2.2.c.Temperature_climatology_Spain_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2.c Temperature climatology in Spain (results)\n",
    "=================================================\n",
    "In this notebook we will finally generate the climatology results of daily temperature observations.<br>\n",
    "Our final goal is still to know whether the temperature anomaly has influence on the power demand or not.<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's import the _classic stack_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do now need to load the compressed filed into memory.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = pd.read_csv(\"aemet_daily_observations_2009-2018.csv.bz2\",compression='bz2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a quick look at the first and last lines of the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>region</th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>tmin</th>\n",
       "      <th>tavg</th>\n",
       "      <th>tmax</th>\n",
       "      <th>prec</th>\n",
       "      <th>widspeed</th>\n",
       "      <th>sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4358X</td>\n",
       "      <td>BADAJOZ</td>\n",
       "      <td>DON BENITO</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C447A</td>\n",
       "      <td>STA. CRUZ DE TENERIFE</td>\n",
       "      <td>TENERIFE NORTE AEROPUERTO</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>13.5</td>\n",
       "      <td>15.2</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code                 region                    station        date  tmin  \\\n",
       "0  4358X                BADAJOZ                 DON BENITO  2009-12-01   3.5   \n",
       "1  C447A  STA. CRUZ DE TENERIFE  TENERIFE NORTE AEROPUERTO  2009-12-01  13.5   \n",
       "\n",
       "   tavg  tmax  prec  widspeed  sun  \n",
       "0   7.7  11.9   1.0       1.7  NaN  \n",
       "1  15.2  16.8   0.6       2.5  0.7  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>code</th>\n",
       "      <th>region</th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>tmin</th>\n",
       "      <th>tavg</th>\n",
       "      <th>tmax</th>\n",
       "      <th>prec</th>\n",
       "      <th>widspeed</th>\n",
       "      <th>sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>815786</th>\n",
       "      <td>0255B</td>\n",
       "      <td>BARCELONA</td>\n",
       "      <td>SANTA SUSANNA</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>3.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815787</th>\n",
       "      <td>5612B</td>\n",
       "      <td>SEVILLA</td>\n",
       "      <td>LA RODA DE ANDALUCÍA</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         code     region               station        date  tmin  tavg  tmax  \\\n",
       "815786  0255B  BARCELONA         SANTA SUSANNA  2018-12-31   3.7  10.6  17.6   \n",
       "815787  5612B    SEVILLA  LA RODA DE ANDALUCÍA  2018-12-31   2.8  10.0  17.3   \n",
       "\n",
       "        prec  widspeed  sun  \n",
       "815786   NaN       0.6  NaN  \n",
       "815787   0.0       3.1  NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observations cover the period end of 2009 end of 2018, 10 years of data. It seems enough to have a representative climatology.<br>\n",
    "And data are already been thorugh some preparation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group by"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **_groupby_** method is a very powerful tool for apply functions based on categories.<br>\n",
    "We will use it to apply it to our _category_ ~ _day-of-the-year_.<br>\n",
    "But, first of all, we add datetime properties to the date column and set it as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA.loc[:,'date'] = pd.DatetimeIndex(pd.to_datetime(DATA['date']))\n",
    "DATA.set_index(['date'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA.loc[:,'doy'] = DATA.index.strftime('Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>region</th>\n",
       "      <th>station</th>\n",
       "      <th>tmin</th>\n",
       "      <th>tavg</th>\n",
       "      <th>tmax</th>\n",
       "      <th>prec</th>\n",
       "      <th>widspeed</th>\n",
       "      <th>sun</th>\n",
       "      <th>doy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-01</th>\n",
       "      <td>4358X</td>\n",
       "      <td>BADAJOZ</td>\n",
       "      <td>DON BENITO</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-01</th>\n",
       "      <td>C447A</td>\n",
       "      <td>STA. CRUZ DE TENERIFE</td>\n",
       "      <td>TENERIFE NORTE AEROPUERTO</td>\n",
       "      <td>13.5</td>\n",
       "      <td>15.2</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Y1201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             code                 region                    station  tmin  \\\n",
       "date                                                                        \n",
       "2009-12-01  4358X                BADAJOZ                 DON BENITO   3.5   \n",
       "2009-12-01  C447A  STA. CRUZ DE TENERIFE  TENERIFE NORTE AEROPUERTO  13.5   \n",
       "\n",
       "            tavg  tmax  prec  widspeed  sun    doy  \n",
       "date                                                \n",
       "2009-12-01   7.7  11.9   1.0       1.7  NaN  Y1201  \n",
       "2009-12-01  15.2  16.8   0.6       2.5  0.7  Y1201  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_group = DATA.groupby(by='station')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's loop throug the station_group object to have a first insight on what the groupby method has produce."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A CORUÑA (A CORUÑA) 3318\n",
      "A CORUÑA AEROPUERTO (A CORUÑA) 3314\n",
      "A POBRA DE TRIVES (OURENSE) 3194\n",
      "ABLA (ALMERIA) 3257\n",
      "ADRA (ALMERIA) 1672\n",
      "ALAJAR (HUELVA) 3220\n",
      "ALBACETE (ALBACETE) 3317\n",
      "ALBACETE BASE AÉREA (ALBACETE) 3318\n",
      "ALBORÁN (ALMERIA) 1711\n",
      "ALCANTARILLA, BASE AÉREA (MURCIA) 3318\n",
      "ALCAÑIZ (TERUEL) 3082\n",
      "ALICANTE-ELCHE AEROPUERTO (ALICANTE) 3318\n",
      "ALICANTE/ALACANT (ALICANTE) 3318\n",
      "ALMERÍA AEROPUERTO (ALMERIA) 3318\n",
      "ANDÚJAR (JAEN) 3087\n",
      "ANTEQUERA (MALAGA) 2933\n",
      "ARAGÜÉS DEL PUERTO (HUESCA) 2915\n",
      "ARANDA DE DUERO (BURGOS) 3301\n",
      "ARANGUREN, ILUNDAIN   (NAVARRA) 2946\n",
      "ARANJUEZ (MADRID) 3093\n",
      "ARENYS DE MAR (BARCELONA) 2975\n",
      "ARROYO DEL OJANCO (JAEN) 3137\n",
      "ASTURIAS AEROPUERTO (ASTURIAS) 3318\n",
      "AUTILLA DEL PINO (PALENCIA) 3257\n",
      "AYAMONTE (HUELVA) 3281\n",
      "BADAJOZ AEROPUERTO (BADAJOZ) 3318\n",
      "BARCELONA (BARCELONA) 3237\n",
      "BARCELONA AEROPUERTO (BARCELONA) 3318\n",
      "BARCELONA, FABRA (BARCELONA) 3318\n",
      "BARDENAS REALES, BASE AÉREA  (NAVARRA) 3145\n",
      "BAZA (GRANADA) 2890\n",
      "BAZTAN, IRURITA  (NAVARRA) 2819\n",
      "BELORADO (BURGOS) 2932\n",
      "BENAVENTE (ZAMORA) 3315\n",
      "BIELSA (HUESCA) 3023\n",
      "BILBAO AEROPUERTO (BIZKAIA) 3318\n",
      "BUITRAGO DEL LOZOYA (MADRID) 3032\n",
      "BUJARALOZ (ZARAGOZA) 2346\n",
      "BURGOS AEROPUERTO (BURGOS) 3318\n",
      "CABO BUSTO (ASTURIAS) 2729\n",
      "CABO PEÑAS (ASTURIAS) 2992\n",
      "CABO VILAN (A CORUÑA) 2962\n",
      "CADREITA (NAVARRA) 2265\n",
      "CALAMOCHA (TERUEL) 3307\n",
      "CALANDA (TERUEL) 2433\n",
      "CALATAYUD (ZARAGOZA) 2732\n",
      "CAPDEPERA (ILLES BALEARS) 3024\n",
      "CARAVACA DE LA CRUZ (MURCIA) 3309\n",
      "CARBALLIÑO, O (OURENSE) 3253\n",
      "CARBONERAS (ALMERIA) 1962\n",
      "CARRIÓN DE LOS CONDES (PALENCIA) 3281\n",
      "CARTAGENA (MURCIA) 3318\n",
      "CASTELLFORT (CASTELLON) 3087\n",
      "CASTELLÓN - ALMASSORA (CASTELLON) 3318\n",
      "CASTRO URDIALES (CANTABRIA) 1628\n",
      "CASTROPOL (ASTURIAS) 1469\n",
      "CAZALLA DE LA SIERRA (SEVILLA) 3213\n",
      "CAZORLA (JAEN) 2839\n",
      "CAÑIZARES (CUENCA) 2603\n",
      "CERVERA DE PISUERGA (PALENCIA) 3211\n",
      "CEUTA (CEUTA) 3305\n",
      "CHINCHILLA (ALBACETE) 2163\n",
      "CIEZA (MURCIA) 3310\n",
      "CIUDAD REAL (CIUDAD REAL) 3318\n",
      "COLMENAR VIEJO (MADRID) 3318\n",
      "CORIA (CACERES) 3273\n",
      "CUENCA (CUENCA) 3318\n",
      "CÁCERES (CACERES) 3318\n",
      "CÁDIZ (CADIZ) 2893\n",
      "CÓRDOBA AEROPUERTO (CORDOBA) 3318\n",
      "DAROCA (ZARAGOZA) 3318\n",
      "DON BENITO (BADAJOZ) 3297\n",
      "DONOSTIA/SAN SEBASTIÁN, IGUELDO (GIPUZKOA) 3318\n",
      "DOÑA MENCÍA (CORDOBA) 3235\n",
      "ELGOIBAR (GIPUZKOA) 3227\n",
      "ESCORCA, LLUC (ILLES BALEARS) 3210\n",
      "ESTACA DE BARES (A CORUÑA) 3189\n",
      "ESTEPONA (MALAGA) 3264\n",
      "FISTERRA (A CORUÑA) 3087\n",
      "FORONDA-TXOKIZA (ARABA/ALAVA) 3318\n",
      "FUENGIROLA (MALAGA) 3293\n",
      "FUERTEVENTURA AEROPUERTO (LAS PALMAS) 3303\n",
      "GETAFE (MADRID) 3318\n",
      "GIJÓN, CAMPUS (ASTURIAS) 1908\n",
      "GIJÓN, PUERTO (ASTURIAS) 3318\n",
      "GIRONA AEROPUERTO (GIRONA) 3316\n",
      "GRAN CANARIA AEROPUERTO (LAS PALMAS) 3318\n",
      "GRANADA AEROPUERTO (GRANADA) 3318\n",
      "GRANADA BASE AÉREA (GRANADA) 3318\n",
      "GRAZALEMA (CADIZ) 3237\n",
      "GUADALAJARA (GUADALAJARA) 2710\n",
      "GUADALAJARA, EL SERRANILLO (GUADALAJARA) 1727\n",
      "GÜEÑES (BIZKAIA) 3145\n",
      "GÜÍMAR (STA. CRUZ DE TENERIFE) 1826\n",
      "HELLÍN (ALBACETE) 3079\n",
      "HERRERA DEL DUQUE (BADAJOZ) 3261\n",
      "HIERRO AEROPUERTO (STA. CRUZ DE TENERIFE) 3318\n",
      "HINOJOSA DEL DUQUE (CORDOBA) 2845\n",
      "HONDARRIBIA, MALKARROA (GIPUZKOA) 3318\n",
      "HUELVA, RONDA ESTE (HUELVA) 3309\n",
      "HUESCA AEROPUERTO (HUESCA) 3318\n",
      "HUÉRCAL-OVERA (ALMERIA) 3263\n",
      "HUÉSCAR (GRANADA) 2321\n",
      "IBIZA, AEROPUERTO (ILLES BALEARS) 3318\n",
      "IZAÑA (STA. CRUZ DE TENERIFE) 3318\n",
      "JACA (HUESCA) 2693\n",
      "JAÉN (JAEN) 3318\n",
      "JAÉN, INSTITUTO (JAEN) 1278\n",
      "JEREZ DE LA FRONTERA AEROPUERTO (CADIZ) 3318\n",
      "JEREZ DE LOS CABALLEROS (BADAJOZ) 3110\n",
      "JÁVEA/ XÀBIA (ALICANTE) 3174\n",
      "LA ALDEA DE SAN NICOLÁS (LAS PALMAS) 1484\n",
      "LA COVATILLA, ESTACIÓN DE ESQUÍ (SALAMANCA) 2952\n",
      "LA MOLINA (GIRONA) 3060\n",
      "LA PALMA AEROPUERTO (STA. CRUZ DE TENERIFE) 3303\n",
      "LA PINILLA, ESTACIÓN DE ESQUÍ (SEGOVIA) 3266\n",
      "LA RODA DE ANDALUCÍA (SEVILLA) 3182\n",
      "LA SEU D'URGELL (LLEIDA) 3180\n",
      "LAGUNAS DE SOMOZA (LEON) 3083\n",
      "LANZAROTE AEROPUERTO (LAS PALMAS) 3318\n",
      "LAS PALMAS DE GRAN CANARIA, PL. DE LA FERIA (LAS PALMAS) 1861\n",
      "LAS PALMAS DE GRAN CANARIA, SAN CRISTOBAL (LAS PALMAS) 2705\n",
      "LEKEITIO (BIZKAIA) 3125\n",
      "LEÓN AEROPUERTO (LEON) 1218\n",
      "LEÓN, VIRGEN DEL CAMINO (LEON) 3318\n",
      "LLANES (ASTURIAS) 3149\n",
      "LLEIDA (LLEIDA) 3318\n",
      "LLERENA (BADAJOZ) 3237\n",
      "LOGROÑO AEROPUERTO (LA RIOJA) 3318\n",
      "LOJA (GRANADA) 3147\n",
      "LORCA (MURCIA) 3282\n",
      "LUGO AEROPUERTO (LUGO) 3318\n",
      "MACHICHACO (BIZKAIA) 3069\n",
      "MADRID AEROPUERTO (MADRID) 3318\n",
      "MADRID, CIUDAD UNIVERSITARIA (MADRID) 3151\n",
      "MADRID, CUATRO VIENTOS (MADRID) 3318\n",
      "MADRID, RETIRO (MADRID) 3318\n",
      "MADRIDEJOS (TOLEDO) 2934\n",
      "MANRESA (BARCELONA) 3199\n",
      "MASPALOMAS (LAS PALMAS) 2867\n",
      "MEDINA DE POMAR (BURGOS) 3145\n",
      "MELILLA (MELILLA) 3318\n",
      "MENORCA, AEROPUERTO (ILLES BALEARS) 3318\n",
      "MOGUER, EL ARENOSILLO (HUELVA) 3239\n",
      "MOGÁN, PUERTO (LAS PALMAS) 3219\n",
      "MOLINA DE ARAGÓN (GUADALAJARA) 3288\n",
      "MONTE IROITE (A CORUÑA) 2513\n",
      "MORÓN DE LA FRONTERA (SEVILLA) 3318\n",
      "MOTRIL (GRANADA) 553\n",
      "MURCIA (MURCIA) 3318\n",
      "MÁLAGA AEROPUERTO (MALAGA) 3318\n",
      "MÁLAGA, CENTRO METEOROLÓGICO (MALAGA) 3310\n",
      "MÁLAGA, PUERTO (MALAGA) 59\n",
      "MÉRIDA (BADAJOZ) 3298\n",
      "NAUT ARAN, ARTIES  (LLEIDA) 2941\n",
      "NAVALMORAL DE LA MATA (CACERES) 3312\n",
      "NAVARREDONDA DE GREDOS (AVILA) 3130\n",
      "OLIVA (VALENCIA) 3203\n",
      "OLMEDO (VALLADOLID) 2886\n",
      "OURENSE (OURENSE) 3318\n",
      "OVIEDO (ASTURIAS) 5814\n",
      "PADRÓN (A CORUÑA) 3131\n",
      "PALACIOS DE LA SIERRA (BURGOS) 3181\n",
      "PALMA, AEROPUERTO (ILLES BALEARS) 3318\n",
      "PALMA, PUERTO (ILLES BALEARS) 3318\n",
      "PAMPLONA (NAVARRA) 3318\n",
      "PAMPLONA AEROPUERTO (NAVARRA) 3318\n",
      "PINOSO  (ALICANTE) 3060\n",
      "PLASENCIA (CACERES) 3238\n",
      "POLINYÀ DE XÚQUER (VALENCIA) 3188\n",
      "PONFERRADA (LEON) 3318\n",
      "PONTEVEDRA (PONTEVEDRA) 3318\n",
      "PORQUERES (GIRONA) 2060\n",
      "PORRERES (ILLES BALEARS) 3203\n",
      "PORTOCOLOM   (ILLES BALEARS) 3106\n",
      "PUEBLA DE DON RODRIGO (CIUDAD REAL) 2660\n",
      "PUERTO ALTO DEL LEÓN (MADRID) 2691\n",
      "PUERTO DE LA CRUZ (STA. CRUZ DE TENERIFE) 3220\n",
      "PUERTO DE LEITARIEGOS (ASTURIAS) 2762\n",
      "PUERTO DE NAVACERRADA (MADRID) 3318\n",
      "PUERTO DE PAJARES (ASTURIAS) 3037\n",
      "PUERTO DE SAN ISIDRO (LEON) 2796\n",
      "PUNTA GALEA (BIZKAIA) 3257\n",
      "PÁJARA (LAS PALMAS) 1443\n",
      "QUINTANAR DE LA ORDEN (TOLEDO) 2859\n",
      "REINOSA (CANTABRIA) 3176\n",
      "REUS AEROPUERTO (TARRAGONA) 3318\n",
      "RIPOLL (GIRONA) 3206\n",
      "ROBLEDO DE CHAVELA (MADRID) 2749\n",
      "RONDA (MALAGA) 558\n",
      "ROQUETAS DE MAR (ALMERIA) 2954\n",
      "ROTA, BASE NAVAL (CADIZ) 3318\n",
      "SA POBLA (ILLES BALEARS) 2744\n",
      "SABADELL AEROPUERTO (BARCELONA) 2241\n",
      "SAELICES EL CHICO (SALAMANCA) 3297\n",
      "SALAMANCA (SALAMANCA) 3308\n",
      "SALAMANCA AEROPUERTO (SALAMANCA) 3318\n",
      "SAN CLEMENTE (CUENCA) 2112\n",
      "SAN FERNANDO (CADIZ) 3284\n",
      "SAN JAVIER AEROPUERTO (MURCIA) 6240\n",
      "SAN PABLO DE LOS MONTES (TOLEDO) 3175\n",
      "SAN SEBASTIÁN AEROPUERTO (GIPUZKOA) 2618\n",
      "SAN SEBASTIÁN DE LA GOMERA (STA. CRUZ DE TENERIFE) 3251\n",
      "SAN VICENTE DE LA BARQUERA (CANTABRIA) 3299\n",
      "SANT JAUME D'ENVEJA (TARRAGONA) 2967\n",
      "SANTA ELENA (JAEN) 3050\n",
      "SANTA SUSANNA (BARCELONA) 2565\n",
      "SANTANDER (CANTABRIA) 5815\n",
      "SANTANDER AEROPUERTO (CANTABRIA) 3318\n",
      "SANTIAGO DE COMPOSTELA (A CORUÑA) 3214\n",
      "SANTIAGO DE COMPOSTELA AEROPUERTO (A CORUÑA) 3318\n",
      "SEGOVIA (SEGOVIA) 3318\n",
      "SEVILLA AEROPUERTO (SEVILLA) 3318\n",
      "SIERRA DE ALFABIA, BUNYOLA (ILLES BALEARS) 3090\n",
      "SIGÜENZA (GUADALAJARA) 3019\n",
      "SOMOSIERRA (MADRID) 2736\n",
      "SORIA (SORIA) 3267\n",
      "SOS DEL REY CATÓLICO (ZARAGOZA) 2810\n",
      "SOTILLO DE LA ADRADA (AVILA) 2991\n",
      "STA.CRUZ DE TENERIFE (STA. CRUZ DE TENERIFE) 3318\n",
      "TALARN (LLEIDA) 2824\n",
      "TALAVERA DE LA REINA (TOLEDO) 3063\n",
      "TARANCÓN (CUENCA) 3259\n",
      "TARIFA (CADIZ) 3102\n",
      "TAZACORTE (STA. CRUZ DE TENERIFE) 3104\n",
      "TEGUISE (LAS PALMAS) 2015\n",
      "TENERIFE NORTE AEROPUERTO (STA. CRUZ DE TENERIFE) 3318\n",
      "TENERIFE SUR AEROPUERTO (STA. CRUZ DE TENERIFE) 3318\n",
      "TERUEL (TERUEL) 3318\n",
      "TOLEDO (TOLEDO) 3315\n",
      "TOMELLOSO (CIUDAD REAL) 2915\n",
      "TORLA (HUESCA) 1729\n",
      "TORREJÓN DE ARDOZ (MADRID) 3318\n",
      "TORROX (MALAGA) 2807\n",
      "TORTOSA (TARRAGONA) 3318\n",
      "TRUJILLO (CACERES) 3211\n",
      "TÀRREGA (LLEIDA) 2715\n",
      "UTIEL (VALENCIA) 3267\n",
      "VALDEPEÑAS (CIUDAD REAL) 3077\n",
      "VALDERREDIBLE, POLIENTES    (CANTABRIA) 3268\n",
      "VALENCIA AEROPUERTO (VALENCIA) 3318\n",
      "VALENCIA DE ALCÁNTARA (CACERES) 3200\n",
      "VALLADOLID (VALLADOLID) 3318\n",
      "VALLADOLID AEROPUERTO (VALLADOLID) 3318\n",
      "VALÈNCIA (VALENCIA) 3318\n",
      "VALÈNCIA, VIVEROS (VALENCIA) 3297\n",
      "VANDELLÒS   (TARRAGONA) 2735\n",
      "VEJER DE LA FRONTERA (CADIZ) 3308\n",
      "VIGO AEROPUERTO (PONTEVEDRA) 3318\n",
      "VILLAFRANCA DEL CID/VILLAFRANCA (CASTELLON) 2096\n",
      "VILLANUEVA DE CÓRDOBA (CORDOBA) 3191\n",
      "VILLARDECIERVOS (ZAMORA) 3258\n",
      "VILLARRODRIGO (JAEN) 2309\n",
      "VINARÒS (CASTELLON) 3206\n",
      "VISO DEL MARQUÉS (CIUDAD REAL) 2895\n",
      "VITIGUDINO (SALAMANCA) 3262\n",
      "VITORIA GASTEIZ AEROPUERTO (ARABA/ALAVA) 2650\n",
      "XINZO DE LIMIA (OURENSE) 2912\n",
      "XÀTIVA (VALENCIA) 3003\n",
      "YECLA (MURCIA) 3303\n",
      "ZAMORA (ZAMORA) 3318\n",
      "ZARAGOZA AEROPUERTO (ZARAGOZA) 3318\n",
      "ZARAGOZA, VALDESPARTERA (ZARAGOZA) 2245\n",
      "ZUMAIA (GIPUZKOA) 3310\n",
      "ZUMARRAGA (GIPUZKOA) 3261\n",
      "ÁGUILAS (MURCIA) 3316\n",
      "ÁVILA (AVILA) 3318\n",
      "ÉCIJA (SEVILLA) 3219\n"
     ]
    }
   ],
   "source": [
    "for name,group_st in station_group:\n",
    "    print ('%s (%s) %s' % (name,group_st.iloc[0].region ,len(group_st)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to groupby date of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y0101 (9) 4.66 10.06 15.43\n",
      "Y0102 (9) 4.17 10.10 16.04\n",
      "Y0103 (9) 4.53 10.09 15.63\n",
      "Y0104 (7) 4.84 10.77 16.69\n",
      "Y0105 (7) 2.37 9.23 16.08\n",
      "Y0106 (7) 3.20 9.37 15.56\n",
      "Y0107 (7) 4.46 9.77 15.07\n",
      "Y0108 (7) 3.57 9.54 15.50\n",
      "Y0109 (7) 2.69 9.06 15.37\n",
      "Y0110 (7) 4.27 10.19 16.07\n",
      "Y0111 (7) 4.67 10.40 16.10\n",
      "Y0112 (8) 4.00 9.74 15.50\n",
      "Y0113 (8) 4.23 9.87 15.53\n",
      "Y0114 (8) 3.39 9.01 14.67\n",
      "Y0115 (8) 1.99 8.04 14.09\n",
      "Y0116 (8) 3.34 8.50 13.67\n",
      "Y0117 (9) 2.43 8.87 15.31\n",
      "Y0118 (9) 3.53 8.91 14.31\n",
      "Y0119 (7) 3.87 8.97 14.05\n",
      "Y0120 (6) 1.62 7.48 13.33\n",
      "Y0121 (9) 1.65 8.02 14.35\n",
      "Y0122 (8) 3.07 9.01 14.97\n",
      "Y0123 (9) 2.33 8.66 14.93\n",
      "Y0124 (9) 2.85 9.46 16.05\n",
      "Y0125 (9) 3.66 9.60 15.52\n",
      "Y0126 (9) 3.77 9.91 16.11\n",
      "Y0127 (9) 4.81 9.62 14.44\n",
      "Y0128 (8) 3.46 8.94 14.40\n",
      "Y0129 (8) 3.15 8.84 14.50\n",
      "Y0130 (8) 3.24 9.41 15.60\n",
      "Y0131 (9) 3.47 9.66 15.84\n",
      "Y0201 (9) 2.79 9.52 16.23\n",
      "Y0202 (9) 2.51 8.24 13.98\n",
      "Y0203 (8) 2.45 8.55 14.62\n",
      "Y0204 (8) 3.59 9.12 14.66\n",
      "Y0205 (9) 3.54 9.52 15.50\n",
      "Y0206 (9) 2.88 9.49 16.13\n",
      "Y0207 (9) 3.10 9.14 15.20\n",
      "Y0208 (9) 3.02 8.68 14.33\n",
      "Y0209 (9) 0.79 7.96 15.11\n",
      "Y0210 (9) 2.33 8.16 13.97\n",
      "Y0211 (9) 2.67 8.74 14.78\n",
      "Y0212 (9) 4.97 9.54 14.08\n",
      "Y0213 (9) 4.56 9.51 14.42\n",
      "Y0214 (9) 3.67 9.77 15.83\n",
      "Y0215 (8) 5.09 10.39 15.68\n",
      "Y0216 (9) 3.66 9.72 15.78\n",
      "Y0217 (9) 4.84 10.09 15.32\n",
      "Y0218 (9) 5.28 10.58 15.93\n",
      "Y0219 (9) 4.58 10.39 16.17\n",
      "Y0220 (9) 3.97 10.48 16.98\n",
      "Y0221 (9) 5.99 11.56 17.14\n",
      "Y0222 (9) 4.43 10.97 17.51\n",
      "Y0223 (9) 4.78 11.11 17.46\n",
      "Y0224 (9) 4.89 11.10 17.31\n",
      "Y0225 (9) 5.21 11.23 17.27\n",
      "Y0226 (9) 3.85 11.00 18.12\n",
      "Y0227 (9) 5.89 11.77 17.63\n",
      "Y0228 (8) 5.50 10.94 16.40\n",
      "Y0229 (2) 3.65 11.90 20.20\n",
      "Y0301 (9) 4.14 10.92 17.69\n",
      "Y0302 (8) 5.89 11.91 17.93\n",
      "Y0303 (9) 6.19 11.81 17.40\n",
      "Y0304 (9) 6.60 11.84 17.10\n",
      "Y0305 (9) 7.50 12.41 17.33\n",
      "Y0306 (9) 6.43 12.07 17.68\n",
      "Y0307 (9) 5.27 11.91 18.56\n",
      "Y0308 (9) 5.67 12.87 20.08\n",
      "Y0309 (9) 6.26 12.71 19.17\n",
      "Y0310 (9) 6.26 12.96 19.68\n",
      "Y0311 (9) 5.47 12.32 19.18\n",
      "Y0312 (9) 6.30 12.64 18.96\n",
      "Y0313 (9) 5.77 11.97 18.21\n",
      "Y0314 (9) 5.48 11.57 17.68\n",
      "Y0315 (9) 5.09 12.07 19.08\n",
      "Y0316 (9) 5.77 12.39 19.01\n",
      "Y0317 (9) 6.81 13.28 19.70\n",
      "Y0318 (9) 6.82 13.47 20.09\n",
      "Y0319 (9) 6.36 13.53 20.71\n",
      "Y0320 (9) 7.69 13.12 18.51\n",
      "Y0321 (9) 7.01 12.69 18.38\n",
      "Y0322 (8) 5.35 11.86 18.36\n",
      "Y0323 (8) 6.70 12.11 17.52\n",
      "Y0324 (8) 7.18 13.03 18.86\n",
      "Y0325 (9) 7.02 12.67 18.33\n",
      "Y0326 (9) 7.86 13.72 19.60\n",
      "Y0327 (8) 6.91 13.47 19.97\n",
      "Y0328 (9) 6.74 14.35 21.99\n",
      "Y0329 (9) 8.30 14.38 20.44\n",
      "Y0330 (9) 7.87 14.86 21.84\n",
      "Y0331 (9) 8.71 15.17 21.62\n",
      "Y0401 (9) 7.76 15.01 22.32\n",
      "Y0402 (9) 8.33 15.04 21.80\n",
      "Y0403 (9) 8.53 14.36 20.18\n",
      "Y0404 (8) 8.43 14.19 19.90\n",
      "Y0405 (9) 8.90 15.32 21.73\n",
      "Y0406 (9) 8.19 15.50 22.86\n",
      "Y0407 (9) 8.66 15.93 23.23\n",
      "Y0408 (8) 8.56 16.18 23.84\n",
      "Y0409 (9) 8.21 15.50 22.84\n",
      "Y0410 (9) 9.83 16.16 22.50\n",
      "Y0411 (8) 11.02 17.23 23.48\n",
      "Y0412 (9) 9.79 15.79 21.80\n",
      "Y0413 (8) 9.30 16.69 24.04\n",
      "Y0414 (9) 10.00 17.26 24.51\n",
      "Y0415 (9) 10.59 17.09 23.58\n",
      "Y0416 (9) 11.50 17.95 24.39\n",
      "Y0417 (9) 9.86 17.79 25.72\n",
      "Y0418 (9) 11.18 18.08 25.01\n",
      "Y0419 (9) 11.72 17.56 23.37\n",
      "Y0420 (9) 11.83 17.26 22.64\n",
      "Y0421 (9) 10.74 16.83 22.91\n",
      "Y0422 (9) 11.22 17.20 23.21\n",
      "Y0423 (9) 11.52 17.78 24.03\n",
      "Y0424 (9) 10.76 17.54 24.29\n",
      "Y0425 (9) 10.64 17.50 24.36\n",
      "Y0426 (9) 11.84 18.53 25.24\n",
      "Y0427 (9) 12.21 18.50 24.80\n",
      "Y0428 (9) 10.50 16.50 22.50\n",
      "Y0429 (9) 10.71 16.10 21.51\n",
      "Y0430 (9) 10.32 16.34 22.33\n",
      "Y0501 (9) 9.22 16.53 23.82\n",
      "Y0502 (9) 9.47 17.34 25.21\n",
      "Y0503 (9) 10.49 18.33 26.16\n",
      "Y0504 (9) 11.34 18.57 25.82\n",
      "Y0505 (8) 11.25 18.34 25.39\n",
      "Y0506 (8) 10.96 18.34 25.69\n",
      "Y0507 (8) 11.30 18.39 25.47\n",
      "Y0508 (9) 12.66 19.88 27.09\n",
      "Y0509 (9) 12.80 20.17 27.56\n",
      "Y0510 (9) 13.43 20.12 26.82\n",
      "Y0511 (9) 13.17 20.16 27.16\n",
      "Y0512 (9) 12.71 20.27 27.83\n",
      "Y0513 (9) 13.37 20.74 28.12\n",
      "Y0514 (9) 12.70 20.76 28.79\n",
      "Y0515 (9) 13.30 20.50 27.71\n",
      "Y0516 (9) 12.84 20.67 28.52\n",
      "Y0517 (9) 12.37 20.82 29.29\n",
      "Y0518 (9) 12.69 19.94 27.17\n",
      "Y0519 (9) 11.87 19.42 26.96\n",
      "Y0520 (9) 11.67 18.97 26.27\n",
      "Y0521 (9) 12.86 19.64 26.43\n",
      "Y0522 (9) 12.16 20.10 28.06\n",
      "Y0523 (9) 12.09 20.90 29.71\n",
      "Y0524 (9) 12.74 20.48 28.23\n",
      "Y0525 (9) 13.37 20.91 28.42\n",
      "Y0526 (9) 13.37 20.44 27.53\n",
      "Y0527 (9) 13.96 21.26 28.53\n",
      "Y0528 (9) 14.54 20.91 27.32\n",
      "Y0529 (9) 13.26 20.56 27.87\n",
      "Y0530 (9) 14.27 21.67 29.04\n",
      "Y0531 (8) 13.59 22.31 31.06\n",
      "Y0601 (9) 14.77 23.42 32.09\n",
      "Y0602 (9) 15.09 23.28 31.49\n",
      "Y0603 (9) 15.15 22.80 30.41\n",
      "Y0604 (9) 14.03 22.34 30.66\n",
      "Y0605 (9) 15.04 23.23 31.42\n",
      "Y0606 (9) 15.48 22.76 30.06\n",
      "Y0607 (8) 14.81 22.02 29.25\n",
      "Y0608 (9) 14.53 21.84 29.13\n",
      "Y0609 (9) 14.02 21.46 28.91\n",
      "Y0610 (9) 14.86 22.28 29.72\n",
      "Y0611 (8) 15.58 22.49 29.43\n",
      "Y0612 (9) 15.37 23.52 31.70\n",
      "Y0613 (9) 15.64 24.26 32.88\n",
      "Y0614 (9) 16.01 24.61 33.20\n",
      "Y0615 (9) 16.80 25.11 33.41\n",
      "Y0616 (9) 16.57 24.91 33.23\n",
      "Y0617 (8) 16.75 25.45 34.16\n",
      "Y0618 (9) 16.61 24.56 32.51\n",
      "Y0619 (9) 16.41 24.43 32.49\n",
      "Y0620 (9) 16.01 25.02 34.04\n",
      "Y0621 (9) 17.14 25.29 33.47\n",
      "Y0622 (9) 16.74 25.71 34.69\n",
      "Y0623 (9) 16.89 26.16 35.39\n",
      "Y0624 (9) 17.79 26.54 35.33\n",
      "Y0625 (9) 18.21 26.17 34.14\n",
      "Y0626 (9) 17.47 26.22 34.94\n",
      "Y0627 (9) 17.76 26.68 35.61\n",
      "Y0628 (9) 18.29 26.83 35.39\n",
      "Y0629 (9) 18.51 26.76 34.96\n",
      "Y0630 (9) 17.27 25.34 33.41\n",
      "Y0701 (9) 16.87 25.38 33.86\n",
      "Y0702 (9) 17.16 25.81 34.44\n",
      "Y0703 (9) 18.08 26.57 35.08\n",
      "Y0704 (9) 17.54 26.57 35.56\n",
      "Y0705 (9) 18.83 27.36 35.90\n",
      "Y0706 (9) 18.66 26.78 34.93\n",
      "Y0707 (9) 18.84 27.22 35.54\n",
      "Y0708 (9) 18.18 27.22 36.28\n",
      "Y0709 (9) 18.27 28.01 37.76\n",
      "Y0710 (8) 19.21 28.23 37.26\n",
      "Y0711 (8) 18.41 27.84 37.29\n",
      "Y0712 (9) 18.40 27.70 36.98\n",
      "Y0713 (9) 18.56 27.60 36.63\n",
      "Y0714 (9) 19.17 27.67 36.16\n",
      "Y0715 (9) 18.11 27.32 36.54\n",
      "Y0716 (9) 20.01 28.66 37.35\n",
      "Y0717 (9) 20.03 28.57 37.14\n",
      "Y0718 (9) 19.23 28.03 36.81\n",
      "Y0719 (9) 19.49 28.50 37.49\n",
      "Y0720 (9) 19.65 27.88 36.08\n",
      "Y0721 (9) 18.63 27.36 36.08\n",
      "Y0722 (9) 18.61 27.51 36.42\n",
      "Y0723 (9) 18.96 27.69 36.42\n",
      "Y0724 (9) 19.38 27.76 36.13\n",
      "Y0725 (9) 18.80 27.78 36.76\n",
      "Y0726 (9) 18.52 27.57 36.63\n",
      "Y0727 (9) 19.36 27.66 35.90\n",
      "Y0728 (9) 19.33 27.92 36.50\n",
      "Y0729 (9) 18.56 27.64 36.73\n",
      "Y0730 (9) 18.93 27.84 36.77\n",
      "Y0731 (9) 18.63 27.67 36.71\n",
      "Y0801 (9) 19.09 28.56 38.02\n",
      "Y0802 (8) 18.72 27.70 36.69\n",
      "Y0803 (9) 19.53 28.83 38.17\n",
      "Y0804 (8) 19.49 29.46 39.40\n",
      "Y0805 (9) 20.58 29.46 38.33\n",
      "Y0806 (9) 20.28 28.98 37.68\n",
      "Y0807 (9) 21.36 29.39 37.46\n",
      "Y0808 (9) 20.96 28.74 36.53\n",
      "Y0809 (9) 20.67 28.79 36.90\n",
      "Y0810 (9) 20.28 29.00 37.69\n",
      "Y0811 (9) 21.10 29.44 37.79\n",
      "Y0812 (9) 20.50 28.80 37.10\n",
      "Y0813 (9) 19.61 27.46 35.29\n",
      "Y0814 (9) 18.43 26.73 35.02\n",
      "Y0815 (9) 19.57 27.64 35.72\n",
      "Y0816 (9) 19.20 27.61 36.02\n",
      "Y0817 (9) 19.83 27.98 36.09\n",
      "Y0818 (9) 20.59 28.58 36.60\n",
      "Y0819 (9) 20.91 29.16 37.41\n",
      "Y0820 (8) 21.00 28.90 36.83\n",
      "Y0821 (8) 20.59 28.85 37.13\n",
      "Y0822 (9) 20.51 28.31 36.09\n",
      "Y0823 (9) 19.30 27.78 36.23\n",
      "Y0824 (9) 18.86 27.18 35.53\n",
      "Y0825 (8) 18.99 27.55 36.15\n",
      "Y0826 (8) 19.80 27.78 35.76\n",
      "Y0827 (9) 19.21 27.96 36.67\n",
      "Y0828 (9) 19.76 27.90 36.02\n",
      "Y0829 (9) 19.26 26.85 34.41\n",
      "Y0830 (9) 18.82 26.48 34.12\n",
      "Y0831 (9) 18.62 26.19 33.74\n",
      "Y0901 (9) 19.77 26.84 33.91\n",
      "Y0902 (9) 19.32 26.32 33.30\n",
      "Y0903 (9) 18.33 25.53 32.76\n",
      "Y0904 (9) 17.94 25.59 33.24\n",
      "Y0905 (9) 17.60 25.58 33.57\n",
      "Y0906 (9) 18.04 26.17 34.33\n",
      "Y0907 (9) 19.38 26.22 33.02\n",
      "Y0908 (9) 17.74 24.80 31.86\n",
      "Y0909 (9) 16.54 24.35 32.11\n",
      "Y0910 (9) 16.19 24.26 32.31\n",
      "Y0911 (9) 16.88 25.08 33.30\n",
      "Y0912 (9) 17.67 26.01 34.36\n",
      "Y0913 (9) 18.07 25.37 32.66\n",
      "Y0914 (8) 17.43 24.48 31.56\n",
      "Y0915 (8) 16.81 23.84 30.84\n",
      "Y0916 (7) 15.94 22.91 29.93\n",
      "Y0917 (7) 15.99 22.30 28.63\n",
      "Y0918 (9) 15.10 22.68 30.19\n",
      "Y0919 (8) 15.81 23.35 30.90\n",
      "Y0920 (8) 17.20 24.49 31.80\n",
      "Y0921 (8) 16.86 24.41 31.95\n",
      "Y0922 (9) 16.86 24.28 31.72\n",
      "Y0923 (9) 15.78 22.98 30.21\n",
      "Y0924 (9) 15.47 22.81 30.13\n",
      "Y0925 (9) 15.18 22.93 30.70\n",
      "Y0926 (9) 15.79 22.79 29.80\n",
      "Y0927 (9) 16.31 22.63 28.96\n",
      "Y0928 (9) 16.78 22.30 27.84\n",
      "Y0929 (9) 16.18 22.33 28.50\n",
      "Y0930 (9) 15.30 22.29 29.26\n",
      "Y1001 (8) 15.97 23.16 30.35\n",
      "Y1002 (9) 15.00 22.86 30.71\n",
      "Y1003 (9) 16.21 22.96 29.74\n",
      "Y1004 (9) 16.34 23.25 30.17\n",
      "Y1005 (9) 14.94 22.04 29.21\n",
      "Y1006 (9) 14.62 21.95 29.29\n",
      "Y1007 (9) 14.70 21.91 29.11\n",
      "Y1008 (9) 14.53 22.14 29.70\n",
      "Y1009 (9) 14.10 21.20 28.30\n",
      "Y1010 (9) 14.27 20.77 27.24\n",
      "Y1011 (9) 14.86 21.21 27.60\n",
      "Y1012 (9) 15.02 20.56 26.09\n",
      "Y1013 (9) 13.46 20.20 26.94\n",
      "Y1014 (9) 13.01 19.40 25.78\n",
      "Y1015 (9) 12.48 19.01 25.54\n",
      "Y1016 (9) 13.23 19.92 26.60\n",
      "Y1017 (9) 12.94 19.93 26.93\n",
      "Y1018 (9) 12.91 18.88 24.84\n",
      "Y1019 (9) 12.84 18.74 24.59\n",
      "Y1020 (9) 12.57 18.99 25.41\n",
      "Y1021 (9) 12.14 18.70 25.29\n",
      "Y1022 (9) 13.22 18.80 24.40\n",
      "Y1023 (9) 13.42 19.09 24.79\n",
      "Y1024 (9) 13.49 18.66 23.82\n",
      "Y1025 (9) 13.08 18.86 24.62\n",
      "Y1026 (8) 13.64 18.43 23.24\n",
      "Y1027 (9) 11.94 18.20 24.46\n",
      "Y1028 (8) 10.72 17.19 23.65\n",
      "Y1029 (9) 9.90 16.57 23.21\n",
      "Y1030 (9) 10.23 16.14 22.04\n",
      "Y1031 (9) 9.72 15.83 21.94\n",
      "Y1101 (9) 10.41 16.49 22.51\n",
      "Y1102 (9) 10.59 16.32 22.04\n",
      "Y1103 (8) 11.01 16.44 21.87\n",
      "Y1104 (9) 11.77 16.06 20.36\n",
      "Y1105 (9) 10.56 15.14 19.71\n",
      "Y1106 (9) 8.53 13.91 19.30\n",
      "Y1107 (9) 9.17 14.27 19.37\n",
      "Y1108 (9) 8.61 13.78 18.96\n",
      "Y1109 (9) 8.21 13.33 18.44\n",
      "Y1110 (9) 6.79 13.09 19.40\n",
      "Y1111 (9) 7.71 14.29 20.88\n",
      "Y1112 (9) 8.44 14.74 21.09\n",
      "Y1113 (9) 8.29 14.94 21.59\n",
      "Y1114 (9) 9.18 14.63 20.06\n",
      "Y1115 (9) 7.76 13.60 19.41\n",
      "Y1116 (8) 6.58 13.06 19.56\n",
      "Y1117 (8) 6.41 12.86 19.30\n",
      "Y1118 (8) 7.31 13.16 18.96\n",
      "Y1119 (9) 7.30 13.27 19.28\n",
      "Y1120 (9) 7.85 13.31 18.77\n",
      "Y1121 (9) 8.53 13.43 18.31\n",
      "Y1122 (8) 7.51 12.46 17.46\n",
      "Y1123 (8) 6.83 12.35 17.91\n",
      "Y1124 (8) 7.02 12.71 18.40\n",
      "Y1125 (8) 8.01 13.30 18.57\n",
      "Y1126 (8) 7.89 12.77 17.63\n",
      "Y1127 (7) 6.21 11.33 16.40\n",
      "Y1128 (7) 5.06 10.86 16.66\n",
      "Y1129 (8) 4.07 10.14 16.17\n",
      "Y1130 (9) 4.67 10.35 16.02\n",
      "Y1201 (10) 4.67 10.64 16.60\n",
      "Y1202 (10) 5.60 10.89 16.19\n",
      "Y1203 (9) 4.51 10.50 16.49\n",
      "Y1204 (10) 4.72 10.95 17.14\n",
      "Y1205 (10) 4.70 10.86 17.02\n",
      "Y1206 (9) 4.87 11.10 17.31\n",
      "Y1207 (10) 5.62 11.27 16.96\n",
      "Y1208 (10) 5.86 11.81 17.77\n",
      "Y1209 (10) 5.52 11.54 17.57\n",
      "Y1210 (10) 6.58 12.36 18.10\n",
      "Y1211 (9) 5.20 11.32 17.44\n",
      "Y1212 (10) 4.70 10.26 15.82\n",
      "Y1213 (9) 6.62 11.42 16.21\n",
      "Y1214 (9) 7.34 11.64 15.93\n",
      "Y1215 (10) 6.19 10.74 15.31\n",
      "Y1216 (9) 5.12 9.53 13.99\n",
      "Y1217 (10) 5.47 10.14 14.83\n",
      "Y1218 (9) 4.54 10.02 15.51\n",
      "Y1219 (10) 4.71 10.29 15.86\n",
      "Y1220 (10) 3.71 9.46 15.20\n",
      "Y1221 (9) 4.24 10.82 17.40\n",
      "Y1222 (9) 4.44 11.11 17.81\n",
      "Y1223 (9) 3.60 10.70 17.77\n",
      "Y1224 (9) 3.28 10.08 16.85\n",
      "Y1225 (9) 4.01 9.99 15.96\n",
      "Y1226 (9) 3.56 9.72 15.88\n",
      "Y1227 (9) 3.45 9.71 15.99\n",
      "Y1228 (9) 4.60 10.64 16.69\n",
      "Y1229 (10) 4.15 10.20 16.28\n",
      "Y1230 (10) 3.31 9.69 16.05\n",
      "Y1231 (10) 4.04 10.10 16.15\n"
     ]
    }
   ],
   "source": [
    "for name,group_doy in group_st.groupby(by='doy'):\n",
    "    print ('%s (%s) %4.2f %4.2f %4.2f' % (name,len(group_doy),np.mean(group_doy['tmin']),\n",
    "                                          np.mean(group_doy['tavg']),np.mean(group_doy['tmax'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "line_of_result = []\n",
    "for station,group_st in station_group:\n",
    "    region = group_st.iloc[0].region\n",
    "    for doy,group_doy in group_st.groupby(by='doy'):\n",
    "        ndata = len(group_doy)\n",
    "        line_of_result.append([station,region,ndata,doy,np.mean(group_doy['tmin']),\n",
    "                                np.mean(group_doy['tavg']),np.mean(group_doy['tmax'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIMATOLOGY = pd.DataFrame.from_records(line_of_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0101</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>11.555556</td>\n",
       "      <td>14.455556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0102</td>\n",
       "      <td>9.611111</td>\n",
       "      <td>12.255556</td>\n",
       "      <td>14.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0103</td>\n",
       "      <td>10.355556</td>\n",
       "      <td>12.866667</td>\n",
       "      <td>15.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0104</td>\n",
       "      <td>10.144444</td>\n",
       "      <td>12.588889</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0105</td>\n",
       "      <td>8.766667</td>\n",
       "      <td>11.733333</td>\n",
       "      <td>14.688889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1  2      3          4          5          6\n",
       "0  A CORUÑA  A CORUÑA  9  Y0101   8.666667  11.555556  14.455556\n",
       "1  A CORUÑA  A CORUÑA  9  Y0102   9.611111  12.255556  14.866667\n",
       "2  A CORUÑA  A CORUÑA  9  Y0103  10.355556  12.866667  15.388889\n",
       "3  A CORUÑA  A CORUÑA  9  Y0104  10.144444  12.588889  15.000000\n",
       "4  A CORUÑA  A CORUÑA  9  Y0105   8.766667  11.733333  14.688889"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CLIMATOLOGY.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add the correct columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIMATOLOGY.columns = ['station','region','ndata','doy','tmin','tavg','tmax']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>station</th>\n",
       "      <th>region</th>\n",
       "      <th>ndata</th>\n",
       "      <th>doy</th>\n",
       "      <th>tmin</th>\n",
       "      <th>tavg</th>\n",
       "      <th>tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0101</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>11.555556</td>\n",
       "      <td>14.455556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0102</td>\n",
       "      <td>9.611111</td>\n",
       "      <td>12.255556</td>\n",
       "      <td>14.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0103</td>\n",
       "      <td>10.355556</td>\n",
       "      <td>12.866667</td>\n",
       "      <td>15.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0104</td>\n",
       "      <td>10.144444</td>\n",
       "      <td>12.588889</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>A CORUÑA</td>\n",
       "      <td>9</td>\n",
       "      <td>Y0105</td>\n",
       "      <td>8.766667</td>\n",
       "      <td>11.733333</td>\n",
       "      <td>14.688889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station    region  ndata    doy       tmin       tavg       tmax\n",
       "0  A CORUÑA  A CORUÑA      9  Y0101   8.666667  11.555556  14.455556\n",
       "1  A CORUÑA  A CORUÑA      9  Y0102   9.611111  12.255556  14.866667\n",
       "2  A CORUÑA  A CORUÑA      9  Y0103  10.355556  12.866667  15.388889\n",
       "3  A CORUÑA  A CORUÑA      9  Y0104  10.144444  12.588889  15.000000\n",
       "4  A CORUÑA  A CORUÑA      9  Y0105   8.766667  11.733333  14.688889"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CLIMATOLOGY.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a quick interactive look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interact_manual,interactive, interactive_output\n",
    "from ipywidgets import fixed, FloatSlider, Dropdown, HBox, Label, VBox, Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to select region and station (based on the selected region...)\n",
    "\n",
    "# Define the region widget (initialised at region = A CORUÑA)\n",
    "region_list = sorted(list(set(CLIMATOLOGY['region'])))\n",
    "region_widget = Dropdown(options=region_list,\n",
    "                         value='A CORUÑA',\n",
    "                         description='Region:'\n",
    "                        )\n",
    "                        \n",
    "# Define the station widget (initialised at region = 'A CORUÑA' & statio = 'A CORUÑA')\n",
    "station_widget = Dropdown(options=sorted(list(set(CLIMATOLOGY[CLIMATOLOGY['region']=='A CORUÑA']['station']))),\n",
    "                         value='A CORUÑA',\n",
    "                         description='Station:'\n",
    "                         )\n",
    "                        \n",
    "# The upodate station list\n",
    "def on_update_brand_widget(*args):\n",
    "    station_widget.options = sorted(list(set(CLIMATOLOGY[CLIMATOLOGY['region']==region_widget.value]['station'])))\n",
    "\n",
    "# The observe method to link station to region\n",
    "region_widget.observe(on_update_brand_widget, 'value')\n",
    "\n",
    "# Function\n",
    "def plot_region_station(df,region,station,column):\n",
    "    fig = plt.figure(figsize=(20,5))\n",
    "    plt.grid()\n",
    "\n",
    "    df_region = df.loc[df['region'] == region]\n",
    "    DataFrame = df_region.loc[df_region['station'] == station]\n",
    "    ndata = DataFrame['ndata'].values[0]\n",
    "    plt.title('%s at %s (%s) ndata=%s' % (column,station,region,ndata),fontsize=10)\n",
    "    display(plt.plot(DataFrame[column]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c2ab16187bd443ea04d52819a77bd9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(VBox(children=(Dropdown(description='Region:', index=8, options=('A CORUÑA', 'AL…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = interactive(plot_region_station,df=fixed(CLIMATOLOGY),region=region_widget, station=station_widget,\n",
    "               column=['tmin','tavg','tmax']);\n",
    "\n",
    "controls_Left = VBox([Y.children[0],Y.children[1]])\n",
    "controls_Right = VBox([Y.children[2]])\n",
    "\n",
    "controls = HBox([controls_Left,controls_Right], layout = Layout(flex_flow='row wrap'))\n",
    "output = Y.children[-1]\n",
    "display(VBox([controls, output]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This climatology doesn't look like a soft climatology... 10 years may not be enough.<br>\n",
    "In order to generate a typical climatology, we need to compute the mean over a window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_widgets = widgets.IntSlider(value=5,min=1,max=24,step=2,description='Window:',disabled=False,\n",
    "    continuous_update=False,orientation='horizontal',readout=True,readout_format='d')\n",
    "\n",
    "doy_list = sorted(list(set(CLIMATOLOGY['doy'])))\n",
    "\n",
    "# Function\n",
    "def plot_window(df,region,station,column,longwindow):\n",
    "    window = (longwindow-1)//2\n",
    "    df_region = df.loc[df['region'] == region]\n",
    "    df_station = df_region.loc[df_region['station'] == station]\n",
    "    df_station.reset_index(inplace=True)\n",
    "    line_of_result = []\n",
    "    for doy in doy_list:\n",
    "        doyindex = df_station[df_station['doy'] == doy].index[0]\n",
    "\n",
    "        if (doyindex-window) < 0:\n",
    "            df_concat = pd.concat([df_station.iloc[doyindex-window:],df_station.iloc[0:doyindex],\n",
    "                                  df_station.iloc[doyindex:1+doyindex+window]])\n",
    "        elif (doyindex+window) > 365:\n",
    "            df_concat = pd.concat([df_station.iloc[doyindex-window:],\n",
    "                                  df_station.iloc[:window-(365-doyindex)]])\n",
    "        else:\n",
    "            df_concat = pd.concat([df_station.iloc[doyindex-window:doyindex],\n",
    "                                       df_station.iloc[doyindex:1+doyindex+window]])\n",
    "\n",
    "        line_of_result.append([doy,np.mean(df_concat[column])])    \n",
    "\n",
    "    DataFrame = pd.DataFrame.from_records(line_of_result)\n",
    "    fig = plt.figure(figsize=(20,5))\n",
    "    plt.grid()\n",
    "\n",
    "    plt.title('%s at %s (%s) - window : %s days' % (column,station,region,longwindow),fontsize=10)\n",
    "    display(plt.plot(DataFrame[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d26d41e8feef4d7497130515f84bb1f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(VBox(children=(Dropdown(description='Region:', options=('A CORUÑA', 'ALBACETE', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = interactive(plot_window,df=fixed(CLIMATOLOGY),region=region_widget, station=station_widget,\n",
    "               column=['tmin','tavg','tmax'], longwindow=window_widgets);\n",
    "\n",
    "controls_Left = VBox([Z.children[0],Z.children[1]])\n",
    "controls_Right = VBox([Z.children[2],Z.children[3]])\n",
    "\n",
    "controls = HBox([controls_Left,controls_Right], layout = Layout(flex_flow='row wrap'))\n",
    "output = Z.children[-1]\n",
    "display(VBox([controls, output]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Climatology results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking the mean value over a window produces a more representative climatology.<br>\n",
    "Given a selected window days, let's recaculate the climatology for all stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I've choosed a window covering 21 days\n",
    "longwindow = 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_group = CLIMATOLOGY.groupby(by='station')\n",
    "window = (longwindow-1)//2\n",
    "line_of_result = []\n",
    "for station,df_station in station_group:\n",
    "    df_station.reset_index(inplace=True)\n",
    "    region = df_station.iloc[0].region\n",
    "    \n",
    "    for doy in doy_list:\n",
    "        try:\n",
    "            doyindex = df_station[df_station['doy'] == doy].index[0]\n",
    "\n",
    "            if (doyindex-window) < 0:\n",
    "                df_concat = pd.concat([df_station.iloc[doyindex-window:],df_station.iloc[0:doyindex],\n",
    "                                      df_station.iloc[doyindex:1+doyindex+window]])\n",
    "            elif (doyindex+window) > 365:\n",
    "                df_concat = pd.concat([df_station.iloc[doyindex-window:],\n",
    "                                      df_station.iloc[:window-(365-doyindex)]])\n",
    "            else:\n",
    "                df_concat = pd.concat([df_station.iloc[doyindex-window:doyindex],\n",
    "                                           df_station.iloc[doyindex:1+doyindex+window]])\n",
    "\n",
    "            line_of_result.append([station,region,doy,np.mean(df_concat['tmin']),\n",
    "                                    np.mean(df_concat['tavg']),np.mean(df_concat['tmax'])])\n",
    "        except:\n",
    "            break\n",
    "\n",
    "CLIMATOLOGY_WINDOW = pd.DataFrame.from_records(line_of_result)\n",
    "CLIMATOLOGY_WINDOW.columns = ['station','region','doy','tmin','tavg','tmax']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIMATOLOGY_WINDOW = pd.DataFrame.from_records(line_of_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The daily temperature climatology is now ready!<br>\n",
    "Let's export the result to a CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIMATOLOGY_WINDOW.to_csv('aemet_daily_temperature_climatology_2009-2018_window-21.csv.bz2',\n",
    "                          compression='bz2',float_format='%1.1f',index=False)"
   ]
  }
 ],
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