lessons/PandasDataExploration.ipynb

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        "# Ein erster Blick auf den Datensatz\n",
        "\n",
        "Wenn wir einen neuen Datensatz bekommen, müssen wir uns zuerst darin zurecht finden, in diesem Notebook sehen wir, worauf wir bei der ersten Orientierung im Datensatz achten.\n",
        "\n",
        "Wir nutzen für dieses Kapitel einen meinen Lieblingsdatensätze: Die [Hundestatistik der Stadt Zürich](https://data.stadt-zuerich.ch/dataset/sid_stapo_hundebestand_od1001#dataset).\n",
        "\n",
        "Zuerst schauen wir uns die grundlegende Struktur des Datensatzes an – was ist alles enthalten, wie heißen die Spalten wie groß ist der Datensatz?"
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              "       StichtagDatJahr  HalterId  AlterV10Cd        AlterV10Lang  \\\n",
              "0                 2015       126          60  60- bis 69-Jährige   \n",
              "1                 2015       574          60  60- bis 69-Jährige   \n",
              "2                 2015       695          40  40- bis 49-Jährige   \n",
              "3                 2015       893          60  60- bis 69-Jährige   \n",
              "4                 2015      1177          50  50- bis 59-Jährige   \n",
              "...                ...       ...         ...                 ...   \n",
              "61467             2022    160175          20  20- bis 29-Jährige   \n",
              "61468             2022    160176          20  20- bis 29-Jährige   \n",
              "61469             2022    160178          50  50- bis 59-Jährige   \n",
              "61470             2022    160182          30  30- bis 39-Jährige   \n",
              "61471             2022    160183          20  20- bis 29-Jährige   \n",
              "\n",
              "       AlterV10Sort  SexCd   SexLang  SexSort  KreisCd KreisLang  ...  \\\n",
              "0                 7      1  männlich        1        9   Kreis 9  ...   \n",
              "1                 7      2  weiblich        2        2   Kreis 2  ...   \n",
              "2                 5      1  männlich        1        6   Kreis 6  ...   \n",
              "3                 7      2  weiblich        2        7   Kreis 7  ...   \n",
              "4                 6      1  männlich        1       10  Kreis 10  ...   \n",
              "...             ...    ...       ...      ...      ...       ...  ...   \n",
              "61467             3      2  weiblich        2        8   Kreis 8  ...   \n",
              "61468             3      2  weiblich        2        8   Kreis 8  ...   \n",
              "61469             6      1  männlich        1       11  Kreis 11  ...   \n",
              "61470             4      2  weiblich        2        7   Kreis 7  ...   \n",
              "61471             3      2  weiblich        2       11  Kreis 11  ...   \n",
              "\n",
              "       RassentypSort  GebDatHundJahr AlterVHundCd  AlterVHundLang  \\\n",
              "0                  1            2011            3       3-Jährige   \n",
              "1                  1            2002           12      12-Jährige   \n",
              "2                  2            2012            2       2-Jährige   \n",
              "3                  2            2010            4       4-Jährige   \n",
              "4                  1            2011            3       3-Jährige   \n",
              "...              ...             ...          ...             ...   \n",
              "61467              1            2021            0       0-Jährige   \n",
              "61468              1            2012            9       9-Jährige   \n",
              "61469              1            2022            0       0-Jährige   \n",
              "61470              1            2022            0       0-Jährige   \n",
              "61471              1            2021            0       0-Jährige   \n",
              "\n",
              "      AlterVHundSort SexHundCd  SexHundLang SexHundSort  HundefarbeText  \\\n",
              "0                  3         2     weiblich           2   schwarz/braun   \n",
              "1                 12         2     weiblich           2         brindle   \n",
              "2                  2         2     weiblich           2           braun   \n",
              "3                  4         2     weiblich           2         schwarz   \n",
              "4                  3         1     männlich           1   schwarz/weiss   \n",
              "...              ...       ...          ...         ...             ...   \n",
              "61467              0         2     weiblich           2       hellbraun   \n",
              "61468              9         2     weiblich           2         apricot   \n",
              "61469              0         2     weiblich           2        tricolor   \n",
              "61470              0         1     männlich           1           braun   \n",
              "61471              0         1     männlich           1       braun/rot   \n",
              "\n",
              "      AnzHunde  \n",
              "0            1  \n",
              "1            1  \n",
              "2            1  \n",
              "3            1  \n",
              "4            1  \n",
              "...        ...  \n",
              "61467        1  \n",
              "61468        1  \n",
              "61469        1  \n",
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          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "dogs = pd.read_csv(\"KUL100OD1001.csv\")\n",
        "dogs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jOmMb7gYhIe"
      },
      "source": [
        "Hier fällt auf, dass nicht alle Spalten abgebildet sind. Pandas zeigt uns das indem in der Mitte eine Spalte mit `...` ist. Deshalb müssen wir eine Option in Pandas setzen, um uns alle Spalten anzeigen zu lassen:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "hI-VVzzzYuVR",
        "outputId": "9d6a8f34-25fa-4e34-e4f2-62b65ca3e376"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-13df37c7-355d-4320-a7ab-64d1b6c22b65\">\n",
              "    <div 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>StichtagDatJahr</th>\n",
              "      <th>HalterId</th>\n",
              "      <th>AlterV10Cd</th>\n",
              "      <th>AlterV10Lang</th>\n",
              "      <th>AlterV10Sort</th>\n",
              "      <th>SexCd</th>\n",
              "      <th>SexLang</th>\n",
              "      <th>SexSort</th>\n",
              "      <th>KreisCd</th>\n",
              "      <th>KreisLang</th>\n",
              "      <th>KreisSort</th>\n",
              "      <th>QuarCd</th>\n",
              "      <th>QuarLang</th>\n",
              "      <th>QuarSort</th>\n",
              "      <th>Rasse1Text</th>\n",
              "      <th>Rasse2Text</th>\n",
              "      <th>RasseMischlingCd</th>\n",
              "      <th>RasseMischlingLang</th>\n",
              "      <th>RasseMischlingSort</th>\n",
              "      <th>RassentypCd</th>\n",
              "      <th>RassentypLang</th>\n",
              "      <th>RassentypSort</th>\n",
              "      <th>GebDatHundJahr</th>\n",
              "      <th>AlterVHundCd</th>\n",
              "      <th>AlterVHundLang</th>\n",
              "      <th>AlterVHundSort</th>\n",
              "      <th>SexHundCd</th>\n",
              "      <th>SexHundLang</th>\n",
              "      <th>SexHundSort</th>\n",
              "      <th>HundefarbeText</th>\n",
              "      <th>AnzHunde</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>60- bis 69-Jährige</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>männlich</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>Kreis 9</td>\n",
              "      <td>9</td>\n",
              "      <td>92</td>\n",
              "      <td>Altstetten</td>\n",
              "      <td>92</td>\n",
              "      <td>Welsh Terrier</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>K</td>\n",
              "      <td>Kleinwüchsig</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>3-Jährige</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>schwarz/braun</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015</td>\n",
              "      <td>574</td>\n",
              "      <td>60</td>\n",
              "      <td>60- bis 69-Jährige</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Kreis 2</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>Leimbach</td>\n",
              "      <td>23</td>\n",
              "      <td>Cairn Terrier</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>K</td>\n",
              "      <td>Kleinwüchsig</td>\n",
              "      <td>1</td>\n",
              "      <td>2002</td>\n",
              "      <td>12</td>\n",
              "      <td>12-Jährige</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>brindle</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015</td>\n",
              "      <td>695</td>\n",
              "      <td>40</td>\n",
              "      <td>40- bis 49-Jährige</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>männlich</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>Kreis 6</td>\n",
              "      <td>6</td>\n",
              "      <td>63</td>\n",
              "      <td>Oberstrass</td>\n",
              "      <td>63</td>\n",
              "      <td>Labrador Retriever</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>I</td>\n",
              "      <td>Rassentypenliste I</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "      <td>2</td>\n",
              "      <td>2-Jährige</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>braun</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015</td>\n",
              "      <td>893</td>\n",
              "      <td>60</td>\n",
              "      <td>60- bis 69-Jährige</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>Kreis 7</td>\n",
              "      <td>7</td>\n",
              "      <td>71</td>\n",
              "      <td>Fluntern</td>\n",
              "      <td>71</td>\n",
              "      <td>Mittelschnauzer</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>I</td>\n",
              "      <td>Rassentypenliste I</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>4</td>\n",
              "      <td>4-Jährige</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>schwarz</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>1177</td>\n",
              "      <td>50</td>\n",
              "      <td>50- bis 59-Jährige</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>männlich</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>Kreis 10</td>\n",
              "      <td>10</td>\n",
              "      <td>102</td>\n",
              "      <td>Wipkingen</td>\n",
              "      <td>102</td>\n",
              "      <td>Shih Tzu</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>K</td>\n",
              "      <td>Kleinwüchsig</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>3-Jährige</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>männlich</td>\n",
              "      <td>1</td>\n",
              "      <td>schwarz/weiss</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61467</th>\n",
              "      <td>2022</td>\n",
              "      <td>160175</td>\n",
              "      <td>20</td>\n",
              "      <td>20- bis 29-Jährige</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>Kreis 8</td>\n",
              "      <td>8</td>\n",
              "      <td>82</td>\n",
              "      <td>Mühlebach</td>\n",
              "      <td>82</td>\n",
              "      <td>Chihuahua</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>K</td>\n",
              "      <td>Kleinwüchsig</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "      <td>0</td>\n",
              "      <td>0-Jährige</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>hellbraun</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61468</th>\n",
              "      <td>2022</td>\n",
              "      <td>160176</td>\n",
              "      <td>20</td>\n",
              "      <td>20- bis 29-Jährige</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>Kreis 8</td>\n",
              "      <td>8</td>\n",
              "      <td>83</td>\n",
              "      <td>Weinegg</td>\n",
              "      <td>83</td>\n",
              "      <td>Zwergpudel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>K</td>\n",
              "      <td>Kleinwüchsig</td>\n",
              "      <td>1</td>\n",
              "      <td>2012</td>\n",
              "      <td>9</td>\n",
              "      <td>9-Jährige</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>apricot</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61469</th>\n",
              "      <td>2022</td>\n",
              "      <td>160178</td>\n",
              "      <td>50</td>\n",
              "      <td>50- bis 59-Jährige</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>männlich</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>Kreis 11</td>\n",
              "      <td>11</td>\n",
              "      <td>111</td>\n",
              "      <td>Affoltern</td>\n",
              "      <td>111</td>\n",
              "      <td>Shetland Sheepdog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>K</td>\n",
              "      <td>Kleinwüchsig</td>\n",
              "      <td>1</td>\n",
              "      <td>2022</td>\n",
              "      <td>0</td>\n",
              "      <td>0-Jährige</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>tricolor</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61470</th>\n",
              "      <td>2022</td>\n",
              "      <td>160182</td>\n",
              "      <td>30</td>\n",
              "      <td>30- bis 39-Jährige</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>Kreis 7</td>\n",
              "      <td>7</td>\n",
              "      <td>72</td>\n",
              "      <td>Hottingen</td>\n",
              "      <td>72</td>\n",
              "      <td>Miniature American Shepard</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>K</td>\n",
              "      <td>Kleinwüchsig</td>\n",
              "      <td>1</td>\n",
              "      <td>2022</td>\n",
              "      <td>0</td>\n",
              "      <td>0-Jährige</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>männlich</td>\n",
              "      <td>1</td>\n",
              "      <td>braun</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61471</th>\n",
              "      <td>2022</td>\n",
              "      <td>160183</td>\n",
              "      <td>20</td>\n",
              "      <td>20- bis 29-Jährige</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>Kreis 11</td>\n",
              "      <td>11</td>\n",
              "      <td>119</td>\n",
              "      <td>Seebach</td>\n",
              "      <td>119</td>\n",
              "      <td>Dachshund</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Rassehund</td>\n",
              "      <td>1</td>\n",
              "      <td>K</td>\n",
              "      <td>Kleinwüchsig</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "      <td>0</td>\n",
              "      <td>0-Jährige</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>männlich</td>\n",
              "      <td>1</td>\n",
              "      <td>braun/rot</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61472 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13df37c7-355d-4320-a7ab-64d1b6c22b65')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-13df37c7-355d-4320-a7ab-64d1b6c22b65');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       StichtagDatJahr  HalterId  AlterV10Cd        AlterV10Lang  \\\n",
              "0                 2015       126          60  60- bis 69-Jährige   \n",
              "1                 2015       574          60  60- bis 69-Jährige   \n",
              "2                 2015       695          40  40- bis 49-Jährige   \n",
              "3                 2015       893          60  60- bis 69-Jährige   \n",
              "4                 2015      1177          50  50- bis 59-Jährige   \n",
              "...                ...       ...         ...                 ...   \n",
              "61467             2022    160175          20  20- bis 29-Jährige   \n",
              "61468             2022    160176          20  20- bis 29-Jährige   \n",
              "61469             2022    160178          50  50- bis 59-Jährige   \n",
              "61470             2022    160182          30  30- bis 39-Jährige   \n",
              "61471             2022    160183          20  20- bis 29-Jährige   \n",
              "\n",
              "       AlterV10Sort  SexCd   SexLang  SexSort  KreisCd KreisLang  KreisSort  \\\n",
              "0                 7      1  männlich        1        9   Kreis 9          9   \n",
              "1                 7      2  weiblich        2        2   Kreis 2          2   \n",
              "2                 5      1  männlich        1        6   Kreis 6          6   \n",
              "3                 7      2  weiblich        2        7   Kreis 7          7   \n",
              "4                 6      1  männlich        1       10  Kreis 10         10   \n",
              "...             ...    ...       ...      ...      ...       ...        ...   \n",
              "61467             3      2  weiblich        2        8   Kreis 8          8   \n",
              "61468             3      2  weiblich        2        8   Kreis 8          8   \n",
              "61469             6      1  männlich        1       11  Kreis 11         11   \n",
              "61470             4      2  weiblich        2        7   Kreis 7          7   \n",
              "61471             3      2  weiblich        2       11  Kreis 11         11   \n",
              "\n",
              "       QuarCd    QuarLang  QuarSort                  Rasse1Text Rasse2Text  \\\n",
              "0          92  Altstetten        92               Welsh Terrier        NaN   \n",
              "1          23    Leimbach        23               Cairn Terrier        NaN   \n",
              "2          63  Oberstrass        63          Labrador Retriever        NaN   \n",
              "3          71    Fluntern        71             Mittelschnauzer        NaN   \n",
              "4         102   Wipkingen       102                    Shih Tzu        NaN   \n",
              "...       ...         ...       ...                         ...        ...   \n",
              "61467      82   Mühlebach        82                   Chihuahua        NaN   \n",
              "61468      83     Weinegg        83                  Zwergpudel        NaN   \n",
              "61469     111   Affoltern       111           Shetland Sheepdog        NaN   \n",
              "61470      72   Hottingen        72  Miniature American Shepard        NaN   \n",
              "61471     119     Seebach       119                   Dachshund        NaN   \n",
              "\n",
              "       RasseMischlingCd RasseMischlingLang  RasseMischlingSort RassentypCd  \\\n",
              "0                     1          Rassehund                   1           K   \n",
              "1                     1          Rassehund                   1           K   \n",
              "2                     1          Rassehund                   1           I   \n",
              "3                     1          Rassehund                   1           I   \n",
              "4                     1          Rassehund                   1           K   \n",
              "...                 ...                ...                 ...         ...   \n",
              "61467                 1          Rassehund                   1           K   \n",
              "61468                 1          Rassehund                   1           K   \n",
              "61469                 1          Rassehund                   1           K   \n",
              "61470                 1          Rassehund                   1           K   \n",
              "61471                 1          Rassehund                   1           K   \n",
              "\n",
              "            RassentypLang  RassentypSort  GebDatHundJahr  AlterVHundCd  \\\n",
              "0            Kleinwüchsig              1            2011             3   \n",
              "1            Kleinwüchsig              1            2002            12   \n",
              "2      Rassentypenliste I              2            2012             2   \n",
              "3      Rassentypenliste I              2            2010             4   \n",
              "4            Kleinwüchsig              1            2011             3   \n",
              "...                   ...            ...             ...           ...   \n",
              "61467        Kleinwüchsig              1            2021             0   \n",
              "61468        Kleinwüchsig              1            2012             9   \n",
              "61469        Kleinwüchsig              1            2022             0   \n",
              "61470        Kleinwüchsig              1            2022             0   \n",
              "61471        Kleinwüchsig              1            2021             0   \n",
              "\n",
              "      AlterVHundLang  AlterVHundSort  SexHundCd SexHundLang  SexHundSort  \\\n",
              "0          3-Jährige               3          2    weiblich            2   \n",
              "1         12-Jährige              12          2    weiblich            2   \n",
              "2          2-Jährige               2          2    weiblich            2   \n",
              "3          4-Jährige               4          2    weiblich            2   \n",
              "4          3-Jährige               3          1    männlich            1   \n",
              "...              ...             ...        ...         ...          ...   \n",
              "61467      0-Jährige               0          2    weiblich            2   \n",
              "61468      9-Jährige               9          2    weiblich            2   \n",
              "61469      0-Jährige               0          2    weiblich            2   \n",
              "61470      0-Jährige               0          1    männlich            1   \n",
              "61471      0-Jährige               0          1    männlich            1   \n",
              "\n",
              "      HundefarbeText  AnzHunde  \n",
              "0      schwarz/braun         1  \n",
              "1            brindle         1  \n",
              "2              braun         1  \n",
              "3            schwarz         1  \n",
              "4      schwarz/weiss         1  \n",
              "...              ...       ...  \n",
              "61467      hellbraun         1  \n",
              "61468        apricot         1  \n",
              "61469       tricolor         1  \n",
              "61470          braun         1  \n",
              "61471      braun/rot         1  \n",
              "\n",
              "[61472 rows x 31 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.set_option(\"display.max_columns\", None)\n",
        "dogs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Z6FR0I1MjH4"
      },
      "source": [
        "Der Datensatz ist ziemlich groß - es sind über 60.000 Hunde darin aufgeführt und die Spaltennamen sind auf den ersten Blick nicht sehr aussagekräftig.\n",
        "\n",
        "Wie finden wir also raus, was genau in diesem Datensatz steckt und was in Spalten die etwa `AlterV10Sort` heißen steht?\n",
        "\n",
        "## Nach dem Weg fragen\n",
        "\n",
        "Wenn man sich nicht auskennt, ist es immer eine gute Idee nach dem Weg zu fragen – das ist bei Datensätzen genauso wie wenn man sich verlaufen hat. Wenn du also die Möglichkeit hast, mit den Personen zu sprechen, die den Datensatz erhoben haben, mach das. Es wird niemanden geben, der\\*die sich besser damit auskennt. \n",
        "\n",
        "Und immerhin bist du Journalist*in – du kannst im Gespräch noch wichtige Hintergrundinfos abgreifen und hast vielleicht direkt die erste Expertin gefunden.\n",
        "\n",
        "## Nachschlagen\n",
        "\n",
        "Wenn du nicht mit niemandem reden kannst, die den Datensatz erhoben haben, dann versuche Informationen über den Datensatz online oder in der Datei selbst zu finden.\n",
        "\n",
        "Die Beschreibung für den Hundedatensatz findest du hier: [https://data.stadt-zuerich.ch/dataset/sid_stapo_hundebestand_od1001#dataset](https://data.stadt-zuerich.ch/dataset/sid_stapo_hundebestand_od1001#dataset)\n",
        "\n",
        "Ein Blick in die Attributbeschreibung zeigt uns z.B. dass die Spalte `AlterV10Sort` eine Variaton der beiden Spalten davor ist und die Altersgruppe der Hundehalter\\*innen anzeigt.\n",
        "\n",
        "## Datensatz reduzieren\n",
        "\n",
        "Bei so großen Datensätzen ist es eine gute Idee, ihn auf die Spalten zu reduzieren, die für uns relevant sind. Was relevant ist, hängt von den Recherchefragen ab die wir haben. \n",
        "\n",
        "Bei diesem Datensatz können wir uns zum Beispiel dafür interessieren wie alt Hunde und Halter\\*innen sind, welche Rasse und Farbe die Hunde haben und in welchen Vierteln sie wohnen.\n",
        "\n",
        "Um den Datensatz auf die für uns wichtigen Spalten zu begrenzen ist es eine gute Idee, sich zuerst alle Spaltennamen ausgeben zu lassen"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFMB0CiyZnuc",
        "outputId": "5308eaf6-c62f-48e7-93c1-c9fb140eccec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['StichtagDatJahr', 'HalterId', 'AlterV10Cd', 'AlterV10Lang',\n",
              "       'AlterV10Sort', 'SexCd', 'SexLang', 'SexSort', 'KreisCd', 'KreisLang',\n",
              "       'KreisSort', 'QuarCd', 'QuarLang', 'QuarSort', 'Rasse1Text',\n",
              "       'Rasse2Text', 'RasseMischlingCd', 'RasseMischlingLang',\n",
              "       'RasseMischlingSort', 'RassentypCd', 'RassentypLang', 'RassentypSort',\n",
              "       'GebDatHundJahr', 'AlterVHundCd', 'AlterVHundLang', 'AlterVHundSort',\n",
              "       'SexHundCd', 'SexHundLang', 'SexHundSort', 'HundefarbeText',\n",
              "       'AnzHunde'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rnm56vW1Zp9F"
      },
      "source": [
        "Die Spaltennamen können wir dann kopieren und alle Spalten entfernen, die uns nicht interessieren:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "2M1wU4aHZ1CK",
        "outputId": "8444e59c-5181-4826-c5ce-b0c1acd1e33d"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>StichtagDatJahr</th>\n",
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              "      <th>KreisCd</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       StichtagDatJahr  HalterId  AlterV10Cd   SexLang  KreisCd  QuarCd  \\\n",
              "0                 2015       126          60  männlich        9      92   \n",
              "1                 2015       574          60  weiblich        2      23   \n",
              "2                 2015       695          40  männlich        6      63   \n",
              "3                 2015       893          60  weiblich        7      71   \n",
              "4                 2015      1177          50  männlich       10     102   \n",
              "...                ...       ...         ...       ...      ...     ...   \n",
              "61467             2022    160175          20  weiblich        8      82   \n",
              "61468             2022    160176          20  weiblich        8      83   \n",
              "61469             2022    160178          50  männlich       11     111   \n",
              "61470             2022    160182          30  weiblich        7      72   \n",
              "61471             2022    160183          20  weiblich       11     119   \n",
              "\n",
              "         QuarLang                  Rasse1Text  GebDatHundJahr  AlterVHundCd  \\\n",
              "0      Altstetten               Welsh Terrier            2011             3   \n",
              "1        Leimbach               Cairn Terrier            2002            12   \n",
              "2      Oberstrass          Labrador Retriever            2012             2   \n",
              "3        Fluntern             Mittelschnauzer            2010             4   \n",
              "4       Wipkingen                    Shih Tzu            2011             3   \n",
              "...           ...                         ...             ...           ...   \n",
              "61467   Mühlebach                   Chihuahua            2021             0   \n",
              "61468     Weinegg                  Zwergpudel            2012             9   \n",
              "61469   Affoltern           Shetland Sheepdog            2022             0   \n",
              "61470   Hottingen  Miniature American Shepard            2022             0   \n",
              "61471     Seebach                   Dachshund            2021             0   \n",
              "\n",
              "      SexHundLang HundefarbeText  \n",
              "0        weiblich  schwarz/braun  \n",
              "1        weiblich        brindle  \n",
              "2        weiblich          braun  \n",
              "3        weiblich        schwarz  \n",
              "4        männlich  schwarz/weiss  \n",
              "...           ...            ...  \n",
              "61467    weiblich      hellbraun  \n",
              "61468    weiblich        apricot  \n",
              "61469    weiblich       tricolor  \n",
              "61470    männlich          braun  \n",
              "61471    männlich      braun/rot  \n",
              "\n",
              "[61472 rows x 12 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs = dogs[['StichtagDatJahr', 'HalterId', 'AlterV10Cd', 'SexLang', 'KreisCd', 'QuarCd', 'QuarLang', 'Rasse1Text',\n",
        "       'GebDatHundJahr', 'AlterVHundCd', 'SexHundLang', 'HundefarbeText']]\n",
        "\n",
        "dogs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfO7ccRva_O-"
      },
      "source": [
        "Damit ist der Datensatz schon ein bisschen übersichtlicher. Als nächstes schauen wir uns die einzelnen Spalten genauer an, ob die Inhalte in den Spalten realistisch klingen oder ob bei der Datenerhebung Fehler unterlaufen sind.\n",
        "\n",
        "**Dieser Schritt ist einer der wichtigsten in jeder Datenrecherche: Wenn der analysierte Datensatz Fehler enthält, dann werden auch die Ergebnisse falsch sein.** Falsche Ergebnisse in der Datenanalyse sind genauso schlimm wie falsche O-Töne oder verdrehte Fakten in der Berichterstattung.\n",
        "\n",
        "Zuerst überprüfen wir mit der `isna()`-Methode ob Felder in unserem Datensatz keinen Eintrag haben. Pandas hat dafür einen eigenen Datentyp: `pd.NA`. Dieser wird beim Import automatisch vergeben, wenn Felder in der Tabelle leer sind.\n",
        "\n",
        "Damit die Tabelle übersichtlicher wird, summieren wir die Spalten auf."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_y-W6q3ioz7",
        "outputId": "9207d144-a48e-4552-80cc-f124964bd665"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "StichtagDatJahr    0\n",
              "HalterId           0\n",
              "AlterV10Cd         0\n",
              "SexLang            0\n",
              "KreisCd            0\n",
              "QuarCd             0\n",
              "QuarLang           0\n",
              "Rasse1Text         0\n",
              "GebDatHundJahr     0\n",
              "AlterVHundCd       0\n",
              "SexHundLang        0\n",
              "HundefarbeText     0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1zq1WD8i5Gs"
      },
      "source": [
        "Wir sehen, dass in unserem Datensatz keine Felder fehlen.\n",
        "\n",
        "Für die weitere Analyse müssen wir in zwei Arten von Daten unterscheiden: Kategorielle und Numerische Daten.\n",
        "\n",
        "## Kategorielle Daten\n",
        "\n",
        "Kategorielle Daten sind Daten, die eine Zeile einer gewissen Kategorie zuordnen. Ein Beispiel in unserem Datensatz hier ist etwa das Geschlecht. Die Spalte `SexHundLang` drückt aus, welches Geschlecht der Hund in der entsprechenden Zeile hat. Meine Erwartung an den Datensatz ist, dass die Hunde nur in männlich und weiblich kategorisiert werden.\n",
        "\n",
        "Mit der Methode `value_counts()` können wir uns anzeigen lassen, welche Kategorien wie häufig in dem Datensatz vorkommen:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBaGUSFZcmn9",
        "outputId": "876a1d43-3c29-4c08-a4db-78857fddead3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "weiblich    30878\n",
              "männlich    30594\n",
              "Name: SexHundLang, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[\"SexHundLang\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsVHq9E_dJNZ"
      },
      "source": [
        "Wir sehen, dass tatsächlich nur die beiden Geschlechter vorkommen. Und auch die Verteilung entspricht meiner Erwartung: Männliche und weibliche Hunde sind etwa 50/50 verteilt.\n",
        "\n",
        "Bei den Halter\\*innen gehe ich davon aus, dass die Verteilung ähnlich ist:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z42VR8f2duYV",
        "outputId": "b85a4a7f-707e-40d5-c53c-18576b912be6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SexLang \n",
              "weiblich    42432\n",
              "männlich    19040\n",
              "dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[[\"SexLang\"]].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gtjtkWgegVd"
      },
      "source": [
        "Hier sehen wir, dass es einen Überhang hin zu Frauen gibt. Ich habe nicht erwartet, dass zwei Drittel der Halter\\*innen Frauen sind. Um sicher zu gehen, schauen wir uns deshalb an, wie viele Halter\\*innen geführt sind, und ob die Zahl der Gesamtzahl entspricht:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RQ8SPGZefte",
        "outputId": "79f41ae2-f1e3-4f85-83ba-8c20993eb74c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "61472"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[\"HalterId\"].size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uD_KOAdsfJbz",
        "outputId": "72c76756-b2fe-45d1-fe47-ae59385b6fab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "61472"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[[\"SexLang\"]].value_counts().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZVNi_2ifR9g"
      },
      "source": [
        "So sehen wir, dass alle Halter\\*innen erfasst sind und anscheinend wirklich viel mehr Frauen als Männer Hunde halten.\n",
        "\n",
        "Jetzt werfen wir einen Blick auf die Rasse des Hundes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24S34MK9floS",
        "outputId": "c4129978-e094-4110-b4b7-432c3bbc8883"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Unbekannt                       7856\n",
              "Chihuahua                       4290\n",
              "Labrador Retriever              3628\n",
              "Yorkshire Terrier               2386\n",
              "Jack Russel Terrier             2293\n",
              "                                ... \n",
              "Ciobanesc Romanesc Mioritic        1\n",
              "Italienische Bracke                1\n",
              "Oesterreichischer Pinscher         1\n",
              "Grand Basset Griffon Vendeen       1\n",
              "Japan-Spitz                        1\n",
              "Name: Rasse1Text, Length: 384, dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[\"Rasse1Text\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "926RQ1tvf63c"
      },
      "source": [
        "Wir sehen, dass 384 verschiedene Rassen aufgeführt sind. Eigentlich sind es nur 383, weil die häufigste Rasse \"Unbekannt\" ist. Das merken wir uns für später, wenn wir den Datensatz bereinigen.\n",
        "\n",
        "Allgemein ist diese Ansicht etwas unübersichtlich. Deshalb lassen wir sie uns schnell als Graph ausgeben.\n",
        "\n",
        "Dafür wandeln wir die Ausgabe von `value_counts()` wieder in einen `DataFrame` um und rufen die `.plot()` Methode auf. Diese Methode gibt uns dann ein einfaches Diagramm zurück, aus dem wir die Verteilung der Daten sehen können.\n",
        "\n",
        "Mit dem Parameter `kind=\"bar\"` in der `plot()`-Methode geben wir an, dass wir einen Barchart, also ein Balkendiagramm haben wollen. Normalerweise würde ein Liniendiagramm zurückgegeben – Liniendiagramme können wir aber nur für numerische Daten verwenden. Mehr dazu im Video zu Visualierung."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "xrLhMqZZgi5J",
        "outputId": "bd4aa013-19c8-493c-e5be-d16f9704cb47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 17,
          "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": [
        "pd.DataFrame(dogs[\"Rasse1Text\"].value_counts()).plot(kind=\"bar\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9f8x7uTDgcpv"
      },
      "source": [
        "Auf diesem Plot erkennt man nicht viel, aber wir sehen die grundlegende Verteilung: Es gibt einige Hunderassen die sehr häufig vorkommen und viele die eher selten sind. Das deckt sich mit meiner Erwartungshaltung.\n",
        "\n",
        "## Numerische Daten\n",
        "\n",
        "Numerische Daten sind einfach gesagt Zahlen. Dabei können wir zwischen steten und unsteten Verteilungen unterscheiden. \n",
        "\n",
        "Stetige Verteilungen sind zum Beispiel Zeitdaten. Wenn wir jede Minute am Tag die Temperatur vor unserer Tür messen, könenn wir eine Linie über den Tag zeichnen, die anzeigt wie sich die Temperatur entwickelt hat. \n",
        "\n",
        "Eine Unstete Verteilung ist etwa das Alter der Hunde in unserem Datensatz. Hier sind nur ganze Jahre angegeben, wir könenn also keinen Verlauf zeigen sondern, wie bei den kategoriellen Daten, nur angeben wie viele Hunde ein gewisses Alter haben.\n",
        "\n",
        "Sehen wir uns die Verteilung des Alters der Hunde genauer an, wieder mit der `value_counts()` Methode:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PowuPuJkwI_j",
        "outputId": "560cd11b-98b2-4fe0-bcf5-9e061c664fd6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1      5463\n",
              "0      5312\n",
              "2      5151\n",
              "3      5070\n",
              "4      4905\n",
              "5      4743\n",
              "6      4540\n",
              "7      4387\n",
              "8      4139\n",
              "9      3871\n",
              "10     3566\n",
              "11     3156\n",
              "12     2599\n",
              "13     1991\n",
              "14     1313\n",
              "15      731\n",
              "16      334\n",
              "17      122\n",
              "18       41\n",
              "19       12\n",
              "20        9\n",
              "999       8\n",
              "21        6\n",
              "22        2\n",
              "23        1\n",
              "Name: AlterVHundCd, dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[\"AlterVHundCd\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sFoEKLNUwYtJ"
      },
      "source": [
        "Hier fällt sofort auf, dass 8 Hunde angeblich 999 Jahre alt sind. Das ist natürlich Quatsch und heißt vermutlich, dass für das Alter des Hundes nichts eingegeben wurde, oder dass das Alter des Hundes nicht bekannt ist.\n",
        "\n",
        "Auch das merken wir uns für die Datenbereinigung später, damit wir diese fehlerhaften Spalten mit `pd.NA` ersetzen können.\n",
        "\n",
        "Sonst zeigt sich ein ähnliches Bild: Viele Hunde sind sehr jung und mit fortschreitendem Alter werden es weniger Hunde. 23 ist zwar sehr alt für einen Hund, aber er ist damit immer noch jünger als [der älteste Hund der Welt](https://www.nordbayern.de/panorama/bobi-ist-mit-30-jahren-der-alteste-hund-der-welt-sein-besitzer-verrat-sein-geheimnis-1.12992998).\n",
        "\n",
        "Wenn wir das Geburtsjahr der Hunde überprüfen ergibt sich für uns das gleiche Bild:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cf1cibn4xPT1",
        "outputId": "6d8c7ddb-bfb3-49f5-c3b2-731ca1474d2e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2012    4806\n",
              "2013    4638\n",
              "2014    4610\n",
              "2010    4269\n",
              "2015    4128\n",
              "2011    4127\n",
              "2009    3895\n",
              "2016    3820\n",
              "2008    3551\n",
              "2017    3284\n",
              "2007    3090\n",
              "2018    2695\n",
              "2006    2582\n",
              "2005    2169\n",
              "2020    2111\n",
              "2019    1973\n",
              "2004    1824\n",
              "2003    1122\n",
              "2021    1022\n",
              "2002     797\n",
              "2001     467\n",
              "2000     268\n",
              "1999     112\n",
              "1998      70\n",
              "1997      22\n",
              "9999       8\n",
              "2022       5\n",
              "1996       4\n",
              "1994       2\n",
              "1995       1\n",
              "Name: GebDatHundJahr, dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[\"GebDatHundJahr\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGKk06C7xg3w"
      },
      "source": [
        "8 Hund sind im Jahr 9999 geboren, was wir später ebenfalls durch den nicht vorhanenden Wert `pd.NA` ersetzen.\n",
        "\n",
        "Hier fällt uns noch etwas auf: Der Datensatz ist von 2022 und der älteste Hund ist im Jahr 1995 geboren, er müsste also 28 Jahre alt sein. Aber laut der Spalte Alter ist der älteste Hund nur 23. \n",
        "\n",
        "Deshalb können wir vermuten, dass es in Zürich gar keine 60.000 Hunde gibt, sondern jeder Hund in jedem Jahr aufgeführt wird.\n",
        "\n",
        "Sehen wir uns deshalb genauer an, wie oft der 23 jährige Hund in unserem Datensatz auftaucht. Leider haben die Hunde keine ID bekommen, sondern nur ihre Herrchen und Frauchen.\n",
        "\n",
        "Also finden wir zuerst die Halter-ID des ältesten Hundes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "1LY3xqhozbOc",
        "outputId": "d649c8eb-a1d4-475f-ea25-3e3c3692a1e7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f7c0f692-7b23-4289-832f-83d45eb4a611\">\n",
              "    <div 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>StichtagDatJahr</th>\n",
              "      <th>HalterId</th>\n",
              "      <th>AlterV10Cd</th>\n",
              "      <th>SexLang</th>\n",
              "      <th>KreisCd</th>\n",
              "      <th>QuarCd</th>\n",
              "      <th>QuarLang</th>\n",
              "      <th>Rasse1Text</th>\n",
              "      <th>GebDatHundJahr</th>\n",
              "      <th>AlterVHundCd</th>\n",
              "      <th>SexHundLang</th>\n",
              "      <th>HundefarbeText</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44522</th>\n",
              "      <td>2021</td>\n",
              "      <td>84494</td>\n",
              "      <td>70</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>10</td>\n",
              "      <td>102</td>\n",
              "      <td>Wipkingen</td>\n",
              "      <td>Dachshund</td>\n",
              "      <td>1997</td>\n",
              "      <td>23</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>schwarz/rot</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7c0f692-7b23-4289-832f-83d45eb4a611')\"\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 0 24 24\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       StichtagDatJahr  HalterId  AlterV10Cd   SexLang  KreisCd  QuarCd  \\\n",
              "44522             2021     84494          70  weiblich       10     102   \n",
              "\n",
              "        QuarLang Rasse1Text  GebDatHundJahr  AlterVHundCd SexHundLang  \\\n",
              "44522  Wipkingen  Dachshund            1997            23    weiblich   \n",
              "\n",
              "      HundefarbeText  \n",
              "44522    schwarz/rot  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[dogs[\"AlterVHundCd\"] == 23]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7285ycY0ztzI"
      },
      "source": [
        "Die HalterId ist also `84494`. Mit einer ähnlichen Abfrage können wir nun alle Jahre abfragen in dem der Hundehalter in der Statistik auftaucht:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
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          "height": 364
        },
        "id": "EeDDF8VNz-dG",
        "outputId": "d4cf8bfc-73fb-4074-8330-5eb7f2088501"
      },
      "outputs": [
        {
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              "    <tr>\n",
              "      <th>21869</th>\n",
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              "      <th>44522</th>\n",
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              "      <td>84494</td>\n",
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              "      <td>2022</td>\n",
              "      <td>84494</td>\n",
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              "      <td>Wipkingen</td>\n",
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              "      <td>2014</td>\n",
              "      <td>7</td>\n",
              "      <td>weiblich</td>\n",
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              "          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",
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              "  "
            ],
            "text/plain": [
              "       StichtagDatJahr  HalterId  AlterV10Cd   SexLang  KreisCd  QuarCd  \\\n",
              "1055              2015     84494          60  weiblich       10     102   \n",
              "7955              2016     84494          60  weiblich       10     102   \n",
              "14806             2017     84494          70  weiblich       10     102   \n",
              "21869             2018     84494          70  weiblich       10     102   \n",
              "29173             2019     84494          70  weiblich       10     102   \n",
              "36736             2020     84494          70  weiblich       10     102   \n",
              "44522             2021     84494          70  weiblich       10     102   \n",
              "53015             2022     84494          70  weiblich       10     102   \n",
              "\n",
              "        QuarLang Rasse1Text  GebDatHundJahr  AlterVHundCd SexHundLang  \\\n",
              "1055   Wipkingen  Dachshund            1997            17    weiblich   \n",
              "7955   Wipkingen  Dachshund            1997            18    weiblich   \n",
              "14806  Wipkingen  Dachshund            1997            19    weiblich   \n",
              "21869  Wipkingen  Dachshund            1997            20    weiblich   \n",
              "29173  Wipkingen  Dachshund            1997            21    weiblich   \n",
              "36736  Wipkingen  Dachshund            1997            22    weiblich   \n",
              "44522  Wipkingen  Dachshund            1997            23    weiblich   \n",
              "53015  Wipkingen  Dachshund            2014             7    weiblich   \n",
              "\n",
              "      HundefarbeText  \n",
              "1055     schwarz/rot  \n",
              "7955     schwarz/rot  \n",
              "14806    schwarz/rot  \n",
              "21869    schwarz/rot  \n",
              "29173    schwarz/rot  \n",
              "36736    schwarz/rot  \n",
              "44522    schwarz/rot  \n",
              "53015        schwarz  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[dogs[\"HalterId\"] == 84494]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0w34-3KH0QAW"
      },
      "source": [
        "Mit dieser Abfrage können wir sicher sein, dass der gleiche Hund mehrfach im Datensatz auftaucht und wir nicht einfach die Zeilen zusammenzählen können.\n",
        "\n",
        "Diese Information stand nicht in der Beschreibung des Datensatzes - wir sehen also wie wichtig es ist, sich ein genaues Bild vom Datensatz zu machen, um nicht aus Versehen falsche Zahlen zu veröffentlichen.\n",
        "\n",
        "## Datenstories ohne Daten\n",
        "\n",
        "In dieser Abfrage steckt außerdem eine ganze Geschichte, die wir schon veröffentlichen können: Wir sehen dass die Halterin des ältesten Hundes Zürich anscheinend großer Fan von Dachshunden ist, und dass der Kleine 2021 anscheinend gestorben ist. Die Halterin hat sich dann einen neuen Hund gekauft.\n",
        "\n",
        "Diese Geschichte ist nicht sonderlich investigativ, aber ein schönes Beispiel dass Datengeschichten auch ganz ohne Visualisierungen oder komplizierte Grafiken auskommen können. Wir können hier eine ein bisschen traurige Homestory über den ältesten Hund Zürichs machen.\n",
        "\n",
        "Eine andere Geschichte, ergibt sich, wenn wir uns die IDs der Halter\\*innen ansehen: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "InH-42Xd2V1B",
        "outputId": "2332b2d1-006f-4dff-f676-04e47590d780"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0           126\n",
              "1           574\n",
              "2           695\n",
              "3           893\n",
              "4          1177\n",
              "          ...  \n",
              "61467    160175\n",
              "61468    160176\n",
              "61469    160178\n",
              "61470    160182\n",
              "61471    160183\n",
              "Name: HalterId, Length: 61472, dtype: int64"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[\"HalterId\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVStxdM32ZdW"
      },
      "source": [
        "Diese sind anscheinend fortlaufend nummeriert. Das heißt, dass der Halter mit der ID 126 anscheinend schon sehr lange Hunde hält. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "pqv2OlL62izR",
        "outputId": "be432178-e009-4e10-80e7-cc35a81dbf73"
      },
      "outputs": [
        {
          "data": {
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              "      <th>StichtagDatJahr</th>\n",
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              "      <td>9</td>\n",
              "      <td>92</td>\n",
              "      <td>Altstetten</td>\n",
              "      <td>Welsh Terrier</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>schwarz/braun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6951</th>\n",
              "      <td>2016</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>männlich</td>\n",
              "      <td>9</td>\n",
              "      <td>92</td>\n",
              "      <td>Altstetten</td>\n",
              "      <td>Welsh Terrier</td>\n",
              "      <td>2011</td>\n",
              "      <td>4</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>schwarz/braun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13850</th>\n",
              "      <td>2017</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>männlich</td>\n",
              "      <td>9</td>\n",
              "      <td>92</td>\n",
              "      <td>Altstetten</td>\n",
              "      <td>Welsh Terrier</td>\n",
              "      <td>2011</td>\n",
              "      <td>5</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>schwarz/braun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20974</th>\n",
              "      <td>2018</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>männlich</td>\n",
              "      <td>9</td>\n",
              "      <td>92</td>\n",
              "      <td>Altstetten</td>\n",
              "      <td>Welsh Terrier</td>\n",
              "      <td>2011</td>\n",
              "      <td>6</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>schwarz/braun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28346</th>\n",
              "      <td>2019</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>männlich</td>\n",
              "      <td>9</td>\n",
              "      <td>92</td>\n",
              "      <td>Altstetten</td>\n",
              "      <td>Welsh Terrier</td>\n",
              "      <td>2011</td>\n",
              "      <td>7</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>schwarz/braun</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ],
            "text/plain": [
              "       StichtagDatJahr  HalterId  AlterV10Cd   SexLang  KreisCd  QuarCd  \\\n",
              "0                 2015       126          60  männlich        9      92   \n",
              "6951              2016       126          60  männlich        9      92   \n",
              "13850             2017       126          60  männlich        9      92   \n",
              "20974             2018       126          60  männlich        9      92   \n",
              "28346             2019       126          60  männlich        9      92   \n",
              "\n",
              "         QuarLang     Rasse1Text  GebDatHundJahr  AlterVHundCd SexHundLang  \\\n",
              "0      Altstetten  Welsh Terrier            2011             3    weiblich   \n",
              "6951   Altstetten  Welsh Terrier            2011             4    weiblich   \n",
              "13850  Altstetten  Welsh Terrier            2011             5    weiblich   \n",
              "20974  Altstetten  Welsh Terrier            2011             6    weiblich   \n",
              "28346  Altstetten  Welsh Terrier            2011             7    weiblich   \n",
              "\n",
              "      HundefarbeText  \n",
              "0      schwarz/braun  \n",
              "6951   schwarz/braun  \n",
              "13850  schwarz/braun  \n",
              "20974  schwarz/braun  \n",
              "28346  schwarz/braun  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[dogs[\"HalterId\"] == 126]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJV8BJE72xa0"
      },
      "source": [
        "Auch dieser Hundehalter ist vielleicht eine interessante Person, die eine ganz besondere Geschichte erzählen kann."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyOZnsW5gep44/kkiAoASCYA",
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      "display_name": "Python 3",
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