capacidad_embalses_2023.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1cfaf617",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b1a4ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7bdbc38",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_file = './data/embalses_1988_2023.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e026aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62b73586",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMBITO_NOMBRE</th>\n",
       "      <th>EMBALSE_NOMBRE</th>\n",
       "      <th>FECHA</th>\n",
       "      <th>AGUA_TOTAL</th>\n",
       "      <th>AGUA_ACTUAL</th>\n",
       "      <th>ELECTRICO_FLAG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Miño - Sil</td>\n",
       "      <td>Albarellos</td>\n",
       "      <td>01/05/88 00:00:00</td>\n",
       "      <td>91,00</td>\n",
       "      <td>32,00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miño - Sil</td>\n",
       "      <td>Albarellos</td>\n",
       "      <td>01/12/88 00:00:00</td>\n",
       "      <td>91,00</td>\n",
       "      <td>44,00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miño - Sil</td>\n",
       "      <td>Albarellos</td>\n",
       "      <td>01/19/88 00:00:00</td>\n",
       "      <td>91,00</td>\n",
       "      <td>42,00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miño - Sil</td>\n",
       "      <td>Albarellos</td>\n",
       "      <td>01/26/88 00:00:00</td>\n",
       "      <td>91,00</td>\n",
       "      <td>43,00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Miño - Sil</td>\n",
       "      <td>Albarellos</td>\n",
       "      <td>02/02/88 00:00:00</td>\n",
       "      <td>91,00</td>\n",
       "      <td>65,00</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655066</th>\n",
       "      <td>Guadalete-Barbate</td>\n",
       "      <td>Zahara-El Gastor</td>\n",
       "      <td>03/14/23 00:00:00</td>\n",
       "      <td>223,00</td>\n",
       "      <td>46,00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655067</th>\n",
       "      <td>Guadalete-Barbate</td>\n",
       "      <td>Zahara-El Gastor</td>\n",
       "      <td>03/21/23 00:00:00</td>\n",
       "      <td>223,00</td>\n",
       "      <td>46,00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655068</th>\n",
       "      <td>Guadalete-Barbate</td>\n",
       "      <td>Zahara-El Gastor</td>\n",
       "      <td>03/28/23 00:00:00</td>\n",
       "      <td>223,00</td>\n",
       "      <td>46,00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655069</th>\n",
       "      <td>Guadalete-Barbate</td>\n",
       "      <td>Zahara-El Gastor</td>\n",
       "      <td>04/04/23 00:00:00</td>\n",
       "      <td>223,00</td>\n",
       "      <td>46,00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655070</th>\n",
       "      <td>Guadalete-Barbate</td>\n",
       "      <td>Zahara-El Gastor</td>\n",
       "      <td>04/11/23 00:00:00</td>\n",
       "      <td>223,00</td>\n",
       "      <td>46,00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>655071 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AMBITO_NOMBRE    EMBALSE_NOMBRE              FECHA AGUA_TOTAL  \\\n",
       "0              Miño - Sil        Albarellos  01/05/88 00:00:00      91,00   \n",
       "1              Miño - Sil        Albarellos  01/12/88 00:00:00      91,00   \n",
       "2              Miño - Sil        Albarellos  01/19/88 00:00:00      91,00   \n",
       "3              Miño - Sil        Albarellos  01/26/88 00:00:00      91,00   \n",
       "4              Miño - Sil        Albarellos  02/02/88 00:00:00      91,00   \n",
       "...                   ...               ...                ...        ...   \n",
       "655066  Guadalete-Barbate  Zahara-El Gastor  03/14/23 00:00:00     223,00   \n",
       "655067  Guadalete-Barbate  Zahara-El Gastor  03/21/23 00:00:00     223,00   \n",
       "655068  Guadalete-Barbate  Zahara-El Gastor  03/28/23 00:00:00     223,00   \n",
       "655069  Guadalete-Barbate  Zahara-El Gastor  04/04/23 00:00:00     223,00   \n",
       "655070  Guadalete-Barbate  Zahara-El Gastor  04/11/23 00:00:00     223,00   \n",
       "\n",
       "       AGUA_ACTUAL  ELECTRICO_FLAG  \n",
       "0            32,00               1  \n",
       "1            44,00               1  \n",
       "2            42,00               1  \n",
       "3            43,00               1  \n",
       "4            65,00               1  \n",
       "...            ...             ...  \n",
       "655066       46,00               0  \n",
       "655067       46,00               0  \n",
       "655068       46,00               0  \n",
       "655069       46,00               0  \n",
       "655070       46,00               0  \n",
       "\n",
       "[655071 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "369960fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AGUA_TOTAL'] = df['AGUA_TOTAL'].str.replace(',','.').astype('float')\n",
    "df['AGUA_ACTUAL'] = df['AGUA_ACTUAL'].str.replace(',','.').astype('float')\n",
    "df['ELECTRICO_FLAG'] = df['ELECTRICO_FLAG'].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cfeeb0a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27.5 s, sys: 4.63 ms, total: 27.5 s\n",
      "Wall time: 27.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "df['FECHA'] = pd.to_datetime(df['FECHA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49ce7a93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 655071 entries, 0 to 655070\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   AMBITO_NOMBRE   655071 non-null  object        \n",
      " 1   EMBALSE_NOMBRE  655071 non-null  object        \n",
      " 2   FECHA           655071 non-null  datetime64[ns]\n",
      " 3   AGUA_TOTAL      655069 non-null  float64       \n",
      " 4   AGUA_ACTUAL     655069 non-null  float64       \n",
      " 5   ELECTRICO_FLAG  655071 non-null  bool          \n",
      "dtypes: bool(1), datetime64[ns](1), float64(2), object(2)\n",
      "memory usage: 25.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8f7a6b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 32.4 s, sys: 7.77 ms, total: 32.4 s\n",
      "Wall time: 32.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_csv(data_file, parse_dates=['FECHA'], dtype={'AGUA_TOTAL':'float', 'AGUA_ACTUAL':'float', 'ELECTRICO_FLAG':'bool'}, decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80f4c8ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 655071 entries, 0 to 655070\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   AMBITO_NOMBRE   655071 non-null  object        \n",
      " 1   EMBALSE_NOMBRE  655071 non-null  object        \n",
      " 2   FECHA           655071 non-null  datetime64[ns]\n",
      " 3   AGUA_TOTAL      655069 non-null  float64       \n",
      " 4   AGUA_ACTUAL     655069 non-null  float64       \n",
      " 5   ELECTRICO_FLAG  655071 non-null  bool          \n",
      "dtypes: bool(1), datetime64[ns](1), float64(2), object(2)\n",
      "memory usage: 25.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "42bfc2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGUA_TOTAL</th>\n",
       "      <th>AGUA_ACTUAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>655069.000000</td>\n",
       "      <td>655069.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>148.649006</td>\n",
       "      <td>84.219786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>351.455727</td>\n",
       "      <td>217.509846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3219.000000</td>\n",
       "      <td>3193.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AGUA_TOTAL    AGUA_ACTUAL\n",
       "count  655069.000000  655069.000000\n",
       "mean      148.649006      84.219786\n",
       "std       351.455727     217.509846\n",
       "min         0.000000       0.000000\n",
       "25%        14.000000       7.000000\n",
       "50%        37.000000      20.000000\n",
       "75%       126.000000      69.000000\n",
       "max      3219.000000    3193.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "030c616f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_192899/3139707767.py:1: FutureWarning: ['AMBITO_NOMBRE'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  df.groupby('EMBALSE_NOMBRE').agg(['max','mean', 'min'])\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">FECHA</th>\n",
       "      <th colspan=\"3\" halign=\"left\">AGUA_TOTAL</th>\n",
       "      <th colspan=\"3\" halign=\"left\">AGUA_ACTUAL</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ELECTRICO_FLAG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMBALSE_NOMBRE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aceña</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-15 21:59:40.466630400</td>\n",
       "      <td>1987-10-06</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.437330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.948454</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agavanzal, Nª Sª de</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-23 00:00:00.000000128</td>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.619229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>20.630636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agrio (Aznalcollar)</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-23 00:00:00.000000128</td>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.432917</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>15.824009</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aguascebas</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-23 00:00:00.000000128</td>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.966866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.521456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aguijón, El</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-23 00:00:00.000000128</td>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.522542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.456274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zocueca</th>\n",
       "      <td>2012-06-26</td>\n",
       "      <td>2011-05-06 12:00:00.000000000</td>\n",
       "      <td>2010-03-16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zufre</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-23 22:33:07.826087040</td>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>179.0</td>\n",
       "      <td>151.289674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>105.097826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zújar</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-23 00:00:00.000000128</td>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>723.0</td>\n",
       "      <td>331.677892</td>\n",
       "      <td>275.0</td>\n",
       "      <td>569.0</td>\n",
       "      <td>198.140684</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Águeda</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-23 00:00:00.000000128</td>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.922325</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.716458</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Úzquiza</th>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2005-08-23 00:00:00.000000128</td>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.926127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>44.636611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>399 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         FECHA                                           \\\n",
       "                           max                          mean        min   \n",
       "EMBALSE_NOMBRE                                                            \n",
       "Aceña               2023-04-11 2005-08-15 21:59:40.466630400 1987-10-06   \n",
       "Agavanzal, Nª Sª de 2023-04-11 2005-08-23 00:00:00.000000128 1988-01-05   \n",
       "Agrio (Aznalcollar) 2023-04-11 2005-08-23 00:00:00.000000128 1988-01-05   \n",
       "Aguascebas          2023-04-11 2005-08-23 00:00:00.000000128 1988-01-05   \n",
       "Aguijón, El         2023-04-11 2005-08-23 00:00:00.000000128 1988-01-05   \n",
       "...                        ...                           ...        ...   \n",
       "Zocueca             2012-06-26 2011-05-06 12:00:00.000000000 2010-03-16   \n",
       "Zufre               2023-04-11 2005-08-23 22:33:07.826087040 1988-01-05   \n",
       "Zújar               2023-04-11 2005-08-23 00:00:00.000000128 1988-01-05   \n",
       "Águeda              2023-04-11 2005-08-23 00:00:00.000000128 1988-01-05   \n",
       "Úzquiza             2023-04-11 2005-08-23 00:00:00.000000128 1988-01-05   \n",
       "\n",
       "                    AGUA_TOTAL                    AGUA_ACTUAL              \\\n",
       "                           max        mean    min         max        mean   \n",
       "EMBALSE_NOMBRE                                                              \n",
       "Aceña                     24.0   22.437330    0.0        24.0   12.948454   \n",
       "Agavanzal, Nª Sª de       36.0   24.619229    0.0        36.0   20.630636   \n",
       "Agrio (Aznalcollar)       40.0   23.432917   20.0        40.0   15.824009   \n",
       "Aguascebas                 6.0    4.966866    0.0        17.0    3.521456   \n",
       "Aguijón, El               11.0    7.522542    0.0        12.0    6.456274   \n",
       "...                        ...         ...    ...         ...         ...   \n",
       "Zocueca                    5.0    5.000000    5.0         1.0    1.000000   \n",
       "Zufre                    179.0  151.289674    0.0       178.0  105.097826   \n",
       "Zújar                    723.0  331.677892  275.0       569.0  198.140684   \n",
       "Águeda                    24.0   22.922325   22.0        23.0   14.716458   \n",
       "Úzquiza                   75.0   70.926127    0.0        75.0   44.636611   \n",
       "\n",
       "                         ELECTRICO_FLAG              \n",
       "                     min            max mean    min  \n",
       "EMBALSE_NOMBRE                                       \n",
       "Aceña                0.0          False  0.0  False  \n",
       "Agavanzal, Nª Sª de  0.0           True  1.0   True  \n",
       "Agrio (Aznalcollar)  4.0          False  0.0  False  \n",
       "Aguascebas           0.0          False  0.0  False  \n",
       "Aguijón, El          0.0          False  0.0  False  \n",
       "...                  ...            ...  ...    ...  \n",
       "Zocueca              1.0          False  0.0  False  \n",
       "Zufre                0.0          False  0.0  False  \n",
       "Zújar                4.0          False  0.0  False  \n",
       "Águeda               1.0          False  0.0  False  \n",
       "Úzquiza              0.0          False  0.0  False  \n",
       "\n",
       "[399 rows x 12 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('EMBALSE_NOMBRE').agg(['max','mean', 'min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8e986588",
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre_embalse = 'Sau'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7b1b71e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_192899/861154356.py:1: FutureWarning: ['AMBITO_NOMBRE'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  df.groupby('EMBALSE_NOMBRE').agg(['max','mean', 'min']).get('AGUA_TOTAL').sort_values('max').loc[[nombre_embalse]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMBALSE_NOMBRE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sau</th>\n",
       "      <td>169.0</td>\n",
       "      <td>166.692558</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  max        mean    min\n",
       "EMBALSE_NOMBRE                          \n",
       "Sau             169.0  166.692558  165.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('EMBALSE_NOMBRE').agg(['max','mean', 'min']).get('AGUA_TOTAL').sort_values('max').loc[[nombre_embalse]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "08ebd4e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='FECHA'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.query(f'EMBALSE_NOMBRE==\"{nombre_embalse}\"').sort_values('FECHA').get(['FECHA', 'AGUA_ACTUAL']).set_index('FECHA').resample('Y').max().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e5d4aff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "embalse = df.query(f'EMBALSE_NOMBRE==\"{nombre_embalse}\"').sort_values('FECHA').set_index('FECHA').resample('M').mean(numeric_only=True).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a09994ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "embalse['YEAR'] = embalse['FECHA'].dt.year\n",
    "embalse['MONTH'] = embalse['FECHA'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0b9d4658",
   "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>FECHA</th>\n",
       "      <th>AGUA_TOTAL</th>\n",
       "      <th>AGUA_ACTUAL</th>\n",
       "      <th>ELECTRICO_FLAG</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1988-01-31</td>\n",
       "      <td>169.0</td>\n",
       "      <td>117.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1988-02-29</td>\n",
       "      <td>169.0</td>\n",
       "      <td>117.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1988-03-31</td>\n",
       "      <td>169.0</td>\n",
       "      <td>94.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1988-04-30</td>\n",
       "      <td>169.0</td>\n",
       "      <td>99.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1988-05-31</td>\n",
       "      <td>169.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>165.0</td>\n",
       "      <td>30.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>165.0</td>\n",
       "      <td>31.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>165.0</td>\n",
       "      <td>28.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>165.0</td>\n",
       "      <td>16.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>165.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>424 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         FECHA  AGUA_TOTAL  AGUA_ACTUAL  ELECTRICO_FLAG  YEAR  MONTH\n",
       "0   1988-01-31       169.0       117.50             0.0  1988      1\n",
       "1   1988-02-29       169.0       117.75             0.0  1988      2\n",
       "2   1988-03-31       169.0        94.20             0.0  1988      3\n",
       "3   1988-04-30       169.0        99.75             0.0  1988      4\n",
       "4   1988-05-31       169.0       126.20             0.0  1988      5\n",
       "..         ...         ...          ...             ...   ...    ...\n",
       "419 2022-12-31       165.0        30.75             0.0  2022     12\n",
       "420 2023-01-31       165.0        31.60             0.0  2023      1\n",
       "421 2023-02-28       165.0        28.25             0.0  2023      2\n",
       "422 2023-03-31       165.0        16.25             0.0  2023      3\n",
       "423 2023-04-30       165.0        11.00             0.0  2023      4\n",
       "\n",
       "[424 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embalse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "46dd9e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = embalse.pivot(index='MONTH', columns='YEAR', values='AGUA_ACTUAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "30e3c7e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f877dbfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def draw_heatmap(data, title='Embalse'):\n",
    "    # Create a pivot table to aggregate the temperature anomaly values by year and month\n",
    "    \n",
    "    # Define a color palette ranging from blue to red\n",
    "    cmap = sns.color_palette(\"coolwarm_r\", as_cmap=True)\n",
    "\n",
    "    # Create the warming stripe plot using the heatmap function from Seaborn\n",
    "    fig, ax = plt.subplots(figsize=(8, 2))\n",
    "    ax = sns.heatmap(data, cmap=cmap, cbar=False, linewidths=0, linecolor='gray')\n",
    "    \n",
    "    # Hide the x-axis and y-axis labels and ticks\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "\n",
    "\n",
    "    # Set the title of the plot to 'Warming Stripe'\n",
    "    ax.set_title(title)\n",
    "    \n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "04dfdf7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_heatmap(data, nombre_embalse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22e0d728",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ironhack",
   "language": "python",
   "name": "ironhack"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}