lessons/PandasDatenAnalyse.ipynb

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        "# Datenanalyse\n",
        "\n",
        "In diesem Abschnitt führen wir die eigentliche Analyse durch, dafür müssen wir uns zuerst klar werden welche Recherchefragen wir haben. Wir wollen anhand dieses Datensatzes folgende Fragen beantworten:\n",
        "\n",
        "1. Haben sich die Menschen über die Corona-Zeit mehr oder weniger Hunde geholt?\n",
        "2. In welchem Stadtteil gibt es die meisten/wenigsten Hunde pro Einwohner\\*in?\n",
        "3. Halten alte Menschen andere Hunde als Junge?\n",
        "\n",
        "Dafür laden wir zuerst den Datensatz den wir im letzten Schritt bereinigt haben:\n",
        "\n"
      ]
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        "id": "j_locY0I2Jk4"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "dogs = pd.read_csv(\"dogs.csv\", index_col = 0)"
      ]
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        "# Die `groupby`-Methode\n",
        "\n",
        "Wir wissen, dass wir den Datensatz nur jahresweise verwenden können, müssen wir ihn auf die einzelnen Jahre aufteilen.\n",
        "\n",
        "Die naheliegende Variante dafür wäre, ihn für jedes Jahr einzeln zu filtern und die Berechnungen auf jedem entstandenen Datensatz zu wiederholen, also etwa so:"
      ]
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              "       StichtagDatJahr  HalterId  AlterV10Cd   SexLang  KreisCd KreisLang  \\\n",
              "52323             2022       574        70.0  weiblich      2.0   Kreis 2   \n",
              "52324             2022       695        50.0  männlich      6.0   Kreis 6   \n",
              "52325             2022       893        70.0  weiblich      7.0   Kreis 7   \n",
              "52326             2022      1177        60.0  männlich     10.0  Kreis 10   \n",
              "52327             2022      4054        50.0  weiblich     11.0  Kreis 11   \n",
              "...                ...       ...         ...       ...      ...       ...   \n",
              "61467             2022    160175        20.0  weiblich      8.0   Kreis 8   \n",
              "61468             2022    160176        20.0  weiblich      8.0   Kreis 8   \n",
              "61469             2022    160178        50.0  männlich     11.0  Kreis 11   \n",
              "61470             2022    160182        30.0  weiblich      7.0   Kreis 7   \n",
              "61471             2022    160183        20.0  weiblich     11.0  Kreis 11   \n",
              "\n",
              "       QuarCd    QuarLang                  Rasse1Text  GebDatHundJahr  \\\n",
              "52323    23.0    Leimbach                   Unbekannt          2013.0   \n",
              "52324    63.0  Oberstrass          Labrador Retriever          2012.0   \n",
              "52325    71.0    Fluntern             Mittelschnauzer          2010.0   \n",
              "52326   102.0   Wipkingen                    Shih Tzu          2011.0   \n",
              "52327   111.0   Affoltern           Lagotto Romagnolo          2016.0   \n",
              "...       ...         ...                         ...             ...   \n",
              "61467    82.0   Mühlebach                   Chihuahua          2021.0   \n",
              "61468    83.0     Weinegg                  Zwergpudel          2012.0   \n",
              "61469   111.0   Affoltern           Shetland Sheepdog          2022.0   \n",
              "61470    72.0   Hottingen  Miniature American Shepard          2022.0   \n",
              "61471   119.0     Seebach                   Dachshund          2021.0   \n",
              "\n",
              "       AlterVHundCd SexHundLang HundefarbeText  \n",
              "52323           8.0    weiblich        schwarz  \n",
              "52324           9.0    weiblich          braun  \n",
              "52325          11.0    weiblich        schwarz  \n",
              "52326          10.0    männlich  schwarz/weiss  \n",
              "52327           5.0    weiblich    weiss/beige  \n",
              "...             ...         ...            ...  \n",
              "61467           0.0    weiblich      hellbraun  \n",
              "61468           9.0    weiblich        apricot  \n",
              "61469           0.0    weiblich       tricolor  \n",
              "61470           0.0    männlich          braun  \n",
              "61471           0.0    männlich      braun/rot  \n",
              "\n",
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      "source": [
        "year22 = dogs[dogs[\"StichtagDatJahr\"] == 2022]\n",
        "year21 = dogs[dogs[\"StichtagDatJahr\"] == 2022]\n",
        "# und so weiter...\n",
        "year22"
      ]
    },
    {
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      "metadata": {
        "id": "uIxqSUv_EotK"
      },
      "source": [
        "Dann könnten wir zählen wie viele Zeilen der Datensatz hat, und so herausfinden wie viele Hunde im Jahr gemeldet wurden:"
      ]
    },
    {
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      "execution_count": 3,
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      "source": [
        "year22[\"StichtagDatJahr\"].size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6flnKWdFgg2"
      },
      "source": [
        "Das über alle Jahre zu wiederholen ist aber ziemlich umständlich. Deshalb gibt es die `groupby()`-Methode.\n",
        "\n",
        "Diese sortiert alle Zeilen anhand des Werts in einem Feld. Wenn wir zum Beispiel nach dem Jahr gruppieren wollen, dann wählen wir einfach die entsprechende Spalte \"StichtagDatJahr\" aus und übergeben sie der groupby-Funktion:"
      ]
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              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f8536c86a90>"
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      "source": [
        "dogs.groupby(\"StichtagDatJahr\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BM0mLwfkGT0r"
      },
      "source": [
        "Das sieht nicht sehr hilfreich aus. Das liegt daran, dass wir pandas sagen müssen was es mit all den Zeilen machen soll, die für jedes Jahr im Datensatz sind.\n",
        "\n",
        "Nachdem wir wissen wollen, wie viele Hunde es in jedem Jahr gab, nutzen wir die `count()`-Funktion, um die einzelnen Zeilen zu zählen:"
      ]
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HalterId</th>\n",
              "      <th>AlterV10Cd</th>\n",
              "      <th>SexLang</th>\n",
              "      <th>KreisCd</th>\n",
              "      <th>KreisLang</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",
              "    <tr>\n",
              "      <th>StichtagDatJahr</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>6951</td>\n",
              "      <td>6905</td>\n",
              "      <td>6951</td>\n",
              "      <td>6950</td>\n",
              "      <td>6951</td>\n",
              "      <td>6948</td>\n",
              "      <td>6948</td>\n",
              "      <td>6951</td>\n",
              "      <td>6948</td>\n",
              "      <td>6948</td>\n",
              "      <td>6951</td>\n",
              "      <td>6951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>6899</td>\n",
              "      <td>6859</td>\n",
              "      <td>6899</td>\n",
              "      <td>6898</td>\n",
              "      <td>6899</td>\n",
              "      <td>6896</td>\n",
              "      <td>6896</td>\n",
              "      <td>6899</td>\n",
              "      <td>6897</td>\n",
              "      <td>6897</td>\n",
              "      <td>6899</td>\n",
              "      <td>6899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>7124</td>\n",
              "      <td>7090</td>\n",
              "      <td>7124</td>\n",
              "      <td>7124</td>\n",
              "      <td>7124</td>\n",
              "      <td>7122</td>\n",
              "      <td>7122</td>\n",
              "      <td>7124</td>\n",
              "      <td>7123</td>\n",
              "      <td>7123</td>\n",
              "      <td>7124</td>\n",
              "      <td>7124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>7372</td>\n",
              "      <td>7340</td>\n",
              "      <td>7372</td>\n",
              "      <td>7372</td>\n",
              "      <td>7372</td>\n",
              "      <td>7370</td>\n",
              "      <td>7370</td>\n",
              "      <td>7372</td>\n",
              "      <td>7371</td>\n",
              "      <td>7371</td>\n",
              "      <td>7372</td>\n",
              "      <td>7372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>7616</td>\n",
              "      <td>7591</td>\n",
              "      <td>7616</td>\n",
              "      <td>7616</td>\n",
              "      <td>7616</td>\n",
              "      <td>7613</td>\n",
              "      <td>7613</td>\n",
              "      <td>7616</td>\n",
              "      <td>7615</td>\n",
              "      <td>7615</td>\n",
              "      <td>7616</td>\n",
              "      <td>7616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>7815</td>\n",
              "      <td>7803</td>\n",
              "      <td>7815</td>\n",
              "      <td>7815</td>\n",
              "      <td>7815</td>\n",
              "      <td>7814</td>\n",
              "      <td>7814</td>\n",
              "      <td>7815</td>\n",
              "      <td>7815</td>\n",
              "      <td>7815</td>\n",
              "      <td>7815</td>\n",
              "      <td>7815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>8546</td>\n",
              "      <td>8532</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "      <td>8546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022</th>\n",
              "      <td>9149</td>\n",
              "      <td>9137</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "      <td>9149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                 HalterId  AlterV10Cd  SexLang  KreisCd  KreisLang  QuarCd  \\\n",
              "StichtagDatJahr                                                              \n",
              "2015                 6951        6905     6951     6950       6951    6948   \n",
              "2016                 6899        6859     6899     6898       6899    6896   \n",
              "2017                 7124        7090     7124     7124       7124    7122   \n",
              "2018                 7372        7340     7372     7372       7372    7370   \n",
              "2019                 7616        7591     7616     7616       7616    7613   \n",
              "2020                 7815        7803     7815     7815       7815    7814   \n",
              "2021                 8546        8532     8546     8546       8546    8546   \n",
              "2022                 9149        9137     9149     9149       9149    9149   \n",
              "\n",
              "                 QuarLang  Rasse1Text  GebDatHundJahr  AlterVHundCd  \\\n",
              "StichtagDatJahr                                                       \n",
              "2015                 6948        6951            6948          6948   \n",
              "2016                 6896        6899            6897          6897   \n",
              "2017                 7122        7124            7123          7123   \n",
              "2018                 7370        7372            7371          7371   \n",
              "2019                 7613        7616            7615          7615   \n",
              "2020                 7814        7815            7815          7815   \n",
              "2021                 8546        8546            8546          8546   \n",
              "2022                 9149        9149            9149          9149   \n",
              "\n",
              "                 SexHundLang  HundefarbeText  \n",
              "StichtagDatJahr                               \n",
              "2015                    6951            6951  \n",
              "2016                    6899            6899  \n",
              "2017                    7124            7124  \n",
              "2018                    7372            7372  \n",
              "2019                    7616            7616  \n",
              "2020                    7815            7815  \n",
              "2021                    8546            8546  \n",
              "2022                    9149            9149  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.groupby(\"StichtagDatJahr\").count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNO57EitIeeW"
      },
      "source": [
        "Und hier sehen wir, dass der Datensatz jetzt nach dem Erfassungsjahr aufgeteilt ist und wir im Jahr 2022 das gleiche Ergebnis wie oben haben: 9149 Hunde wurden erfasst.\n",
        "\n",
        "Wir sehen auch schon, dass sich unsere Recherchefrage bewahrheitet: Es scheinen über Corona mehr Menschen einen Hund geholt zu haben.\n",
        "\n",
        "Im Plot sieht man das auch sehr gut:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "bbuCRJOAJOVx",
        "outputId": "36d2c668-4189-4e7b-e928-6e5221c02954"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='StichtagDatJahr'>"
            ]
          },
          "execution_count": 6,
          "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": [
        "dogs.groupby(\"StichtagDatJahr\")[\"HalterId\"].count().plot(ylim=(0,10000), kind=\"bar\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2KcxFKRJ3-s"
      },
      "source": [
        "`groupby()` braucht also immer noch eine andere Funktion, die auf den Gruppen angewandt wird, man nennt diese Funktionen Aggregationsfunktionen. Es gibt [viele verschiedene](https://www.shanelynn.ie/summarising-aggregation-and-grouping-data-in-python-pandas/), aber die, die wir am häufigsten verwenden sind der Maximalwert:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9kAZoYmJ0IJ",
        "outputId": "87c5f369-dee8-496f-9c29-2a1b3fc1a560"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "StichtagDatJahr\n",
              "2015    20.0\n",
              "2016    21.0\n",
              "2017    20.0\n",
              "2018    21.0\n",
              "2019    21.0\n",
              "2020    22.0\n",
              "2021    23.0\n",
              "2022    21.0\n",
              "Name: AlterVHundCd, dtype: float64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.groupby(\"StichtagDatJahr\")[\"AlterVHundCd\"].max()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhsgWPddK6Xq"
      },
      "source": [
        "Der Minimalwert:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VaK0kCwK77y",
        "outputId": "28458f9a-b431-49c1-8489-d5e2866b4db7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "StichtagDatJahr\n",
              "2015    126\n",
              "2016    126\n",
              "2017    126\n",
              "2018    126\n",
              "2019    126\n",
              "2020    574\n",
              "2021    574\n",
              "2022    574\n",
              "Name: HalterId, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.groupby(\"StichtagDatJahr\")[\"HalterId\"].min()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDT3FAaELIbH"
      },
      "source": [
        "Der Mittelwert:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CIf77dI6LHux",
        "outputId": "9d3b0ab2-94d4-48fb-87ca-7803c500f593"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "StichtagDatJahr\n",
              "2015    5.654865\n",
              "2016    6.224010\n",
              "2017    6.188685\n",
              "2018    6.127934\n",
              "2019    6.055417\n",
              "2020    6.047729\n",
              "2021    5.882401\n",
              "2022    5.535796\n",
              "Name: AlterVHundCd, dtype: float64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.groupby(\"StichtagDatJahr\")[\"AlterVHundCd\"].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ldAOAh-LPid"
      },
      "source": [
        "und der Median:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPLZo-5yLPQI",
        "outputId": "2956ded3-78ed-4ba5-d782-519dbdb32e70"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "StichtagDatJahr\n",
              "2015    5.0\n",
              "2016    6.0\n",
              "2017    6.0\n",
              "2018    6.0\n",
              "2019    6.0\n",
              "2020    6.0\n",
              "2021    5.0\n",
              "2022    5.0\n",
              "Name: AlterVHundCd, dtype: float64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.groupby(\"StichtagDatJahr\")[\"AlterVHundCd\"].median()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLx3Q9kvM1nO"
      },
      "source": [
        "Außerdem ist es möglich auf mehr als eine Spalte zu gruppieren. So können wir uns die Entwicklung für jeden Kreis nach dem Jahr ansehen, indem wir der groupby-Methode einfach eine Liste an Spalten übergeben:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "wMO4MMjXNiyE",
        "outputId": "c40cbe6f-24cc-4faa-efdc-ee4661d5a055"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></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",
              "    <tr>\n",
              "      <th>StichtagDatJahr</th>\n",
              "      <th>KreisLang</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2015</th>\n",
              "      <th>Kreis 1</th>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 10</th>\n",
              "      <td>689</td>\n",
              "      <td>681</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "      <td>689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 11</th>\n",
              "      <td>1163</td>\n",
              "      <td>1151</td>\n",
              "      <td>1163</td>\n",
              "      <td>1163</td>\n",
              "      <td>1163</td>\n",
              "      <td>1163</td>\n",
              "      <td>1163</td>\n",
              "      <td>1163</td>\n",
              "      <td>1163</td>\n",
              "      <td>1163</td>\n",
              "      <td>1163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 12</th>\n",
              "      <td>491</td>\n",
              "      <td>490</td>\n",
              "      <td>491</td>\n",
              "      <td>491</td>\n",
              "      <td>491</td>\n",
              "      <td>491</td>\n",
              "      <td>491</td>\n",
              "      <td>491</td>\n",
              "      <td>491</td>\n",
              "      <td>491</td>\n",
              "      <td>491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 2</th>\n",
              "      <td>603</td>\n",
              "      <td>600</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2022</th>\n",
              "      <th>Kreis 5</th>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "      <td>321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 6</th>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "      <td>647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 7</th>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "      <td>1252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 8</th>\n",
              "      <td>476</td>\n",
              "      <td>475</td>\n",
              "      <td>476</td>\n",
              "      <td>476</td>\n",
              "      <td>476</td>\n",
              "      <td>476</td>\n",
              "      <td>476</td>\n",
              "      <td>476</td>\n",
              "      <td>476</td>\n",
              "      <td>476</td>\n",
              "      <td>476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 9</th>\n",
              "      <td>1149</td>\n",
              "      <td>1148</td>\n",
              "      <td>1149</td>\n",
              "      <td>1149</td>\n",
              "      <td>1149</td>\n",
              "      <td>1149</td>\n",
              "      <td>1149</td>\n",
              "      <td>1149</td>\n",
              "      <td>1149</td>\n",
              "      <td>1149</td>\n",
              "      <td>1149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>98 rows × 11 columns</p>\n",
              "</div>\n",
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            ],
            "text/plain": [
              "                           HalterId  AlterV10Cd  SexLang  KreisCd  QuarCd  \\\n",
              "StichtagDatJahr KreisLang                                                   \n",
              "2015            Kreis 1         117         117      117      117     117   \n",
              "                Kreis 10        689         681      689      689     689   \n",
              "                Kreis 11       1163        1151     1163     1163    1163   \n",
              "                Kreis 12        491         490      491      491     491   \n",
              "                Kreis 2         603         600      603      603     603   \n",
              "...                             ...         ...      ...      ...     ...   \n",
              "2022            Kreis 5         321         321      321      321     321   \n",
              "                Kreis 6         647         647      647      647     647   \n",
              "                Kreis 7        1252        1252     1252     1252    1252   \n",
              "                Kreis 8         476         475      476      476     476   \n",
              "                Kreis 9        1149        1148     1149     1149    1149   \n",
              "\n",
              "                           QuarLang  Rasse1Text  GebDatHundJahr  AlterVHundCd  \\\n",
              "StichtagDatJahr KreisLang                                                       \n",
              "2015            Kreis 1         117         117             117           117   \n",
              "                Kreis 10        689         689             689           689   \n",
              "                Kreis 11       1163        1163            1163          1163   \n",
              "                Kreis 12        491         491             491           491   \n",
              "                Kreis 2         603         603             603           603   \n",
              "...                             ...         ...             ...           ...   \n",
              "2022            Kreis 5         321         321             321           321   \n",
              "                Kreis 6         647         647             647           647   \n",
              "                Kreis 7        1252        1252            1252          1252   \n",
              "                Kreis 8         476         476             476           476   \n",
              "                Kreis 9        1149        1149            1149          1149   \n",
              "\n",
              "                           SexHundLang  HundefarbeText  \n",
              "StichtagDatJahr KreisLang                               \n",
              "2015            Kreis 1            117             117  \n",
              "                Kreis 10           689             689  \n",
              "                Kreis 11          1163            1163  \n",
              "                Kreis 12           491             491  \n",
              "                Kreis 2            603             603  \n",
              "...                                ...             ...  \n",
              "2022            Kreis 5            321             321  \n",
              "                Kreis 6            647             647  \n",
              "                Kreis 7           1252            1252  \n",
              "                Kreis 8            476             476  \n",
              "                Kreis 9           1149            1149  \n",
              "\n",
              "[98 rows x 11 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.groupby([\"StichtagDatJahr\", \"KreisLang\"]).count()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yim55Q4-Ti3k"
      },
      "source": [
        "Diese Tabelle ist ziemlich groß und unübersichtlich deshalb plotten wir sie:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "tju4e4MUTVwR",
        "outputId": "87a27da9-fef1-429f-af04-d62e221d08ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='StichtagDatJahr'>"
            ]
          },
          "execution_count": 13,
          "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": [
        "dogs.groupby([\"StichtagDatJahr\", \"KreisLang\"])[\"HalterId\"].count().unstack(\"KreisLang\").plot()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TYxHw-D2phs"
      },
      "source": [
        "# Der `join()`\n",
        "\n",
        "Für die nächste Recherchefrage wollen wir uns ansehen in welchem Stadtteil es die meisten Hunde pro Einwohner gibt. \n",
        "\n",
        "Dafür berechnen wir zuerst die Gesamtzahl an Hunden pro Kreis im Jahresmittel und teilen diese Zahl durch die Anzahl der Menschen, die im jeweiligen Kreis wohnen. \n",
        "\n",
        "Das machen wir wieder mit der `groupby()`-Methode und teilen durch die Anzahl an Jahren im Datensatz. Wir speichern sie in eine neue Variable `kreise`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "k5mNtV7xPWiI",
        "outputId": "f20de941-8b1f-4449-b80b-af109b9d4deb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-728b053c-99d7-4b57-9c83-e51926275d47\">\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>HalterId</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KreisLang</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Kreis 1</th>\n",
              "      <td>124.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 10</th>\n",
              "      <td>713.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 11</th>\n",
              "      <td>1294.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 12</th>\n",
              "      <td>544.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 2</th>\n",
              "      <td>713.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 3</th>\n",
              "      <td>690.125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 4</th>\n",
              "      <td>401.125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 5</th>\n",
              "      <td>245.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 6</th>\n",
              "      <td>527.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 7</th>\n",
              "      <td>1069.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 8</th>\n",
              "      <td>407.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 9</th>\n",
              "      <td>952.125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unbekannt (Stadt Zürich)</th>\n",
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              "  "
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            "text/plain": [
              "                          HalterId\n",
              "KreisLang                         \n",
              "Kreis 1                    124.875\n",
              "Kreis 10                   713.875\n",
              "Kreis 11                  1294.375\n",
              "Kreis 12                   544.000\n",
              "Kreis 2                    713.000\n",
              "Kreis 3                    690.125\n",
              "Kreis 4                    401.125\n",
              "Kreis 5                    245.500\n",
              "Kreis 6                    527.625\n",
              "Kreis 7                   1069.750\n",
              "Kreis 8                    407.375\n",
              "Kreis 9                    952.125\n",
              "Unbekannt (Stadt Zürich)     0.250"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "kreise = dogs.groupby(\"KreisLang\")[\"HalterId\"].count()/8\n",
        "kreise = pd.DataFrame(kreise)\n",
        "kreise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-V_hrJw3hbQ"
      },
      "source": [
        "Doch in unserem Datensatz ist gar nicht aufgeführt, wie viele Menschen in dem jeweligen Kreis leben. Diese Daten müssen wir uns aus einem anderen Datensatz holen. \n",
        "\n",
        "Zum Glück liefert die Stadt Zürich uns auch dafür die nötigen Zahlen. Wir können sie auf der [Homepage](https://www.stadt-zuerich.ch/prd/de/index/statistik/themen/bevoelkerung/daten.html) als [Excel-Datei herunterladen](https://www.stadt-zuerich.ch/content/dam/stzh/prd/Deutsch/Statistik/Themen/Bevoelkerung/BEV326T3260_Bevoelkerung_nach-Geschlecht-Stadtkreis-Stadtquartier.xlsx).\n",
        "\n",
        "Der Datensatz der Stadt Zürich ist leider in keinem guten Format, weshalb ich ihn bereits bereinigt und in ein gutes Format gebracht habe. Den bereinigten Datensatz findest du [hier](https://github.com/redadmiral/python-for-journalists/blob/main/datasets/population_zurich.csv). Wenn du dich dafür interessierst, wie der Datensatz bereinigt wurde, kannst du das Notebook dazu [hier](https://github.com/redadmiral/python-for-journalists/blob/main/CleanPopulationZurich.ipynb) herunterladen.\n",
        "\n",
        "Lass uns einen Blick in den Datensatz mit den Einwohnerzahlen werfen. Dafür laden wir den Datensatz zuerst hoch und dann mit `read_csv()` in den Workspace:"
      ]
    },
    {
      "cell_type": "code",
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        "id": "nNEAPojCcWYh",
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        {
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            "text/plain": [
              "                    Gebiet  Total  Jahr\n",
              "0                  Kreis 1   5817  2021\n",
              "1                  Rathaus   3275  2021\n",
              "2              Hochschulen    701  2021\n",
              "3                Lindenhof   1024  2021\n",
              "4                     City    817  2021\n",
              "...                    ...    ...   ...\n",
              "1329               Seebach  17614  1993\n",
              "1330              Kreis 12  27481  1993\n",
              "1331               Saatlen   6472  1993\n",
              "1332  Schwamendingen-Mitte  10175  1993\n",
              "1333            Hirzenbach  10834  1993\n",
              "\n",
              "[1334 rows x 3 columns]"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "population = pd.read_csv(\"population_zurich.csv\", index_col=0)\n",
        "population"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1mHq4lG96TW"
      },
      "source": [
        "Wir sehen drei Spalten: Gebiet, Total und Jahr. \n",
        "\n",
        "In Gebiet stehen die Kreise und Quartiere, in Total die Einwohnerzahlen und in Jahr das Erhebungsjahr.\n",
        "\n",
        "Sehen wir uns die kreise nochmal an:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "CfL-7oC_-ODw",
        "outputId": "d91a0371-1bde-4a62-f936-1e5d25be4302"
      },
      "outputs": [
        {
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            "text/plain": [
              "                          HalterId\n",
              "KreisLang                         \n",
              "Kreis 1                    124.875\n",
              "Kreis 10                   713.875\n",
              "Kreis 11                  1294.375\n",
              "Kreis 12                   544.000\n",
              "Kreis 2                    713.000\n",
              "Kreis 3                    690.125\n",
              "Kreis 4                    401.125\n",
              "Kreis 5                    245.500\n",
              "Kreis 6                    527.625\n",
              "Kreis 7                   1069.750\n",
              "Kreis 8                    407.375\n",
              "Kreis 9                    952.125\n",
              "Unbekannt (Stadt Zürich)     0.250"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "kreise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yo3s6zzd_H1u"
      },
      "source": [
        "Wir sehen das die Kreise aus der Spalte \"Gebiet\" in `population` sich in der Spalte `KreisLang` wiederfinden. Hier sind sie aber fett gedruckt.\n",
        "\n",
        "Das heißt, dass pandas diese Spalte als Index benutzt. Der Index ist eine besondere Spalte in Pandas: Sie wird intern dazu genutzt, um auf die Zeilen zuzugreifen, ein bisschen wie die Seitenzahlen in einem Buch dabei helfen, schneller eine bestimmte Seite wiederzufinden.\n",
        "\n",
        "Wir wollen jetzt die Einwohnerzahlen aus `population` den Kreisen im DataFrame `kreise` zuordnen. Das können wir indem wir die beiden Datensätze verbinden. Pandas hat dafür die `join()`-Methode.\n",
        "\n",
        "Die join-Methode schaut sich die Indizes von zwei DataFrames an, und wenn die Indizes gleich sind, klebt sie die Spalten einer Tabelle an die andere.\n",
        "\n",
        "Nachdem wir uns die Zahlen für die Jahresmittel ansehen wollen und oben auch deshalb die Anzahl an Hunden durch die Zahl an Jahren geteilt haben, müssen wir noch die mittlere Einwohnerzahl der Jahre 2015 bis 2021 berechnen. \n",
        "\n",
        "Die Einwoherzahlen beginnen in 1993, wir müssen also die Jahre herausfiltern, die wir nicht in unserem Hundedatensatz haben."
      ]
    },
    {
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      "execution_count": 89,
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              "                   Gebiet  Total  Jahr\n",
              "0                 Kreis 1   5817  2021\n",
              "1                 Rathaus   3275  2021\n",
              "2             Hochschulen    701  2021\n",
              "3               Lindenhof   1024  2021\n",
              "4                    City    817  2021\n",
              "..                    ...    ...   ...\n",
              "317               Seebach  24991  2015\n",
              "318              Kreis 12  31753  2015\n",
              "319               Saatlen   8508  2015\n",
              "320  Schwamendingen-Mitte  11315  2015\n",
              "321            Hirzenbach  11930  2015\n",
              "\n",
              "[322 rows x 3 columns]"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "population_after_15 = population[population[\"Jahr\"] >= 2015]\n",
        "population_after_15"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hhq_jdDyBhRi"
      },
      "source": [
        "Den Mittelwert der Gebiete über die Jahre berechnen wir wieder mit der `groupby()`-Methode."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
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        },
        "id": "XykW3AbIevfK",
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      "outputs": [
        {
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "                             Total\n",
              "Gebiet                            \n",
              "Affoltern             26359.285714\n",
              "Albisrieden           21720.571429\n",
              "Alt-Wiedikon          17603.714286\n",
              "Altstetten            33262.714286\n",
              "City                    816.714286\n",
              "Enge                   9552.142857\n",
              "Escher Wyss            6006.142857\n",
              "Fluntern               8399.714286\n",
              "Friesenberg           10946.857143\n",
              "Gewerbeschule          9552.285714\n",
              "Hard                  12918.714286\n",
              "Hirslanden             7413.000000\n",
              "Hirzenbach            12686.285714\n",
              "Hochschulen             668.000000\n",
              "Hottingen             11230.857143\n",
              "Höngg                 23838.142857\n",
              "Kreis 1                5740.285714\n",
              "Kreis 10              40191.142857\n",
              "Kreis 11              75052.000000\n",
              "Kreis 12              32402.142857\n",
              "Kreis 2               34121.000000\n",
              "Kreis 3               50184.000000\n",
              "Kreis 4               28894.285714\n",
              "Kreis 5               15558.428571\n",
              "Kreis 6               34219.714286\n",
              "Kreis 7               37959.571429\n",
              "Kreis 8               16866.857143\n",
              "Kreis 9               54983.285714\n",
              "Langstrasse           11449.571429\n",
              "Leimbach               6205.285714\n",
              "Lindenhof               990.571429\n",
              "Mühlebach              6268.714286\n",
              "Oberstrass            10817.285714\n",
              "Oerlikon              22981.714286\n",
              "Rathaus                3265.000000\n",
              "Saatlen                8598.428571\n",
              "Schwamendingen-Mitte  11117.428571\n",
              "Seebach               25711.000000\n",
              "Seefeld                5297.000000\n",
              "Sihlfeld              21633.428571\n",
              "Unterstrass           23402.428571\n",
              "Weinegg                5301.142857\n",
              "Werd                   4526.000000\n",
              "Wipkingen             16353.000000\n",
              "Witikon               10916.000000\n",
              "Wollishofen           18363.571429"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "population_mean = population_after_15.groupby(\"Gebiet\")[\"Total\"].mean()\n",
        "population_mean = pd.DataFrame(population_mean)\n",
        "population_mean"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ol4MmGEsBvud"
      },
      "source": [
        "Hier sehen wir nun die mittleren Einwohnerzahlen für die Jahre 2015-2021 für alle Quartiere und Kreise. Außerdem bemerken wir, dass durch das `groupby()` die Gebiete jetzt auch der Index der Tabelle sind.\n",
        "\n",
        "Dass nicht nur die Kreise, sondern auch die Quartiere mit in der Tabelle aufgeführt sind ist kein Problem: Nachdem sie in `kreise` nicht vorkommen, werden sie beim `join()` einfach weggelassen."
      ]
    },
    {
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      "execution_count": 91,
      "metadata": {
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          "height": 488
        },
        "id": "gn0Bgo0Cd1sQ",
        "outputId": "6db125cf-01ef-4846-8844-222d0b42569c"
      },
      "outputs": [
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              "      <th>Kreis 8</th>\n",
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              "      <td>54983.285714</td>\n",
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              "      <th>Unbekannt (Stadt Zürich)</th>\n",
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            "text/plain": [
              "                          HalterId         Total\n",
              "KreisLang                                       \n",
              "Kreis 1                    124.875   5740.285714\n",
              "Kreis 10                   713.875  40191.142857\n",
              "Kreis 11                  1294.375  75052.000000\n",
              "Kreis 12                   544.000  32402.142857\n",
              "Kreis 2                    713.000  34121.000000\n",
              "Kreis 3                    690.125  50184.000000\n",
              "Kreis 4                    401.125  28894.285714\n",
              "Kreis 5                    245.500  15558.428571\n",
              "Kreis 6                    527.625  34219.714286\n",
              "Kreis 7                   1069.750  37959.571429\n",
              "Kreis 8                    407.375  16866.857143\n",
              "Kreis 9                    952.125  54983.285714\n",
              "Unbekannt (Stadt Zürich)     0.250           NaN"
            ]
          },
          "execution_count": 91,
          "metadata": {},
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      ],
      "source": [
        "kreise_pop = kreise.join(population_mean)\n",
        "kreise_pop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfVzOLucDEfX"
      },
      "source": [
        "Wir sehen dass nach dem `join()` die beiden Tabellen verbunden sind und für jeden Kreis nun auch die Einwohnerzahl angegeben ist.\n",
        "\n",
        "Außerdem sehen wir, dass in der letzten Zeile bei \"Unbekannt (Stadt Zürich)\" keine Einwohnerzahl angegeben ist. Das liegt daran, dass dieser Index in der Tabelle `population_mean` nicht existiert. \n",
        "\n",
        "Die Standardeinstellung von `join()` ist es, dass die Tabelle auf der die `join()`-Methode aufgerufen wird immer ganz erhalten bleibt. Nachdem wir hier `kreise.join()` aufgerufen haben, ist `kreise` erhalten geblieben.\n",
        "\n",
        "Diese Art des joins nennt sich Left-Join, weil die linke Tabelle ganz bleibt und die rechte Tabelle, also die als zweites aufgerufen wird angestückelt wird.\n",
        "\n",
        "Es gibt noch andere Arten des Joins:\n",
        "\n",
        "- `inner`: Nur Indizes, die in beiden DataFrames enthalten sind, werden gejoined.\n",
        "- `outer`: Alle Indizes werden gejoined\n",
        "- `right`: Alle Indizes im rechten DataFrame sind im Ergebnis enthalten.\n",
        "\n",
        "Wir können mit dem Argument `how` auswählen auf welche Art wir joinen möchten:"
      ]
    },
    {
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      "execution_count": 96,
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-3fa1d89f-f374-4f6c-8153-5e7ef3b05075 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3fa1d89f-f374-4f6c-8153-5e7ef3b05075');\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": [
              "          HalterId         Total\n",
              "Kreis 1    124.875   5740.285714\n",
              "Kreis 10   713.875  40191.142857\n",
              "Kreis 11  1294.375  75052.000000\n",
              "Kreis 12   544.000  32402.142857\n",
              "Kreis 2    713.000  34121.000000\n",
              "Kreis 3    690.125  50184.000000\n",
              "Kreis 4    401.125  28894.285714\n",
              "Kreis 5    245.500  15558.428571\n",
              "Kreis 6    527.625  34219.714286\n",
              "Kreis 7   1069.750  37959.571429\n",
              "Kreis 8    407.375  16866.857143\n",
              "Kreis 9    952.125  54983.285714"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "kreise_pop = kreise.join(population_mean, how=\"inner\")\n",
        "kreise_pop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67qpXk_SF2IV"
      },
      "source": [
        "Die Spaltennamen sind noch etwas nichtssagend, deshalb passen wir sie kurz an:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "EZAj8jPdC-gq",
        "outputId": "eef70bfc-3e86-4aa0-b996-68dcc2ad7a9b"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-350f3ffd-bc14-49c1-941b-22d39bc73709\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AnzahlHunde</th>\n",
              "      <th>Einwohner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Kreis 1</th>\n",
              "      <td>124.875</td>\n",
              "      <td>5740.285714</td>\n",
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              "    <tr>\n",
              "      <th>Kreis 10</th>\n",
              "      <td>713.875</td>\n",
              "      <td>40191.142857</td>\n",
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              "    <tr>\n",
              "      <th>Kreis 11</th>\n",
              "      <td>1294.375</td>\n",
              "      <td>75052.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 12</th>\n",
              "      <td>544.000</td>\n",
              "      <td>32402.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 2</th>\n",
              "      <td>713.000</td>\n",
              "      <td>34121.000000</td>\n",
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              "    <tr>\n",
              "      <th>Kreis 3</th>\n",
              "      <td>690.125</td>\n",
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              "    <tr>\n",
              "      <th>Kreis 4</th>\n",
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              "      <th>Kreis 5</th>\n",
              "      <td>245.500</td>\n",
              "      <td>15558.428571</td>\n",
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              "    <tr>\n",
              "      <th>Kreis 6</th>\n",
              "      <td>527.625</td>\n",
              "      <td>34219.714286</td>\n",
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              "    <tr>\n",
              "      <th>Kreis 7</th>\n",
              "      <td>1069.750</td>\n",
              "      <td>37959.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 8</th>\n",
              "      <td>407.375</td>\n",
              "      <td>16866.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 9</th>\n",
              "      <td>952.125</td>\n",
              "      <td>54983.285714</td>\n",
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              "                                                     [key], {});\n",
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              "\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          AnzahlHunde     Einwohner\n",
              "Kreis 1       124.875   5740.285714\n",
              "Kreis 10      713.875  40191.142857\n",
              "Kreis 11     1294.375  75052.000000\n",
              "Kreis 12      544.000  32402.142857\n",
              "Kreis 2       713.000  34121.000000\n",
              "Kreis 3       690.125  50184.000000\n",
              "Kreis 4       401.125  28894.285714\n",
              "Kreis 5       245.500  15558.428571\n",
              "Kreis 6       527.625  34219.714286\n",
              "Kreis 7      1069.750  37959.571429\n",
              "Kreis 8       407.375  16866.857143\n",
              "Kreis 9       952.125  54983.285714"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "kreise_pop.columns = [\"AnzahlHunde\", \"Einwohner\"]\n",
        "kreise_pop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8c0D-JRkF7-r"
      },
      "source": [
        "Jetzt können wir mit der eigentlichen Analyse starten: Wir müssen die Zahl der Hunde in jedem Kreis durch die Zahl der Einwohner teilen.\n",
        "\n",
        "Dafür benutzen wir die `apply()`-Methode. Wenn wir diese auf einem DataFrame ausführen, dann wird jede Spalte an eine vorher definierte Funktion übergeben. \n",
        "\n",
        "Wir spielen das kurz an einem einfachen Beispiel durch:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "T2pSc26-HxFh",
        "outputId": "818d1e20-ea5a-4ae6-a5b0-d8e9fbd5a240"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3b21bce0-f2e7-48cd-89b6-1f74134c7a88\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Spalte1</th>\n",
              "      <th>Spalte2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              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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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\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-3b21bce0-f2e7-48cd-89b6-1f74134c7a88 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-3b21bce0-f2e7-48cd-89b6-1f74134c7a88');\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": [
              "   Spalte1  Spalte2\n",
              "0        1        4\n",
              "1        2        5\n",
              "2        3        6"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.DataFrame({\"Spalte1\": [1, 2, 3], \"Spalte2\": [4, 5, 6]})\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqTY6rugIDdQ"
      },
      "source": [
        "Wir können jetzt eine Funktion schreiben, die eine Spalte als Input nimmt und damit irgendetwas macht. Als Beispiel berechnen wir den Mittelwert jeder Spalte:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9_9tN34ICqW",
        "outputId": "d12c9e2b-b372-4231-e3c0-6504438ceb40"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Spalte1    2.0\n",
              "Spalte2    5.0\n",
              "dtype: float64"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def calc_mean(col):\n",
        "  return col.mean()\n",
        "\n",
        "df.apply(calc_mean)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRcYtV_8IaEa"
      },
      "source": [
        "Wir können die `mean()`-Methode hier anwenden, weil die `apply()`-Methode die einzelnen Spalten als `Series`-Objekt übergibt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sP4_rQqlIo2a",
        "outputId": "2ec97823-0f9b-46d3-fd7c-28b288aa5d83"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Spalte1    <class 'pandas.core.series.Series'>\n",
              "Spalte2    <class 'pandas.core.series.Series'>\n",
              "dtype: object"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def calc_mean(col):\n",
        "  return str(type(col))\n",
        "\n",
        "df.apply(calc_mean)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8e6ZyWhFI4CM"
      },
      "source": [
        "Wir können hier also jede Methode und jede Funktion anwenden, die wir auf `Series`-Objekte anwenden können. Welche das sind können wir [hier in der Dokumentation](https://pandas.pydata.org/docs/reference/series.html) nachlesen.\n",
        "\n",
        "Wir können die `apply()`-Methode nicht nur auf Spalten sondern auch auf Zeilen anwenden. Dafür müssen wir das Argument `axis=1` mitgeben. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NyJI-iGJgfU",
        "outputId": "f978409c-d9c4-440f-c450-29fa9efd9d9e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    2.5\n",
              "1    3.5\n",
              "2    4.5\n",
              "dtype: float64"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def calc_mean(col):\n",
        "  return col.mean()\n",
        "\n",
        "df.apply(calc_mean, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YM-yEPhhJkgb"
      },
      "source": [
        "Und nach diesem Prinzip können wir jetzt die beiden Elemente `Einwohner` und `AnzahlHunde` aus jeder Spalte nehmen und durcheinander teilen.\n",
        "\n",
        "Das Ergebnis schreiben wir in eine neue Spalte `AnzahlHunde`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "SyODvl6oh_be",
        "outputId": "9d1ff6cd-615c-4635-8c68-ffa168acc164"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-583b6e27-158f-46b0-8968-845aa6195ea7\">\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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AnzahlHunde</th>\n",
              "      <th>Einwohner</th>\n",
              "      <th>HundePro100Einwohner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Kreis 1</th>\n",
              "      <td>124.875</td>\n",
              "      <td>5740.285714</td>\n",
              "      <td>2.175414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 10</th>\n",
              "      <td>713.875</td>\n",
              "      <td>40191.142857</td>\n",
              "      <td>1.776200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 11</th>\n",
              "      <td>1294.375</td>\n",
              "      <td>75052.000000</td>\n",
              "      <td>1.724638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 12</th>\n",
              "      <td>544.000</td>\n",
              "      <td>32402.142857</td>\n",
              "      <td>1.678901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 2</th>\n",
              "      <td>713.000</td>\n",
              "      <td>34121.000000</td>\n",
              "      <td>2.089622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 3</th>\n",
              "      <td>690.125</td>\n",
              "      <td>50184.000000</td>\n",
              "      <td>1.375189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 4</th>\n",
              "      <td>401.125</td>\n",
              "      <td>28894.285714</td>\n",
              "      <td>1.388250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 5</th>\n",
              "      <td>245.500</td>\n",
              "      <td>15558.428571</td>\n",
              "      <td>1.577923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 6</th>\n",
              "      <td>527.625</td>\n",
              "      <td>34219.714286</td>\n",
              "      <td>1.541874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 7</th>\n",
              "      <td>1069.750</td>\n",
              "      <td>37959.571429</td>\n",
              "      <td>2.818130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 8</th>\n",
              "      <td>407.375</td>\n",
              "      <td>16866.857143</td>\n",
              "      <td>2.415240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kreis 9</th>\n",
              "      <td>952.125</td>\n",
              "      <td>54983.285714</td>\n",
              "      <td>1.731663</td>\n",
              "    </tr>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          AnzahlHunde     Einwohner  HundePro100Einwohner\n",
              "Kreis 1       124.875   5740.285714              2.175414\n",
              "Kreis 10      713.875  40191.142857              1.776200\n",
              "Kreis 11     1294.375  75052.000000              1.724638\n",
              "Kreis 12      544.000  32402.142857              1.678901\n",
              "Kreis 2       713.000  34121.000000              2.089622\n",
              "Kreis 3       690.125  50184.000000              1.375189\n",
              "Kreis 4       401.125  28894.285714              1.388250\n",
              "Kreis 5       245.500  15558.428571              1.577923\n",
              "Kreis 6       527.625  34219.714286              1.541874\n",
              "Kreis 7      1069.750  37959.571429              2.818130\n",
              "Kreis 8       407.375  16866.857143              2.415240\n",
              "Kreis 9       952.125  54983.285714              1.731663"
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def percentage(row):\n",
        "  return row[\"AnzahlHunde\"]/row[\"Einwohner\"]*100\n",
        "\n",
        "kreise_pop[\"HundePro100Einwohner\"] = kreise_pop.apply(percentage, axis=1)\n",
        "kreise_pop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-Kmf8pBJ3mb"
      },
      "source": [
        "Und weil es übersichtlicher ist, erstellen wir noch schnell einen Barplot:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "-lKkMhMCh6kf",
        "outputId": "89ced7da-94a3-4778-cc42-fc6142d10a6f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 100,
          "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": [
        "kreise_pop[\"HundePro100Einwohner\"].plot(kind=\"bar\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sIhhrt--knBT"
      },
      "source": [
        "# Hunderassen nach Alter der Halter\\*innen"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "nOeOOlcfjPNq",
        "outputId": "c8906726-b7f1-4f98-fc48-ef1b735415b3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-307ca91e-14c9-4275-a1ee-fbd9898dbdb8\">\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>KreisLang</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>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>126</td>\n",
              "      <td>60.0</td>\n",
              "      <td>männlich</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Kreis 9</td>\n",
              "      <td>92.0</td>\n",
              "      <td>Altstetten</td>\n",
              "      <td>Welsh Terrier</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>schwarz/braun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015</td>\n",
              "      <td>574</td>\n",
              "      <td>60.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Kreis 2</td>\n",
              "      <td>23.0</td>\n",
              "      <td>Leimbach</td>\n",
              "      <td>Cairn Terrier</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>brindle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015</td>\n",
              "      <td>695</td>\n",
              "      <td>40.0</td>\n",
              "      <td>männlich</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Kreis 6</td>\n",
              "      <td>63.0</td>\n",
              "      <td>Oberstrass</td>\n",
              "      <td>Labrador Retriever</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>braun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015</td>\n",
              "      <td>893</td>\n",
              "      <td>60.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Kreis 7</td>\n",
              "      <td>71.0</td>\n",
              "      <td>Fluntern</td>\n",
              "      <td>Mittelschnauzer</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>schwarz</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>1177</td>\n",
              "      <td>50.0</td>\n",
              "      <td>männlich</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Kreis 10</td>\n",
              "      <td>102.0</td>\n",
              "      <td>Wipkingen</td>\n",
              "      <td>Shih Tzu</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>männlich</td>\n",
              "      <td>schwarz/weiss</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61467</th>\n",
              "      <td>2022</td>\n",
              "      <td>160175</td>\n",
              "      <td>20.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Kreis 8</td>\n",
              "      <td>82.0</td>\n",
              "      <td>Mühlebach</td>\n",
              "      <td>Chihuahua</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>hellbraun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61468</th>\n",
              "      <td>2022</td>\n",
              "      <td>160176</td>\n",
              "      <td>20.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Kreis 8</td>\n",
              "      <td>83.0</td>\n",
              "      <td>Weinegg</td>\n",
              "      <td>Zwergpudel</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>apricot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61469</th>\n",
              "      <td>2022</td>\n",
              "      <td>160178</td>\n",
              "      <td>50.0</td>\n",
              "      <td>männlich</td>\n",
              "      <td>11.0</td>\n",
              "      <td>Kreis 11</td>\n",
              "      <td>111.0</td>\n",
              "      <td>Affoltern</td>\n",
              "      <td>Shetland Sheepdog</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>tricolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61470</th>\n",
              "      <td>2022</td>\n",
              "      <td>160182</td>\n",
              "      <td>30.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Kreis 7</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Hottingen</td>\n",
              "      <td>Miniature American Shepard</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>männlich</td>\n",
              "      <td>braun</td>\n",
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              "    <tr>\n",
              "      <th>61471</th>\n",
              "      <td>2022</td>\n",
              "      <td>160183</td>\n",
              "      <td>20.0</td>\n",
              "      <td>weiblich</td>\n",
              "      <td>11.0</td>\n",
              "      <td>Kreis 11</td>\n",
              "      <td>119.0</td>\n",
              "      <td>Seebach</td>\n",
              "      <td>Dachshund</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>männlich</td>\n",
              "      <td>braun/rot</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61472 rows × 13 columns</p>\n",
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            "text/plain": [
              "       StichtagDatJahr  HalterId  AlterV10Cd   SexLang  KreisCd KreisLang  \\\n",
              "0                 2015       126        60.0  männlich      9.0   Kreis 9   \n",
              "1                 2015       574        60.0  weiblich      2.0   Kreis 2   \n",
              "2                 2015       695        40.0  männlich      6.0   Kreis 6   \n",
              "3                 2015       893        60.0  weiblich      7.0   Kreis 7   \n",
              "4                 2015      1177        50.0  männlich     10.0  Kreis 10   \n",
              "...                ...       ...         ...       ...      ...       ...   \n",
              "61467             2022    160175        20.0  weiblich      8.0   Kreis 8   \n",
              "61468             2022    160176        20.0  weiblich      8.0   Kreis 8   \n",
              "61469             2022    160178        50.0  männlich     11.0  Kreis 11   \n",
              "61470             2022    160182        30.0  weiblich      7.0   Kreis 7   \n",
              "61471             2022    160183        20.0  weiblich     11.0  Kreis 11   \n",
              "\n",
              "       QuarCd    QuarLang                  Rasse1Text  GebDatHundJahr  \\\n",
              "0        92.0  Altstetten               Welsh Terrier          2011.0   \n",
              "1        23.0    Leimbach               Cairn Terrier          2002.0   \n",
              "2        63.0  Oberstrass          Labrador Retriever          2012.0   \n",
              "3        71.0    Fluntern             Mittelschnauzer          2010.0   \n",
              "4       102.0   Wipkingen                    Shih Tzu          2011.0   \n",
              "...       ...         ...                         ...             ...   \n",
              "61467    82.0   Mühlebach                   Chihuahua          2021.0   \n",
              "61468    83.0     Weinegg                  Zwergpudel          2012.0   \n",
              "61469   111.0   Affoltern           Shetland Sheepdog          2022.0   \n",
              "61470    72.0   Hottingen  Miniature American Shepard          2022.0   \n",
              "61471   119.0     Seebach                   Dachshund          2021.0   \n",
              "\n",
              "       AlterVHundCd SexHundLang HundefarbeText  \n",
              "0               3.0    weiblich  schwarz/braun  \n",
              "1              12.0    weiblich        brindle  \n",
              "2               2.0    weiblich          braun  \n",
              "3               4.0    weiblich        schwarz  \n",
              "4               3.0    männlich  schwarz/weiss  \n",
              "...             ...         ...            ...  \n",
              "61467           0.0    weiblich      hellbraun  \n",
              "61468           9.0    weiblich        apricot  \n",
              "61469           0.0    weiblich       tricolor  \n",
              "61470           0.0    männlich          braun  \n",
              "61471           0.0    männlich      braun/rot  \n",
              "\n",
              "[61472 rows x 13 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "aeWcqFUDtGh6",
        "outputId": "a68a660d-9202-485e-b235-9d25a69ca8dc"
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      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
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              "      <th>StichtagDatJahr</th>\n",
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              "      <th rowspan=\"2\" valign=\"top\">90.0</th>\n",
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              "      <th rowspan=\"2\" valign=\"top\">80.0</th>\n",
              "      <th>Unbekannt</th>\n",
              "      <td>192</td>\n",
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              "      <td>192</td>\n",
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              "      <td>192</td>\n",
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              "      <td>192</td>\n",
              "      <td>192</td>\n",
              "      <td>192</td>\n",
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              "      <th>Pudel</th>\n",
              "      <td>112</td>\n",
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              "      <td>112</td>\n",
              "      <td>112</td>\n",
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              "      <td>112</td>\n",
              "      <td>112</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">70.0</th>\n",
              "      <th>Unbekannt</th>\n",
              "      <td>771</td>\n",
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              "      <td>771</td>\n",
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              "      <td>771</td>\n",
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              "      <td>771</td>\n",
              "      <td>771</td>\n",
              "      <td>771</td>\n",
              "      <td>771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>343</td>\n",
              "      <td>343</td>\n",
              "      <td>343</td>\n",
              "      <td>343</td>\n",
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              "      <td>343</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">60.0</th>\n",
              "      <th>Unbekannt</th>\n",
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              "      <td>1164</td>\n",
              "      <td>1164</td>\n",
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              "      <th>Labrador Retriever</th>\n",
              "      <td>536</td>\n",
              "      <td>536</td>\n",
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              "      <td>536</td>\n",
              "      <td>536</td>\n",
              "      <td>536</td>\n",
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              "      <td>536</td>\n",
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              "      <td>536</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">50.0</th>\n",
              "      <th>Unbekannt</th>\n",
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              "      <td>1688</td>\n",
              "      <td>1688</td>\n",
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              "      <th>Labrador Retriever</th>\n",
              "      <td>847</td>\n",
              "      <td>847</td>\n",
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              "      <th>Unbekannt</th>\n",
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              "      <th>Chihuahua</th>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
              "      <td>888</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">30.0</th>\n",
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              "      <td>1738</td>\n",
              "      <td>1738</td>\n",
              "      <td>1738</td>\n",
              "      <td>1738</td>\n",
              "      <td>1738</td>\n",
              "      <td>1738</td>\n",
              "      <td>1738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>1130</td>\n",
              "      <td>1130</td>\n",
              "      <td>1130</td>\n",
              "      <td>1130</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">20.0</th>\n",
              "      <th>Unbekannt</th>\n",
              "      <td>778</td>\n",
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              "      <td>778</td>\n",
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              "      <td>778</td>\n",
              "      <td>778</td>\n",
              "      <td>778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
              "      <td>759</td>\n",
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              "      <td>18</td>\n",
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              "      <td>18</td>\n",
              "      <td>18</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35a7f52a-2615-49b2-9a4a-a678de1bda59')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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            ],
            "text/plain": [
              "                               StichtagDatJahr  HalterId  SexLang  KreisCd  \\\n",
              "AlterV10Cd Rasse1Text                                                        \n",
              "90.0       Unbekannt                        25        25       25       25   \n",
              "           Pudel                            21        21       21       21   \n",
              "80.0       Unbekannt                       192       192      192      192   \n",
              "           Pudel                           112       112      112      112   \n",
              "70.0       Unbekannt                       771       771      771      771   \n",
              "           Labrador Retriever              343       343      343      343   \n",
              "60.0       Unbekannt                      1164      1164     1164     1164   \n",
              "           Labrador Retriever              536       536      536      536   \n",
              "50.0       Unbekannt                      1688      1688     1688     1688   \n",
              "           Labrador Retriever              847       847      847      847   \n",
              "40.0       Unbekannt                      1479      1479     1479     1479   \n",
              "           Chihuahua                       888       888      888      888   \n",
              "30.0       Unbekannt                      1738      1738     1738     1738   \n",
              "           Chihuahua                      1130      1130     1130     1130   \n",
              "20.0       Unbekannt                       778       778      778      778   \n",
              "           Chihuahua                       759       759      759      759   \n",
              "10.0       Unbekannt                        18        18       18       18   \n",
              "           Yorkshire Terrier                16        16       16       16   \n",
              "\n",
              "                               KreisLang  QuarCd  QuarLang  GebDatHundJahr  \\\n",
              "AlterV10Cd Rasse1Text                                                        \n",
              "90.0       Unbekannt                  25      25        25              25   \n",
              "           Pudel                      21      21        21              21   \n",
              "80.0       Unbekannt                 192     192       192             192   \n",
              "           Pudel                     112     112       112             112   \n",
              "70.0       Unbekannt                 771     771       771             771   \n",
              "           Labrador Retriever        343     343       343             343   \n",
              "60.0       Unbekannt                1164    1164      1164            1164   \n",
              "           Labrador Retriever        536     536       536             536   \n",
              "50.0       Unbekannt                1688    1688      1688            1688   \n",
              "           Labrador Retriever        847     847       847             847   \n",
              "40.0       Unbekannt                1479    1479      1479            1479   \n",
              "           Chihuahua                 888     888       888             888   \n",
              "30.0       Unbekannt                1738    1738      1738            1738   \n",
              "           Chihuahua                1130    1130      1130            1130   \n",
              "20.0       Unbekannt                 778     778       778             778   \n",
              "           Chihuahua                 759     759       759             759   \n",
              "10.0       Unbekannt                  18      18        18              18   \n",
              "           Yorkshire Terrier          16      16        16              16   \n",
              "\n",
              "                               AlterVHundCd  SexHundLang  HundefarbeText  \n",
              "AlterV10Cd Rasse1Text                                                     \n",
              "90.0       Unbekannt                     25           25              25  \n",
              "           Pudel                         21           21              21  \n",
              "80.0       Unbekannt                    192          192             192  \n",
              "           Pudel                        112          112             112  \n",
              "70.0       Unbekannt                    771          771             771  \n",
              "           Labrador Retriever           343          343             343  \n",
              "60.0       Unbekannt                   1164         1164            1164  \n",
              "           Labrador Retriever           536          536             536  \n",
              "50.0       Unbekannt                   1688         1688            1688  \n",
              "           Labrador Retriever           847          847             847  \n",
              "40.0       Unbekannt                   1479         1479            1479  \n",
              "           Chihuahua                    888          888             888  \n",
              "30.0       Unbekannt                   1738         1738            1738  \n",
              "           Chihuahua                   1130         1130            1130  \n",
              "20.0       Unbekannt                    778          778             778  \n",
              "           Chihuahua                    759          759             759  \n",
              "10.0       Unbekannt                     18           18              18  \n",
              "           Yorkshire Terrier             16           16              16  "
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs.groupby([\"AlterV10Cd\", \"Rasse1Text\"]).count().sort_values([\"AlterV10Cd\", \"HalterId\"], ascending=False).groupby(level=0).head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DQFn7OYSxMmw",
        "outputId": "25c49b7d-b98f-4e9c-f285-0b421489fcea"
      },
      "outputs": [
        {
          "data": {
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              "      <th>StichtagDatJahr</th>\n",
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              "      <th>AlterV10Cd</th>\n",
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              "      <th rowspan=\"5\" valign=\"top\">90.0</th>\n",
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              "      <td>2.625</td>\n",
              "      <td>2.625</td>\n",
              "      <td>2.625</td>\n",
              "      <td>2.625</td>\n",
              "      <td>2.625</td>\n",
              "      <td>2.625</td>\n",
              "      <td>2.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malteser</th>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yorkshire Terrier</th>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "      <td>1.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">80.0</th>\n",
              "      <th>Pudel</th>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "      <td>14.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yorkshire Terrier</th>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "      <td>10.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "      <td>9.875</td>\n",
              "    </tr>\n",
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              "      <th>Zwergpudel</th>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
              "      <td>7.375</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">70.0</th>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "      <td>42.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dachshund</th>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
              "      <td>31.250</td>\n",
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              "    <tr>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "      <td>28.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yorkshire Terrier</th>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jack Russel Terrier</th>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "      <td>21.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">60.0</th>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "      <td>67.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "      <td>49.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jack Russel Terrier</th>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "      <td>46.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yorkshire Terrier</th>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "      <td>46.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malteser</th>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "      <td>38.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">50.0</th>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "      <td>105.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "      <td>95.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yorkshire Terrier</th>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "      <td>77.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jack Russel Terrier</th>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "      <td>69.125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malteser</th>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "      <td>48.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">40.0</th>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "      <td>111.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "      <td>100.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jack Russel Terrier</th>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "      <td>61.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yorkshire Terrier</th>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "      <td>57.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malteser</th>\n",
              "      <td>52.500</td>\n",
              "      <td>52.500</td>\n",
              "      <td>52.500</td>\n",
              "      <td>52.500</td>\n",
              "      <td>52.500</td>\n",
              "      <td>52.500</td>\n",
              "      <td>52.500</td>\n",
              "      <td>52.375</td>\n",
              "      <td>52.375</td>\n",
              "      <td>52.500</td>\n",
              "      <td>52.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">30.0</th>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "      <td>141.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Französische Bulldogge</th>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "      <td>101.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "      <td>96.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mops</th>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "      <td>57.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jack Russel Terrier</th>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "      <td>56.625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">20.0</th>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "      <td>94.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Französische Bulldogge</th>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "      <td>62.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zwergspitz</th>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "      <td>31.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "      <td>26.125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mops</th>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "      <td>23.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">10.0</th>\n",
              "      <th>Yorkshire Terrier</th>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chihuahua</th>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "      <td>1.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Französische Bulldogge</th>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "      <td>1.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zwergspitz</th>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Labrador Retriever</th>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18f5dc05-28d5-4c16-91ac-69b9af4af15f')\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-18f5dc05-28d5-4c16-91ac-69b9af4af15f');\n",
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              "                                                     [key], {});\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  SexLang  \\\n",
              "AlterV10Cd Rasse1Text                                                   \n",
              "90.0       Pudel                             2.625     2.625    2.625   \n",
              "           Chihuahua                         2.000     2.000    2.000   \n",
              "           Malteser                          1.875     1.875    1.875   \n",
              "           Yorkshire Terrier                 1.625     1.625    1.625   \n",
              "           Labrador Retriever                1.375     1.375    1.375   \n",
              "80.0       Pudel                            14.000    14.000   14.000   \n",
              "           Chihuahua                        10.500    10.500   10.500   \n",
              "           Yorkshire Terrier                10.500    10.500   10.500   \n",
              "           Labrador Retriever                9.875     9.875    9.875   \n",
              "           Zwergpudel                        7.375     7.375    7.375   \n",
              "70.0       Labrador Retriever               42.875    42.875   42.875   \n",
              "           Dachshund                        31.250    31.250   31.250   \n",
              "           Chihuahua                        28.375    28.375   28.375   \n",
              "           Yorkshire Terrier                27.750    27.750   27.750   \n",
              "           Jack Russel Terrier              21.000    21.000   21.000   \n",
              "60.0       Labrador Retriever               67.000    67.000   67.000   \n",
              "           Chihuahua                        49.000    49.000   49.000   \n",
              "           Jack Russel Terrier              46.375    46.375   46.375   \n",
              "           Yorkshire Terrier                46.250    46.250   46.250   \n",
              "           Malteser                         38.625    38.625   38.625   \n",
              "50.0       Labrador Retriever              105.875   105.875  105.875   \n",
              "           Chihuahua                        95.750    95.750   95.750   \n",
              "           Yorkshire Terrier                77.750    77.750   77.750   \n",
              "           Jack Russel Terrier              69.125    69.125   69.125   \n",
              "           Malteser                         48.750    48.750   48.750   \n",
              "40.0       Chihuahua                       111.000   111.000  111.000   \n",
              "           Labrador Retriever              100.500   100.500  100.500   \n",
              "           Jack Russel Terrier              61.875    61.875   61.875   \n",
              "           Yorkshire Terrier                57.375    57.375   57.375   \n",
              "           Malteser                         52.500    52.500   52.500   \n",
              "30.0       Chihuahua                       141.250   141.250  141.250   \n",
              "           Französische Bulldogge          101.750   101.750  101.750   \n",
              "           Labrador Retriever               96.375    96.375   96.375   \n",
              "           Mops                             57.750    57.750   57.750   \n",
              "           Jack Russel Terrier              56.625    56.625   56.625   \n",
              "20.0       Chihuahua                        94.875    94.875   94.875   \n",
              "           Französische Bulldogge           62.875    62.875   62.875   \n",
              "           Zwergspitz                       31.500    31.500   31.500   \n",
              "           Labrador Retriever               26.125    26.125   26.125   \n",
              "           Mops                             23.875    23.875   23.875   \n",
              "10.0       Yorkshire Terrier                 2.000     2.000    2.000   \n",
              "           Chihuahua                         1.875     1.875    1.875   \n",
              "           Französische Bulldogge            1.375     1.375    1.375   \n",
              "           Zwergspitz                        1.250     1.250    1.250   \n",
              "           Labrador Retriever                1.125     1.125    1.125   \n",
              "\n",
              "                                   KreisCd  KreisLang   QuarCd  QuarLang  \\\n",
              "AlterV10Cd Rasse1Text                                                      \n",
              "90.0       Pudel                     2.625      2.625    2.625     2.625   \n",
              "           Chihuahua                 2.000      2.000    2.000     2.000   \n",
              "           Malteser                  1.875      1.875    1.875     1.875   \n",
              "           Yorkshire Terrier         1.625      1.625    1.625     1.625   \n",
              "           Labrador Retriever        1.375      1.375    1.375     1.375   \n",
              "80.0       Pudel                    14.000     14.000   14.000    14.000   \n",
              "           Chihuahua                10.500     10.500   10.500    10.500   \n",
              "           Yorkshire Terrier        10.500     10.500   10.500    10.500   \n",
              "           Labrador Retriever        9.875      9.875    9.875     9.875   \n",
              "           Zwergpudel                7.375      7.375    7.375     7.375   \n",
              "70.0       Labrador Retriever       42.875     42.875   42.875    42.875   \n",
              "           Dachshund                31.250     31.250   31.250    31.250   \n",
              "           Chihuahua                28.375     28.375   28.375    28.375   \n",
              "           Yorkshire Terrier        27.750     27.750   27.750    27.750   \n",
              "           Jack Russel Terrier      21.000     21.000   21.000    21.000   \n",
              "60.0       Labrador Retriever       67.000     67.000   67.000    67.000   \n",
              "           Chihuahua                49.000     49.000   49.000    49.000   \n",
              "           Jack Russel Terrier      46.375     46.375   46.375    46.375   \n",
              "           Yorkshire Terrier        46.250     46.250   46.250    46.250   \n",
              "           Malteser                 38.625     38.625   38.625    38.625   \n",
              "50.0       Labrador Retriever      105.875    105.875  105.875   105.875   \n",
              "           Chihuahua                95.750     95.750   95.750    95.750   \n",
              "           Yorkshire Terrier        77.750     77.750   77.750    77.750   \n",
              "           Jack Russel Terrier      69.125     69.125   69.125    69.125   \n",
              "           Malteser                 48.750     48.750   48.750    48.750   \n",
              "40.0       Chihuahua               111.000    111.000  111.000   111.000   \n",
              "           Labrador Retriever      100.500    100.500  100.500   100.500   \n",
              "           Jack Russel Terrier      61.875     61.875   61.875    61.875   \n",
              "           Yorkshire Terrier        57.375     57.375   57.375    57.375   \n",
              "           Malteser                 52.500     52.500   52.500    52.500   \n",
              "30.0       Chihuahua               141.250    141.250  141.250   141.250   \n",
              "           Französische Bulldogge  101.750    101.750  101.750   101.750   \n",
              "           Labrador Retriever       96.375     96.375   96.375    96.375   \n",
              "           Mops                     57.750     57.750   57.750    57.750   \n",
              "           Jack Russel Terrier      56.625     56.625   56.625    56.625   \n",
              "20.0       Chihuahua                94.875     94.875   94.875    94.875   \n",
              "           Französische Bulldogge   62.875     62.875   62.875    62.875   \n",
              "           Zwergspitz               31.500     31.500   31.500    31.500   \n",
              "           Labrador Retriever       26.125     26.125   26.125    26.125   \n",
              "           Mops                     23.875     23.875   23.875    23.875   \n",
              "10.0       Yorkshire Terrier         2.000      2.000    2.000     2.000   \n",
              "           Chihuahua                 1.875      1.875    1.875     1.875   \n",
              "           Französische Bulldogge    1.375      1.375    1.375     1.375   \n",
              "           Zwergspitz                1.250      1.250    1.250     1.250   \n",
              "           Labrador Retriever        1.125      1.125    1.125     1.125   \n",
              "\n",
              "                                   GebDatHundJahr  AlterVHundCd  SexHundLang  \\\n",
              "AlterV10Cd Rasse1Text                                                          \n",
              "90.0       Pudel                            2.625         2.625        2.625   \n",
              "           Chihuahua                        2.000         2.000        2.000   \n",
              "           Malteser                         1.875         1.875        1.875   \n",
              "           Yorkshire Terrier                1.625         1.625        1.625   \n",
              "           Labrador Retriever               1.375         1.375        1.375   \n",
              "80.0       Pudel                           14.000        14.000       14.000   \n",
              "           Chihuahua                       10.500        10.500       10.500   \n",
              "           Yorkshire Terrier               10.500        10.500       10.500   \n",
              "           Labrador Retriever               9.875         9.875        9.875   \n",
              "           Zwergpudel                       7.375         7.375        7.375   \n",
              "70.0       Labrador Retriever              42.875        42.875       42.875   \n",
              "           Dachshund                       31.250        31.250       31.250   \n",
              "           Chihuahua                       28.375        28.375       28.375   \n",
              "           Yorkshire Terrier               27.750        27.750       27.750   \n",
              "           Jack Russel Terrier             21.000        21.000       21.000   \n",
              "60.0       Labrador Retriever              67.000        67.000       67.000   \n",
              "           Chihuahua                       49.000        49.000       49.000   \n",
              "           Jack Russel Terrier             46.375        46.375       46.375   \n",
              "           Yorkshire Terrier               46.250        46.250       46.250   \n",
              "           Malteser                        38.625        38.625       38.625   \n",
              "50.0       Labrador Retriever             105.875       105.875      105.875   \n",
              "           Chihuahua                       95.750        95.750       95.750   \n",
              "           Yorkshire Terrier               77.750        77.750       77.750   \n",
              "           Jack Russel Terrier             69.125        69.125       69.125   \n",
              "           Malteser                        48.750        48.750       48.750   \n",
              "40.0       Chihuahua                      111.000       111.000      111.000   \n",
              "           Labrador Retriever             100.500       100.500      100.500   \n",
              "           Jack Russel Terrier             61.875        61.875       61.875   \n",
              "           Yorkshire Terrier               57.375        57.375       57.375   \n",
              "           Malteser                        52.375        52.375       52.500   \n",
              "30.0       Chihuahua                      141.250       141.250      141.250   \n",
              "           Französische Bulldogge         101.750       101.750      101.750   \n",
              "           Labrador Retriever              96.375        96.375       96.375   \n",
              "           Mops                            57.750        57.750       57.750   \n",
              "           Jack Russel Terrier             56.625        56.625       56.625   \n",
              "20.0       Chihuahua                       94.875        94.875       94.875   \n",
              "           Französische Bulldogge          62.875        62.875       62.875   \n",
              "           Zwergspitz                      31.500        31.500       31.500   \n",
              "           Labrador Retriever              26.125        26.125       26.125   \n",
              "           Mops                            23.875        23.875       23.875   \n",
              "10.0       Yorkshire Terrier                2.000         2.000        2.000   \n",
              "           Chihuahua                        1.875         1.875        1.875   \n",
              "           Französische Bulldogge           1.375         1.375        1.375   \n",
              "           Zwergspitz                       1.250         1.250        1.250   \n",
              "           Labrador Retriever               1.125         1.125        1.125   \n",
              "\n",
              "                                   HundefarbeText  \n",
              "AlterV10Cd Rasse1Text                              \n",
              "90.0       Pudel                            2.625  \n",
              "           Chihuahua                        2.000  \n",
              "           Malteser                         1.875  \n",
              "           Yorkshire Terrier                1.625  \n",
              "           Labrador Retriever               1.375  \n",
              "80.0       Pudel                           14.000  \n",
              "           Chihuahua                       10.500  \n",
              "           Yorkshire Terrier               10.500  \n",
              "           Labrador Retriever               9.875  \n",
              "           Zwergpudel                       7.375  \n",
              "70.0       Labrador Retriever              42.875  \n",
              "           Dachshund                       31.250  \n",
              "           Chihuahua                       28.375  \n",
              "           Yorkshire Terrier               27.750  \n",
              "           Jack Russel Terrier             21.000  \n",
              "60.0       Labrador Retriever              67.000  \n",
              "           Chihuahua                       49.000  \n",
              "           Jack Russel Terrier             46.375  \n",
              "           Yorkshire Terrier               46.250  \n",
              "           Malteser                        38.625  \n",
              "50.0       Labrador Retriever             105.875  \n",
              "           Chihuahua                       95.750  \n",
              "           Yorkshire Terrier               77.750  \n",
              "           Jack Russel Terrier             69.125  \n",
              "           Malteser                        48.750  \n",
              "40.0       Chihuahua                      111.000  \n",
              "           Labrador Retriever             100.500  \n",
              "           Jack Russel Terrier             61.875  \n",
              "           Yorkshire Terrier               57.375  \n",
              "           Malteser                        52.500  \n",
              "30.0       Chihuahua                      141.250  \n",
              "           Französische Bulldogge         101.750  \n",
              "           Labrador Retriever              96.375  \n",
              "           Mops                            57.750  \n",
              "           Jack Russel Terrier             56.625  \n",
              "20.0       Chihuahua                       94.875  \n",
              "           Französische Bulldogge          62.875  \n",
              "           Zwergspitz                      31.500  \n",
              "           Labrador Retriever              26.125  \n",
              "           Mops                            23.875  \n",
              "10.0       Yorkshire Terrier                2.000  \n",
              "           Chihuahua                        1.875  \n",
              "           Französische Bulldogge           1.375  \n",
              "           Zwergspitz                       1.250  \n",
              "           Labrador Retriever               1.125  "
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dogs[dogs[\"Rasse1Text\"] != \"Unbekannt\"].groupby([\"AlterV10Cd\", \"Rasse1Text\"]).count().sort_values([\"AlterV10Cd\", \"HalterId\"], ascending=False).groupby(level=0).head(5).apply(lambda x: x/8)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyPw4zOhICSIsegGnVbeURSh",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}