{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Covid 19 Daten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "RKIData = pd.read_csv('https://opendata.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0.csv');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>ObjectId</th>\n",
       "      <th>Meldedatum</th>\n",
       "      <th>IdLandkreis</th>\n",
       "      <th>Datenstand</th>\n",
       "      <th>NeuerFall</th>\n",
       "      <th>NeuerTodesfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A15-A34</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>588874</td>\n",
       "      <td>2020-03-27T00:00:00.000Z</td>\n",
       "      <td>3461</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A15-A34</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>588875</td>\n",
       "      <td>2020-03-22T00:00:00.000Z</td>\n",
       "      <td>3461</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A15-A34</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>588876</td>\n",
       "      <td>2020-03-25T00:00:00.000Z</td>\n",
       "      <td>3461</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>588877</td>\n",
       "      <td>2020-03-18T00:00:00.000Z</td>\n",
       "      <td>3461</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>588878</td>\n",
       "      <td>2020-03-22T00:00:00.000Z</td>\n",
       "      <td>3461</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IdBundesland     Bundesland       Landkreis Altersgruppe Geschlecht  \\\n",
       "0             3  Niedersachsen  LK Wesermarsch      A15-A34          M   \n",
       "1             3  Niedersachsen  LK Wesermarsch      A15-A34          W   \n",
       "2             3  Niedersachsen  LK Wesermarsch      A15-A34          W   \n",
       "3             3  Niedersachsen  LK Wesermarsch      A35-A59          M   \n",
       "4             3  Niedersachsen  LK Wesermarsch      A35-A59          M   \n",
       "\n",
       "   AnzahlFall  AnzahlTodesfall  ObjectId                Meldedatum  \\\n",
       "0           2                0    588874  2020-03-27T00:00:00.000Z   \n",
       "1           2                0    588875  2020-03-22T00:00:00.000Z   \n",
       "2           3                0    588876  2020-03-25T00:00:00.000Z   \n",
       "3           3                0    588877  2020-03-18T00:00:00.000Z   \n",
       "4           1                0    588878  2020-03-22T00:00:00.000Z   \n",
       "\n",
       "   IdLandkreis        Datenstand  NeuerFall  NeuerTodesfall  \n",
       "0         3461  31.03.2020 00:00          0              -9  \n",
       "1         3461  31.03.2020 00:00          0              -9  \n",
       "2         3461  31.03.2020 00:00          0              -9  \n",
       "3         3461  31.03.2020 00:00          0              -9  \n",
       "4         3461  31.03.2020 00:00          0              -9  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RKIData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "RKIData = RKIData[RKIData.Geschlecht.str.contains(\"unbekannt\") == False]\n",
    "RKIData = RKIData[RKIData.Altersgruppe.str.contains(\"unbekannt\") == False]\n",
    "\n",
    "RKIData = RKIData.drop(['NeuerFall','NeuerTodesfall','ObjectId','Meldedatum','IdBundesland','IdLandkreis'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bundesland</th>\n",
       "      <th>Landkreis</th>\n",
       "      <th>Altersgruppe</th>\n",
       "      <th>Geschlecht</th>\n",
       "      <th>AnzahlFall</th>\n",
       "      <th>AnzahlTodesfall</th>\n",
       "      <th>Datenstand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A15-A34</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A15-A34</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A15-A34</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>LK Wesermarsch</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31.03.2020 00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Bundesland       Landkreis Altersgruppe Geschlecht  AnzahlFall  \\\n",
       "0  Niedersachsen  LK Wesermarsch      A15-A34          M           2   \n",
       "1  Niedersachsen  LK Wesermarsch      A15-A34          W           2   \n",
       "2  Niedersachsen  LK Wesermarsch      A15-A34          W           3   \n",
       "3  Niedersachsen  LK Wesermarsch      A35-A59          M           3   \n",
       "4  Niedersachsen  LK Wesermarsch      A35-A59          M           1   \n",
       "\n",
       "   AnzahlTodesfall        Datenstand  \n",
       "0                0  31.03.2020 00:00  \n",
       "1                0  31.03.2020 00:00  \n",
       "2                0  31.03.2020 00:00  \n",
       "3                0  31.03.2020 00:00  \n",
       "4                0  31.03.2020 00:00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RKIData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RKIData['Bundesland'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 25438 entries, 0 to 25775\n",
      "Data columns (total 7 columns):\n",
      "Bundesland         25438 non-null object\n",
      "Landkreis          25438 non-null object\n",
      "Altersgruppe       25438 non-null object\n",
      "Geschlecht         25438 non-null object\n",
      "AnzahlFall         25438 non-null int64\n",
      "AnzahlTodesfall    25438 non-null int64\n",
      "Datenstand         25438 non-null object\n",
      "dtypes: int64(2), object(5)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "RKIData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>AnzahlFall</th>\n",
       "      <th>AnzahlTodesfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Altersgruppe</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A00-A04</td>\n",
       "      <td>452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>1203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>15809</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>29515</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>11238</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>3257</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AnzahlFall  AnzahlTodesfall\n",
       "Altersgruppe                             \n",
       "A00-A04              452                0\n",
       "A05-A14             1203                0\n",
       "A15-A34            15809                1\n",
       "A35-A59            29515               30\n",
       "A60-A79            11238              173\n",
       "A80+                3257              376"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RKIData.groupby(['Altersgruppe']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AnzahlFall</th>\n",
       "      <th>AnzahlTodesfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bundesland</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>12262</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bayern</td>\n",
       "      <td>14626</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Berlin</td>\n",
       "      <td>2553</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Brandenburg</td>\n",
       "      <td>798</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bremen</td>\n",
       "      <td>290</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hamburg</td>\n",
       "      <td>2175</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hessen</td>\n",
       "      <td>3270</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mecklenburg-Vorpommern</td>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>4037</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nordrhein-Westfalen</td>\n",
       "      <td>13194</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rheinland-Pfalz</td>\n",
       "      <td>2681</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Saarland</td>\n",
       "      <td>774</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sachsen</td>\n",
       "      <td>1882</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sachsen-Anhalt</td>\n",
       "      <td>679</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>1105</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Thüringen</td>\n",
       "      <td>783</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        AnzahlFall  AnzahlTodesfall\n",
       "Bundesland                                         \n",
       "Baden-Württemberg            12262              164\n",
       "Bayern                       14626              160\n",
       "Berlin                        2553               13\n",
       "Brandenburg                    798                2\n",
       "Bremen                         290                4\n",
       "Hamburg                       2175                9\n",
       "Hessen                        3270               17\n",
       "Mecklenburg-Vorpommern         365                1\n",
       "Niedersachsen                 4037               34\n",
       "Nordrhein-Westfalen          13194              117\n",
       "Rheinland-Pfalz               2681               19\n",
       "Saarland                       774                7\n",
       "Sachsen                       1882               12\n",
       "Sachsen-Anhalt                 679                6\n",
       "Schleswig-Holstein            1105                9\n",
       "Thüringen                      783                6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RKIData.groupby(['Bundesland']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots = RKIData.groupby(['Geschlecht']).sum().plot(kind=\"bar\",figsize=(20,10),secondary_y=\"AnzahlTodesfall\")\n",
    "\n",
    "plots.get_figure().savefig('geschlecht.png',dpi=200,pad_inches=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112c65990>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RKIData.groupby(['Bundesland','Geschlecht']).sum().sort_values(by=\"AnzahlFall\").plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GADaten = RKIData.groupby(['Geschlecht','Altersgruppe']).sum().sort_values(by=[\"AnzahlFall\"])\n",
    "\n",
    "thePlotGADaten = GADaten.plot(kind=\"bar\",secondary_y=\"AnzahlTodesfall\",figsize=(20,10))\n",
    "\n",
    "thePlotGADaten.get_figure().savefig('geschlecht-altersgruppe.png',dpi=200,pad_inches=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>AnzahlFall</th>\n",
       "      <th>AnzahlTodesfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bundesland</th>\n",
       "      <th>Altersgruppe</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Baden-Württemberg</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>218</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>3060</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>5772</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>2440</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>678</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Bayern</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>3792</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>6677</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>2624</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>981</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Berlin</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>933</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>1147</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>309</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>65</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Brandenburg</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>423</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Bremen</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>131</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Hamburg</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>997</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>306</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Hessen</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>759</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>1741</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>591</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>121</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">Mecklenburg-Vorpommern</td>\n",
       "      <td>A05-A14</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>166</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Niedersachsen</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>964</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>1944</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>795</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>246</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Nordrhein-Westfalen</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>3209</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>6695</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>2403</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>647</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Rheinland-Pfalz</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>713</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>1332</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>481</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>98</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Saarland</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>399</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>129</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Sachsen</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>467</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>375</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>80</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Sachsen-Anhalt</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>324</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Schleswig-Holstein</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>487</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">Thüringen</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A05-A14</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A15-A34</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A60-A79</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A35-A59</td>\n",
       "      <td>379</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A80+</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     AnzahlFall  AnzahlTodesfall\n",
       "Bundesland             Altersgruppe                             \n",
       "Baden-Württemberg      A00-A04               94                0\n",
       "                       A05-A14              218                0\n",
       "                       A15-A34             3060                0\n",
       "                       A35-A59             5772                5\n",
       "                       A60-A79             2440               55\n",
       "                       A80+                 678              104\n",
       "Bayern                 A00-A04              146                0\n",
       "                       A05-A14              406                0\n",
       "                       A15-A34             3792                1\n",
       "                       A35-A59             6677                8\n",
       "                       A60-A79             2624               41\n",
       "                       A80+                 981              110\n",
       "Berlin                 A00-A04               30                0\n",
       "                       A05-A14               69                0\n",
       "                       A15-A34              933                0\n",
       "                       A35-A59             1147                2\n",
       "                       A60-A79              309                2\n",
       "                       A80+                  65                9\n",
       "Brandenburg            A00-A04                3                0\n",
       "                       A05-A14               12                0\n",
       "                       A15-A34              175                0\n",
       "                       A35-A59              423                0\n",
       "                       A60-A79              161                0\n",
       "                       A80+                  24                2\n",
       "Bremen                 A00-A04                1                0\n",
       "                       A05-A14                3                0\n",
       "                       A15-A34               81                0\n",
       "                       A35-A59              131                1\n",
       "                       A60-A79               63                1\n",
       "                       A80+                  11                2\n",
       "Hamburg                A00-A04               18                0\n",
       "                       A05-A14               76                0\n",
       "                       A15-A34              706                0\n",
       "                       A35-A59              997                1\n",
       "                       A80+                  72                3\n",
       "                       A60-A79              306                5\n",
       "Hessen                 A00-A04               15                0\n",
       "                       A05-A14               43                0\n",
       "                       A15-A34              759                0\n",
       "                       A35-A59             1741                1\n",
       "                       A60-A79              591                5\n",
       "                       A80+                 121               11\n",
       "Mecklenburg-Vorpommern A05-A14                6                0\n",
       "                       A15-A34              120                0\n",
       "                       A60-A79               60                0\n",
       "                       A80+                  13                0\n",
       "                       A35-A59              166                1\n",
       "Niedersachsen          A00-A04               21                0\n",
       "                       A05-A14               67                0\n",
       "                       A15-A34              964                0\n",
       "                       A35-A59             1944                2\n",
       "                       A60-A79              795                8\n",
       "                       A80+                 246               24\n",
       "Nordrhein-Westfalen    A00-A04               69                0\n",
       "                       A05-A14              171                0\n",
       "                       A15-A34             3209                0\n",
       "                       A35-A59             6695                5\n",
       "                       A60-A79             2403               35\n",
       "                       A80+                 647               77\n",
       "Rheinland-Pfalz        A00-A04               16                0\n",
       "                       A05-A14               41                0\n",
       "                       A15-A34              713                0\n",
       "                       A35-A59             1332                1\n",
       "                       A60-A79              481                6\n",
       "                       A80+                  98               12\n",
       "Saarland               A00-A04                2                0\n",
       "                       A05-A14                9                0\n",
       "                       A15-A34              198                0\n",
       "                       A35-A59              399                1\n",
       "                       A60-A79              129                3\n",
       "                       A80+                  37                3\n",
       "Sachsen                A00-A04               19                0\n",
       "                       A05-A14               40                0\n",
       "                       A15-A34              467                0\n",
       "                       A35-A59              901                0\n",
       "                       A60-A79              375                4\n",
       "                       A80+                  80                8\n",
       "Sachsen-Anhalt         A00-A04                3                0\n",
       "                       A05-A14               11                0\n",
       "                       A15-A34              156                0\n",
       "                       A35-A59              324                0\n",
       "                       A60-A79              130                3\n",
       "                       A80+                  55                3\n",
       "Schleswig-Holstein     A00-A04                8                0\n",
       "                       A05-A14               16                0\n",
       "                       A15-A34              277                0\n",
       "                       A35-A59              487                1\n",
       "                       A80+                  78                3\n",
       "                       A60-A79              239                5\n",
       "Thüringen              A00-A04                7                0\n",
       "                       A05-A14               15                0\n",
       "                       A15-A34              199                0\n",
       "                       A60-A79              132                0\n",
       "                       A35-A59              379                1\n",
       "                       A80+                  51                5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RKIData.groupby(['Bundesland','Altersgruppe']).sum().sort_values(by=[\"Bundesland\",\"AnzahlTodesfall\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>AnzahlFall</th>\n",
       "      <th>AnzahlTodesfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Landkreis</th>\n",
       "      <th>Altersgruppe</th>\n",
       "      <th>Geschlecht</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>LK Heinsberg</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>W</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SK Köln</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>M</td>\n",
       "      <td>274</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">SK Hamburg</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">A15-A34</td>\n",
       "      <td>W</td>\n",
       "      <td>342</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>364</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">SK München</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">A15-A34</td>\n",
       "      <td>M</td>\n",
       "      <td>411</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>W</td>\n",
       "      <td>427</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SK Hamburg</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>W</td>\n",
       "      <td>459</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SK München</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>W</td>\n",
       "      <td>530</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SK Hamburg</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>M</td>\n",
       "      <td>538</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SK München</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>M</td>\n",
       "      <td>609</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      AnzahlFall  AnzahlTodesfall\n",
       "Landkreis    Altersgruppe Geschlecht                             \n",
       "LK Heinsberg A35-A59      W                  272                0\n",
       "SK Köln      A35-A59      M                  274                0\n",
       "SK Hamburg   A15-A34      W                  342                0\n",
       "                          M                  364                0\n",
       "SK München   A15-A34      M                  411                0\n",
       "                          W                  427                0\n",
       "SK Hamburg   A35-A59      W                  459                0\n",
       "SK München   A35-A59      W                  530                0\n",
       "SK Hamburg   A35-A59      M                  538                1\n",
       "SK München   A35-A59      M                  609                1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RKIData.groupby(['Landkreis','Altersgruppe','Geschlecht']).sum().sort_values(by=[\"AnzahlFall\"])[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>AnzahlFall</th>\n",
       "      <th>AnzahlTodesfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Altersgruppe</th>\n",
       "      <th>Geschlecht</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">A00-A04</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">A05-A14</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">A15-A34</td>\n",
       "      <td>M</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>W</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">A35-A59</td>\n",
       "      <td>M</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>W</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">A60-A79</td>\n",
       "      <td>M</td>\n",
       "      <td>149</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>W</td>\n",
       "      <td>130</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">A80+</td>\n",
       "      <td>M</td>\n",
       "      <td>57</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>W</td>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         AnzahlFall  AnzahlTodesfall\n",
       "Altersgruppe Geschlecht                             \n",
       "A00-A04      M                    5                0\n",
       "             W                    4                0\n",
       "A05-A14      M                    5                0\n",
       "             W                    7                0\n",
       "A15-A34      M                  136                0\n",
       "             W                  170                0\n",
       "A35-A59      M                  244                0\n",
       "             W                  272                0\n",
       "A60-A79      M                  149                3\n",
       "             W                  130                6\n",
       "A80+         M                   57               13\n",
       "             W                   55               10"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RKIData.loc[RKIData['Landkreis'] == \"LK Heinsberg\"].groupby(['Altersgruppe','Geschlecht']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LandkreisTodesfall = RKIData.groupby(['Landkreis']).sum().sort_values(by=[\"AnzahlTodesfall\"])[-10:].plot(kind=\"bar\",figsize=(20,10))\n",
    "\n",
    "LandkreisTodesfall.get_figure().savefig('todesfall-landkreis.png',dpi=200,pad_inches=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LandkreisTodesfall = RKIData.groupby(['Landkreis']).sum().sort_values(by=[\"AnzahlFall\"])[-10:].plot(kind=\"bar\",figsize=(20,10))\n",
    "\n",
    "LandkreisTodesfall.get_figure().savefig('fall-landkreis.png',dpi=200,pad_inches=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115e11910>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RKIData.loc[RKIData['Landkreis'] == \"LK Heinsberg\"].groupby(['Altersgruppe']).sum().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame = RKIData.groupby(['Altersgruppe']).sum().reset_index()\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,10))\n",
    "ax.plot(frame['Altersgruppe'], frame['AnzahlTodesfall'], color=\"black\")\n",
    "ax.set_xlabel(\"Altersgruppe\",fontsize=14)\n",
    "ax.set_ylabel(\"Anzahl Todesfall\",color=\"black\",fontsize=14)\n",
    "\n",
    "ax2=ax.twinx()\n",
    "ax2.plot(frame['Altersgruppe'], frame['AnzahlFall'],color=\"red\")\n",
    "ax2.set_ylabel(\"Anzahl Fälle\",color=\"red\",fontsize=14)\n",
    "\n",
    "fig.savefig('data.png',dpi=200,pad_inches=5) # Bild Speichern\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Altersgruppe</th>\n",
       "      <th>AnzahlFall</th>\n",
       "      <th>AnzahlTodesfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>A00-A04</td>\n",
       "      <td>452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>A05-A14</td>\n",
       "      <td>1203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>A15-A34</td>\n",
       "      <td>15809</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>A35-A59</td>\n",
       "      <td>29515</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>A60-A79</td>\n",
       "      <td>11238</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Altersgruppe  AnzahlFall  AnzahlTodesfall\n",
       "0      A00-A04         452                0\n",
       "1      A05-A14        1203                0\n",
       "2      A15-A34       15809                1\n",
       "3      A35-A59       29515               30\n",
       "4      A60-A79       11238              173"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vielen Dank fürs Lesen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://hellocoding.de/impressum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://hellocoding.de/datenschutz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://hellocoding.de/blog/coding-language/python/covid-19-data-science"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}