Distrito}'], inplace=True, axis=1)\n",
        "\n",
        "# District data from San Jose Canton\n",
        "san_jose_district_dataset = district_dataset[district_dataset['Canton']=='SAN JOSE']\n",
        "san_jose_district_dataset['Porcentaje de población de 65 años y más'] = san_jose_district_dataset['Porcentaje de población de 65 años y más'].astype(float)\n",
        "\n",
        "# Street harassment data from San Jose Province\n",
        "san_jose_street_harassement_dataset = street_harassement_dataset[street_harassement_dataset['canton']=='San Jose']\n",
        "\n",
        "# Crime records data from San Jose Province\n",
        "san_jose_crime_data = crime_data_dataset[crime_data_dataset['Canton']=='SAN JOSE']\n",
        "\n",
        "# Merge Date and Time columns\n",
        "san_jose_crime_data = merge_date_time(san_jose_crime_data)\n",
        "\n",
        "# Obtain datetime features\n",
        "san_jose_crime_data = get_date_features(san_jose_crime_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Exploratory Data Analysis\n",
        "\n",
        "Analyse demographic data by district in San Jose canton"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Económica</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>77.02</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68.48</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>65.22</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57.68</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>50.73</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47.08</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>46.26</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>42.98</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41.70</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40.26</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>39.92</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Económica  Province    Canton                   District\n",
              "7       77.02  SAN JOSE  SAN JOSE               MATA REDONDA\n",
              "0       68.48  SAN JOSE  SAN JOSE                     CARMEN\n",
              "5       65.22  SAN JOSE  SAN JOSE  SAN FRANCISCO DE DOS RIOS\n",
              "4       57.68  SAN JOSE  SAN JOSE                     ZAPOTE\n",
              "8       50.73  SAN JOSE  SAN JOSE                      PAVAS\n",
              "3       47.08  SAN JOSE  SAN JOSE                   CATEDRAL\n",
              "10      46.26  SAN JOSE  SAN JOSE              SAN SEBASTIAN\n",
              "9       42.98  SAN JOSE  SAN JOSE                    HATILLO\n",
              "1       41.70  SAN JOSE  SAN JOSE                     MERCED\n",
              "2       40.26  SAN JOSE  SAN JOSE                   HOSPITAL\n",
              "6       39.92  SAN JOSE  SAN JOSE                      URUCA"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Participación electoral</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>51.82</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>49.94</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.73</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43.00</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>42.80</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41.11</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>39.99</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.93</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>39.80</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>39.71</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36.12</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Participación electoral  Province    Canton                   District\n",
              "5                     51.82  SAN JOSE  SAN JOSE  SAN FRANCISCO DE DOS RIOS\n",
              "7                     49.94  SAN JOSE  SAN JOSE               MATA REDONDA\n",
              "4                     49.73  SAN JOSE  SAN JOSE                     ZAPOTE\n",
              "3                     43.00  SAN JOSE  SAN JOSE                   CATEDRAL\n",
              "10                    42.80  SAN JOSE  SAN JOSE              SAN SEBASTIAN\n",
              "0                     41.11  SAN JOSE  SAN JOSE                     CARMEN\n",
              "9                     39.99  SAN JOSE  SAN JOSE                    HATILLO\n",
              "1                     39.93  SAN JOSE  SAN JOSE                     MERCED\n",
              "6                     39.80  SAN JOSE  SAN JOSE                      URUCA\n",
              "8                     39.71  SAN JOSE  SAN JOSE                      PAVAS\n",
              "2                     36.12  SAN JOSE  SAN JOSE                   HOSPITAL"
            ]
          },
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Salud</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>91.35</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>83.26</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>83.23</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82.95</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>82.77</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>82.56</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>78.15</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>76.35</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>74.53</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>73.96</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>72.17</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Salud  Province    Canton                   District\n",
              "7   91.35  SAN JOSE  SAN JOSE               MATA REDONDA\n",
              "3   83.26  SAN JOSE  SAN JOSE                   CATEDRAL\n",
              "8   83.23  SAN JOSE  SAN JOSE                      PAVAS\n",
              "4   82.95  SAN JOSE  SAN JOSE                     ZAPOTE\n",
              "10  82.77  SAN JOSE  SAN JOSE              SAN SEBASTIAN\n",
              "5   82.56  SAN JOSE  SAN JOSE  SAN FRANCISCO DE DOS RIOS\n",
              "0   78.15  SAN JOSE  SAN JOSE                     CARMEN\n",
              "1   76.35  SAN JOSE  SAN JOSE                     MERCED\n",
              "2   74.53  SAN JOSE  SAN JOSE                   HOSPITAL\n",
              "6   73.96  SAN JOSE  SAN JOSE                      URUCA\n",
              "9   72.17  SAN JOSE  SAN JOSE                    HATILLO"
            ]
          },
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Educación</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90.67</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>74.56</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>73.79</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>73.68</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>73.15</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68.90</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>66.98</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>64.52</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63.08</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>62.57</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>58.98</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Educación  Province    Canton                   District\n",
              "0       90.67  SAN JOSE  SAN JOSE                     CARMEN\n",
              "7       74.56  SAN JOSE  SAN JOSE               MATA REDONDA\n",
              "3       73.79  SAN JOSE  SAN JOSE                   CATEDRAL\n",
              "10      73.68  SAN JOSE  SAN JOSE              SAN SEBASTIAN\n",
              "4       73.15  SAN JOSE  SAN JOSE                     ZAPOTE\n",
              "1       68.90  SAN JOSE  SAN JOSE                     MERCED\n",
              "9       66.98  SAN JOSE  SAN JOSE                    HATILLO\n",
              "5       64.52  SAN JOSE  SAN JOSE  SAN FRANCISCO DE DOS RIOS\n",
              "2       63.08  SAN JOSE  SAN JOSE                   HOSPITAL\n",
              "8       62.57  SAN JOSE  SAN JOSE                      PAVAS\n",
              "6       58.98  SAN JOSE  SAN JOSE                      URUCA"
            ]
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Seguridad</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>92.20</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>91.90</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>90.07</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>90.06</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>87.39</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>85.89</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>85.07</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84.17</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>76.23</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70.54</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>36.94</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "    Seguridad  Province    Canton                   District\n",
              "5       92.20  SAN JOSE  SAN JOSE  SAN FRANCISCO DE DOS RIOS\n",
              "4       91.90  SAN JOSE  SAN JOSE                     ZAPOTE\n",
              "8       90.07  SAN JOSE  SAN JOSE                      PAVAS\n",
              "9       90.06  SAN JOSE  SAN JOSE                    HATILLO\n",
              "10      87.39  SAN JOSE  SAN JOSE              SAN SEBASTIAN\n",
              "7       85.89  SAN JOSE  SAN JOSE               MATA REDONDA\n",
              "6       85.07  SAN JOSE  SAN JOSE                      URUCA\n",
              "3       84.17  SAN JOSE  SAN JOSE                   CATEDRAL\n",
              "1       76.23  SAN JOSE  SAN JOSE                     MERCED\n",
              "2       70.54  SAN JOSE  SAN JOSE                   HOSPITAL\n",
              "0       36.94  SAN JOSE  SAN JOSE                     CARMEN"
            ]
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IDS 2017</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>93.16</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>82.87</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82.86</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81.16</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>76.19</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>76.17</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>73.72</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>68.71</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67.73</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>63.87</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62.50</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    IDS 2017  Province    Canton                   District\n",
              "7      93.16  SAN JOSE  SAN JOSE               MATA REDONDA\n",
              "5      82.87  SAN JOSE  SAN JOSE  SAN FRANCISCO DE DOS RIOS\n",
              "4      82.86  SAN JOSE  SAN JOSE                     ZAPOTE\n",
              "0      81.16  SAN JOSE  SAN JOSE                     CARMEN\n",
              "10     76.19  SAN JOSE  SAN JOSE              SAN SEBASTIAN\n",
              "3      76.17  SAN JOSE  SAN JOSE                   CATEDRAL\n",
              "8      73.72  SAN JOSE  SAN JOSE                      PAVAS\n",
              "9      68.71  SAN JOSE  SAN JOSE                    HATILLO\n",
              "1      67.73  SAN JOSE  SAN JOSE                     MERCED\n",
              "6      63.87  SAN JOSE  SAN JOSE                      URUCA\n",
              "2      62.50  SAN JOSE  SAN JOSE                   HOSPITAL"
            ]
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Relación hombres mujeres</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>94.528569</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>93.063162</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>92.387380</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>90.758463</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>90.326881</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>89.233470</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>88.783824</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>87.571932</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>86.995695</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>81.229562</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>77.646285</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Relación hombres mujeres  Province    Canton                   District\n",
              "2                  94.528569  SAN JOSE  SAN JOSE                   HOSPITAL\n",
              "6                  93.063162  SAN JOSE  SAN JOSE                      URUCA\n",
              "1                  92.387380  SAN JOSE  SAN JOSE                     MERCED\n",
              "10                 90.758463  SAN JOSE  SAN JOSE              SAN SEBASTIAN\n",
              "8                  90.326881  SAN JOSE  SAN JOSE                      PAVAS\n",
              "3                  89.233470  SAN JOSE  SAN JOSE                   CATEDRAL\n",
              "9                  88.783824  SAN JOSE  SAN JOSE                    HATILLO\n",
              "5                  87.571932  SAN JOSE  SAN JOSE  SAN FRANCISCO DE DOS RIOS\n",
              "4                  86.995695  SAN JOSE  SAN JOSE                     ZAPOTE\n",
              "7                  81.229562  SAN JOSE  SAN JOSE               MATA REDONDA\n",
              "0                  77.646285  SAN JOSE  SAN JOSE                     CARMEN"
            ]
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Relación dependencia demográfica</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>51.121696</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47.132931</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>46.927678</td>\n",
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              "      <th>9</th>\n",
              "      <td>46.783099</td>\n",
              "      <td>SAN JOSE</td>\n",
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              "      <th>3</th>\n",
              "      <td>45.774172</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>45.750046</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>45.586690</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45.345666</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>44.283949</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>43.186448</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>40.135913</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Relación dependencia demográfica  Province    Canton  \\\n",
              "6                          51.121696  SAN JOSE  SAN JOSE   \n",
              "2                          47.132931  SAN JOSE  SAN JOSE   \n",
              "0                          46.927678  SAN JOSE  SAN JOSE   \n",
              "9                          46.783099  SAN JOSE  SAN JOSE   \n",
              "3                          45.774172  SAN JOSE  SAN JOSE   \n",
              "8                          45.750046  SAN JOSE  SAN JOSE   \n",
              "7                          45.586690  SAN JOSE  SAN JOSE   \n",
              "1                          45.345666  SAN JOSE  SAN JOSE   \n",
              "4                          44.283949  SAN JOSE  SAN JOSE   \n",
              "10                         43.186448  SAN JOSE  SAN JOSE   \n",
              "5                          40.135913  SAN JOSE  SAN JOSE   \n",
              "\n",
              "                     District  \n",
              "6                       URUCA  \n",
              "2                    HOSPITAL  \n",
              "0                      CARMEN  \n",
              "9                     HATILLO  \n",
              "3                    CATEDRAL  \n",
              "8                       PAVAS  \n",
              "7                MATA REDONDA  \n",
              "1                      MERCED  \n",
              "4                      ZAPOTE  \n",
              "10              SAN SEBASTIAN  \n",
              "5   SAN FRANCISCO DE DOS RIOS  "
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              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>16.444124</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.440322</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.914021</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12.242070</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.373638</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10.181941</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.969813</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9.115188</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7.751037</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.427383</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
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              "    Porcentaje de población de 65 años y más  Province    Canton  \\\n",
              "0                                  21.909697  SAN JOSE  SAN JOSE   \n",
              "7                                  16.444124  SAN JOSE  SAN JOSE   \n",
              "3                                  14.440322  SAN JOSE  SAN JOSE   \n",
              "4                                  13.914021  SAN JOSE  SAN JOSE   \n",
              "5                                  12.242070  SAN JOSE  SAN JOSE   \n",
              "2                                  10.373638  SAN JOSE  SAN JOSE   \n",
              "9                                  10.181941  SAN JOSE  SAN JOSE   \n",
              "1                                   9.969813  SAN JOSE  SAN JOSE   \n",
              "10                                  9.115188  SAN JOSE  SAN JOSE   \n",
              "8                                   7.751037  SAN JOSE  SAN JOSE   \n",
              "6                                   5.427383  SAN JOSE  SAN JOSE   \n",
              "\n",
              "                     District  \n",
              "0                      CARMEN  \n",
              "7                MATA REDONDA  \n",
              "3                    CATEDRAL  \n",
              "4                      ZAPOTE  \n",
              "5   SAN FRANCISCO DE DOS RIOS  \n",
              "2                    HOSPITAL  \n",
              "9                     HATILLO  \n",
              "1                      MERCED  \n",
              "10              SAN SEBASTIAN  \n",
              "8                       PAVAS  \n",
              "6                       URUCA  "
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              "      <th>6</th>\n",
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              "      <td>SAN JOSE</td>\n",
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              "      <td>SAN JOSE</td>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>22.374594</td>\n",
              "      <td>SAN JOSE</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.338590</td>\n",
              "      <td>SAN JOSE</td>\n",
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              "      <th>2</th>\n",
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              "      <th>8</th>\n",
              "      <td>16.336994</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>12.479720</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.401306</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.508004</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6.505514</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
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              "    Porcentaje de población nacida en el extranjero  Province    Canton  \\\n",
              "6                                         33.320726  SAN JOSE  SAN JOSE   \n",
              "1                                         27.763727  SAN JOSE  SAN JOSE   \n",
              "0                                         24.537380  SAN JOSE  SAN JOSE   \n",
              "7                                         22.374594  SAN JOSE  SAN JOSE   \n",
              "3                                         20.338590  SAN JOSE  SAN JOSE   \n",
              "2                                         20.249092  SAN JOSE  SAN JOSE   \n",
              "8                                         16.336994  SAN JOSE  SAN JOSE   \n",
              "10                                        12.479720  SAN JOSE  SAN JOSE   \n",
              "5                                         10.401306  SAN JOSE  SAN JOSE   \n",
              "4                                          9.508004  SAN JOSE  SAN JOSE   \n",
              "9                                          6.505514  SAN JOSE  SAN JOSE   \n",
              "\n",
              "                     District  \n",
              "6                       URUCA  \n",
              "1                      MERCED  \n",
              "0                      CARMEN  \n",
              "7                MATA REDONDA  \n",
              "3                    CATEDRAL  \n",
              "2                    HOSPITAL  \n",
              "8                       PAVAS  \n",
              "10              SAN SEBASTIAN  \n",
              "5   SAN FRANCISCO DE DOS RIOS  \n",
              "4                      ZAPOTE  \n",
              "9                     HATILLO  "
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              "      <td>SAN JOSE</td>\n",
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              "      <th>8</th>\n",
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              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45.705552</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>45.268512</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>44.940949</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>44.264355</td>\n",
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              "      <td>HOSPITAL</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>43.330606</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>42.773424</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.947956</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40.436915</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>36.151369</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Porcentaje de personas unidas o casadas  Province    Canton  \\\n",
              "6                                 51.204307  SAN JOSE  SAN JOSE   \n",
              "8                                 47.673736  SAN JOSE  SAN JOSE   \n",
              "1                                 45.705552  SAN JOSE  SAN JOSE   \n",
              "10                                45.268512  SAN JOSE  SAN JOSE   \n",
              "5                                 44.940949  SAN JOSE  SAN JOSE   \n",
              "2                                 44.264355  SAN JOSE  SAN JOSE   \n",
              "7                                 43.330606  SAN JOSE  SAN JOSE   \n",
              "9                                 42.773424  SAN JOSE  SAN JOSE   \n",
              "4                                 41.947956  SAN JOSE  SAN JOSE   \n",
              "3                                 40.436915  SAN JOSE  SAN JOSE   \n",
              "0                                 36.151369  SAN JOSE  SAN JOSE   \n",
              "\n",
              "                     District  \n",
              "6                       URUCA  \n",
              "8                       PAVAS  \n",
              "1                      MERCED  \n",
              "10              SAN SEBASTIAN  \n",
              "5   SAN FRANCISCO DE DOS RIOS  \n",
              "2                    HOSPITAL  \n",
              "7                MATA REDONDA  \n",
              "9                     HATILLO  \n",
              "4                      ZAPOTE  \n",
              "3                    CATEDRAL  \n",
              "0                      CARMEN  "
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              "      <th>2</th>\n",
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              "      <th>9</th>\n",
              "      <td>14.913583</td>\n",
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              "      <th>3</th>\n",
              "      <td>12.755102</td>\n",
              "      <td>SAN JOSE</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11.925642</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11.776735</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.547727</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11.405727</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>11.135275</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10.989642</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9.886684</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "    Porcentaje de discapacidad  Province    Canton                   District\n",
              "0                    15.692080  SAN JOSE  SAN JOSE                     CARMEN\n",
              "2                    15.474831  SAN JOSE  SAN JOSE                   HOSPITAL\n",
              "9                    14.913583  SAN JOSE  SAN JOSE                    HATILLO\n",
              "3                    12.755102  SAN JOSE  SAN JOSE                   CATEDRAL\n",
              "8                    11.925642  SAN JOSE  SAN JOSE                      PAVAS\n",
              "7                    11.776735  SAN JOSE  SAN JOSE               MATA REDONDA\n",
              "4                    11.547727  SAN JOSE  SAN JOSE                     ZAPOTE\n",
              "1                    11.405727  SAN JOSE  SAN JOSE                     MERCED\n",
              "6                    11.135275  SAN JOSE  SAN JOSE                      URUCA\n",
              "10                   10.989642  SAN JOSE  SAN JOSE              SAN SEBASTIAN\n",
              "5                     9.886684  SAN JOSE  SAN JOSE  SAN FRANCISCO DE DOS RIOS"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Porcentaje de población no asegurada</th>\n",
              "      <th>Province</th>\n",
              "      <th>Canton</th>\n",
              "      <th>District</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>20.798664</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>URUCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.593669</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HOSPITAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.103941</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MERCED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>14.486440</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>PAVAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.169759</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CATEDRAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.286454</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>CARMEN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>12.432298</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN SEBASTIAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12.290392</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>HATILLO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11.704559</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>MATA REDONDA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.027303</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>ZAPOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9.683804</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN JOSE</td>\n",
              "      <td>SAN FRANCISCO DE DOS RIOS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Porcentaje de población no asegurada  Province    Canton  \\\n",
              "6                              20.798664  SAN JOSE  SAN JOSE   \n",
              "2                              18.593669  SAN JOSE  SAN JOSE   \n",
              "1                              18.103941  SAN JOSE  SAN JOSE   \n",
              "8                              14.486440  SAN JOSE  SAN JOSE   \n",
              "3                              14.169759  SAN JOSE  SAN JOSE   \n",
              "0                              13.286454  SAN JOSE  SAN JOSE   \n",
              "10                             12.432298  SAN JOSE  SAN JOSE   \n",
              "9                              12.290392  SAN JOSE  SAN JOSE   \n",
              "7                              11.704559  SAN JOSE  SAN JOSE   \n",
              "4                              10.027303  SAN JOSE  SAN JOSE   \n",
              "5                               9.683804  SAN JOSE  SAN JOSE   \n",
              "\n",
              "                     District  \n",
              "6                       URUCA  \n",
              "2                    HOSPITAL  \n",
              "1                      MERCED  \n",
              "8                       PAVAS  \n",
              "3                    CATEDRAL  \n",
              "0                      CARMEN  \n",
              "10              SAN SEBASTIAN  \n",
              "9                     HATILLO  \n",
              "7                MATA REDONDA  \n",
              "4                      ZAPOTE  \n",
              "5   SAN FRANCISCO DE DOS RIOS  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Economy: Residential electricity, internet access\n",
        "display(san_jose_district_dataset[['Económica','Province','Canton','District']].sort_values('Económica', ascending=False))\n",
        "\n",
        "# Citizen participation: National elections\n",
        "display(san_jose_district_dataset[['Participación electoral','Province','Canton','District']].sort_values('Participación electoral', ascending=False))\n",
        "\n",
        "# Health: Low weight in children, Mortality in children with less than 5 years old, Coverage of drinking water, births in single moms younger than 19 years old\n",
        "display(san_jose_district_dataset[['Salud','Province','Canton','District']].sort_values('Salud', ascending=False))\n",
        "\n",
        "# Education: Infrastruture in education, Special educational programmes, non-single teach schools, educational lag, high school coverage\n",
        "display(san_jose_district_dataset[['Educación','Province','Canton','District']].sort_values('Educación', ascending=False))\n",
        "\n",
        "# Security: Deaths in traffic accidents, intentional homicide\n",
        "display(san_jose_district_dataset[['Seguridad','Province','Canton','District']].sort_values('Seguridad', ascending=False))\n",
        "\n",
        "# Social Development Index calculated by the Ministry of Planing and Economic Policy of Costa Rica. It uses Education, Citizen participation, Health, Security, and Economy\n",
        "display(san_jose_district_dataset[['IDS 2017','Province','Canton','District']].sort_values('IDS 2017', ascending=False))\n",
        "\n",
        "# Ratio men to women 2011 census\n",
        "display(san_jose_district_dataset[['Relación hombres mujeres','Province','Canton','District']].sort_values('Relación hombres mujeres', ascending=False))\n",
        "\n",
        "# Ratio active to inactive population 2011 census\n",
        "display(san_jose_district_dataset[['Relación dependencia demográfica','Province','Canton','District']].sort_values('Relación dependencia demográfica', ascending=False))\n",
        "\n",
        "# Percentage of people with more than 65 years old 2011 census\n",
        "display(san_jose_district_dataset[['Porcentaje de población de 65 años y más','Province','Canton','District']].sort_values('Porcentaje de población de 65 años y más', ascending=False))\n",
        "\n",
        "# Percentage of people born abroad 2011 census\n",
        "display(san_jose_district_dataset[['Porcentaje de población nacida en el extranjero','Province','Canton','District']].sort_values('Porcentaje de población nacida en el extranjero', ascending=False))\n",
        "\n",
        "# Percentage of people in union or married 2011 census\n",
        "display(san_jose_district_dataset[['Porcentaje de personas unidas o casadas','Province','Canton','District']].sort_values('Porcentaje de personas unidas o casadas', ascending=False))\n",
        "\n",
        "# Percentage of handicaped people 2011 census\n",
        "display(san_jose_district_dataset[['Porcentaje de discapacidad','Province','Canton','District']].sort_values('Porcentaje de discapacidad', ascending=False))\n",
        "\n",
        "# Percentage of people without social security 2011 census\n",
        "display(san_jose_district_dataset[['Porcentaje de población no asegurada','Province','Canton','District']].sort_values('Porcentaje de población no asegurada', ascending=False))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Analyse street harassment by district"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>province</th>\n",
              "      <th>canton</th>\n",
              "      <th>district</th>\n",
              "      <th>public_prosecutors_office</th>\n",
              "      <th>sexual_harassement</th>\n",
              "      <th>exhibitionism</th>\n",
              "      <th>pursuit_or_cornering</th>\n",
              "      <th>production_of_audiovisual_material</th>\n",
              "      <th>sexual_harassement_law</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>Catedral</td>\n",
              "      <td>Unidad Trámite Rápido</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>Hatillo</td>\n",
              "      <td>Fiscalía Hatillo</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>Pavas</td>\n",
              "      <td>Fiscalía Pavas</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>Catedral</td>\n",
              "      <td>Fiscalía Turno Extraordinario San José</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>Catedral</td>\n",
              "      <td>Fiscalía I Circuito San José</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>San Jose</td>\n",
              "      <td>Catedral</td>\n",
              "      <td>Fiscalía Adjunta Penal Juvenil</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    province    canton  district               public_prosecutors_office  \\\n",
              "8   San Jose  San Jose  Catedral                   Unidad Trámite Rápido   \n",
              "12  San Jose  San Jose   Hatillo                        Fiscalía Hatillo   \n",
              "13  San Jose  San Jose     Pavas                          Fiscalía Pavas   \n",
              "17  San Jose  San Jose  Catedral  Fiscalía Turno Extraordinario San José   \n",
              "32  San Jose  San Jose  Catedral            Fiscalía I Circuito San José   \n",
              "54  San Jose  San Jose  Catedral          Fiscalía Adjunta Penal Juvenil   \n",
              "\n",
              "    sexual_harassement  exhibitionism  pursuit_or_cornering  \\\n",
              "8                    0              1                     5   \n",
              "12                   7              1                     0   \n",
              "13                   5              4                     0   \n",
              "17                   0              3                     2   \n",
              "32                   0              2                     1   \n",
              "54                   0              1                     0   \n",
              "\n",
              "    production_of_audiovisual_material  sexual_harassement_law  \n",
              "8                                    2                       4  \n",
              "12                                   1                       0  \n",
              "13                                   0                       0  \n",
              "17                                   3                       0  \n",
              "32                                   0                       1  \n",
              "54                                   0                       0  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Sexual harassement - Harassment can include \"sexual harassment\" or unwelcome sexual advances, requests for sexual favors, and other verbal or physical harassment of a sexual nature\n",
        "# Exhibitionism - Exhibitionism involves exposing the genitals to become sexually excited or having a strong desire to be observed by other people during sexual activity\n",
        "# Pursuit and cornering - Following the victim\n",
        "# Production of audiovisual material\n",
        "# Offences against the sexual harassement law of 2020\n",
        "display(san_jose_street_harassement_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Crime reports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {},
      "outputs": [],
      "source": [
        "san_jose_districts = ['CARMEN', 'CATEDRAL', 'HATILLO', 'HOSPITAL', 'MATA REDONDA', 'MERCED',\n",
        "                      'PAVAS',  'SAN FRANCISCO DE DOS RIOS', 'SAN SEBASTIAN', 'URUCA', 'ZAPOTE']\n",
        "                        \n",
        "san_jose_district_crime_count_by_year = get_district_crime_count_by_year(san_jose_crime_data, san_jose_districts)\n",
        "san_jose_district_crime_count_by_trimester = get_district_crime_count_by_trimester(san_jose_crime_data, san_jose_districts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "san_jose_crime_males = san_jose_crime_data[san_jose_crime_data['Gender']=='male']\n",
        "san_jose_crime_females = san_jose_crime_data[san_jose_crime_data['Gender']=='female']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate ratio of crimes by type and subtype for males and females"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ASSAULT              0.459104\n",
              "THEFT                0.293094\n",
              "VEHICLE VANDALISM    0.093807\n",
              "ROBBERY              0.078896\n",
              "VEHICLE THEFT        0.067005\n",
              "HOMICIDE             0.008094\n",
              "Name: Crime, dtype: float64"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "THEFT                0.454592\n",
              "ASSAULT              0.391579\n",
              "ROBBERY              0.074295\n",
              "VEHICLE VANDALISM    0.054431\n",
              "VEHICLE THEFT        0.023312\n",
              "HOMICIDE             0.001791\n",
              "Name: Crime, dtype: float64"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "san_jose_crime_type_males_count = san_jose_crime_males['Crime'].value_counts('mean')\n",
        "san_jose_crime_type_females_count = san_jose_crime_females['Crime'].value_counts('mean')\n",
        "\n",
        "display(san_jose_crime_type_males_count)\n",
        "display(san_jose_crime_type_females_count)\n",
        "\n",
        "san_jose_crime_subtype_males_count = san_jose_crime_males['SubtypeCrime'].value_counts('mean')\n",
        "san_jose_crime_subtype_females_count = san_jose_crime_females['SubtypeCrime'].value_counts('mean')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Number of crimes by year for males and females (Anos do COVID.....)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f971e25db80>]"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "san_jose_crime_males_count_by_year = san_jose_crime_males['Year'].value_counts().sort_index()\n",
        "san_jose_crime_females_count_by_year = san_jose_crime_females['Year'].value_counts().sort_index()\n",
        "\n",
        "plt.plot(san_jose_crime_males_count_by_year)\n",
        "plt.plot(san_jose_crime_females_count_by_year)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Number of crimes by month for males and females"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'Crime'}, xlabel='Month'>"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "san_jose_crime_males_count_by_month = san_jose_crime_males.groupby(['Year','Month']).count()['Crime'].reset_index()\n",
        "san_jose_crime_females_count_by_month = san_jose_crime_females.groupby(['Year','Month']).count()['Crime'].reset_index()\n",
        "\n",
        "san_jose_crime_males_count_by_month.boxplot(column=['Crime'],by='Month')\n",
        "san_jose_crime_females_count_by_month.boxplot(column=['Crime'],by='Month')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Number of crimes by hour for males and females"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'Crime'}, xlabel='Start Hour'>"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "san_jose_crime_males_count_by_hour = san_jose_crime_males.groupby(['Year','Month','Day','Start Hour']).count()['Crime'].reset_index()\n",
        "san_jose_crime_females_count_by_hour = san_jose_crime_females.groupby(['Year','Month','Day','Start Hour']).count()['Crime'].reset_index()\n",
        "\n",
        "san_jose_crime_males_count_by_hour.boxplot(column=['Crime'],by='Start Hour')\n",
        "san_jose_crime_females_count_by_hour.boxplot(column=['Crime'],by='Start Hour')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Number of crimes by weekday for males and females"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'Crime'}, xlabel='Weekday'>"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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/GTCnhnGZNYzPebB2pEqOyEs6H7grIv63pK0RsWuaL+Dpvul+28wH5gN0dHRMW7lyZS3iHlRvby8TJkzItYy8uQ6NtWrVKrq6ujj99NOZPHkyGzdu5JxzzqGzs5PZs2c3OryKnXT9c1xw9PhGhzEi7VAHyP9zMWvWrDURMX3AhYMNyRpgiNY44EmgI01v7bf86eH2MW3atMhbd3d37mXkzXVovOXLl8fUqVNj1KhRMXXq1Fi+fHmjQ6ra/guubXQII9YOdYjI/3MB3BmD5N9KRuO8n6xV/3iaflzSxIjYLGkisKW67yKz5jN37lzmzp1LT08PM2fObHQ4ZiNWSbKfC5QONr4GmAeclf5eXcO4CqEdznhshzqYFUFZB2gljQfeB1xRMvss4H2SHgCOTNNWgcF+brXSGY/tUAezIiirZR8RzwF79Jv3B7LROWZm1uR8uQQzswJwsjczKwAnezOzAnCyNzMrAF/10gqvmuGj4CGk1lrcsrfCG2yIqIeQWjtxsjczK4CW7Mbxz24zs8q0ZMveP7vNzCrTki17M7Nm1ozXjGrJlr2ZWTNrxp4HJ3szswJwsjczKwAnezOzAnCyNzMrACd7M7MCKPdOVbtKulzS/ZLWSzpc0u6SbpL0QPq7W97BmplZdcpt2X8XuD4i3gwcAqwHFgKrIuJAYFWaNjOzJjRsspe0C3AE0AUQES9FxFbgWGBZWm0ZMCefEM3MbKTKadlPBp4AfizpbknnpRuQd0TE5rTOY0BHXkGamdnIlHO5hDHA24FTIuJ2Sd+lX5dNRISkAU8BkzQfmA/Q0dFBT0/PyCIuQz3KyJvr0DzaoR6uQ/NoVD3KSfabgE0RcXuavpws2T8uaWJEbJY0Edgy0MYRsRRYCjB9+vSYOXPmyKMeyvXXkXsZeXMdmkc71MN1aB4NrMew3TgR8RjwiKQ3pVmzgXXANcC8NG8ecHUuEZqZ2YiVe9XLU4CLJY0DHgQ+SfZFcamkTuBh4Ph8QjQzs5EqK9lHxD3A9AEWza5pNGZmlgufQWtmVgBO9mZmBeBkb2ZWAE72ZmYF4GRvZlYATvZmZgXgZG9mVgBO9mZmBeBkb2ZWAE72ZmYF4GRvZlYATvZmZgXgZG9mVgBO9mZmBeBkb2ZWAE72ZmYFUNbNSyQ9BDwLvAxsi4jpknYHLgEmAQ8Bx0fE0/mEaWZmI1HubQkBZkXEkyXTC4FVEXGWpIVpekFNozOztnTIV2/kmRf+XNE2kxZeV9H6u+w4ll9/5aiKtmlnlST7/o4FZqbny4AenOzNrAzPvPBnHjrrg2Wv39PTw8yZMysqo9Ivh3ZXbp99ADdKWiNpfprXERGb0/PHgI6aR2dmZjVRbst+RkQ8Kum1wE2S7i9dGBEhKQbaMH05zAfo6Oigp6dnJPGWpR5l5M11aB7tUI9mrEMlMfX29lZVh1avdy2Vlewj4tH0d4ukK4FDgcclTYyIzZImAlsG2XYpsBRg+vTpUelPsYpdf13FP/eajuvQPNqhHs1YhwpjqqYbpx3qXUvDduNIGi9p577nwFHAWuAaYF5abR5wdV5BmpnZyJTTsu8ArpTUt/7yiLhe0h3ApZI6gYeB4/ML08zMRmLYZB8RDwKHDDD/D8DsPIIyM7PaGsnQSzNrgLzHqHt8entysjdrMXmPUff49Pbka+OYmRWAk72ZWQE42ZuZFYCTvZlZATjZm5kVQFOPxqlmiBl4mJmZWX9NnewrHWIGHmZmZjYQd+OYmRVAU7fsrXn4zkJmrc3J3sriOwuZtTZ345iZFYCTvZlZAbgbpw58lcLm4KG8VmRO9nXgqxQ2Bw/ltSIruxtH0mhJd0u6Nk1PlnS7pA2SLpE0Lr8wzcxsJCrpsz8VWF8yfTZwbkQcADwNdNYyMDMzq52ykr2kfYEPAuelaQHvBS5PqywD5uQQn5mZ1UC5LfvvAP8C/CVN7wFsjYhtaXoTsE9tQzMzs1oZ9gCtpGOALRGxRtLMSguQNB+YD9DR0UFPT09F21e6fm9vb+5lVKOSMopah0rLqIb/n2q//2q1w/9TNRoWU0QM+QC+QdZyfwh4DHgeuBh4EhiT1jkcuGG4fU2bNi0qsf+CaytaPyKiu7s79zIqVWkZRaxDNWXUY/9FfC/aoQ7VlFEPeccE3BmD5N9hu3Ei4vMRsW9ETAI+BvwsIk4EuoHj0mrzgKtr+B1kZmY1NJIzaBcAp0naQNaH31WbkMzMrNYqOqkqInqAnvT8QeDQ2odkZkPZecpC3rJsYWUbLatk/5ANvrOhtNoZ2T6D1qzFPLv+LJ+R3QRa7YxsXwjNzKwAnOzNzArAyd7MrADcZ29mdZf3QeasDPCB5lc42ZtZ3eV9kBl8oLk/d+OYmRWAk72ZWQE42ZuZFYCTvZlZATT1AdqqjtiDTw3PgUdPmLW2pk72lR6xB58anhePnjBrbe7GMTMrgKZu2ZvVkrsFrcic7K0w3C1oReZkXwe+/riZNZqTfR34+uNm1mjDHqCVtIOkX0n6taT7JH01zZ8s6XZJGyRdImlc/uGamVk1ymnZ/wl4b0T0ShoLrJb0b8BpwLkRsVLSD4FO4Ac5xmpmScW/5q6v7FZ41n6GTfYREUBvmhybHgG8FzghzV8GnIGTvVnuKj3IPGnhdRVvY+2nrD57SaOBNcABwPeB3wJbI2JbWmUTsM8g284H5gN0dHTQ09NTUYCVrt/b25t7GdWopIyi1qHSMqrRLv9PlWr1mPz/VAMRUfYD2BXoBmYAG0rm7wesHW77adOmRSX2X3BtRetHRHR3d+deRqUqLaOIdaimjHrsvxnfi0q1Q0z+fyoPcGcMkn8rOoM2IramZH84sKukvl8G+wKP1uLLx8zMam/YbhxJewF/joitknYE3gecTZb0jwNWAvOAq/MM1MzaS54HmcEHmvsrp89+IrAs9duPAi6NiGslrQNWSvoacDfQlWOcZtZGfJC5/soZjfPvwNsGmP8gcGgeQZWq6oQhDzMzs5y12rWWmvoM2mq+yd0CMLN6aLVrLfkSx2ZmBdDULXtrLj6gZta6nOytLD6gZtba3I1jZlYAbtlboXh0lxWVk70Vhkd3WZG5G8fMrACc7M3MCsDdOHXim02YWSM52deBhy2aWaO5G8fMrACc7M3MCsDJ3sysAJzszcwKwMnezKwAhk32kvaT1C1pnaT7JJ2a5u8u6SZJD6S/u+UfrpmZVaOclv024J8i4iDgncA/SjoIWAisiogDgVVp2szMmtCwyT4iNkfEXen5s8B6YB/gWF65wdYyYE5OMZqZ2QhVdFKVpElk96O9HeiIiM1p0WNAxyDbzAfmA3R0dNDT01NtrGWrRxl5cx2aRzvUw3XIR6Ux9fb2VrxNzeodEWU9gAnAGuAjaXprv+VPD7ePadOmRd72X3Bt7mXkzXVoHu1QD9chH9XE1N3dnWsZwJ0xSP4tazSOpLHAT4CLI+KKNPtxSRPT8onAltp8/ZiZWa0N240jSUAXsD4ivl2y6BpgHnBW+nt1LhGamTWpVroZTjl99u8GPgHcK+meNO8LZEn+UkmdwMPA8TWLysysybXazXCGTfYRsRrQIItn1zYcMzPLg8+gNTMrACd7M7MCcLI3MysAJ3szswJwsjczKwAnezOzAnCyNzMrACd7M7MCcLI3MysAJ3szswJwsjczKwAnezOzAnCyNzMrACd7M7MCqOgetGb9Zfe2GWTZ2QPPz+6e1jyGqgO0Tj3MhuKWvY3IYPe77O7uHup+xk1lsDhbrR5mQxk22Us6X9IWSWtL5u0u6SZJD6S/u+UbppmZjUQ5LfsLgKP7zVsIrIqIA4FVadrMzJrUsMk+Im4Gnuo3+1hgWXq+DJhT27DMzKyWqj1A2xERm9Pzx4COwVaUNB+YD9DR0UFPT0+VRZavHmXUwqxZswZdNthBwe7u7pyiqa3e3t6WeR+G0i71aJU6VPOZgNb5XEDj3osRj8aJiJA06NGqiFgKLAWYPn16zJw5c6RFDu3668i9jBoZ7CBfT09Py9RhMO1QB2iTevgz0Twa+F5UOxrncUkTAdLfLbULyczMaq3alv01wDzgrPT36ppFVAaPizZ7tXY458HyU87QyxXArcCbJG2S1EmW5N8n6QHgyDRdNx4XbfZq/kzYUIZt2UfE3EEWza5xLGZmlhOfQWtmVgBO9mZmBeALoZmZ1VgzHix3y97MrMaa8WC5k72ZWQE42ZuZFYCTvZlZATjZm5kVgJO9mVkBONmbmRWAk72ZWQE42ZuZFYDqeeU7SU8AD+dczJ7AkzmXkTfXoXm0Qz1ch+aRdz32j4i9BlpQ12RfD5LujIjpjY5jJFyH5tEO9XAdmkcj6+FuHDOzAnCyNzMrgHZM9ksbHUANuA7Nox3q4To0j4bVo+367M3M7NXasWVvZmb9tE2yl3S0pN9I2iBpYaPjqYak8yVtkbS20bFUS9J+krolrZN0n6RTGx1TpSTtIOlXkn6d6vDVRsdULUmjJd0t6dpGx1ItSQ9JulfSPZLubHQ81ZC0q6TLJd0vab2kw+seQzt040gaDfwH8D5gE3AHMDci1jU0sApJOgLoBS6MiIMbHU81JE0EJkbEXZJ2BtYAc1rpvVB2m6HxEdEraSywGjg1Im5rcGgVk3QaMB34m4g4ptHxVEPSQ8D0iGjZcfaSlgG/iIjzJI0DdoqIrfWMoV1a9ocCGyLiwYh4CVgJHNvgmCoWETcDTzU6jpGIiM0RcVd6/iywHtinsVFVJjK9aXJserRcq0jSvsAHgfMaHUuRSdoFOALoAoiIl+qd6KF9kv0+wCMl05tosQTTjiRNAt4G3N7gUCqWuj/uAbYAN0VEy9UB+A7wL8BfGhzHSAVwo6Q1kuY3OpgqTAaeAH6cutTOkzS+3kG0S7K3JiNpAvAT4HMR8cdGx1OpiHg5It4K7AscKqmlutUkHQNsiYg1jY6lBmZExNuB9wP/mLo7W8kY4O3ADyLibcBzQN2PK7ZLsn8U2K9ket80zxog9XP/BLg4Iq5odDwjkX5udwNHNziUSr0b+LvU370SeK+kixobUnUi4tH0dwtwJVm3bSvZBGwq+XV4OVnyr6t2SfZ3AAdKmpwOfnwMuKbBMRVSOrjZBayPiG83Op5qSNpL0q7p+Y5kB/7vb2hQFYqIz0fEvhExiezz8LOI+HiDw6qYpPHpQD+p6+MooKVGq0XEY8Ajkt6UZs0G6j5gYUy9C8xDRGyTdDJwAzAaOD8i7mtwWBWTtAKYCewpaRPwlYjoamxUFXs38Ang3tTnDfCFiPh/jQupYhOBZWmU1yjg0oho2aGLLa4DuDJrQzAGWB4R1zc2pKqcAlycGqMPAp+sdwBtMfTSzMyG1i7dOGZmNgQnezOzAnCyNzMrACd7M7MCcLI3MysAJ3treZLOlfS5kukbJJ1XMv2tdEGwSvZ5gaTjhllnZitfTdKKxcne2sEvgXcBSBoF7AlMLVn+LuCWBsRl1jSc7K0d3AL0XR98KtkZls9K2k3Sa4ApQEj6ebqY1g3pUsxIeqOk69P8X0h6c/+dSzoztfRHp/sm3C/pLuAjJescKunWdKGrW/rOlpR0s6S3lqy3WtIheb0QZoNxsreWFxG/B7ZJej1ZK/5WsittHk52Lff1wLnAcRExDTgfWJw2Xwqckub/M/CvpfuWdA6wF9kZj2OBHwEfAqYBrytZ9X7gPelCV18Gvp7mdwEnpX39J2CHiPh1repuVq62uFyCGVnr/l3p8W2yS1y/C3iG7KJ4RwE3pdPuRwOb05U53wVcluYDvKZkn18Cbo+I+QCp1b8xIh5I0xcBfZfc3YXsEgsHkl2Sd2yafxnwJUmnA58CLqhprc3K5GRv7aKv3/4tZN04jwD/BPwR6AH2iYjtbgUn6W+ArelSxgO5A5gmafeIGO6mMmcC3RHx4XQd/x6AiHhe0k1kN9M5nuwXgVnduRvH2sUtwDHAU+la9E8Bu5J15awA9uq776eksZKmpuvsb5T00TRf/frTrwfOAq5LV168H5gk6Y1p+dySdXfhlctqn9QvtvOA7wF3RMTTNamtWYWc7K1d3Es2Cue2fvOeSddBPw44W9KvgXtIo3eAE4HONP8++t3OMiIuI+unvwYQWbfNdekA7ZaSVb8JfEPS3fT7xZxuIPJH4Mcjr6ZZdXzVS7OcSdqbrFvnzRHR6rcItBbllr1ZjiT9PdnIoEVO9NZIbtmbmRWAW/ZmZgXgZG9mVgBO9mZmBeBkb2ZWAE72ZmYF4GRvZlYA/x/qV1T0Uc7OwQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "san_jose_crime_males_count_by_weekday = san_jose_crime_males.groupby(['Year','Month','Weekday']).count()['Crime'].reset_index()\n",
        "san_jose_crime_females_count_by_weekday = san_jose_crime_females.groupby(['Year','Month','Weekday']).count()['Crime'].reset_index()\n",
        "\n",
        "san_jose_crime_males_count_by_weekday.boxplot(column=['Crime'],by='Weekday')\n",
        "san_jose_crime_females_count_by_weekday.boxplot(column=['Crime'],by='Weekday')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Number of crimes by season for males and females"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'Crime'}, xlabel='Season'>"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "san_jose_crime_males_count_by_season = san_jose_crime_males.groupby(['Year','Season']).count()['Crime'].reset_index()\n",
        "san_jose_crime_females_count_by_season = san_jose_crime_females.groupby(['Year','Season']).count()['Crime'].reset_index()\n",
        "\n",
        "san_jose_crime_males_count_by_season.boxplot(column=['Crime'],by='Season')\n",
        "san_jose_crime_females_count_by_season.boxplot(column=['Crime'],by='Season')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Number of crimes per district for males and females normalised using district population"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'0'}, xlabel='[District]'>"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "san_jose_crime_males_count_by_district = san_jose_crime_males.groupby(['Year','District']).count()['Crime'].sort_values(ascending=False).reset_index()\n",
        "san_jose_crime_females_count_by_district = san_jose_crime_females.groupby(['Year','District']).count()['Crime'].sort_values(ascending=False).reset_index()\n",
        "\n",
        "san_jose_crime_males_count_by_district_norm = san_jose_crime_males_count_by_district.set_index('District')['Crime'].div(san_jose_district_dataset.set_index('District')['Población total']).reset_index()\n",
        "san_jose_crime_males_count_by_district_norm = san_jose_crime_males_count_by_district_norm[san_jose_crime_males_count_by_district_norm['District']!='DESCONOCIDO']\n",
        "san_jose_crime_females_count_by_district_norm = san_jose_crime_females_count_by_district.set_index('District')['Crime'].div(san_jose_district_dataset.set_index('District')['Población total']).reset_index()\n",
        "san_jose_crime_females_count_by_district_norm = san_jose_crime_females_count_by_district_norm[san_jose_crime_females_count_by_district_norm['District']!='DESCONOCIDO']\n",
        "\n",
        "san_jose_crime_males_count_by_district_norm.boxplot(by='District', rot=90)\n",
        "san_jose_crime_females_count_by_district_norm.boxplot(by='District', rot=90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Agrupar por trimestre deve ser so fixar o ano e juntar de 3 em 3 meses"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCcVSUTsasBP"
      },
      "source": [
        "## 🖼️ Visualisations\n",
        "Copy here the most important visualizations (graphs, charts, maps, images, etc). You can refer to them in the Executive Summary.\n",
        "\n",
        "Technical note: If not all the visualisations are visible, you can still include them as an image or link - in this case please upload them to your own repository."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2REdi5_rThg"
      },
      "source": [
        "## 👓 References\n",
        "List all of the external links (even if they are already linked above), such as external datasets, papers, blog posts, code repositories and any other materials."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLBpGOl5o0ix"
      },
      "source": [
        "## ⏭️ Appendix\n",
        "Add here any code, images or text that you still find relevant, but that was too long to include in the main report. This section is optional.\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "C8rCpNajszur"
      ],
      "name": "Submission Template.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "d810581116dce4f4becb3cf7f73fc42d9b89a6e86f44ce881666ea9525d5b6a4"
    },
    "kernelspec": {
      "display_name": "Python 3.8.3 64-bit ('base': conda)",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}