{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"top\">\n",
    "<!-- <div style=\"background-image: url(https://www.emtmadrid.es/getattachment/da3be644-cb9d-44db-8011-e3f40f1c5c34); opacity: 0.2\"/> -->\n",
    "<img src=\"https://www.gmv.com/sites/default/files/content/image/2021/11/03/115/gmv_rgbredblack.png\" alt=\"GMV Logo\" style=\"width: 200px\">\n",
    "<img src=\"https://www.upm.es/sfs/Rectorado/Gabinete%20del%20Rector/Logos/UPM/CEI/LOGOTIPO%20leyenda%20color%20JPG%20p.png\" alt=\"UPM Logo\" style=\"float: right; width: 200px\">\n",
    "<h1><b>EDA: AEMET dataset 🌥️</b></h1>\n",
    "<h5 style=\"text-align: right\">INESDATA-MOV</h5>\n",
    "</div>\n",
    "\n",
    "# Análisis EDA\n",
    "\n",
    "En este cuaderno se realiza el análisis de datos exploratorio (EDA) del dataset de [AEMET](https://www.aemet.es/es/portada). Una vez realizado el análisis de calidad se estudiará para el dataset resultante:\n",
    "\n",
    "* Valores nulos y unicidad de las variables\n",
    "* Correlaciones entre las variables\n",
    "* Outliers de las variables seleccionadas\n",
    "\n",
    "Con ello se pretende hacer un filtrado de las variables que no dispongan de información relevante para el modelo o variables con la misma información. Por otro lado se hará un estudio de los valores nulos para, en caso de que sea pertinente, reconstruir estos datos. \n",
    "\n",
    "Además es conveniente realizar un tratamiento de outliers ya que podrían distorsionar el entrenamiento del modelo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(os.getcwd())))\n",
    "DATA_PATH = os.path.join(ROOT_PATH, \"notebooks\", \"aemet_dataset_qa.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>estado_cielo</th>\n",
       "      <th>precipitacion_value</th>\n",
       "      <th>probPrecipitacion_value</th>\n",
       "      <th>probTormenta_value</th>\n",
       "      <th>nieve_value</th>\n",
       "      <th>probNieve_value</th>\n",
       "      <th>temperatura_value</th>\n",
       "      <th>sensTermica_value</th>\n",
       "      <th>humedadRelativa_value</th>\n",
       "      <th>direccion</th>\n",
       "      <th>velocidad</th>\n",
       "      <th>vientoAndRachaMax_value</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>SO</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>O</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.0</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>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>O</td>\n",
       "      <td>17.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</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>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>SO</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</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>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>O</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   estado_cielo  precipitacion_value  probPrecipitacion_value  \\\n",
       "0          12.0                  0.0                      NaN   \n",
       "1          15.0                  0.0                      NaN   \n",
       "2          15.0                  0.0                      0.0   \n",
       "3          16.0                  0.0                      0.0   \n",
       "4          16.0                  0.0                      0.0   \n",
       "\n",
       "   probTormenta_value  nieve_value  probNieve_value  temperatura_value  \\\n",
       "0                 NaN          0.0              NaN                8.0   \n",
       "1                 NaN          0.0              NaN                8.0   \n",
       "2                 0.0          0.0              0.0                8.0   \n",
       "3                 0.0          0.0              0.0                8.0   \n",
       "4                 0.0          0.0              0.0                8.0   \n",
       "\n",
       "   sensTermica_value  humedadRelativa_value direccion  velocidad  \\\n",
       "0                5.0                   77.0        SO       16.0   \n",
       "1                5.0                   80.0         O       17.0   \n",
       "2                5.0                   82.0         O       17.0   \n",
       "3                5.0                   84.0        SO       20.0   \n",
       "4                4.0                   86.0         O       25.0   \n",
       "\n",
       "   vientoAndRachaMax_value  day  month  year  hour  minute  second  \n",
       "0                     27.0    2      3  2024     0       0       0  \n",
       "1                     27.0    2      3  2024     1       0       0  \n",
       "2                     33.0    2      3  2024     2       0       0  \n",
       "3                     40.0    2      3  2024     3       0       0  \n",
       "4                     35.0    2      3  2024     4       0       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aemet_dataset = pd.read_csv(DATA_PATH)\n",
    "aemet_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valores nulos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h10>En esta sección se observará si existe alguna variable que disponga de valores nulos y si estos pueden ser reconstruidos. </h10>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 661 entries, 0 to 660\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   estado_cielo             661 non-null    float64\n",
      " 1   precipitacion_value      661 non-null    float64\n",
      " 2   probPrecipitacion_value  659 non-null    float64\n",
      " 3   probTormenta_value       659 non-null    float64\n",
      " 4   nieve_value              661 non-null    float64\n",
      " 5   probNieve_value          659 non-null    float64\n",
      " 6   temperatura_value        661 non-null    float64\n",
      " 7   sensTermica_value        661 non-null    float64\n",
      " 8   humedadRelativa_value    661 non-null    float64\n",
      " 9   direccion                651 non-null    object \n",
      " 10  velocidad                651 non-null    float64\n",
      " 11  vientoAndRachaMax_value  661 non-null    float64\n",
      " 12  day                      661 non-null    int64  \n",
      " 13  month                    661 non-null    int64  \n",
      " 14  year                     661 non-null    int64  \n",
      " 15  hour                     661 non-null    int64  \n",
      " 16  minute                   661 non-null    int64  \n",
      " 17  second                   661 non-null    int64  \n",
      "dtypes: float64(11), int64(6), object(1)\n",
      "memory usage: 93.1+ KB\n"
     ]
    }
   ],
   "source": [
    "aemet_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>estado_cielo</th>\n",
       "      <th>precipitacion_value</th>\n",
       "      <th>probPrecipitacion_value</th>\n",
       "      <th>probTormenta_value</th>\n",
       "      <th>nieve_value</th>\n",
       "      <th>probNieve_value</th>\n",
       "      <th>temperatura_value</th>\n",
       "      <th>sensTermica_value</th>\n",
       "      <th>humedadRelativa_value</th>\n",
       "      <th>direccion</th>\n",
       "      <th>velocidad</th>\n",
       "      <th>vientoAndRachaMax_value</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>SO</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>O</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.0</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>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>O</td>\n",
       "      <td>17.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</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>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>SO</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</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>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>O</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   estado_cielo  precipitacion_value  probPrecipitacion_value  \\\n",
       "0          12.0                  0.0                      NaN   \n",
       "1          15.0                  0.0                      NaN   \n",
       "2          15.0                  0.0                      0.0   \n",
       "3          16.0                  0.0                      0.0   \n",
       "4          16.0                  0.0                      0.0   \n",
       "\n",
       "   probTormenta_value  nieve_value  probNieve_value  temperatura_value  \\\n",
       "0                 NaN          0.0              NaN                8.0   \n",
       "1                 NaN          0.0              NaN                8.0   \n",
       "2                 0.0          0.0              0.0                8.0   \n",
       "3                 0.0          0.0              0.0                8.0   \n",
       "4                 0.0          0.0              0.0                8.0   \n",
       "\n",
       "   sensTermica_value  humedadRelativa_value direccion  velocidad  \\\n",
       "0                5.0                   77.0        SO       16.0   \n",
       "1                5.0                   80.0         O       17.0   \n",
       "2                5.0                   82.0         O       17.0   \n",
       "3                5.0                   84.0        SO       20.0   \n",
       "4                4.0                   86.0         O       25.0   \n",
       "\n",
       "   vientoAndRachaMax_value  day  month  year  hour  minute  second  \n",
       "0                     27.0    2      3  2024     0       0       0  \n",
       "1                     27.0    2      3  2024     1       0       0  \n",
       "2                     33.0    2      3  2024     2       0       0  \n",
       "3                     40.0    2      3  2024     3       0       0  \n",
       "4                     35.0    2      3  2024     4       0       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aemet_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h10> Se observa que todas las variables están completas a excepción de las variables que indican probabilidad (ya sea de lluvia, tormenta o nieve) o las variables de dirección y velocidad. De las primeras variables, sólo existen un par de valores nulos debido a no tener datos del día anterior (aparecen para el primer día de todos). Además es probable que estas variables se eliminen por fuerte correlación con la precipitación y la nieve previstas.\n",
    "\n",
    "De las segundas variables no se posee información para pocos valores. En la tabla posterior vemos si existe algún patrón para esto.</h10>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>estado_cielo</th>\n",
       "      <th>precipitacion_value</th>\n",
       "      <th>probPrecipitacion_value</th>\n",
       "      <th>probTormenta_value</th>\n",
       "      <th>nieve_value</th>\n",
       "      <th>probNieve_value</th>\n",
       "      <th>temperatura_value</th>\n",
       "      <th>sensTermica_value</th>\n",
       "      <th>humedadRelativa_value</th>\n",
       "      <th>direccion</th>\n",
       "      <th>velocidad</th>\n",
       "      <th>vientoAndRachaMax_value</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>12.0</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>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>17.0</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>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>12.0</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>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     estado_cielo  precipitacion_value  probPrecipitacion_value  \\\n",
       "432          12.0                  0.0                      0.0   \n",
       "455          17.0                  0.0                      0.0   \n",
       "478          12.0                  0.0                      0.0   \n",
       "501          17.0                  0.0                     10.0   \n",
       "524          16.0                  0.0                     95.0   \n",
       "547          12.0                  0.0                     60.0   \n",
       "570          16.0                  0.0                     50.0   \n",
       "593          16.0                  0.0                    100.0   \n",
       "616          16.0                  0.0                     40.0   \n",
       "639          16.0                  0.0                    100.0   \n",
       "\n",
       "     probTormenta_value  nieve_value  probNieve_value  temperatura_value  \\\n",
       "432                 0.0          0.0              0.0               17.0   \n",
       "455                 0.0          0.0              0.0               17.0   \n",
       "478                 0.0          0.0              0.0               18.0   \n",
       "501                10.0          0.0              0.0               18.0   \n",
       "524                75.0          0.0              0.0               12.0   \n",
       "547                60.0          0.0             10.0                5.0   \n",
       "570                50.0          0.0              0.0               11.0   \n",
       "593                60.0          0.0              0.0               13.0   \n",
       "616                40.0          0.0              0.0                5.0   \n",
       "639                90.0          0.0              0.0                8.0   \n",
       "\n",
       "     sensTermica_value  humedadRelativa_value direccion  velocidad  \\\n",
       "432               17.0                   51.0       NaN        NaN   \n",
       "455               17.0                   42.0       NaN        NaN   \n",
       "478               18.0                   42.0       NaN        NaN   \n",
       "501               18.0                   43.0       NaN        NaN   \n",
       "524               12.0                   76.0       NaN        NaN   \n",
       "547                1.0                   85.0       NaN        NaN   \n",
       "570               11.0                   75.0       NaN        NaN   \n",
       "593               13.0                   82.0       NaN        NaN   \n",
       "616                3.0                   93.0       NaN        NaN   \n",
       "639                6.0                   82.0       NaN        NaN   \n",
       "\n",
       "     vientoAndRachaMax_value  day  month  year  hour  minute  second  \n",
       "432                      0.0   22      3  2024     1       0       0  \n",
       "455                      0.0   23      3  2024     1       0       0  \n",
       "478                      0.0   24      3  2024     1       0       0  \n",
       "501                      0.0   25      3  2024     1       0       0  \n",
       "524                      0.0   26      3  2024     1       0       0  \n",
       "547                      0.0   27      3  2024     1       0       0  \n",
       "570                      0.0   28      3  2024     1       0       0  \n",
       "593                      0.0   29      3  2024     1       0       0  \n",
       "616                      0.0   30      3  2024     1       0       0  \n",
       "639                      0.0   31      3  2024     1       0       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aemet_dataset[aemet_dataset.direccion.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unicidad de las variables\n",
    "\n",
    "<h10>En algunos casos es probable que una variable disponga de un único valor o de poca variabilidad. En estos casos sería recomendable quitar la variable del dataset.</h10>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['estado_cielo', 'precipitacion_value', 'probPrecipitacion_value',\n",
       "       'probTormenta_value', 'nieve_value', 'probNieve_value',\n",
       "       'temperatura_value', 'sensTermica_value', 'humedadRelativa_value',\n",
       "       'direccion', 'velocidad', 'vientoAndRachaMax_value', 'day', 'month',\n",
       "       'year', 'hour', 'minute', 'second'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aemet_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "estado_cielo\n",
       "17.0    169\n",
       "11.0    163\n",
       "16.0    105\n",
       "12.0     61\n",
       "46.0     47\n",
       "64.0     47\n",
       "15.0     27\n",
       "43.0     15\n",
       "14.0     10\n",
       "36.0      3\n",
       "54.0      3\n",
       "45.0      2\n",
       "26.0      2\n",
       "82.0      2\n",
       "63.0      1\n",
       "44.0      1\n",
       "81.0      1\n",
       "62.0      1\n",
       "13.0      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estado cielo\n",
    "aemet_dataset.estado_cielo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precipitacion_value\n",
       "0.0    539\n",
       "0.1     52\n",
       "0.2     17\n",
       "1.0     14\n",
       "0.9      7\n",
       "0.5      6\n",
       "0.4      6\n",
       "0.3      5\n",
       "0.8      3\n",
       "0.7      3\n",
       "3.0      3\n",
       "2.0      3\n",
       "0.6      1\n",
       "6.0      1\n",
       "4.0      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precipitacion\n",
    "aemet_dataset.precipitacion_value.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probPrecipitacion_value\n",
       "0.0      369\n",
       "100.0    130\n",
       "5.0       33\n",
       "90.0      24\n",
       "95.0      23\n",
       "55.0      12\n",
       "10.0      11\n",
       "65.0       6\n",
       "70.0       6\n",
       "80.0       6\n",
       "15.0       6\n",
       "85.0       6\n",
       "25.0       6\n",
       "35.0       6\n",
       "60.0       5\n",
       "50.0       5\n",
       "40.0       5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Probabilidad de precipitacion\n",
    "aemet_dataset.probPrecipitacion_value.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probTormenta_value\n",
       "0.0     375\n",
       "80.0     42\n",
       "5.0      33\n",
       "95.0     24\n",
       "55.0     24\n",
       "75.0     23\n",
       "60.0     22\n",
       "70.0     18\n",
       "25.0     18\n",
       "50.0     17\n",
       "65.0     12\n",
       "85.0     12\n",
       "90.0     11\n",
       "10.0     11\n",
       "15.0      6\n",
       "35.0      6\n",
       "40.0      5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Probabilidad de tormenta\n",
    "aemet_dataset.probTormenta_value.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nieve_value\n",
       "0.0    661\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nieve\n",
    "aemet_dataset.nieve_value.value_counts()\n",
    "\n",
    "# En este caso se observa que sólo existe  un valor para la variable \n",
    "# nieve pero, a pesar de ello en el caso de que sí que existiese nieve\n",
    "# este dato sería relevante para nuestro problema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probNieve_value\n",
       "0.0     648\n",
       "30.0      6\n",
       "10.0      5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Probabilidad nieve\n",
    "aemet_dataset.probNieve_value.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temperatura_value\n",
       "8.0     66\n",
       "6.0     60\n",
       "7.0     51\n",
       "9.0     49\n",
       "10.0    48\n",
       "14.0    44\n",
       "12.0    43\n",
       "11.0    41\n",
       "13.0    41\n",
       "15.0    34\n",
       "16.0    24\n",
       "5.0     22\n",
       "18.0    21\n",
       "17.0    20\n",
       "19.0    15\n",
       "20.0    13\n",
       "21.0    12\n",
       "24.0    11\n",
       "22.0    11\n",
       "4.0     10\n",
       "3.0      7\n",
       "23.0     6\n",
       "25.0     5\n",
       "2.0      3\n",
       "26.0     2\n",
       "1.0      1\n",
       "27.0     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Temperatura\n",
    "aemet_dataset.temperatura_value.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sensTermica_value\n",
       " 4.0     56\n",
       " 10.0    48\n",
       " 5.0     46\n",
       " 14.0    44\n",
       " 12.0    43\n",
       " 11.0    41\n",
       " 13.0    41\n",
       " 3.0     38\n",
       " 6.0     36\n",
       " 15.0    34\n",
       " 16.0    24\n",
       " 2.0     21\n",
       " 18.0    21\n",
       " 17.0    20\n",
       " 7.0     20\n",
       " 8.0     19\n",
       " 1.0     19\n",
       " 19.0    15\n",
       " 20.0    13\n",
       " 21.0    12\n",
       " 22.0    11\n",
       " 24.0    11\n",
       " 9.0      6\n",
       " 23.0     6\n",
       " 25.0     5\n",
       "-1.0      4\n",
       " 0.0      3\n",
       " 26.0     2\n",
       "-3.0      1\n",
       " 27.0     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sensación térmica\n",
    "aemet_dataset.sensTermica_value.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "humedadRelativa_value\n",
       "76.0    20\n",
       "86.0    19\n",
       "74.0    19\n",
       "82.0    18\n",
       "81.0    18\n",
       "        ..\n",
       "95.0     2\n",
       "99.0     2\n",
       "23.0     1\n",
       "24.0     1\n",
       "28.0     1\n",
       "Name: count, Length: 76, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Humedad relativa\n",
    "aemet_dataset.humedadRelativa_value.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "direccion\n",
       "SO    143\n",
       "NE    135\n",
       "O     117\n",
       "S     104\n",
       "SE     54\n",
       "E      48\n",
       "NO     37\n",
       "N      13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dirección\n",
    "aemet_dataset.direccion.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "velocidad\n",
       "10.0    54\n",
       "8.0     47\n",
       "9.0     46\n",
       "7.0     39\n",
       "6.0     34\n",
       "17.0    32\n",
       "11.0    31\n",
       "12.0    31\n",
       "16.0    29\n",
       "13.0    28\n",
       "5.0     26\n",
       "15.0    26\n",
       "14.0    20\n",
       "20.0    19\n",
       "19.0    18\n",
       "18.0    16\n",
       "22.0    16\n",
       "24.0    16\n",
       "21.0    14\n",
       "4.0     14\n",
       "23.0    13\n",
       "25.0     9\n",
       "3.0      7\n",
       "31.0     7\n",
       "27.0     7\n",
       "35.0     6\n",
       "26.0     6\n",
       "28.0     5\n",
       "2.0      5\n",
       "37.0     5\n",
       "32.0     4\n",
       "29.0     4\n",
       "33.0     4\n",
       "1.0      3\n",
       "38.0     3\n",
       "30.0     2\n",
       "34.0     2\n",
       "45.0     1\n",
       "39.0     1\n",
       "40.0     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Velocidad\n",
    "aemet_dataset.velocidad.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vientoAndRachaMax_value\n",
       "16.0    27\n",
       "14.0    26\n",
       "15.0    26\n",
       "17.0    26\n",
       "18.0    26\n",
       "        ..\n",
       "45.0     1\n",
       "1.0      1\n",
       "2.0      1\n",
       "49.0     1\n",
       "64.0     1\n",
       "Name: count, Length: 65, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viento and racha máxima\n",
    "aemet_dataset.vientoAndRachaMax_value.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlaciones\n",
    "\n",
    "En este punto se verá la correlación de las variables entre sí. Para este punto omitiremos las variables referentes al tiempo (año, mes, día, hora, minutos y segundos), la variable categórica de dirección y nieve (siempre tiene el mismo valor)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "aemet_dataset_corr = aemet_dataset.drop(columns=[\"nieve_value\", \"direccion\", \"year\", \"day\", \"month\", \"hour\", \"minute\", \"second\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcular la matriz de correlación\n",
    "correlation_matrix = aemet_dataset_corr.corr(numeric_only=True)\n",
    "\n",
    "plt.figure(figsize=(10, 8))  # Tamaño de la figura\n",
    "sns.heatmap(correlation_matrix, vmin=-1, vmax=1, annot=True, cmap='coolwarm')  \n",
    "\n",
    "plt.title('Matriz de correlación')\n",
    "plt.xticks(rotation=90)  # Rotar las etiquetas del eje x para mayor legibilidad\n",
    "plt.yticks(rotation=0)   # No rotar las etiquetas del eje y\n",
    "plt.tight_layout()       # Ajustar el diseño para evitar que las etiquetas se superpongan\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h10> En la matriz de correlación anterior se observa que existen variables en nuestro dataset que proporcionan la misma información que otras. Por ejemplo:\n",
    "\n",
    "* probPrecipitacion_value <-> probTormenta_value\n",
    "* sensTermica_value <-> temperatura_value\n",
    "* vientoAndRachaMax_value <-> velocidad\n",
    "\n",
    "Por lo tanto, podríamos prescindir de *probPrecipitacion_value*, *sensTermica_value* y *vientoAndRachaMax_value*. Además, vemos que *probPrecipitacion_value*, *probTormenta_value* y *probNieve_value* no están muy correladas con la precipitación o la nieve por lo que puede que nos estén dando información no fiable.\n",
    "</h10>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "aemet_dataset.drop(columns=[\"probTormenta_value\", \"probNieve_value\", \"probPrecipitacion_value\", \"sensTermica_value\", \"vientoAndRachaMax_value\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 661 entries, 0 to 660\n",
      "Data columns (total 13 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   estado_cielo           661 non-null    float64\n",
      " 1   precipitacion_value    661 non-null    float64\n",
      " 2   nieve_value            661 non-null    float64\n",
      " 3   temperatura_value      661 non-null    float64\n",
      " 4   humedadRelativa_value  661 non-null    float64\n",
      " 5   direccion              651 non-null    object \n",
      " 6   velocidad              651 non-null    float64\n",
      " 7   day                    661 non-null    int64  \n",
      " 8   month                  661 non-null    int64  \n",
      " 9   year                   661 non-null    int64  \n",
      " 10  hour                   661 non-null    int64  \n",
      " 11  minute                 661 non-null    int64  \n",
      " 12  second                 661 non-null    int64  \n",
      "dtypes: float64(6), int64(6), object(1)\n",
      "memory usage: 67.3+ KB\n"
     ]
    }
   ],
   "source": [
    "aemet_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers\n",
    "\n",
    "<h10>En el apartado de unicidad de variables ya se observa que no existen outliers para ninguna de las variables. </h10>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}