RKI_daten_abrufen.ipynb

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    "colab": {
      "name": "RKI_daten_abrufen2.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhmPZNvzSs_M"
      },
      "source": [
        "# RKI Daten abrufen 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eou_w0tDSvOj"
      },
      "source": [
        "Jetzt nehmen wir uns die RKI-Daten im Detail vor. Das Gute daran: Sie liegen im CSV-Format vor, das wir leicht mit Pandas öffnen können. Zunächst muss man das Pandas Modul in Python laden:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_5K1ViHv8Wc"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOfxfgdcmVl9"
      },
      "source": [
        "Das ist die URL zu dem RKI-Datensatz. Die URL ist jeden Tag zum Glück gleich. Damit die Spalten \"IdLandkreis\" und \"IdBundesland\" als Text erkannt werden, können wir vor dem Laden der CSV Tabelle bestimmen, welchen Datentyp die Spalten haben sollen. Außerdem können wir gleich bestimmen, dass die Spalten \"RefDatum\", \"Meldedatum\" und \"Datenstand\" als Typ Datumsangaben enthalten und als solche \"geparst\" - also verarbeitet - werden sollen.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSFQ7_9qwzy4"
      },
      "source": [
        "dtypes = {'IdLandkreis': str, 'IdBundesland': str}\n",
        "df = pd.read_csv('https://opendata.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0.csv', parse_dates=['Meldedatum', 'Refdatum', 'Datenstand'], dtype=dtypes)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1qAmyYRnNKW"
      },
      "source": [
        "Jetzt können wir den DataFrame aufrufen:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWWpU5No3fln",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "outputId": "529184fb-827f-4cb9-817c-0d858ff81957"
      },
      "source": [
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Meldedatum</th>\n",
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              "      <th>Datenstand</th>\n",
              "      <th>NeuerFall</th>\n",
              "      <th>NeuerTodesfall</th>\n",
              "      <th>Refdatum</th>\n",
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              "      <td>A80+</td>\n",
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              "</table>\n",
              "<p>490873 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        ObjectId IdBundesland  ... IstErkrankungsbeginn      Altersgruppe2\n",
              "0              1            1  ...                    0  Nicht übermittelt\n",
              "1              2            1  ...                    1  Nicht übermittelt\n",
              "2              3            1  ...                    1  Nicht übermittelt\n",
              "3              4            1  ...                    1  Nicht übermittelt\n",
              "4              5            1  ...                    1  Nicht übermittelt\n",
              "...          ...          ...  ...                  ...                ...\n",
              "490868    490869           16  ...                    0  Nicht übermittelt\n",
              "490869    490870           16  ...                    1  Nicht übermittelt\n",
              "490870    490871           16  ...                    1  Nicht übermittelt\n",
              "490871    490872           16  ...                    1  Nicht übermittelt\n",
              "490872    490873           16  ...                    0  Nicht übermittelt\n",
              "\n",
              "[490873 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M94njjKlMhn3"
      },
      "source": [
        "Mit `.head()` oder `.tail()` lassen sich die ersten oder letzten Zeilen des Dataframes anzeigen:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FP5bkf2hMpK4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "f83c1b40-ae55-4067-9b38-77dafe203dee"
      },
      "source": [
        "df.tail(10)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>16077</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-11-14</td>\n",
              "      <td>-9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490869</th>\n",
              "      <td>490870</td>\n",
              "      <td>16</td>\n",
              "      <td>Thüringen</td>\n",
              "      <td>LK Altenburger Land</td>\n",
              "      <td>A80+</td>\n",
              "      <td>W</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-11-15</td>\n",
              "      <td>16077</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-11-13</td>\n",
              "      <td>-9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490870</th>\n",
              "      <td>490871</td>\n",
              "      <td>16</td>\n",
              "      <td>Thüringen</td>\n",
              "      <td>LK Altenburger Land</td>\n",
              "      <td>A80+</td>\n",
              "      <td>W</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>16077</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-11-02</td>\n",
              "      <td>-9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490871</th>\n",
              "      <td>490872</td>\n",
              "      <td>16</td>\n",
              "      <td>Thüringen</td>\n",
              "      <td>LK Altenburger Land</td>\n",
              "      <td>A80+</td>\n",
              "      <td>W</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-11-17</td>\n",
              "      <td>16077</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>1</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-11-15</td>\n",
              "      <td>-9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490872</th>\n",
              "      <td>490873</td>\n",
              "      <td>16</td>\n",
              "      <td>Thüringen</td>\n",
              "      <td>LK Altenburger Land</td>\n",
              "      <td>A80+</td>\n",
              "      <td>W</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-11-17</td>\n",
              "      <td>16077</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>1</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-11-17</td>\n",
              "      <td>-9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        ObjectId IdBundesland  ... IstErkrankungsbeginn      Altersgruppe2\n",
              "490863    490864           16  ...                    1  Nicht übermittelt\n",
              "490864    490865           16  ...                    0  Nicht übermittelt\n",
              "490865    490866           16  ...                    0  Nicht übermittelt\n",
              "490866    490867           16  ...                    0  Nicht übermittelt\n",
              "490867    490868           16  ...                    0  Nicht übermittelt\n",
              "490868    490869           16  ...                    0  Nicht übermittelt\n",
              "490869    490870           16  ...                    1  Nicht übermittelt\n",
              "490870    490871           16  ...                    1  Nicht übermittelt\n",
              "490871    490872           16  ...                    1  Nicht übermittelt\n",
              "490872    490873           16  ...                    0  Nicht übermittelt\n",
              "\n",
              "[10 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CTtSAx6IzmP"
      },
      "source": [
        "Wenn man wissen will, welche Spalten es gibt, kann man das mit `df.info()` herausfinden:<br>\n",
        "- `object` bedeutet, dass die Spalte nur Nicht-Zahlen (Objekte) enthält, meist Strings\n",
        "- `int64` bedeutet Integer\n",
        "- `Datetime64` steht für Datums- und Zeitangaben. (Wie sehen auch, dass oben beim Laden die Spalte \"Datenstand\" nicht richtig als Datums- und Zeitformat erkannt wurde) \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9on_zfO3I51E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d92423a0-6b91-4ad4-ff42-5edc871f61e4"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 490873 entries, 0 to 490872\n",
            "Data columns (total 18 columns):\n",
            " #   Column                Non-Null Count   Dtype         \n",
            "---  ------                --------------   -----         \n",
            " 0   ObjectId              490873 non-null  int64         \n",
            " 1   IdBundesland          490873 non-null  object        \n",
            " 2   Bundesland            490873 non-null  object        \n",
            " 3   Landkreis             490873 non-null  object        \n",
            " 4   Altersgruppe          490873 non-null  object        \n",
            " 5   Geschlecht            490873 non-null  object        \n",
            " 6   AnzahlFall            490873 non-null  int64         \n",
            " 7   AnzahlTodesfall       490873 non-null  int64         \n",
            " 8   Meldedatum            490873 non-null  datetime64[ns]\n",
            " 9   IdLandkreis           490873 non-null  object        \n",
            " 10  Datenstand            490873 non-null  object        \n",
            " 11  NeuerFall             490873 non-null  int64         \n",
            " 12  NeuerTodesfall        490873 non-null  int64         \n",
            " 13  Refdatum              490873 non-null  datetime64[ns]\n",
            " 14  NeuGenesen            490873 non-null  int64         \n",
            " 15  AnzahlGenesen         490873 non-null  int64         \n",
            " 16  IstErkrankungsbeginn  490873 non-null  int64         \n",
            " 17  Altersgruppe2         490873 non-null  object        \n",
            "dtypes: datetime64[ns](2), int64(8), object(8)\n",
            "memory usage: 67.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNGcDyrdI-2R"
      },
      "source": [
        "Und mit dem Befehl .describe() lassen sich auf die Schnelle ein paar statistische Auswertungen darstellen:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRpE6RzkJJf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "8914efaf-ca70-49e4-c941-d8e78a9e1037"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ObjectId</th>\n",
              "      <th>AnzahlFall</th>\n",
              "      <th>AnzahlTodesfall</th>\n",
              "      <th>NeuerFall</th>\n",
              "      <th>NeuerTodesfall</th>\n",
              "      <th>NeuGenesen</th>\n",
              "      <th>AnzahlGenesen</th>\n",
              "      <th>IstErkrankungsbeginn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>490873.000000</td>\n",
              "      <td>490873.000000</td>\n",
              "      <td>490873.000000</td>\n",
              "      <td>490873.000000</td>\n",
              "      <td>490873.000000</td>\n",
              "      <td>490873.000000</td>\n",
              "      <td>490873.000000</td>\n",
              "      <td>490873.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>245437.000000</td>\n",
              "      <td>1.697109</td>\n",
              "      <td>0.026712</td>\n",
              "      <td>0.013670</td>\n",
              "      <td>-8.775484</td>\n",
              "      <td>-1.876253</td>\n",
              "      <td>1.113062</td>\n",
              "      <td>0.664508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>141702.973681</td>\n",
              "      <td>2.796958</td>\n",
              "      <td>0.177214</td>\n",
              "      <td>0.120133</td>\n",
              "      <td>1.405846</td>\n",
              "      <td>3.680502</td>\n",
              "      <td>1.453323</td>\n",
              "      <td>0.472163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>122719.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>245437.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>368155.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>490873.000000</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ObjectId     AnzahlFall  ...  AnzahlGenesen  IstErkrankungsbeginn\n",
              "count  490873.000000  490873.000000  ...  490873.000000         490873.000000\n",
              "mean   245437.000000       1.697109  ...       1.113062              0.664508\n",
              "std    141702.973681       2.796958  ...       1.453323              0.472163\n",
              "min         1.000000      -2.000000  ...      -1.000000              0.000000\n",
              "25%    122719.000000       1.000000  ...       1.000000              0.000000\n",
              "50%    245437.000000       1.000000  ...       1.000000              1.000000\n",
              "75%    368155.000000       1.000000  ...       1.000000              1.000000\n",
              "max    490873.000000     147.000000  ...     147.000000              1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6dvsx22nYQt"
      },
      "source": [
        "Je nachdem, was einen interssiert, kann man nun die Summenwerte für einzelne Spalten abrufen:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCYQK2tAne93",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48ff885b-6638-4d4b-d8f3-4d95eb09f01c"
      },
      "source": [
        "df['AnzahlFall'].sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "833065"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKWIiZOXKpDk"
      },
      "source": [
        "Neben `.sum()` gibt es auch <br>\n",
        "- `.max()` für den Maximalwert<br>\n",
        "- `.min()` für den Minimalwert<br>\n",
        "- `.mean()` für das artihmetische Mittel<br>\n",
        "- `.median()` für den Medianwert<br>\n",
        "- `.count()` für die Anzahl der beobachteten Werte<br>\n",
        "\n",
        "Ihr könnt hier ja mal berechnen, wie sich Median und Arithmetisches Mittel verhalten und was das bedeutet...."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVN63ksmL2rG"
      },
      "source": [
        "# Dataframe filtern<br>\n",
        "Dataframes lassen sich anhand einzelner Spalten filtern. Dazu kann man Zahlen vergleichen (zum Beispiel mit `<`, `>` oder `==`) oder Textspalten filtern (mit `==`):\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeOInjMNKnyU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d92a006-bd85-42ef-e768-b0783e0680a8"
      },
      "source": [
        "df['Bundesland'] == 'Nordrhein-Westfalen'"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         False\n",
              "1         False\n",
              "2         False\n",
              "3         False\n",
              "4         False\n",
              "          ...  \n",
              "490868    False\n",
              "490869    False\n",
              "490870    False\n",
              "490871    False\n",
              "490872    False\n",
              "Name: Bundesland, Length: 490873, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tKTUcrXMZaV"
      },
      "source": [
        "Dadurch wird ein Wahrheitswert zurück gegeben. Will man nur die Zeilen, bei denen der Wahrheitswert `True`lautet, muss man noch `df[(  )]` um den Filterausdruck herumsetzen. Und dann die Wert in einen neuen Dataframe speichern:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQihvQJyM_nw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "8f88becb-de48-4f78-e159-0efeae27fe53"
      },
      "source": [
        "df_nrw = df[(df['Bundesland'] == 'Nordrhein-Westfalen')]\n",
        "df_nrw"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ObjectId</th>\n",
              "      <th>IdBundesland</th>\n",
              "      <th>Bundesland</th>\n",
              "      <th>Landkreis</th>\n",
              "      <th>Altersgruppe</th>\n",
              "      <th>Geschlecht</th>\n",
              "      <th>AnzahlFall</th>\n",
              "      <th>AnzahlTodesfall</th>\n",
              "      <th>Meldedatum</th>\n",
              "      <th>IdLandkreis</th>\n",
              "      <th>Datenstand</th>\n",
              "      <th>NeuerFall</th>\n",
              "      <th>NeuerTodesfall</th>\n",
              "      <th>Refdatum</th>\n",
              "      <th>NeuGenesen</th>\n",
              "      <th>AnzahlGenesen</th>\n",
              "      <th>IstErkrankungsbeginn</th>\n",
              "      <th>Altersgruppe2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57500</th>\n",
              "      <td>57501</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>SK Düsseldorf</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>M</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-07</td>\n",
              "      <td>05111</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-07</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57501</th>\n",
              "      <td>57502</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>SK Düsseldorf</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-08</td>\n",
              "      <td>05111</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-08</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57502</th>\n",
              "      <td>57503</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>SK Düsseldorf</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>M</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-09</td>\n",
              "      <td>05111</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-09</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57503</th>\n",
              "      <td>57504</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>SK Düsseldorf</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>M</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-10</td>\n",
              "      <td>05111</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-10</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57504</th>\n",
              "      <td>57505</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>SK Düsseldorf</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>M</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-11</td>\n",
              "      <td>05111</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-11</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172495</th>\n",
              "      <td>172496</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>LK Unna</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>W</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-08</td>\n",
              "      <td>05978</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-05</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172496</th>\n",
              "      <td>172497</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>LK Unna</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>W</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-09</td>\n",
              "      <td>05978</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-09</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172497</th>\n",
              "      <td>172498</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>LK Unna</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>W</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-10</td>\n",
              "      <td>05978</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-02</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172498</th>\n",
              "      <td>172499</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>LK Unna</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>W</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-10</td>\n",
              "      <td>05978</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-05</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172499</th>\n",
              "      <td>172500</td>\n",
              "      <td>5</td>\n",
              "      <td>Nordrhein-Westfalen</td>\n",
              "      <td>LK Unna</td>\n",
              "      <td>A35-A59</td>\n",
              "      <td>W</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-10-10</td>\n",
              "      <td>05978</td>\n",
              "      <td>18.11.2020, 00:00 Uhr</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>2020-10-08</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Nicht übermittelt</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>113152 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        ObjectId IdBundesland  ... IstErkrankungsbeginn      Altersgruppe2\n",
              "57500      57501            5  ...                    0  Nicht übermittelt\n",
              "57501      57502            5  ...                    0  Nicht übermittelt\n",
              "57502      57503            5  ...                    0  Nicht übermittelt\n",
              "57503      57504            5  ...                    0  Nicht übermittelt\n",
              "57504      57505            5  ...                    0  Nicht übermittelt\n",
              "...          ...          ...  ...                  ...                ...\n",
              "172495    172496            5  ...                    1  Nicht übermittelt\n",
              "172496    172497            5  ...                    0  Nicht übermittelt\n",
              "172497    172498            5  ...                    1  Nicht übermittelt\n",
              "172498    172499            5  ...                    1  Nicht übermittelt\n",
              "172499    172500            5  ...                    1  Nicht übermittelt\n",
              "\n",
              "[113152 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OEAV05BNGMk"
      },
      "source": [
        "(Wenn ihr jetzt verzweifelt und gar nicht mehr wisst, wann man welche Klammer setzt - willkommen im Club. Ich mache es auch noch jeden Tag falsch...)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lAcGT9_NSfv"
      },
      "source": [
        "## Pivot Tabellen\n",
        "\n",
        "Mit Pivot Tabellen kann man die Spalten und Zeilen neu \"überkreuzen\", deshalb werden sie auch Kreuztabellen genannt. Es ist eines der mächtigsten Tools, um große Datenmengen zu verstehen.\n",
        "<br>\n",
        "Um in Pandas eine Pivot Tabelle zu bauen, braucht man folgende Zutaten\n",
        " 1. Quell-DF \n",
        " 2. Index = Was soll in den Zeilen / in der \"horizontalen\" Achse angezeigt werden\n",
        " 3. columns = sollen die Daten noch in Spalten aufgefächert werden?\n",
        " 4. values = Was soll gezählt werden? Welche Spalte im DF ist es?\n",
        " 5. aggunc = Wie soll es gezählt werden (sum, mean, ect.)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHWnHiZuOhYG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d5337027-3ba0-4e31-8ba5-631c1fb4b773"
      },
      "source": [
        "pd.pivot_table(df_nrw, index='Altersgruppe', columns=\"Geschlecht\", values='AnzahlFall', aggfunc='sum')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Geschlecht</th>\n",
              "      <th>M</th>\n",
              "      <th>W</th>\n",
              "      <th>unbekannt</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Altersgruppe</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A00-A04</th>\n",
              "      <td>2373</td>\n",
              "      <td>2176</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A05-A14</th>\n",
              "      <td>7105</td>\n",
              "      <td>6569</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A15-A34</th>\n",
              "      <td>36098</td>\n",
              "      <td>35228</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A35-A59</th>\n",
              "      <td>41028</td>\n",
              "      <td>41039</td>\n",
              "      <td>229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A60-A79</th>\n",
              "      <td>13442</td>\n",
              "      <td>13191</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A80+</th>\n",
              "      <td>4247</td>\n",
              "      <td>7466</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unbekannt</th>\n",
              "      <td>255</td>\n",
              "      <td>241</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Geschlecht        M      W  unbekannt\n",
              "Altersgruppe                         \n",
              "A00-A04        2373   2176         30\n",
              "A05-A14        7105   6569        125\n",
              "A15-A34       36098  35228        309\n",
              "A35-A59       41028  41039        229\n",
              "A60-A79       13442  13191         45\n",
              "A80+           4247   7466          9\n",
              "unbekannt       255    241        123"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I6oxkd_JtdHT"
      },
      "source": [
        "# Zellen ansteuern mit `iloc`<br>\n",
        "\n",
        "Wenn man aus einem Dataframe ein bestimmten Wert extrahieren möchte, kann man dazu den \"Integer Locator\" nutzen - also den Locator nach Zahlen (gemeint sind die Werte für Zeile und Spalte).<br>\n",
        "Damit kann man unterschiedliche Dinge machen. Angenommen, ich habe einen Dataframe nach Meldedatum und den Bundesländern in den Spalten:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwObgMwEuG45"
      },
      "source": [
        "df_bl_nach_datum = pd.pivot_table(df, index='Meldedatum', columns=\"Bundesland\", values='AnzahlFall', aggfunc='sum')"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "ThJN6XaSuQW9",
        "outputId": "af5de085-738b-4a0f-ce3d-340f8a85c668"
      },
      "source": [
        "df_bl_nach_datum"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Bundesland</th>\n",
              "      <th>Baden-Württemberg</th>\n",
              "      <th>Bayern</th>\n",
              "      <th>Berlin</th>\n",
              "      <th>Brandenburg</th>\n",
              "      <th>Bremen</th>\n",
              "      <th>Hamburg</th>\n",
              "      <th>Hessen</th>\n",
              "      <th>Mecklenburg-Vorpommern</th>\n",
              "      <th>Niedersachsen</th>\n",
              "      <th>Nordrhein-Westfalen</th>\n",
              "      <th>Rheinland-Pfalz</th>\n",
              "      <th>Saarland</th>\n",
              "      <th>Sachsen</th>\n",
              "      <th>Sachsen-Anhalt</th>\n",
              "      <th>Schleswig-Holstein</th>\n",
              "      <th>Thüringen</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meldedatum</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-03</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-13</th>\n",
              "      <td>2643.0</td>\n",
              "      <td>4067.0</td>\n",
              "      <td>1403.0</td>\n",
              "      <td>382.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>423.0</td>\n",
              "      <td>1752.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>1394.0</td>\n",
              "      <td>5120.0</td>\n",
              "      <td>1116.0</td>\n",
              "      <td>206.0</td>\n",
              "      <td>1317.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>285.0</td>\n",
              "      <td>381.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-14</th>\n",
              "      <td>1973.0</td>\n",
              "      <td>3107.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>268.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>1307.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>1070.0</td>\n",
              "      <td>4153.0</td>\n",
              "      <td>660.0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>1079.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>246.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-15</th>\n",
              "      <td>1074.0</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>600.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>788.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>405.0</td>\n",
              "      <td>2320.0</td>\n",
              "      <td>489.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-16</th>\n",
              "      <td>1416.0</td>\n",
              "      <td>2395.0</td>\n",
              "      <td>1548.0</td>\n",
              "      <td>294.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>1054.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>790.0</td>\n",
              "      <td>3722.0</td>\n",
              "      <td>692.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>677.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>208.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-17</th>\n",
              "      <td>1557.0</td>\n",
              "      <td>2166.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1359.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>3383.0</td>\n",
              "      <td>457.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>339.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>279 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Bundesland  Baden-Württemberg  Bayern  ...  Schleswig-Holstein  Thüringen\n",
              "Meldedatum                             ...                               \n",
              "2020-01-03                1.0     NaN  ...                 NaN        NaN\n",
              "2020-01-28                NaN     2.0  ...                 NaN        NaN\n",
              "2020-01-29                NaN     2.0  ...                 NaN        NaN\n",
              "2020-01-31                NaN     5.0  ...                 NaN        NaN\n",
              "2020-02-03                NaN     1.0  ...                 NaN        NaN\n",
              "...                       ...     ...  ...                 ...        ...\n",
              "2020-11-13             2643.0  4067.0  ...               285.0      381.0\n",
              "2020-11-14             1973.0  3107.0  ...               190.0      246.0\n",
              "2020-11-15             1074.0  1508.0  ...                83.0      117.0\n",
              "2020-11-16             1416.0  2395.0  ...               124.0      208.0\n",
              "2020-11-17             1557.0  2166.0  ...               222.0      339.0\n",
              "\n",
              "[279 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-ioBM_xuYHz"
      },
      "source": [
        "Mit `.iloc[0]` lässt sich die erste Zeile anzeigen. (Wie immer in Python 0 = 1. Wert)<br>\n",
        "Fachwissen: In diesem Fall ist das eine Pandas Serie (und kein Dataframe)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6afJEo2NuT5z",
        "outputId": "d7845409-3aad-430c-984d-e1266bff5f86"
      },
      "source": [
        "df_bl_nach_datum.iloc[0]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bundesland\n",
              "Baden-Württemberg         1.0\n",
              "Bayern                    NaN\n",
              "Berlin                    NaN\n",
              "Brandenburg               NaN\n",
              "Bremen                    NaN\n",
              "Hamburg                   NaN\n",
              "Hessen                    NaN\n",
              "Mecklenburg-Vorpommern    NaN\n",
              "Niedersachsen             NaN\n",
              "Nordrhein-Westfalen       NaN\n",
              "Rheinland-Pfalz           NaN\n",
              "Saarland                  NaN\n",
              "Sachsen                   NaN\n",
              "Sachsen-Anhalt            NaN\n",
              "Schleswig-Holstein        NaN\n",
              "Thüringen                 NaN\n",
              "Name: 2020-01-03 00:00:00, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkedgyQoummE"
      },
      "source": [
        "Um das ganze als Dataframe anzuzeigen, kann man die erste Zeile bis exklusive der 2. Zeile anzeigen:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "id": "GK2ERbcxuvoI",
        "outputId": "e5c79e8d-5d47-43a1-beca-e41968705089"
      },
      "source": [
        "df_bl_nach_datum.iloc[0:1]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Bundesland</th>\n",
              "      <th>Baden-Württemberg</th>\n",
              "      <th>Bayern</th>\n",
              "      <th>Berlin</th>\n",
              "      <th>Brandenburg</th>\n",
              "      <th>Bremen</th>\n",
              "      <th>Hamburg</th>\n",
              "      <th>Hessen</th>\n",
              "      <th>Mecklenburg-Vorpommern</th>\n",
              "      <th>Niedersachsen</th>\n",
              "      <th>Nordrhein-Westfalen</th>\n",
              "      <th>Rheinland-Pfalz</th>\n",
              "      <th>Saarland</th>\n",
              "      <th>Sachsen</th>\n",
              "      <th>Sachsen-Anhalt</th>\n",
              "      <th>Schleswig-Holstein</th>\n",
              "      <th>Thüringen</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meldedatum</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Bundesland  Baden-Württemberg  Bayern  ...  Schleswig-Holstein  Thüringen\n",
              "Meldedatum                             ...                               \n",
              "2020-01-03                1.0     NaN  ...                 NaN        NaN\n",
              "\n",
              "[1 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ut7F6UMGu8ZB"
      },
      "source": [
        "Genau so lässt sich die letzte Zeile anzeigen:"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBlBLmGivBce",
        "outputId": "46c9daca-771f-4d52-a203-81068537356a"
      },
      "source": [
        "# Als Serie\n",
        "df_bl_nach_datum.iloc[-1]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bundesland\n",
              "Baden-Württemberg         1557.0\n",
              "Bayern                    2166.0\n",
              "Berlin                     598.0\n",
              "Brandenburg                 93.0\n",
              "Bremen                     123.0\n",
              "Hamburg                      NaN\n",
              "Hessen                    1359.0\n",
              "Mecklenburg-Vorpommern     157.0\n",
              "Niedersachsen               67.0\n",
              "Nordrhein-Westfalen       3383.0\n",
              "Rheinland-Pfalz            457.0\n",
              "Saarland                   142.0\n",
              "Sachsen                    370.0\n",
              "Sachsen-Anhalt             142.0\n",
              "Schleswig-Holstein         222.0\n",
              "Thüringen                  339.0\n",
              "Name: 2020-11-17 00:00:00, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "id": "Xm6NqICWvE-m",
        "outputId": "53b10106-5378-471e-bd4b-a5490260689f"
      },
      "source": [
        "# Als Dataframe\n",
        "df_bl_nach_datum.iloc[-1:]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Bundesland</th>\n",
              "      <th>Baden-Württemberg</th>\n",
              "      <th>Bayern</th>\n",
              "      <th>Berlin</th>\n",
              "      <th>Brandenburg</th>\n",
              "      <th>Bremen</th>\n",
              "      <th>Hamburg</th>\n",
              "      <th>Hessen</th>\n",
              "      <th>Mecklenburg-Vorpommern</th>\n",
              "      <th>Niedersachsen</th>\n",
              "      <th>Nordrhein-Westfalen</th>\n",
              "      <th>Rheinland-Pfalz</th>\n",
              "      <th>Saarland</th>\n",
              "      <th>Sachsen</th>\n",
              "      <th>Sachsen-Anhalt</th>\n",
              "      <th>Schleswig-Holstein</th>\n",
              "      <th>Thüringen</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meldedatum</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-11-17</th>\n",
              "      <td>1557.0</td>\n",
              "      <td>2166.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1359.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>3383.0</td>\n",
              "      <td>457.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>339.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Bundesland  Baden-Württemberg  Bayern  ...  Schleswig-Holstein  Thüringen\n",
              "Meldedatum                             ...                               \n",
              "2020-11-17             1557.0  2166.0  ...               222.0      339.0\n",
              "\n",
              "[1 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEHtx8qdvMpL"
      },
      "source": [
        "Man kann sich so zum Beispiel die letzten 7 Tage des Dataframes anzeigen lassen:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "sewV8hvavLyn",
        "outputId": "db00fe80-3837-4552-b622-491239681bf6"
      },
      "source": [
        "df_bl_nach_datum.iloc[-7:]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Bundesland</th>\n",
              "      <th>Baden-Württemberg</th>\n",
              "      <th>Bayern</th>\n",
              "      <th>Berlin</th>\n",
              "      <th>Brandenburg</th>\n",
              "      <th>Bremen</th>\n",
              "      <th>Hamburg</th>\n",
              "      <th>Hessen</th>\n",
              "      <th>Mecklenburg-Vorpommern</th>\n",
              "      <th>Niedersachsen</th>\n",
              "      <th>Nordrhein-Westfalen</th>\n",
              "      <th>Rheinland-Pfalz</th>\n",
              "      <th>Saarland</th>\n",
              "      <th>Sachsen</th>\n",
              "      <th>Sachsen-Anhalt</th>\n",
              "      <th>Schleswig-Holstein</th>\n",
              "      <th>Thüringen</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meldedatum</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-11-11</th>\n",
              "      <td>2976.0</td>\n",
              "      <td>4571.0</td>\n",
              "      <td>1669.0</td>\n",
              "      <td>385.0</td>\n",
              "      <td>224.0</td>\n",
              "      <td>438.0</td>\n",
              "      <td>1871.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1623.0</td>\n",
              "      <td>5148.0</td>\n",
              "      <td>1126.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>1146.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>285.0</td>\n",
              "      <td>351.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-12</th>\n",
              "      <td>2946.0</td>\n",
              "      <td>4602.0</td>\n",
              "      <td>1561.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>1823.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>1682.0</td>\n",
              "      <td>5721.0</td>\n",
              "      <td>1181.0</td>\n",
              "      <td>279.0</td>\n",
              "      <td>1362.0</td>\n",
              "      <td>272.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>401.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-13</th>\n",
              "      <td>2643.0</td>\n",
              "      <td>4067.0</td>\n",
              "      <td>1403.0</td>\n",
              "      <td>382.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>423.0</td>\n",
              "      <td>1752.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>1394.0</td>\n",
              "      <td>5120.0</td>\n",
              "      <td>1116.0</td>\n",
              "      <td>206.0</td>\n",
              "      <td>1317.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>285.0</td>\n",
              "      <td>381.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-14</th>\n",
              "      <td>1973.0</td>\n",
              "      <td>3107.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>268.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>1307.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>1070.0</td>\n",
              "      <td>4153.0</td>\n",
              "      <td>660.0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>1079.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>246.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-15</th>\n",
              "      <td>1074.0</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>600.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>788.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>405.0</td>\n",
              "      <td>2320.0</td>\n",
              "      <td>489.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-16</th>\n",
              "      <td>1416.0</td>\n",
              "      <td>2395.0</td>\n",
              "      <td>1548.0</td>\n",
              "      <td>294.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>1054.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>790.0</td>\n",
              "      <td>3722.0</td>\n",
              "      <td>692.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>677.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>208.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-17</th>\n",
              "      <td>1557.0</td>\n",
              "      <td>2166.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1359.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>3383.0</td>\n",
              "      <td>457.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>339.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Bundesland  Baden-Württemberg  Bayern  ...  Schleswig-Holstein  Thüringen\n",
              "Meldedatum                             ...                               \n",
              "2020-11-11             2976.0  4571.0  ...               285.0      351.0\n",
              "2020-11-12             2946.0  4602.0  ...               229.0      401.0\n",
              "2020-11-13             2643.0  4067.0  ...               285.0      381.0\n",
              "2020-11-14             1973.0  3107.0  ...               190.0      246.0\n",
              "2020-11-15             1074.0  1508.0  ...                83.0      117.0\n",
              "2020-11-16             1416.0  2395.0  ...               124.0      208.0\n",
              "2020-11-17             1557.0  2166.0  ...               222.0      339.0\n",
              "\n",
              "[7 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJL0ERdVvYC-"
      },
      "source": [
        "Bislang bekommen wir hier die vollen Zeilen. Wenn man nur einen oder ausgewählte Spalten anzeigen lassen möchte, geht das ebenfalls mit `.iloc` - die Spaltenwerte folgen nach dem Komma in den eckigen Klammern:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "v1RKkfXCvnbz",
        "outputId": "592ec61a-62ef-404f-daf3-8211b1ae71e4"
      },
      "source": [
        "df_bl_nach_datum.iloc[-1:,4:6]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\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>Bundesland</th>\n",
              "      <th>Bremen</th>\n",
              "      <th>Hamburg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meldedatum</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-11-17</th>\n",
              "      <td>123.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Bundesland  Bremen  Hamburg\n",
              "Meldedatum                 \n",
              "2020-11-17   123.0      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4CO555NxElQ"
      },
      "source": [
        "...oder, um nur einen Wert zu extrahieren:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QsjVtmYExHB-",
        "outputId": "1c601308-1e4a-41fa-93a4-44b0af0edd0e"
      },
      "source": [
        "df_bl_nach_datum.iloc[-1,4:5].item()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "123.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXQw2mUSx9EG"
      },
      "source": [
        "Neben dem Integer-Locator gibt es auch noch Namens-Locator `.loc()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JA-HSaH9x4qL",
        "outputId": "cc3f63e3-8609-4d85-ed1d-860435b046d9"
      },
      "source": [
        "df_bl_nach_datum.loc[:,'Nordrhein-Westfalen']"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Meldedatum\n",
              "2020-01-03       NaN\n",
              "2020-01-28       NaN\n",
              "2020-01-29       NaN\n",
              "2020-01-31       NaN\n",
              "2020-02-03       NaN\n",
              "               ...  \n",
              "2020-11-13    5120.0\n",
              "2020-11-14    4153.0\n",
              "2020-11-15    2320.0\n",
              "2020-11-16    3722.0\n",
              "2020-11-17    3383.0\n",
              "Name: Nordrhein-Westfalen, Length: 279, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "060CWaWhyj7T"
      },
      "source": [
        "`loc` sowohl für Zeilen als auch Spalten:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJtkL5YeyTJV",
        "outputId": "107735e1-f1d9-4657-f1e5-faad1084b77f"
      },
      "source": [
        "df_bl_nach_datum.loc['2020-11-17','Nordrhein-Westfalen']"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3383.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlRLYuynyIsi"
      },
      "source": [
        "Da sich in der Automation aber immer das neueste Datum ändert, kann man auch `loc` und `iloc` kombinieren:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S4gETUXnyNON",
        "outputId": "f0b82594-5635-4bf4-ca6b-69c9da456f4b"
      },
      "source": [
        "df_bl_nach_datum.loc[:,'Nordrhein-Westfalen'].iloc[-1]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3383.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJcCazzvywDA"
      },
      "source": [
        "# Rolling\n",
        "\n",
        "Es gibt praktische Pandas-Funktionen, um zum Beispiel die Summe der letzten 7 Tage zu berechnen. Das geht mit `rolling`:\n",
        "- Wie viele Tage sollen zur Berechnung herangezogen werden?\n",
        "- min_periods= Wie viele Tage sollen minimal beobachtet werden (z.B. wenn die Zahl der Tage nicht glatt durch 7 teilbar ist)?\n",
        "- Axis = Soll das ganze per Zeile (1) oder per Spalte (0) berechnet werden?\n",
        "- Schließlich muss noch bestimmt werden, ob man die Summe oder etwas anderes berechnen will (`.sum()`)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "8meMgDbsy-UT",
        "outputId": "7c99eaf8-8e9a-4f76-f5fe-095b3e86c1b6"
      },
      "source": [
        "df_bl_nach_datum.rolling(7, min_periods=7, axis=0).sum()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Bundesland</th>\n",
              "      <th>Baden-Württemberg</th>\n",
              "      <th>Bayern</th>\n",
              "      <th>Berlin</th>\n",
              "      <th>Brandenburg</th>\n",
              "      <th>Bremen</th>\n",
              "      <th>Hamburg</th>\n",
              "      <th>Hessen</th>\n",
              "      <th>Mecklenburg-Vorpommern</th>\n",
              "      <th>Niedersachsen</th>\n",
              "      <th>Nordrhein-Westfalen</th>\n",
              "      <th>Rheinland-Pfalz</th>\n",
              "      <th>Saarland</th>\n",
              "      <th>Sachsen</th>\n",
              "      <th>Sachsen-Anhalt</th>\n",
              "      <th>Schleswig-Holstein</th>\n",
              "      <th>Thüringen</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meldedatum</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-03</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-13</th>\n",
              "      <td>16148.0</td>\n",
              "      <td>25368.0</td>\n",
              "      <td>8119.0</td>\n",
              "      <td>2129.0</td>\n",
              "      <td>1113.0</td>\n",
              "      <td>2960.0</td>\n",
              "      <td>11625.0</td>\n",
              "      <td>715.0</td>\n",
              "      <td>8907.0</td>\n",
              "      <td>31154.0</td>\n",
              "      <td>6079.0</td>\n",
              "      <td>1680.0</td>\n",
              "      <td>7230.0</td>\n",
              "      <td>1403.0</td>\n",
              "      <td>1661.0</td>\n",
              "      <td>1969.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-14</th>\n",
              "      <td>15749.0</td>\n",
              "      <td>25371.0</td>\n",
              "      <td>8173.0</td>\n",
              "      <td>2160.0</td>\n",
              "      <td>1116.0</td>\n",
              "      <td>2760.0</td>\n",
              "      <td>11182.0</td>\n",
              "      <td>686.0</td>\n",
              "      <td>8683.0</td>\n",
              "      <td>31562.0</td>\n",
              "      <td>6159.0</td>\n",
              "      <td>1617.0</td>\n",
              "      <td>7530.0</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>1601.0</td>\n",
              "      <td>1980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-15</th>\n",
              "      <td>15663.0</td>\n",
              "      <td>24705.0</td>\n",
              "      <td>8477.0</td>\n",
              "      <td>2130.0</td>\n",
              "      <td>1128.0</td>\n",
              "      <td>2673.0</td>\n",
              "      <td>11111.0</td>\n",
              "      <td>680.0</td>\n",
              "      <td>8430.0</td>\n",
              "      <td>30934.0</td>\n",
              "      <td>6081.0</td>\n",
              "      <td>1563.0</td>\n",
              "      <td>7618.0</td>\n",
              "      <td>1476.0</td>\n",
              "      <td>1529.0</td>\n",
              "      <td>1999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-16</th>\n",
              "      <td>15436.0</td>\n",
              "      <td>24092.0</td>\n",
              "      <td>8542.0</td>\n",
              "      <td>2161.0</td>\n",
              "      <td>1065.0</td>\n",
              "      <td>2450.0</td>\n",
              "      <td>10464.0</td>\n",
              "      <td>664.0</td>\n",
              "      <td>8321.0</td>\n",
              "      <td>30525.0</td>\n",
              "      <td>6149.0</td>\n",
              "      <td>1476.0</td>\n",
              "      <td>7463.0</td>\n",
              "      <td>1490.0</td>\n",
              "      <td>1404.0</td>\n",
              "      <td>2002.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-17</th>\n",
              "      <td>14585.0</td>\n",
              "      <td>22416.0</td>\n",
              "      <td>7839.0</td>\n",
              "      <td>1882.0</td>\n",
              "      <td>1072.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9954.0</td>\n",
              "      <td>697.0</td>\n",
              "      <td>7031.0</td>\n",
              "      <td>29567.0</td>\n",
              "      <td>5721.0</td>\n",
              "      <td>1339.0</td>\n",
              "      <td>6539.0</td>\n",
              "      <td>1365.0</td>\n",
              "      <td>1418.0</td>\n",
              "      <td>2043.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>279 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Bundesland  Baden-Württemberg   Bayern  ...  Schleswig-Holstein  Thüringen\n",
              "Meldedatum                              ...                               \n",
              "2020-01-03                NaN      NaN  ...                 NaN        NaN\n",
              "2020-01-28                NaN      NaN  ...                 NaN        NaN\n",
              "2020-01-29                NaN      NaN  ...                 NaN        NaN\n",
              "2020-01-31                NaN      NaN  ...                 NaN        NaN\n",
              "2020-02-03                NaN      NaN  ...                 NaN        NaN\n",
              "...                       ...      ...  ...                 ...        ...\n",
              "2020-11-13            16148.0  25368.0  ...              1661.0     1969.0\n",
              "2020-11-14            15749.0  25371.0  ...              1601.0     1980.0\n",
              "2020-11-15            15663.0  24705.0  ...              1529.0     1999.0\n",
              "2020-11-16            15436.0  24092.0  ...              1404.0     2002.0\n",
              "2020-11-17            14585.0  22416.0  ...              1418.0     2043.0\n",
              "\n",
              "[279 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    }
  ]
}