lessons/VisualisierungDatawrapperBreak-ins.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/redadmiral/python-for-journalists/blob/main/lessons/VisualisierungDatawrapperBreak-ins.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualisierung mit Datawrapper: Diagramme\n",
        "\n",
        "In dieser Sitzung widmen wir uns dem Visualisieren unserer Ergebnisse mit [Datawrapper](https://app.datawrapper.de/chart/RFPfn/publish).\n",
        "\n",
        "Datawrapper ist ein Visualisierungstool, das in den meisten Fällen gratis genutzt werden kann und in vielen Redaktionen verbreitet ist.\n",
        "\n",
        "In diesem Notebook erzeugen wir nur die Daten für die Visualisierung, der Rest passiert über die Oberfläche in Datawrapper.\n",
        "\n",
        "Wir behandeln hier nur sehr elementare Datenvisualisierungen. Der [Visual Almanach der Financial Times](https://ft-interactive.github.io/visual-vocabulary/) liefert aber einen sehr guten Überblick über verschiedene Darstellungsformen für verschiedene Zwecke."
      ],
      "metadata": {
        "id": "_f4mRsDEqYm2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        ":Im ersten Schritt importieren wir Pandas und laden den Datensatz mit den Züricher Hundedaten:"
      ],
      "metadata": {
        "id": "nnb-NM3x9bI6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "TTNfCv0sqU95",
        "outputId": "5df641ea-37a5-4868-feae-39a58b91d6be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Ausgangsjahr  Gemeinde_BFS_Nr Gemeindename  Stadtkreis_BFS_Nr  \\\n",
              "0          2009              131     Adliswil                NaN   \n",
              "1          2009              131     Adliswil                NaN   \n",
              "2          2009              131     Adliswil                NaN   \n",
              "3          2009              241        Aesch                NaN   \n",
              "4          2009              241        Aesch                NaN   \n",
              "\n",
              "  Stadtkreis_Name  Gesetz_Nummer Gesetz_Abk            Tatbestand  \\\n",
              "0             NaN          311.0       StGB     Einbruchdiebstahl   \n",
              "1             NaN          311.0       StGB  Einschleichdiebstahl   \n",
              "2             NaN          311.0       StGB   Einbrüche insgesamt   \n",
              "3             NaN          311.0       StGB     Einbruchdiebstahl   \n",
              "4             NaN          311.0       StGB  Einschleichdiebstahl   \n",
              "\n",
              "   Straftaten_total  Straftaten_vollendet  Straftaten_versucht  Einwohner  \\\n",
              "0               159                   114                   45    16052.0   \n",
              "1                33                    32                    1    16052.0   \n",
              "2               192                   146                   46    16052.0   \n",
              "3                10                     7                    3      987.0   \n",
              "4                 2                     2                    0      987.0   \n",
              "\n",
              "   Häufigkeitszahl  \n",
              "0              9.9  \n",
              "1              2.1  \n",
              "2             12.0  \n",
              "3             10.1  \n",
              "4              2.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-371fd9ee-fedf-47f2-aabc-cfd849251605\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ausgangsjahr</th>\n",
              "      <th>Gemeinde_BFS_Nr</th>\n",
              "      <th>Gemeindename</th>\n",
              "      <th>Stadtkreis_BFS_Nr</th>\n",
              "      <th>Stadtkreis_Name</th>\n",
              "      <th>Gesetz_Nummer</th>\n",
              "      <th>Gesetz_Abk</th>\n",
              "      <th>Tatbestand</th>\n",
              "      <th>Straftaten_total</th>\n",
              "      <th>Straftaten_vollendet</th>\n",
              "      <th>Straftaten_versucht</th>\n",
              "      <th>Einwohner</th>\n",
              "      <th>Häufigkeitszahl</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009</td>\n",
              "      <td>131</td>\n",
              "      <td>Adliswil</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>311.0</td>\n",
              "      <td>StGB</td>\n",
              "      <td>Einbruchdiebstahl</td>\n",
              "      <td>159</td>\n",
              "      <td>114</td>\n",
              "      <td>45</td>\n",
              "      <td>16052.0</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009</td>\n",
              "      <td>131</td>\n",
              "      <td>Adliswil</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>311.0</td>\n",
              "      <td>StGB</td>\n",
              "      <td>Einschleichdiebstahl</td>\n",
              "      <td>33</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>16052.0</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009</td>\n",
              "      <td>131</td>\n",
              "      <td>Adliswil</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>311.0</td>\n",
              "      <td>StGB</td>\n",
              "      <td>Einbrüche insgesamt</td>\n",
              "      <td>192</td>\n",
              "      <td>146</td>\n",
              "      <td>46</td>\n",
              "      <td>16052.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009</td>\n",
              "      <td>241</td>\n",
              "      <td>Aesch</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>311.0</td>\n",
              "      <td>StGB</td>\n",
              "      <td>Einbruchdiebstahl</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>987.0</td>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009</td>\n",
              "      <td>241</td>\n",
              "      <td>Aesch</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>311.0</td>\n",
              "      <td>StGB</td>\n",
              "      <td>Einschleichdiebstahl</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>987.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-371fd9ee-fedf-47f2-aabc-cfd849251605')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-371fd9ee-fedf-47f2-aabc-cfd849251605 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-371fd9ee-fedf-47f2-aabc-cfd849251605');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a8913592-9656-4c6e-8924-ea0283f3497b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a8913592-9656-4c6e-8924-ea0283f3497b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a8913592-9656-4c6e-8924-ea0283f3497b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "break_ins",
              "summary": "{\n  \"name\": \"break_ins\",\n  \"rows\": 7785,\n  \"fields\": [\n    {\n      \"column\": \"Ausgangsjahr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2009,\n        \"max\": 2023,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          2018,\n          2020,\n          2009\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gemeinde_BFS_Nr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 572,\n        \"min\": 1,\n        \"max\": 7601,\n        \"num_unique_values\": 161,\n        \"samples\": [\n          95,\n          138,\n          200\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gemeindename\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 161,\n        \"samples\": [\n          \"Regensberg\",\n          \"Richterswil\",\n          \"Wangen-Br\\u00fcttisellen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stadtkreis_BFS_Nr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4552533206993785,\n        \"min\": 2610001.0,\n        \"max\": 2610012.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          2610011.0,\n          2610010.0,\n          2610001.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stadtkreis_Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Kreis 12\",\n          \"Kreis 10\",\n          \"Kreis 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gesetz_Nummer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 311.0,\n        \"max\": 311.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          311.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gesetz_Abk\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"StGB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tatbestand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Einbruchdiebstahl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Straftaten_total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84,\n        \"min\": 0,\n        \"max\": 1060,\n        \"num_unique_values\": 417,\n        \"samples\": [\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Straftaten_vollendet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 0,\n        \"max\": 724,\n        \"num_unique_values\": 327,\n        \"samples\": [\n          255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Straftaten_versucht\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 0,\n        \"max\": 336,\n        \"num_unique_values\": 216,\n        \"samples\": [\n          130\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Einwohner\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12653.067385945527,\n        \"min\": 296.0,\n        \"max\": 116906.0,\n        \"num_unique_values\": 2296,\n        \"samples\": [\n          5188.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"H\\u00e4ufigkeitszahl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.977122111645075,\n        \"min\": 0.0,\n        \"max\": 74.6,\n        \"num_unique_values\": 218,\n        \"samples\": [\n          6.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "break_ins = pd.read_csv(\"https://www.web.statistik.zh.ch/ogd/daten/ressourcen/KTZH_00002042_00004083.csv\")\n",
        "\n",
        "break_ins.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Barchart\n",
        "\n",
        "Zuerst erzeugen wir ein Barchart - also ein Balkendiagramm.\n",
        "\n",
        "Balkendiagramme sind meistens die sichere Variante, um kategorielle Daten zu visualisieren. In diesem Fall sind die Jahre unsere Kategorien. Linien- und Flächendiagramme sollten wir nur dann wählen, wenn die Daten kontinuierlich erhoben sind und nicht auf Monate oder Jahre aggregiert sind.\n",
        "\n",
        "Wir wollen ein Diagramm für die Recherchefrage \"Wie hat sich der Hundebestand in Zürich über die Jahre entwickelt?\" erstellen. Wir gruppieren den Datensatz dafür wieder nach Jahren."
      ],
      "metadata": {
        "id": "iksHha069mYh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Make a quick bar plot on the total number of break-ins per year. Only use columns where Einbrüche insgesamt.\n",
        "# Ausgangsjahr\tGemeinde_BFS_Nr\tGemeindename\tStadtkreis_BFS_Nr\tStadtkreis_Name\tGesetz_Nummer\tGesetz_Abk\tTatbestand\tStraftaten_total\tStraftaten_vollendet\tStraftaten_versucht\tEinwohner\tHäufigkeitszahl\n",
        "# 0\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinbruchdiebstahl\t159\t114\t45\t16052.0\t9.9\n",
        "# 1\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinschleichdiebstahl\t33\t32\t1\t16052.0\t2.1\n",
        "# 2\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinbrüche insgesamt\t192\t146\t46\t16052.0\t12.0\n",
        "# 3\t2009\t241\tAesch\tNaN\tNaN\t311.0\tStGB\tEinbruchdiebstahl\t10\t7\t3\t987.0\t10.1\n",
        "# 4\t2009\t241\tAesch\tNaN\tNaN\t311.0\tStGB\tEinschleichdiebstahl\t2\t2\t0\t987.0\t2.0\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming 'break_ins' DataFrame is already loaded as in the previous code\n",
        "\n",
        "# Filter for \"Einbrüche insgesamt\"\n",
        "break_ins_total = break_ins[break_ins[\"Tatbestand\"] == \"Einbrüche insgesamt\"]\n",
        "\n",
        "# Group by year and sum the total break-ins\n",
        "break_ins_by_year = break_ins_total.groupby(\"Ausgangsjahr\")[\"Straftaten_total\"].sum()\n",
        "\n",
        "# Create the bar plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "break_ins_by_year.plot(kind=\"bar\")\n",
        "plt.title(\"Total Number of Break-ins per Year\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Total Break-ins\")\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
        "plt.tight_layout()  # Adjust layout to prevent labels from overlapping\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "YmFKIRYtqqSM",
        "outputId": "2fe4648f-5060-4d79-f9d2-51ac949b58bf"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Und schreiben ihn als `dogs_by_year.csv` auf die Festplatte."
      ],
      "metadata": {
        "id": "ICCYPgvq_T8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "break_ins_by_year.to_csv(\"break_ins_by_year.csv\")"
      ],
      "metadata": {
        "id": "tB2Ix24HrSEc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stacked Barcharts\n",
        "\n",
        "Eine komplexere Form des Balkendiagramms ist das gestapelte oder das gruppierte Balkendiagramm, wo eine Hauptkategorie (in unserem Fall das Jahr) eine Unterkategorie mit mehreren Ausprägungen haben kann. Als Unterkategorie bietet sich bei unserem Datensatz das Geschlecht der Hunde an.\n",
        "\n"
      ],
      "metadata": {
        "id": "tno2mgZp_hkJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dafür gruppieren wir den Datensatz nach Jahr und Geschlecht.\n",
        "\n",
        "Mit der `unstack()`-Methode lösen wir den Multi-Index auf, so dass wir eine normale Tabelle bekommen, denn Datawrapper kann nicht mit Multi-Indizes arbeiten."
      ],
      "metadata": {
        "id": "7i1UxZinAwtS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Make a stacked bar plot on the total number of break-ins per year. Stack Einbruchdiebstahl and Einschleichdiebstahl.\n",
        "# Ausgangsjahr\tGemeinde_BFS_Nr\tGemeindename\tStadtkreis_BFS_Nr\tStadtkreis_Name\tGesetz_Nummer\tGesetz_Abk\tTatbestand\tStraftaten_total\tStraftaten_vollendet\tStraftaten_versucht\tEinwohner\tHäufigkeitszahl\n",
        "# 0\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinbruchdiebstahl\t159\t114\t45\t16052.0\t9.9\n",
        "# 1\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinschleichdiebstahl\t33\t32\t1\t16052.0\t2.1\n",
        "# 2\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinbrüche insgesamt\t192\t146\t46\t16052.0\t12.0\n",
        "# 3\t2009\t241\tAesch\tNaN\tNaN\t311.0\tStGB\tEinbruchdiebstahl\t10\t7\t3\t987.0\t10.1\n",
        "# 4\t2009\t241\tAesch\tNaN\tNaN\t311.0\tStGB\tEinschleichdiebstahl\t2\t2\t0\t987.0\t2.0\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "break_ins = pd.read_csv(\"https://www.web.statistik.zh.ch/ogd/daten/ressourcen/KTZH_00002042_00004083.csv\")\n",
        "\n",
        "# Filter for Einbruchdiebstahl and Einschleichdiebstahl\n",
        "filtered_break_ins = break_ins[break_ins[\"Tatbestand\"].isin([\"Einbruchdiebstahl\", \"Einschleichdiebstahl\"])]\n",
        "\n",
        "# Group by year and Tatbestand, then sum Straftaten_total\n",
        "break_ins_grouped = filtered_break_ins.groupby([\"Ausgangsjahr\", \"Tatbestand\"])[\"Straftaten_total\"].sum().unstack()\n",
        "\n",
        "# Create the stacked bar plot\n",
        "break_ins_grouped.plot(kind=\"bar\", stacked=True, figsize=(10, 6))\n",
        "plt.title(\"Total Number of Break-ins per Year (Stacked)\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Total Break-ins\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(title=\"Tatbestand\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "yhLFHR42vuAe",
        "outputId": "fc327e94-342d-4f7a-e0f1-d50581028c08"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Karten\n",
        "\n",
        "Man kann zwei Hauptkategorien von Karten erstellen, Locator Maps und Choropleth Maps. Wir beschränken uns heute auf Choropleth Karten.\n",
        "\n",
        "Choropleth Karten zeichenen eine vorher definierte Form mit einer Farbe aus, die sich am Wert orientert. Für Locator-Maps bräuchten wir Punkte auf der Karte - und die haben wir nicht.\n",
        "\n",
        "Für die Kartendarstellung gruppieren wir den Datensatz nach Quartieren."
      ],
      "metadata": {
        "id": "pRQEwhvdBMcv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Count the number of Einbrüche insgesamt for the most recent year and group by Gemeindename.\n",
        "# Ausgangsjahr\tGemeinde_BFS_Nr\tGemeindename\tStadtkreis_BFS_Nr\tStadtkreis_Name\tGesetz_Nummer\tGesetz_Abk\tTatbestand\tStraftaten_total\tStraftaten_vollendet\tStraftaten_versucht\tEinwohner\tHäufigkeitszahl\n",
        "# 0\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinbruchdiebstahl\t159\t114\t45\t16052.0\t9.9\n",
        "# 1\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinschleichdiebstahl\t33\t32\t1\t16052.0\t2.1\n",
        "# 2\t2009\t131\tAdliswil\tNaN\tNaN\t311.0\tStGB\tEinbrüche insgesamt\t192\t146\t46\t16052.0\t12.0\n",
        "# 3\t2009\t241\tAesch\tNaN\tNaN\t311.0\tStGB\tEinbruchdiebstahl\t10\t7\t3\t987.0\t10.1\n",
        "# 4\t2009\t241\tAesch\tNaN\tNaN\t311.0\tStGB\tEinschleichdiebstahl\t2\t2\t0\t987.0\t2.0\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming 'break_ins' DataFrame is already loaded as in the previous code\n",
        "break_ins = pd.read_csv(\"https://www.web.statistik.zh.ch/ogd/daten/ressourcen/KTZH_00002042_00004083.csv\")\n",
        "\n",
        "# Find the most recent year\n",
        "most_recent_year = break_ins[\"Ausgangsjahr\"].max()\n",
        "\n",
        "# Filter data for the most recent year and \"Einbrüche insgesamt\"\n",
        "recent_break_ins = break_ins[\n",
        "    (break_ins[\"Ausgangsjahr\"] == most_recent_year)\n",
        "    & (break_ins[\"Tatbestand\"] == \"Einbrüche insgesamt\")\n",
        "]\n",
        "\n",
        "# Group by Gemeindename and sum Einbrüche insgesamt\n",
        "break_ins_by_gemeinde = recent_break_ins.groupby(\"Gemeindename\")[\"Straftaten_total\"].sum()\n",
        "\n",
        "break_ins_by_gemeinde"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "yUU7k37i3PZu",
        "outputId": "408bc694-3140-44e2-d46f-9c1c1c825c8b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gemeindename\n",
              "Adliswil          60\n",
              "Aesch              7\n",
              "Aeugst             7\n",
              "Affoltern         54\n",
              "Altikon            1\n",
              "                ... \n",
              "Zell               9\n",
              "Zollikon          84\n",
              "Zumikon           40\n",
              "Zürich          2941\n",
              "unbekannt ZH       2\n",
              "Name: Straftaten_total, Length: 161, dtype: int64"
            ],
            "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>Straftaten_total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gemeindename</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Adliswil</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aesch</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aeugst</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Affoltern</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Altikon</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zell</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zollikon</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zumikon</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zürich</th>\n",
              "      <td>2941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unbekannt ZH</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>161 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "break_ins_by_gemeinde.to_csv(\"break_ins_by_gemeinde.csv\")"
      ],
      "metadata": {
        "id": "faS1mFdL3w0b"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}