Precipitation_Result.ipynb

{
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   "id": "d1f9fd49-f5d3-4379-8fd0-19c24f863a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e903275-238e-448f-9843-f4caa17e5b17",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1590</th>\n",
       "      <th>5733</th>\n",
       "      <th>13670</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1941-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941-01-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941-01-03</th>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941-01-04</th>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>0.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>8.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>7.2</td>\n",
       "      <td>20.1</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29220 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1590  5733  13670\n",
       "MESS_DATUM                   \n",
       "1941-01-01   0.0   NaN    NaN\n",
       "1941-01-02   0.0   NaN    NaN\n",
       "1941-01-03   0.2   NaN    NaN\n",
       "1941-01-04   0.3   NaN    NaN\n",
       "1941-01-05   0.0   NaN    NaN\n",
       "...          ...   ...    ...\n",
       "2022-12-27   0.3   2.1    0.9\n",
       "2022-12-28   8.7   3.9    6.0\n",
       "2022-12-29   1.6   0.5    3.0\n",
       "2022-12-30   7.2  20.1    3.3\n",
       "2022-12-31   0.0   0.1    0.2\n",
       "\n",
       "[29220 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('FINAL_DATA.CSV', sep =\",\", index_col = \"MESS_DATUM\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3cc46f2-bac5-442b-93ee-b17a9ff57cb3",
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    {
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       "      <th>1590</th>\n",
       "      <th>5733</th>\n",
       "      <th>13670</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>6.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>12.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>5.6</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>0.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>8.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>7.2</td>\n",
       "      <td>20.1</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1826 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1590  5733  13670\n",
       "MESS_DATUM                   \n",
       "2018-01-01   6.3   5.9    5.0\n",
       "2018-01-02  12.7  16.2   12.3\n",
       "2018-01-03   5.6   6.5    3.1\n",
       "2018-01-04   3.0   2.6    2.4\n",
       "2018-01-05   0.0   2.3    0.0\n",
       "...          ...   ...    ...\n",
       "2022-12-27   0.3   2.1    0.9\n",
       "2022-12-28   8.7   3.9    6.0\n",
       "2022-12-29   1.6   0.5    3.0\n",
       "2022-12-30   7.2  20.1    3.3\n",
       "2022-12-31   0.0   0.1    0.2\n",
       "\n",
       "[1826 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = '2018-01-01'\n",
    "end = '2022-12-31'\n",
    "df_5_years = df.loc[start:end]\n",
    "df_5_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14ab6c45-3045-488e-945c-702319ebf66e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No\n"
     ]
    }
   ],
   "source": [
    "if pd.api.types.is_datetime64_any_dtype(df_5_years.index):\n",
    "    print('yes')\n",
    "else:\n",
    "    print('No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22904b7a-b025-4231-b97c-1cc26288e090",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>1590</th>\n",
       "      <th>5733</th>\n",
       "      <th>13670</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</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>2018-01-01</th>\n",
       "      <td>6.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>12.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>5.6</td>\n",
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       "      <td>3.1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</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>2022-12-27</th>\n",
       "      <td>0.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>8.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>7.2</td>\n",
       "      <td>20.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1826 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1590  5733  13670  Year  Month\n",
       "MESS_DATUM                                \n",
       "2018-01-01   6.3   5.9    5.0  2018      1\n",
       "2018-01-02  12.7  16.2   12.3  2018      1\n",
       "2018-01-03   5.6   6.5    3.1  2018      1\n",
       "2018-01-04   3.0   2.6    2.4  2018      1\n",
       "2018-01-05   0.0   2.3    0.0  2018      1\n",
       "...          ...   ...    ...   ...    ...\n",
       "2022-12-27   0.3   2.1    0.9  2022     12\n",
       "2022-12-28   8.7   3.9    6.0  2022     12\n",
       "2022-12-29   1.6   0.5    3.0  2022     12\n",
       "2022-12-30   7.2  20.1    3.3  2022     12\n",
       "2022-12-31   0.0   0.1    0.2  2022     12\n",
       "\n",
       "[1826 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.mode.chained_assignment = None\n",
    "df_5_years.index = pd.to_datetime(df_5_years.index)\n",
    "df_5_years['Year'] = df_5_years.index.year\n",
    "df_5_years['Month'] = df_5_years.index.month\n",
    "df_5_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6a98646-1fd3-42c3-8bcc-1923d20f7bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1590</th>\n",
       "      <th>5733</th>\n",
       "      <th>13670</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Precipitation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</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>2018-01-01</th>\n",
       "      <td>6.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>12.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>41.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>5.6</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1590  5733  13670  Year  Month  Total_Precipitation\n",
       "MESS_DATUM                                                     \n",
       "2018-01-01   6.3   5.9    5.0  2018      1                 17.2\n",
       "2018-01-02  12.7  16.2   12.3  2018      1                 41.2\n",
       "2018-01-03   5.6   6.5    3.1  2018      1                 15.2\n",
       "2018-01-04   3.0   2.6    2.4  2018      1                  8.0\n",
       "2018-01-05   0.0   2.3    0.0  2018      1                  2.3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_5_years['Total_Precipitation'] = df_5_years[['1590', '5733', '13670']].sum(axis=1) # Daily sum of three stations\n",
    "df_5_years.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee74b099-81fb-4328-a570-0d39ba6e7a60",
   "metadata": {},
   "source": [
    "## Resampling data to a monthly frequency and then calculating the sum within each month. ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cfb4b1c-dfdd-4f40-90f1-6a9c8a9b0e5f",
   "metadata": {},
   "outputs": [
    {
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       "\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>1590</th>\n",
       "      <th>5733</th>\n",
       "      <th>13670</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Precipitation</th>\n",
       "      <th>Monthly_Accumulated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</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>2018-01-01</th>\n",
       "      <td>6.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>17.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>12.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>41.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>5.6</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>1.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-15</th>\n",
       "      <td>5.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>3.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>5.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>16.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>3.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-20</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-21</th>\n",
       "      <td>1.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>1.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-27</th>\n",
       "      <td>0.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>3.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>1.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>5.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3</td>\n",
       "      <td>235.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1590  5733  13670  Year  Month  Total_Precipitation  \\\n",
       "MESS_DATUM                                                        \n",
       "2018-01-01   6.3   5.9    5.0  2018      1                 17.2   \n",
       "2018-01-02  12.7  16.2   12.3  2018      1                 41.2   \n",
       "2018-01-03   5.6   6.5    3.1  2018      1                 15.2   \n",
       "2018-01-04   3.0   2.6    2.4  2018      1                  8.0   \n",
       "2018-01-05   0.0   2.3    0.0  2018      1                  2.3   \n",
       "2018-01-06   0.0   0.0    0.0  2018      1                  0.0   \n",
       "2018-01-07   0.0   0.0    0.0  2018      1                  0.0   \n",
       "2018-01-08   0.0   0.0    0.0  2018      1                  0.0   \n",
       "2018-01-09   0.1   0.1    0.0  2018      1                  0.2   \n",
       "2018-01-10   1.8   1.4    0.2  2018      1                  3.4   \n",
       "2018-01-11   0.0   0.0    0.0  2018      1                  0.0   \n",
       "2018-01-12   0.0   0.0    0.0  2018      1                  0.0   \n",
       "2018-01-13   0.0   0.0    0.0  2018      1                  0.0   \n",
       "2018-01-14   0.0   0.0    0.0  2018      1                  0.0   \n",
       "2018-01-15   5.1   8.5    8.9  2018      1                 22.5   \n",
       "2018-01-16   3.9   4.0    9.2  2018      1                 17.1   \n",
       "2018-01-17   5.8   6.3    4.1  2018      1                 16.2   \n",
       "2018-01-18   5.9   7.6    4.9  2018      1                 18.4   \n",
       "2018-01-19   3.6   3.2    1.8  2018      1                  8.6   \n",
       "2018-01-20   2.4   1.3    3.2  2018      1                  6.9   \n",
       "2018-01-21   1.7   1.8    1.0  2018      1                  4.5   \n",
       "2018-01-22   0.2   0.4    0.9  2018      1                  1.5   \n",
       "2018-01-23   0.0   0.2    0.0  2018      1                  0.2   \n",
       "2018-01-24   1.7   1.6    1.9  2018      1                  5.2   \n",
       "2018-01-25   1.3   0.5    0.7  2018      1                  2.5   \n",
       "2018-01-26   0.0   0.0    0.0  2018      1                  0.0   \n",
       "2018-01-27   0.9   2.6    2.2  2018      1                  5.7   \n",
       "2018-01-28   0.0   0.2    0.0  2018      1                  0.2   \n",
       "2018-01-29   3.2   3.9    3.0  2018      1                 10.1   \n",
       "2018-01-30   1.7   3.0    1.4  2018      1                  6.1   \n",
       "2018-01-31   5.1  11.2    6.0  2018      1                 22.3   \n",
       "\n",
       "            Monthly_Accumulated  \n",
       "MESS_DATUM                       \n",
       "2018-01-01                  NaN  \n",
       "2018-01-02                  NaN  \n",
       "2018-01-03                  NaN  \n",
       "2018-01-04                  NaN  \n",
       "2018-01-05                  NaN  \n",
       "2018-01-06                  NaN  \n",
       "2018-01-07                  NaN  \n",
       "2018-01-08                  NaN  \n",
       "2018-01-09                  NaN  \n",
       "2018-01-10                  NaN  \n",
       "2018-01-11                  NaN  \n",
       "2018-01-12                  NaN  \n",
       "2018-01-13                  NaN  \n",
       "2018-01-14                  NaN  \n",
       "2018-01-15                  NaN  \n",
       "2018-01-16                  NaN  \n",
       "2018-01-17                  NaN  \n",
       "2018-01-18                  NaN  \n",
       "2018-01-19                  NaN  \n",
       "2018-01-20                  NaN  \n",
       "2018-01-21                  NaN  \n",
       "2018-01-22                  NaN  \n",
       "2018-01-23                  NaN  \n",
       "2018-01-24                  NaN  \n",
       "2018-01-25                  NaN  \n",
       "2018-01-26                  NaN  \n",
       "2018-01-27                  NaN  \n",
       "2018-01-28                  NaN  \n",
       "2018-01-29                  NaN  \n",
       "2018-01-30                  NaN  \n",
       "2018-01-31                235.5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_accumulated = df_5_years['Total_Precipitation'].resample('M').sum() # In order to get monthly data \n",
    "df_5_years['Monthly_Accumulated'] = monthly_accumulated\n",
    "df_5_years.head(31)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286d386b-c376-4a08-96ed-bf7be7c156a9",
   "metadata": {},
   "source": [
    "## Get the monthly data ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "067ce35a-b273-471a-b320-d83ca154f773",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1590</th>\n",
       "      <th>5733</th>\n",
       "      <th>13670</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>5.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2018</td>\n",
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       "      <th>2018-02-28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
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       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
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       "      <td>149.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>4.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>161.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>0.3</td>\n",
       "      <td>315.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>170.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>143.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>240.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>96.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-31</th>\n",
       "      <td>11.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>13.4</td>\n",
       "      <td>228.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>8.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>24.2</td>\n",
       "      <td>165.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>0.2</td>\n",
       "      <td>207.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1590  5733  13670  Year  Month  Total_Precipitation  \\\n",
       "MESS_DATUM                                                        \n",
       "2018-01-31   5.1  11.2    6.0  2018      1                 22.3   \n",
       "2018-02-28   0.0   0.0    0.0  2018      2                  0.0   \n",
       "2018-03-31   4.8   2.6    0.9  2018      3                  8.3   \n",
       "2018-04-30   4.4   4.9    4.7  2018      4                 14.0   \n",
       "2018-05-31   0.0   0.0    0.0  2018      5                  0.0   \n",
       "2018-06-30   0.0   0.0    0.0  2018      6                  0.0   \n",
       "2018-07-31   0.0   0.0    0.0  2018      7                  0.0   \n",
       "2018-08-31   0.0   0.0    0.0  2018      8                  0.0   \n",
       "2018-09-30   0.0   0.0    0.0  2018      9                  0.0   \n",
       "2018-10-31   0.0   0.0    0.0  2018     10                  0.0   \n",
       "2018-11-30   0.0   0.0    0.0  2018     11                  0.0   \n",
       "2018-12-31   0.3   0.0    0.0  2018     12                  0.3   \n",
       "2019-01-31   1.9   0.9    2.0  2019      1                  4.8   \n",
       "2019-02-28   0.9   0.0    0.8  2019      2                  1.7   \n",
       "2019-03-31   0.1   0.0    0.0  2019      3                  0.1   \n",
       "2019-04-30   0.0   0.0    0.0  2019      4                  0.0   \n",
       "2019-05-31   0.0   0.0    0.0  2019      5                  0.0   \n",
       "2019-06-30   0.0   0.0    0.0  2019      6                  0.0   \n",
       "2019-07-31   0.0   0.0    1.1  2019      7                  1.1   \n",
       "2019-08-31  11.2   2.0    0.2  2019      8                 13.4   \n",
       "2019-09-30   8.3   7.8    8.1  2019      9                 24.2   \n",
       "2019-10-31   0.0   0.0    0.0  2019     10                  0.0   \n",
       "2019-11-30   0.0   0.0    0.0  2019     11                  0.0   \n",
       "2019-12-31   0.0   0.0    0.2  2019     12                  0.2   \n",
       "\n",
       "            Monthly_Accumulated  \n",
       "MESS_DATUM                       \n",
       "2018-01-31                235.5  \n",
       "2018-02-28                 44.4  \n",
       "2018-03-31                149.8  \n",
       "2018-04-30                161.5  \n",
       "2018-05-31                111.6  \n",
       "2018-06-30                 62.0  \n",
       "2018-07-31                 21.5  \n",
       "2018-08-31                168.4  \n",
       "2018-09-30                 96.1  \n",
       "2018-10-31                 77.0  \n",
       "2018-11-30                 52.8  \n",
       "2018-12-31                315.2  \n",
       "2019-01-31                170.9  \n",
       "2019-02-28                143.4  \n",
       "2019-03-31                240.8  \n",
       "2019-04-30                 81.2  \n",
       "2019-05-31                 94.5  \n",
       "2019-06-30                147.0  \n",
       "2019-07-31                 96.9  \n",
       "2019-08-31                228.9  \n",
       "2019-09-30                165.1  \n",
       "2019-10-31                317.5  \n",
       "2019-11-30                266.0  \n",
       "2019-12-31                207.1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Monthly = df_5_years.groupby(['Year','Month'])['Monthly_Accumulated'].idxmax()\n",
    "df_Monthly = df_5_years.loc[Monthly]\n",
    "df_Monthly.head(24)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fe39b4-f90d-40eb-8f6c-80f17507d5b8",
   "metadata": {},
   "source": [
    "## Get the highest precipitation value within each year ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4fd61b7a-9c50-43fa-8bb8-1e6095448c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1590</th>\n",
       "      <th>5733</th>\n",
       "      <th>13670</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Precipitation</th>\n",
       "      <th>Monthly_Accumulated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESS_DATUM</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>2018-12-31</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>0.3</td>\n",
       "      <td>315.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>433.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>2.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>343.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>6.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>343.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1590  5733  13670  Year  Month  Total_Precipitation  \\\n",
       "MESS_DATUM                                                        \n",
       "2018-12-31   0.3   0.0    0.0  2018     12                  0.3   \n",
       "2019-10-31   0.0   0.0    0.0  2019     10                  0.0   \n",
       "2020-02-29   1.4   1.5    1.1  2020      2                  4.0   \n",
       "2021-06-30   2.2   2.1    1.5  2021      6                  5.8   \n",
       "2022-09-30   6.3   6.4   10.0  2022      9                 22.7   \n",
       "\n",
       "            Monthly_Accumulated  \n",
       "MESS_DATUM                       \n",
       "2018-12-31                315.2  \n",
       "2019-10-31                317.5  \n",
       "2020-02-29                433.5  \n",
       "2021-06-30                343.8  \n",
       "2022-09-30                343.1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_precip_month = df_Monthly.groupby(['Year'])['Monthly_Accumulated'].idxmax()\n",
    "df_most_precip_month = df_Monthly.loc[most_precip_month]\n",
    "df_most_precip_month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67b25b7-2b8e-4239-abd9-6ebce286ceb2",
   "metadata": {},
   "source": [
    "## Match the most precipitation month and the 5-year data ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a415dbd-8758-4061-be8e-573da44fe025",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_match = pd.DataFrame()\n",
    "for year, month in zip(df_most_precip_month['Year'], df_most_precip_month['Month']):\n",
    "    mask = (df_5_years.index.year == year) & (df_5_years.index.month == month)\n",
    "    df_match = pd.concat([df_match, df_5_years[mask]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0763a258-acbe-44cb-8083-a136da6d4d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5733</th>\n",
       "      <th>13670</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>9.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>24.5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2018-12-02</th>\n",
       "      <td>10.9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>44.6</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2018-12-03</th>\n",
       "      <td>9.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>17.5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2018-12-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-05</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-10-29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-10-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1590  5733  13670  Year  Month  Total_Precipitation  \\\n",
       "MESS_DATUM                                                        \n",
       "2018-12-01   9.9   7.6    7.0  2018     12                 24.5   \n",
       "2018-12-02  10.9  21.0   12.7  2018     12                 44.6   \n",
       "2018-12-03   9.7   4.0    3.8  2018     12                 17.5   \n",
       "2018-12-04   0.0   0.0    0.0  2018     12                  0.0   \n",
       "2018-12-05   0.5   0.8    0.2  2018     12                  1.5   \n",
       "...          ...   ...    ...   ...    ...                  ...   \n",
       "2019-10-27   0.0   0.0    0.0  2019     10                  0.0   \n",
       "2019-10-28   0.0   0.0    0.0  2019     10                  0.0   \n",
       "2019-10-29   0.0   0.0    0.0  2019     10                  0.0   \n",
       "2019-10-30   0.0   0.0    0.0  2019     10                  0.0   \n",
       "2019-10-31   0.0   0.0    0.0  2019     10                  0.0   \n",
       "\n",
       "            Monthly_Accumulated  \n",
       "MESS_DATUM                       \n",
       "2018-12-01                  NaN  \n",
       "2018-12-02                  NaN  \n",
       "2018-12-03                  NaN  \n",
       "2018-12-04                  NaN  \n",
       "2018-12-05                  NaN  \n",
       "...                         ...  \n",
       "2019-10-27                  NaN  \n",
       "2019-10-28                  NaN  \n",
       "2019-10-29                  NaN  \n",
       "2019-10-30                  NaN  \n",
       "2019-10-31                317.5  \n",
       "\n",
       "[62 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_match.head(62) # A daily data only with the months with the highest precipitaion for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c7a50aa4-3c62-4f4d-b0f8-33c9ba6e089b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2018-12-01</th>\n",
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       "      <td>2018</td>\n",
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       "      <th>2022-09-26</th>\n",
       "      <td>7.9</td>\n",
       "      <td>10.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>9</td>\n",
       "      <td>33.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.963541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-27</th>\n",
       "      <td>8.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2022</td>\n",
       "      <td>9</td>\n",
       "      <td>18.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.544357</td>\n",
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       "      <th>2022-09-28</th>\n",
       "      <td>17.1</td>\n",
       "      <td>0.7</td>\n",
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       "      <td>2022</td>\n",
       "      <td>9</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>10.0</td>\n",
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       "      <td>9</td>\n",
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      ],
      "text/plain": [
       "            1590  5733  13670  Year  Month  Total_Precipitation  \\\n",
       "MESS_DATUM                                                        \n",
       "2018-12-01   9.9   7.6    7.0  2018     12                 24.5   \n",
       "2018-12-02  10.9  21.0   12.7  2018     12                 44.6   \n",
       "2018-12-03   9.7   4.0    3.8  2018     12                 17.5   \n",
       "2018-12-04   0.0   0.0    0.0  2018     12                  0.0   \n",
       "2018-12-05   0.5   0.8    0.2  2018     12                  1.5   \n",
       "...          ...   ...    ...   ...    ...                  ...   \n",
       "2022-09-26   7.9  10.2   15.0  2022      9                 33.1   \n",
       "2022-09-27   8.2   3.8    6.7  2022      9                 18.7   \n",
       "2022-09-28  17.1   0.7    0.6  2022      9                 18.4   \n",
       "2022-09-29   0.0   0.0    0.0  2022      9                  0.0   \n",
       "2022-09-30   6.3   6.4   10.0  2022      9                 22.7   \n",
       "\n",
       "            Monthly_Accumulated  vol_water(m^3)  \n",
       "MESS_DATUM                                       \n",
       "2018-12-01                  NaN        0.713195  \n",
       "2018-12-02                  NaN        1.298306  \n",
       "2018-12-03                  NaN        0.509425  \n",
       "2018-12-04                  NaN        0.000000  \n",
       "2018-12-05                  NaN        0.043665  \n",
       "...                         ...             ...  \n",
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       "2022-09-27                  NaN        0.544357  \n",
       "2022-09-28                  NaN        0.535624  \n",
       "2022-09-29                  NaN        0.000000  \n",
       "2022-09-30                343.1        0.660797  \n",
       "\n",
       "[151 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GH_roof_area = 7.1*4.1 #m^2\n",
    "df_match['vol_water(m^3)'] = GH_roof_area * (df_match[\"Total_Precipitation\"]/1000)\n",
    "# divided 1000 to convert millimeters to meters\n",
    "# the column 'vol_water(m^3)' represents the volume of rain fell on the roof of the Green House\n",
    "df_match"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fac00bc-4198-44ff-9e93-8eea19642e77",
   "metadata": {},
   "source": [
    "## Daily average precipitation on the green house "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db099d26-ee95-43b3-99a6-f9e63c18ed21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The daily average precipitation on the green house is 0.3380 m^3 which is equal to 338.0 Liters\n"
     ]
    }
   ],
   "source": [
    "avg_daily_precip = df_match['vol_water(m^3)'].mean()\n",
    "avg_daily_precip\n",
    "\n",
    "print(f'The daily average precipitation on the green house is {avg_daily_precip:.4f} m^3 which is equal to {avg_daily_precip*1000:.1f} Liters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d79b1dfb-c255-47e8-8958-238b66399441",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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