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Example python code, in order to parse DWD-Precipitation data  
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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Updated precipitation script from DWD / CDC    
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       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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Example python code, in order to parse DWD-Precipitation data  
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 365 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "datetime  1    2    3    4    5    6    7    8    9    10   ...  356  357  \\\n",
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       "00:00:00  0.0  0.0  0.0  0.6  0.8  0.0  0.0  0.0  0.0  0.0  ...  0.0  1.2   \n",
       "01:00:00  0.0  0.0  0.0  1.1  0.4  0.0  0.0  0.0  0.3  0.0  ...  0.0  0.6   \n",
       "02:00:00  0.0  0.0  2.4  1.1  0.0  0.0  0.0  0.0  0.3  0.0  ...  0.1  0.2   \n",
       "03:00:00  0.0  0.0  0.1  0.6  0.6  0.0  0.0  0.3  0.4  0.0  ...  0.4  0.3   \n",
       "04:00:00  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.2  0.8  0.0  ...  0.0  0.2   \n",
       "05:00:00  0.0  0.0  0.0  1.6  0.0  0.0  0.0  1.0  0.9  0.0  ...  0.0  0.0   \n",
       "06:00:00  0.0  0.0  0.2  2.5  0.0  0.0  0.0  0.4  0.1  0.0  ...  0.1  0.1   \n",
       "07:00:00  0.0  0.0  1.2  1.6  0.0  0.0  0.0  0.1  0.2  0.0  ...  1.4  0.0   \n",
       "08:00:00  0.0  0.0  2.8  1.1  0.0  0.0  0.0  0.0  0.8  0.0  ...  0.3  0.0   \n",
       "09:00:00  0.0  0.0  0.1  1.2  0.0  0.1  0.0  0.0  0.2  0.0  ...  1.1  0.0   \n",
       "10:00:00  0.0  0.0  0.1  1.2  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "11:00:00  0.0  0.0  0.9  1.5  0.0  0.0  0.1  0.0  0.1  0.0  ...  0.0  0.0   \n",
       "12:00:00  0.0  0.0  0.0  3.2  0.0  0.0  0.2  0.0  0.0  0.0  ...  0.1  0.1   \n",
       "13:00:00  0.0  0.0  0.0  1.9  0.0  0.0  0.1  0.0  0.0  0.0  ...  0.0  0.5   \n",
       "14:00:00  0.0  0.0  0.0  0.5  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.2  1.5   \n",
       "15:00:00  0.0  0.0  0.2  0.6  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.4  2.3   \n",
       "16:00:00  0.0  0.0  0.1  0.8  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.3  2.3   \n",
       "17:00:00  0.0  0.0  0.0  0.4  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  1.1   \n",
       "18:00:00  0.0  0.0  0.0  0.1  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.2  1.4   \n",
       "19:00:00  0.0  0.0  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.2  1.8   \n",
       "20:00:00  0.0  0.0  0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.6   \n",
       "21:00:00  0.0  0.0  0.3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.1  1.5   \n",
       "22:00:00  0.0  0.0  0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.7  0.1   \n",
       "23:00:00  0.0  0.0  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  1.3  0.0   \n",
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Example python code, in order to parse DWD-Precipitation data  
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       "\n",
       "datetime  358  359  360  361  362  363  364  365  \n",
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Updated precipitation script from DWD / CDC    
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       "00:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "01:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "02:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "03:00:00  0.0  0.0  0.0  0.0  0.0  0.1  0.0  0.0  \n",
       "04:00:00  0.0  0.0  0.0  0.0  0.0  0.1  0.0  0.0  \n",
       "05:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "06:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "07:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "08:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "09:00:00  0.0  0.0  0.4  0.0  0.0  0.0  0.0  0.0  \n",
       "10:00:00  0.0  0.0  1.1  0.0  0.0  0.0  0.0  0.0  \n",
       "11:00:00  0.0  0.0  0.6  0.0  0.0  0.0  0.0  0.0  \n",
       "12:00:00  0.2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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Example python code, in order to parse DWD-Precipitation data  
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       "13:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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Updated precipitation script from DWD / CDC    
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       "14:00:00  0.2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "15:00:00  0.2  0.0  0.1  0.0  0.0  0.0  0.0  0.0  \n",
       "16:00:00  0.0  0.0  0.0  0.0  0.0  0.7  0.1  0.0  \n",
       "17:00:00  0.0  0.0  0.0  0.0  0.0  1.4  1.1  0.0  \n",
       "18:00:00  0.0  0.0  2.2  0.0  0.0  0.0  0.1  0.0  \n",
       "19:00:00  0.0  0.0  1.0  0.0  0.0  0.0  0.1  0.0  \n",
       "20:00:00  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  \n",
       "22:00:00  0.0  0.0  0.1  0.0  0.0  0.0  0.0  0.0  \n",
       "23:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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       "\n",
       "[24 rows x 365 columns]"
      ]
     },
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Updated precipitation script from DWD / CDC    
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     "execution_count": 12,
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Example python code, in order to parse DWD-Precipitation data  
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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Updated precipitation script from DWD / CDC    
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    "rain2022 = pd.pivot_table(df3, values='precipitation', index=df3.index.time, columns=df3.index.dayofyear)\n",
    "rain2022"
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   ]
  },
  {
   "cell_type": "code",
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Updated precipitation script from DWD / CDC    
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   "execution_count": 13,
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Example python code, in order to parse DWD-Precipitation data  
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   "id": "42ddd82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
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Updated precipitation script from DWD / CDC    
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   "execution_count": 14,
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Example python code, in order to parse DWD-Precipitation data  
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   "id": "f1b17ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Updated precipitation script from DWD / CDC    
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\n",
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      "text/plain": [
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       "<Figure size 2000x1000 with 2 Axes>"
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      ]
     },
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     "metadata": {},
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "sns.heatmap(rain2022, annot=False, cmap='viridis', vmax=5)\n",
    "plt.title('mm/h Regen in Würzburg, 2022')\n",
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    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 15,
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   "id": "29be9114",
   "metadata": {},
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   "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",
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       "      <th>2023-06-30</th>\n",
       "      <td>21.3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-07-31</th>\n",
       "      <td>54.2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-08-31</th>\n",
       "      <td>69.9</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-09-30</th>\n",
       "      <td>18.6</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-10-31</th>\n",
       "      <td>52.7</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<p>166 rows × 1 columns</p>\n",
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       "</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",
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       "2023-06-30           21.3\n",
       "2023-07-31           54.2\n",
       "2023-08-31           69.9\n",
       "2023-09-30           18.6\n",
       "2023-10-31           52.7\n",
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       "\n",
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       "[166 rows x 1 columns]"
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      ]
     },
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     "execution_count": 15,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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    "df4 = df2[(df2.index.year >= 2010)].resample('M').sum()\n",
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    "df4"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 16,
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monthly_precipitation_variation_wuerzburg    
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   "id": "283cd890",
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   "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",
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       "      <th>2023-06-30</th>\n",
       "      <td>21.3</td>\n",
       "      <td>2023</td>\n",
       "      <td>Jun</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-07-31</th>\n",
       "      <td>54.2</td>\n",
       "      <td>2023</td>\n",
       "      <td>Jul</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-08-31</th>\n",
       "      <td>69.9</td>\n",
       "      <td>2023</td>\n",
       "      <td>Aug</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-09-30</th>\n",
       "      <td>18.6</td>\n",
       "      <td>2023</td>\n",
       "      <td>Sep</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2023-10-31</th>\n",
       "      <td>52.7</td>\n",
       "      <td>2023</td>\n",
       "      <td>Oct</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<p>166 rows × 3 columns</p>\n",
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       "</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",
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       "2023-06-30           21.3  2023   Jun\n",
       "2023-07-31           54.2  2023   Jul\n",
       "2023-08-31           69.9  2023   Aug\n",
       "2023-09-30           18.6  2023   Sep\n",
       "2023-10-31           52.7  2023   Oct\n",
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       "\n",
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       "[166 rows x 3 columns]"
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      ]
     },
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     "execution_count": 16,
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     "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",
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   "execution_count": 17,
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monthly_precipitation_variation_wuerzburg    
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   "id": "28a0fd61",
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   "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>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",
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       "      <th rowspan=\"5\" valign=\"top\">2023</th>\n",
       "      <th>Jun</th>\n",
       "      <td>21.3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>Jul</th>\n",
       "      <td>54.2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>Aug</th>\n",
       "      <td>69.9</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>Sep</th>\n",
       "      <td>18.6</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>Oct</th>\n",
       "      <td>52.7</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<p>166 rows × 1 columns</p>\n",
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       "</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",
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       "2023 Jun             21.3\n",
       "     Jul             54.2\n",
       "     Aug             69.9\n",
       "     Sep             18.6\n",
       "     Oct             52.7\n",
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       "\n",
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       "[166 rows x 1 columns]"
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      ]
     },
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     "execution_count": 17,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4.set_index(['Year', 'Month'])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 18,
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monthly_precipitation_variation_wuerzburg    
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   "id": "8e3bd7d1",
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   "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>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",
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       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
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       "    </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",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    </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",
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       "      <td>50.7</td>\n",
       "      <td>41.8</td>\n",
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       "    </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",
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       "      <td>51.0</td>\n",
       "      <td>20.5</td>\n",
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       "    </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",
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       "      <td>18.4</td>\n",
       "      <td>72.5</td>\n",
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       "    </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",
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       "      <td>87.8</td>\n",
       "      <td>49.3</td>\n",
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       "    </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",
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       "      <td>28.9</td>\n",
       "      <td>21.9</td>\n",
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       "    </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",
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       "      <td>23.2</td>\n",
       "      <td>21.3</td>\n",
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       "    </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",
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       "      <td>17.5</td>\n",
       "      <td>54.2</td>\n",
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       "    </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",
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       "      <td>40.2</td>\n",
       "      <td>69.9</td>\n",
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       "    </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",
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       "      <td>109.1</td>\n",
       "      <td>18.6</td>\n",
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       "    </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",
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       "      <td>43.9</td>\n",
       "      <td>52.7</td>\n",
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       "    </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",
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       "      <td>43.7</td>\n",
       "      <td>NaN</td>\n",
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       "    </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",
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       "      <td>46.9</td>\n",
       "      <td>NaN</td>\n",
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       "    </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",
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       "Year    2021   2022  2023  \n",
       "Month                      \n",
       "Jan     54.6   50.7  41.8  \n",
       "Feb     48.6   51.0  20.5  \n",
       "Mar     35.8   18.4  72.5  \n",
       "Apr     17.6   87.8  49.3  \n",
       "May     69.8   28.9  21.9  \n",
       "Jun    100.9   23.2  21.3  \n",
       "Jul    138.7   17.5  54.2  \n",
       "Aug     82.0   40.2  69.9  \n",
       "Sep      6.0  109.1  18.6  \n",
       "Oct     42.5   43.9  52.7  \n",
       "Nov     26.3   43.7   NaN  \n",
       "Dec     60.5   46.9   NaN  "
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      ]
     },
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     "execution_count": 18,
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     "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",
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   "execution_count": 19,
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monthly_precipitation_variation_wuerzburg    
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   "id": "fa12c1b9",
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   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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Update. Parse Niederschlag.    
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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Updated precipitation script from DWD / CDC    
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    "ax=sns.heatmap(df_precipitation, cmap='viridis_r', annot=True, fmt=\".0f\")\n",
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Update. Parse Niederschlag.    
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    "ax.invert_yaxis()\n",
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Updated precipitation script from DWD / CDC    
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    "plt.title(\"Precipitation in Würzburg [mm / month]\");\n",
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Updated script and diagrams    
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    "plt.savefig('wuerzburg_monthly_precipitation.png', facecolor='w', bbox_inches='tight')"
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Update. Parse Niederschlag.    
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   ]
  },
  {
   "cell_type": "code",
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Updated precipitation script from DWD / CDC    
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   "execution_count": 20,
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monthly_precipitation_variation_wuerzburg    
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   "id": "470cf56e",
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Update. Parse Niederschlag.    
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   "metadata": {},
   "outputs": [
    {
     "data": {
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Updated precipitation script from DWD / CDC    
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      "image/png": 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\n",
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Update. Parse Niederschlag.    
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      "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",
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Updated precipitation script from DWD / CDC    
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   "execution_count": 21,
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monthly_precipitation_variation_wuerzburg    
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   "id": "40727a96",
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Update. Parse Niederschlag.    
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   "metadata": {},
   "outputs": [
    {
     "data": {
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Updated precipitation script from DWD / CDC    
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      "image/png": 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\n",
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Update. Parse Niederschlag.    
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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Updated precipitation script from DWD / CDC    
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    "df_precipitation.mean(axis=1).plot.bar(title='Mittlerer Niederschlag in Würzburg\\n1966 -> 2022\\n[mm / Monat]',\n",
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Update. Parse Niederschlag.    
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    "                                       ylim=(0, None));"
   ]
  },
  {
   "cell_type": "code",
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Updated precipitation script from DWD / CDC    
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   "execution_count": 22,
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monthly_precipitation_variation_wuerzburg    
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   "id": "0a195120",
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   "metadata": {},
   "outputs": [
    {
     "data": {
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Updated script and diagrams    
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      "image/png": 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\n",
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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",
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Updated precipitation script from DWD / CDC    
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    "duration = \"2010 -> 2022\"\n",
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    "plt.title(f\"Variation of monthly precipitation in Würzburg, {duration}\")\n",
    "plt.ylabel(\"[mm / month]\");\n",
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Updated script and diagrams    
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    "plt.savefig('wuerzburg_monthly_precipitation_variation.png', facecolor=\"w\", bbox_inches = 'tight')"
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   ]
  },
  {
   "cell_type": "code",
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Updated precipitation script from DWD / CDC    
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   "execution_count": 23,
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monthly_precipitation_variation_wuerzburg    
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   "id": "e5d682d5",
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   "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,
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monthly_precipitation_variation_wuerzburg    
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   "id": "803223b4",
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   "metadata": {},
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Example python code, in order to parse DWD-Precipitation data  
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   "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",
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   "version": "3.9.13"
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Example python code, in order to parse DWD-Precipitation data  
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  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
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