Parse Niederschlag.ipynb

       "11:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.2  0.0  0.0  ...  0.0  0.0   \n",
       "12:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "13:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "14:00:00  0.0  0.0  0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "15:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "16:00:00  0.0  0.0  0.0  0.0  0.6  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "17:00:00  0.0  0.0  0.0  0.0  0.4  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "18:00:00  0.0  0.0  0.0  0.0  0.2  0.0  0.0  0.1  0.0  0.0  ...  0.0  0.0   \n",
       "19:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "20:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "21:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "22:00:00  0.0  0.0  0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.1   \n",
       "23:00:00  0.0  0.0  0.3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "datetime  358  359  360  361  362  363  364  365  \n",
       "00:00:00  0.0  0.0  0.0  0.0  0.2  1.5  0.0  0.0  \n",
       "01:00:00  0.0  0.0  0.0  1.1  0.0  1.2  0.1  0.0  \n",
       "02:00:00  0.3  0.0  0.0  0.1  0.0  0.3  0.0  0.0  \n",
       "03:00:00  0.1  0.0  0.0  0.1  0.0  0.3  0.0  0.0  \n",
       "04:00:00  0.1  0.0  0.0  0.3  0.0  0.0  0.0  0.0  \n",
       "05:00:00  0.0  0.7  0.0  0.1  0.0  0.0  0.0  0.0  \n",
       "06:00:00  0.0  2.4  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "07:00:00  0.0  2.7  0.0  0.0  1.1  0.0  0.0  0.0  \n",
       "08:00:00  0.0  0.8  0.0  0.0  2.8  0.0  1.1  0.0  \n",
       "09:00:00  0.0  0.4  0.0  0.0  1.5  0.0  0.1  0.0  \n",
       "10:00:00  0.0  0.3  0.0  0.0  0.6  0.0  0.0  0.0  \n",
       "11:00:00  0.0  0.2  0.0  0.0  0.7  0.1  0.0  0.0  \n",
       "12:00:00  0.0  0.0  0.0  0.0  0.2  0.0  0.0  0.0  \n",
       "13:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "14:00:00  0.0  0.2  0.1  0.0  0.0  0.0  0.0  0.0  \n",
       "15:00:00  0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "16:00:00  0.1  0.0  0.0  0.0  1.1  0.0  0.0  0.0  \n",
       "17:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "18:00:00  0.0  0.0  0.0  0.0  0.0  0.4  0.0  0.0  \n",
       "19:00:00  0.0  0.0  0.0  0.1  0.0  1.1  0.0  0.0  \n",
       "20:00:00  0.0  0.0  0.0  0.3  0.4  0.7  0.0  0.0  \n",
       "21:00:00  0.0  0.0  0.1  0.2  1.0  0.2  0.0  0.0  \n",
       "22:00:00  0.0  0.0  0.0  0.1  1.1  0.1  0.0  0.0  \n",
       "23:00:00  0.0  0.0  0.1  0.6  1.1  0.0  0.0  0.0  \n",
       "\n",
       "[24 rows x 365 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain2021 = pd.pivot_table(df3, values='precipitation', index=df3.index.time, columns=df3.index.dayofyear)\n",
    "rain2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "42ddd82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1b17ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(rain2021, annot=False, cmap='viridis', vmax=5)\n",
    "plt.title('mm/h Regen in Würzburg, 2021')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "29be9114",
   "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>precipitation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>42.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precipitation\n",
       "datetime                 \n",
       "2010-01-31           35.9\n",
       "2010-02-28           28.5\n",
       "2010-03-31           29.7\n",
       "2010-04-30           23.7\n",
       "2010-05-31           68.4\n",
       "...                   ...\n",
       "2021-08-31           82.0\n",
       "2021-09-30            6.0\n",
       "2021-10-31           42.5\n",
       "2021-11-30           26.3\n",
       "2021-12-31           60.5\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df2[df2.index.year >= 2010].resample('M').sum()\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "283cd890",
   "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>precipitation</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>35.9</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>28.5</td>\n",
       "      <td>2010</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>29.7</td>\n",
       "      <td>2010</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-30</th>\n",
       "      <td>23.7</td>\n",
       "      <td>2010</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-31</th>\n",
       "      <td>68.4</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>82.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>42.5</td>\n",
       "      <td>2021</td>\n",
       "      <td>Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>26.3</td>\n",
       "      <td>2021</td>\n",
       "      <td>Nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>60.5</td>\n",
       "      <td>2021</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precipitation  Year Month\n",
       "datetime                             \n",
       "2010-01-31           35.9  2010   Jan\n",
       "2010-02-28           28.5  2010   Feb\n",
       "2010-03-31           29.7  2010   Mar\n",
       "2010-04-30           23.7  2010   Apr\n",
       "2010-05-31           68.4  2010   May\n",
       "...                   ...   ...   ...\n",
       "2021-08-31           82.0  2021   Aug\n",
       "2021-09-30            6.0  2021   Sep\n",
       "2021-10-31           42.5  2021   Oct\n",
       "2021-11-30           26.3  2021   Nov\n",
       "2021-12-31           60.5  2021   Dec\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import calendar\n",
    "df4['Year'] = df4.index.year\n",
    "df4['Month'] = df4.index.month\n",
    "df4['Month'] = df4['Month'].apply(lambda x: calendar.month_abbr[x])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "28a0fd61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>precipitation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010</th>\n",
       "      <th>Jan</th>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2021</th>\n",
       "      <th>Aug</th>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>42.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precipitation\n",
       "Year Month               \n",
       "2010 Jan             35.9\n",
       "     Feb             28.5\n",
       "     Mar             29.7\n",
       "     Apr             23.7\n",
       "     May             68.4\n",
       "...                   ...\n",
       "2021 Aug             82.0\n",
       "     Sep              6.0\n",
       "     Oct             42.5\n",
       "     Nov             26.3\n",
       "     Dec             60.5\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4.set_index(['Year', 'Month'])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8e3bd7d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Year</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</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>Jan</th>\n",
       "      <td>35.9</td>\n",
       "      <td>38.6</td>\n",
       "      <td>66.6</td>\n",
       "      <td>29.8</td>\n",
       "      <td>31.1</td>\n",
       "      <td>53.3</td>\n",
       "      <td>62.2</td>\n",
       "      <td>14.7</td>\n",
       "      <td>56.1</td>\n",
       "      <td>37.2</td>\n",
       "      <td>23.2</td>\n",
       "      <td>54.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>28.5</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12.2</td>\n",
       "      <td>35.1</td>\n",
       "      <td>39.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>60.1</td>\n",
       "      <td>21.2</td>\n",
       "      <td>12.4</td>\n",
       "      <td>10.6</td>\n",
       "      <td>106.5</td>\n",
       "      <td>48.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>29.7</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>26.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>43.3</td>\n",
       "      <td>36.5</td>\n",
       "      <td>41.6</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>23.7</td>\n",
       "      <td>14.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>42.1</td>\n",
       "      <td>40.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>49.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>34.5</td>\n",
       "      <td>27.8</td>\n",
       "      <td>10.9</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>68.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>34.7</td>\n",
       "      <td>99.7</td>\n",
       "      <td>73.7</td>\n",
       "      <td>22.4</td>\n",
       "      <td>66.9</td>\n",
       "      <td>118.4</td>\n",
       "      <td>48.4</td>\n",
       "      <td>72.0</td>\n",
       "      <td>41.2</td>\n",
       "      <td>69.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>21.5</td>\n",
       "      <td>110.0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>44.7</td>\n",
       "      <td>16.7</td>\n",
       "      <td>39.3</td>\n",
       "      <td>53.4</td>\n",
       "      <td>49.3</td>\n",
       "      <td>27.3</td>\n",
       "      <td>44.6</td>\n",
       "      <td>75.6</td>\n",
       "      <td>100.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>75.2</td>\n",
       "      <td>94.6</td>\n",
       "      <td>66.8</td>\n",
       "      <td>36.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>26.7</td>\n",
       "      <td>47.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>54.4</td>\n",
       "      <td>25.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>138.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>185.1</td>\n",
       "      <td>43.4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>108.9</td>\n",
       "      <td>95.1</td>\n",
       "      <td>67.6</td>\n",
       "      <td>30.6</td>\n",
       "      <td>61.1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>39.2</td>\n",
       "      <td>61.9</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>55.3</td>\n",
       "      <td>28.3</td>\n",
       "      <td>44.8</td>\n",
       "      <td>88.9</td>\n",
       "      <td>31.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>32.5</td>\n",
       "      <td>68.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>31.2</td>\n",
       "      <td>31.9</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>27.6</td>\n",
       "      <td>36.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>56.5</td>\n",
       "      <td>39.2</td>\n",
       "      <td>28.5</td>\n",
       "      <td>38.1</td>\n",
       "      <td>36.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>65.9</td>\n",
       "      <td>41.6</td>\n",
       "      <td>42.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>87.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>79.7</td>\n",
       "      <td>60.2</td>\n",
       "      <td>35.3</td>\n",
       "      <td>87.8</td>\n",
       "      <td>66.0</td>\n",
       "      <td>62.4</td>\n",
       "      <td>11.4</td>\n",
       "      <td>37.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>106.5</td>\n",
       "      <td>109.5</td>\n",
       "      <td>78.5</td>\n",
       "      <td>30.2</td>\n",
       "      <td>41.3</td>\n",
       "      <td>27.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>49.9</td>\n",
       "      <td>86.2</td>\n",
       "      <td>54.2</td>\n",
       "      <td>48.3</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Year    2010   2011  2012   2013  2014  2015  2016   2017  2018  2019   2020  \\\n",
       "Month                                                                          \n",
       "Jan     35.9   38.6  66.6   29.8  31.1  53.3  62.2   14.7  56.1  37.2   23.2   \n",
       "Feb     28.5   31.4  12.2   35.1  39.6   8.4  60.1   21.2  12.4  10.6  106.5   \n",
       "Mar     29.7    6.2   5.7   26.1   7.7  43.3  36.5   41.6  46.0  44.8   27.0   \n",
       "Apr     23.7   14.2  11.8   42.1  40.7  17.3  49.6   20.9  34.5  27.8   10.9   \n",
       "May     68.4    6.6  34.7   99.7  73.7  22.4  66.9  118.4  48.4  72.0   41.2   \n",
       "Jun     21.5  110.0  67.2   44.7  16.7  39.3  53.4   49.3  27.3  44.6   75.6   \n",
       "Jul     75.2   94.6  66.8   36.3  77.5  26.7  47.5   85.0  54.4  25.3   15.5   \n",
       "Aug    185.1   43.4  28.0  108.9  95.1  67.6  30.6   61.1  22.5  39.2   61.9   \n",
       "Sep     55.3   28.3  44.8   88.9  31.2  26.2  32.5   68.3  24.1  31.2   31.9   \n",
       "Oct     27.6   36.1  41.9   56.5  39.2  28.5  38.1   36.1   8.8  65.9   41.6   \n",
       "Nov     87.0    0.6  79.7   60.2  35.3  87.8  66.0   62.4  11.4  37.2    9.6   \n",
       "Dec    106.5  109.5  78.5   30.2  41.3  27.6   7.4   49.9  86.2  54.2   48.3   \n",
       "\n",
       "Year    2021  \n",
       "Month         \n",
       "Jan     54.6  \n",
       "Feb     48.6  \n",
       "Mar     35.8  \n",
       "Apr     17.6  \n",
       "May     69.8  \n",
       "Jun    100.9  \n",
       "Jul    138.7  \n",
       "Aug     82.0  \n",
       "Sep      6.0  \n",
       "Oct     42.5  \n",
       "Nov     26.3  \n",
       "Dec     60.5  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_precipitation = df4.reset_index().pivot_table(columns='Year',index='Month',values='precipitation', sort=False)\n",
    "df_precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa12c1b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.heatmap(df_precipitation, cmap='viridis_r')\n",
    "ax.invert_yaxis()\n",
    "plt.title(\"Precipitation in Würzburg [mm / month]\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "470cf56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_precipitation.sum().plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40727a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_precipitation.mean(axis=1).plot.bar(title='Mittlerer Niederschlag in Würzburg\\n1966 -> 2021\\n[mm / Monat]',\n",
    "                                       ylim=(0, None));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a195120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_precipitation.transpose().plot.box(showfliers=False, ylim=(0, None))\n",
    "duration = \"2010 -> 2021\"\n",
    "plt.title(f\"Variation of monthly precipitation in Würzburg, {duration}\")\n",
    "plt.ylabel(\"[mm / month]\");\n",
    "plt.savefig('monthly_precipitation_variation_wuerzburg.png', facecolor=\"w\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5d682d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Compare 1995 -> 2021 with 2015 -> 2021\n",
    "# TODO: Get duration from min max index\n",
    "# TODO: Ridgeline for each month?\n",
    "# TODO: apply to Feuerbach\n",
    "# TODO: Push and document\n",
    "# TODO: Show on map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "803223b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}