5_data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data_x = pd.read_csv('./data/weather/v3/2013/merged/merged_2013_h0.csv', index_col = 'Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 8758 entries, 2013-01-01 00:00:00 to 2013-12-31 22:00:00\n",
      "Data columns (total 70 columns):\n",
      "temperature-002    8758 non-null float64\n",
      "cloudiness-002     8758 non-null int64\n",
      "temperature-005    8756 non-null float64\n",
      "cloudiness-005     8757 non-null float64\n",
      "temperature-015    8739 non-null float64\n",
      "cloudiness-015     8741 non-null float64\n",
      "temperature-027    8758 non-null float64\n",
      "cloudiness-027     8758 non-null int64\n",
      "temperature-070    8758 non-null float64\n",
      "cloudiness-070     8758 non-null int64\n",
      "temperature-110    8758 non-null float64\n",
      "cloudiness-110     8758 non-null int64\n",
      "temperature-120    8758 non-null float64\n",
      "cloudiness-120     8758 non-null int64\n",
      "temperature-130    8758 non-null float64\n",
      "cloudiness-130     8758 non-null int64\n",
      "temperature-145    8758 non-null float64\n",
      "cloudiness-145     8758 non-null int64\n",
      "temperature-149    8758 non-null float64\n",
      "cloudiness-149     8758 non-null int64\n",
      "temperature-156    8758 non-null float64\n",
      "cloudiness-156     8758 non-null int64\n",
      "temperature-168    8758 non-null float64\n",
      "cloudiness-168     8758 non-null int64\n",
      "temperature-180    8750 non-null float64\n",
      "cloudiness-180     8751 non-null float64\n",
      "temperature-190    8758 non-null float64\n",
      "cloudiness-190     8758 non-null int64\n",
      "temperature-222    8758 non-null float64\n",
      "cloudiness-222     8758 non-null int64\n",
      "temperature-240    8758 non-null float64\n",
      "cloudiness-240     8758 non-null int64\n",
      "temperature-255    8758 non-null float64\n",
      "cloudiness-255     8758 non-null int64\n",
      "temperature-260    8758 non-null float64\n",
      "cloudiness-260     8758 non-null int64\n",
      "temperature-280    8758 non-null float64\n",
      "cloudiness-280     8758 non-null int64\n",
      "temperature-299    8746 non-null float64\n",
      "cloudiness-299     8748 non-null float64\n",
      "temperature-434    8758 non-null float64\n",
      "cloudiness-434     8758 non-null int64\n",
      "temperature-460    8758 non-null float64\n",
      "cloudiness-460     8758 non-null int64\n",
      "temperature-481    8758 non-null float64\n",
      "cloudiness-481     8758 non-null int64\n",
      "temperature-497    8731 non-null float64\n",
      "cloudiness-497     8735 non-null float64\n",
      "temperature-510    8758 non-null float64\n",
      "cloudiness-510     8758 non-null int64\n",
      "temperature-579    8758 non-null float64\n",
      "cloudiness-579     8758 non-null int64\n",
      "temperature-588    8758 non-null float64\n",
      "cloudiness-588     8758 non-null int64\n",
      "temperature-621    8758 non-null float64\n",
      "cloudiness-621     8758 non-null int64\n",
      "temperature-630    8758 non-null float64\n",
      "cloudiness-630     8758 non-null int64\n",
      "temperature-643    8758 non-null float64\n",
      "cloudiness-643     8758 non-null int64\n",
      "temperature-645    8758 non-null float64\n",
      "cloudiness-645     8758 non-null int64\n",
      "temperature-650    8758 non-null float64\n",
      "cloudiness-650     8758 non-null int64\n",
      "temperature-675    8758 non-null float64\n",
      "cloudiness-675     8758 non-null int64\n",
      "temperature-690    8758 non-null float64\n",
      "cloudiness-690     8758 non-null int64\n",
      "temperature-747    8758 non-null float64\n",
      "cloudiness-747     8758 non-null int64\n",
      "dtypes: float64(40), int64(30)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data_x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3d2da673c8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AF0naABymPZvs1QARsUfSV4E9wMvAb7i7YGZWraLlpu8Al0XED8sPaXEuN1XD5aZBbV/u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\nZma5nCTMzCyXk4SZmeX6/3bPphEP7mLnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2da54978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "data_x['temperature-002'].plot(kind = 'hist', bins = 96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3d2d765ef0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2da41ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_x['cloudiness-002'].plot(kind = 'hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature-002</th>\n",
       "      <th>cloudiness-002</th>\n",
       "      <th>temperature-005</th>\n",
       "      <th>cloudiness-005</th>\n",
       "      <th>temperature-015</th>\n",
       "      <th>cloudiness-015</th>\n",
       "      <th>temperature-027</th>\n",
       "      <th>cloudiness-027</th>\n",
       "      <th>temperature-070</th>\n",
       "      <th>cloudiness-070</th>\n",
       "      <th>...</th>\n",
       "      <th>temperature-645</th>\n",
       "      <th>cloudiness-645</th>\n",
       "      <th>temperature-650</th>\n",
       "      <th>cloudiness-650</th>\n",
       "      <th>temperature-675</th>\n",
       "      <th>cloudiness-675</th>\n",
       "      <th>temperature-690</th>\n",
       "      <th>cloudiness-690</th>\n",
       "      <th>temperature-747</th>\n",
       "      <th>cloudiness-747</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8756.000000</td>\n",
       "      <td>8757.000000</td>\n",
       "      <td>8739.000000</td>\n",
       "      <td>8741.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.000000</td>\n",
       "      <td>8758.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.108666</td>\n",
       "      <td>4.823932</td>\n",
       "      <td>10.093559</td>\n",
       "      <td>4.847551</td>\n",
       "      <td>10.349445</td>\n",
       "      <td>4.784807</td>\n",
       "      <td>10.634860</td>\n",
       "      <td>4.596712</td>\n",
       "      <td>10.275953</td>\n",
       "      <td>4.756109</td>\n",
       "      <td>...</td>\n",
       "      <td>14.622117</td>\n",
       "      <td>2.882964</td>\n",
       "      <td>15.084997</td>\n",
       "      <td>2.602306</td>\n",
       "      <td>14.922973</td>\n",
       "      <td>2.661110</td>\n",
       "      <td>15.842338</td>\n",
       "      <td>2.975794</td>\n",
       "      <td>15.476387</td>\n",
       "      <td>3.37383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.991534</td>\n",
       "      <td>2.877510</td>\n",
       "      <td>6.685689</td>\n",
       "      <td>2.859659</td>\n",
       "      <td>7.335951</td>\n",
       "      <td>2.932697</td>\n",
       "      <td>6.263151</td>\n",
       "      <td>2.933342</td>\n",
       "      <td>7.792500</td>\n",
       "      <td>2.946732</td>\n",
       "      <td>...</td>\n",
       "      <td>8.074588</td>\n",
       "      <td>2.974542</td>\n",
       "      <td>7.943629</td>\n",
       "      <td>2.898217</td>\n",
       "      <td>8.399573</td>\n",
       "      <td>2.923761</td>\n",
       "      <td>6.613972</td>\n",
       "      <td>2.872632</td>\n",
       "      <td>7.335438</td>\n",
       "      <td>2.87766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.900000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.800000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>87.100000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.100000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>35.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>35.300000</td>\n",
       "      <td>8.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       temperature-002  cloudiness-002  temperature-005  cloudiness-005  \\\n",
       "count      8758.000000     8758.000000      8756.000000     8757.000000   \n",
       "mean         10.108666        4.823932        10.093559        4.847551   \n",
       "std           5.991534        2.877510         6.685689        2.859659   \n",
       "min          -9.000000        0.000000        -9.800000        0.000000   \n",
       "25%           6.100000        2.000000         5.300000        2.000000   \n",
       "50%           9.900000        6.000000         9.900000        6.000000   \n",
       "75%          14.800000        7.000000        14.700000        7.000000   \n",
       "max          32.500000        8.000000        33.500000        8.000000   \n",
       "\n",
       "       temperature-015  cloudiness-015  temperature-027  cloudiness-027  \\\n",
       "count      8739.000000     8741.000000      8758.000000     8758.000000   \n",
       "mean         10.349445        4.784807        10.634860        4.596712   \n",
       "std           7.335951        2.932697         6.263151        2.933342   \n",
       "min         -10.300000        0.000000        -4.100000        0.000000   \n",
       "25%           5.100000        2.000000         6.000000        2.000000   \n",
       "50%           9.900000        6.000000        10.300000        5.000000   \n",
       "75%          15.500000        7.000000        15.100000        7.000000   \n",
       "max          87.100000        8.000000        34.400000        8.000000   \n",
       "\n",
       "       temperature-070  cloudiness-070       ...        temperature-645  \\\n",
       "count      8758.000000     8758.000000       ...            8758.000000   \n",
       "mean         10.275953        4.756109       ...              14.622117   \n",
       "std           7.792500        2.946732       ...               8.074588   \n",
       "min          -9.800000        0.000000       ...              -3.600000   \n",
       "25%           4.300000        2.000000       ...               8.600000   \n",
       "50%          10.000000        6.000000       ...              14.300000   \n",
       "75%          15.500000        7.000000       ...              20.600000   \n",
       "max          36.300000        8.000000       ...              36.100000   \n",
       "\n",
       "       cloudiness-645  temperature-650  cloudiness-650  temperature-675  \\\n",
       "count     8758.000000      8758.000000     8758.000000      8758.000000   \n",
       "mean         2.882964        15.084997        2.602306        14.922973   \n",
       "std          2.974542         7.943629        2.898217         8.399573   \n",
       "min          0.000000        -4.200000        0.000000        -4.900000   \n",
       "25%          0.000000         9.100000        0.000000         9.000000   \n",
       "50%          2.000000        14.900000        1.000000        14.700000   \n",
       "75%          6.000000        21.100000        5.000000        20.800000   \n",
       "max          8.000000        35.900000        8.000000        37.500000   \n",
       "\n",
       "       cloudiness-675  temperature-690  cloudiness-690  temperature-747  \\\n",
       "count     8758.000000      8758.000000     8758.000000      8758.000000   \n",
       "mean         2.661110        15.842338        2.975794        15.476387   \n",
       "std          2.923761         6.613972        2.872632         7.335438   \n",
       "min          0.000000         1.300000        0.000000        -2.500000   \n",
       "25%          0.000000        10.500000        0.000000         9.700000   \n",
       "50%          1.000000        15.700000        2.000000        15.300000   \n",
       "75%          5.000000        21.100000        5.000000        21.000000   \n",
       "max          8.000000        31.100000        8.000000        35.300000   \n",
       "\n",
       "       cloudiness-747  \n",
       "count      8758.00000  \n",
       "mean          3.37383  \n",
       "std           2.87766  \n",
       "min           0.00000  \n",
       "25%           0.00000  \n",
       "50%           3.00000  \n",
       "75%           6.00000  \n",
       "max           8.00000  \n",
       "\n",
       "[8 rows x 70 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['temperature-005', 'cloudiness-005', 'temperature-015',\n",
       "       'cloudiness-015', 'temperature-180', 'cloudiness-180',\n",
       "       'temperature-299', 'cloudiness-299', 'temperature-497',\n",
       "       'cloudiness-497'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns with missing values\n",
    "# Choosing columns where number of values is not equal to max number of values -> some values are missing.\n",
    "data_x.columns[data_x.describe().loc['count'] != data_x.describe().loc['count'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data_y = pd.read_csv('./data/energy/v5/2013/Consumption_2013.csv', index_col = 'Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data_y.drop(data_y.index[0], \n",
    "            inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data_y = data_y.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Auvergne-Rhone-Alpes</th>\n",
       "      <th>Bourgogne-Franche-Comte</th>\n",
       "      <th>Bretagne</th>\n",
       "      <th>Centre-Val de Loire</th>\n",
       "      <th>Grand-Est</th>\n",
       "      <th>Hauts-de-France</th>\n",
       "      <th>Ile-de-France</th>\n",
       "      <th>Normandie</th>\n",
       "      <th>Nouvelle-Aquitaine</th>\n",
       "      <th>Occitanie</th>\n",
       "      <th>PACA</th>\n",
       "      <th>Pays-de-la-Loire</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.00000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "      <td>17519.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7717.750271</td>\n",
       "      <td>2535.014784</td>\n",
       "      <td>2613.741652</td>\n",
       "      <td>2247.02386</td>\n",
       "      <td>5427.153662</td>\n",
       "      <td>6043.343684</td>\n",
       "      <td>8642.616816</td>\n",
       "      <td>3430.122324</td>\n",
       "      <td>5110.759005</td>\n",
       "      <td>4340.247902</td>\n",
       "      <td>4875.551059</td>\n",
       "      <td>3191.928306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1708.200281</td>\n",
       "      <td>602.260387</td>\n",
       "      <td>705.329137</td>\n",
       "      <td>627.75084</td>\n",
       "      <td>1141.590972</td>\n",
       "      <td>1196.965309</td>\n",
       "      <td>2363.372858</td>\n",
       "      <td>805.721576</td>\n",
       "      <td>1216.823664</td>\n",
       "      <td>1142.174855</td>\n",
       "      <td>1032.661901</td>\n",
       "      <td>863.558309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4101.000000</td>\n",
       "      <td>1177.000000</td>\n",
       "      <td>1225.000000</td>\n",
       "      <td>1021.00000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>3292.000000</td>\n",
       "      <td>4196.000000</td>\n",
       "      <td>1869.000000</td>\n",
       "      <td>2817.000000</td>\n",
       "      <td>2214.000000</td>\n",
       "      <td>2943.000000</td>\n",
       "      <td>1434.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6319.000000</td>\n",
       "      <td>2056.500000</td>\n",
       "      <td>2067.000000</td>\n",
       "      <td>1748.50000</td>\n",
       "      <td>4532.000000</td>\n",
       "      <td>5122.000000</td>\n",
       "      <td>6832.000000</td>\n",
       "      <td>2804.500000</td>\n",
       "      <td>4164.000000</td>\n",
       "      <td>3478.500000</td>\n",
       "      <td>4103.000000</td>\n",
       "      <td>2519.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7499.000000</td>\n",
       "      <td>2503.000000</td>\n",
       "      <td>2505.000000</td>\n",
       "      <td>2097.00000</td>\n",
       "      <td>5346.000000</td>\n",
       "      <td>5902.000000</td>\n",
       "      <td>8321.000000</td>\n",
       "      <td>3249.000000</td>\n",
       "      <td>4851.000000</td>\n",
       "      <td>4067.000000</td>\n",
       "      <td>4639.000000</td>\n",
       "      <td>3067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9097.000000</td>\n",
       "      <td>2968.000000</td>\n",
       "      <td>3167.000000</td>\n",
       "      <td>2751.00000</td>\n",
       "      <td>6255.000000</td>\n",
       "      <td>6938.000000</td>\n",
       "      <td>10413.000000</td>\n",
       "      <td>4085.000000</td>\n",
       "      <td>6057.000000</td>\n",
       "      <td>5225.000000</td>\n",
       "      <td>5705.500000</td>\n",
       "      <td>3835.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12315.000000</td>\n",
       "      <td>4137.000000</td>\n",
       "      <td>4615.000000</td>\n",
       "      <td>4088.00000</td>\n",
       "      <td>8373.000000</td>\n",
       "      <td>9598.000000</td>\n",
       "      <td>15286.000000</td>\n",
       "      <td>5672.000000</td>\n",
       "      <td>8888.000000</td>\n",
       "      <td>7694.000000</td>\n",
       "      <td>8160.000000</td>\n",
       "      <td>5834.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Auvergne-Rhone-Alpes  Bourgogne-Franche-Comte      Bretagne  \\\n",
       "count          17519.000000             17519.000000  17519.000000   \n",
       "mean            7717.750271              2535.014784   2613.741652   \n",
       "std             1708.200281               602.260387    705.329137   \n",
       "min             4101.000000              1177.000000   1225.000000   \n",
       "25%             6319.000000              2056.500000   2067.000000   \n",
       "50%             7499.000000              2503.000000   2505.000000   \n",
       "75%             9097.000000              2968.000000   3167.000000   \n",
       "max            12315.000000              4137.000000   4615.000000   \n",
       "\n",
       "       Centre-Val de Loire     Grand-Est  Hauts-de-France  Ile-de-France  \\\n",
       "count          17519.00000  17519.000000     17519.000000   17519.000000   \n",
       "mean            2247.02386   5427.153662      6043.343684    8642.616816   \n",
       "std              627.75084   1141.590972      1196.965309    2363.372858   \n",
       "min             1021.00000   2783.000000      3292.000000    4196.000000   \n",
       "25%             1748.50000   4532.000000      5122.000000    6832.000000   \n",
       "50%             2097.00000   5346.000000      5902.000000    8321.000000   \n",
       "75%             2751.00000   6255.000000      6938.000000   10413.000000   \n",
       "max             4088.00000   8373.000000      9598.000000   15286.000000   \n",
       "\n",
       "          Normandie  Nouvelle-Aquitaine     Occitanie          PACA  \\\n",
       "count  17519.000000        17519.000000  17519.000000  17519.000000   \n",
       "mean    3430.122324         5110.759005   4340.247902   4875.551059   \n",
       "std      805.721576         1216.823664   1142.174855   1032.661901   \n",
       "min     1869.000000         2817.000000   2214.000000   2943.000000   \n",
       "25%     2804.500000         4164.000000   3478.500000   4103.000000   \n",
       "50%     3249.000000         4851.000000   4067.000000   4639.000000   \n",
       "75%     4085.000000         6057.000000   5225.000000   5705.500000   \n",
       "max     5672.000000         8888.000000   7694.000000   8160.000000   \n",
       "\n",
       "       Pays-de-la-Loire  \n",
       "count      17519.000000  \n",
       "mean        3191.928306  \n",
       "std          863.558309  \n",
       "min         1434.000000  \n",
       "25%         2519.000000  \n",
       "50%         3067.000000  \n",
       "75%         3835.000000  \n",
       "max         5834.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "data_y.index = data_y.index.to_datetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_y[data_y.columns[0]].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01 00:30:00', '2013-01-01 01:00:00',\n",
       "               '2013-01-01 01:30:00', '2013-01-01 02:00:00',\n",
       "               '2013-01-01 02:30:00', '2013-01-01 03:00:00',\n",
       "               '2013-01-01 03:30:00', '2013-01-01 04:00:00',\n",
       "               '2013-01-01 04:30:00', '2013-01-01 05:00:00',\n",
       "               ...\n",
       "               '2013-12-31 19:00:00', '2013-12-31 19:30:00',\n",
       "               '2013-12-31 20:00:00', '2013-12-31 20:30:00',\n",
       "               '2013-12-31 21:00:00', '2013-12-31 21:30:00',\n",
       "               '2013-12-31 22:00:00', '2013-12-31 22:30:00',\n",
       "               '2013-12-31 23:00:00', '2013-12-31 23:30:00'],\n",
       "              dtype='datetime64[ns]', length=17519, freq=None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17519"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_y[data_y.columns[0]].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "choice = 0\n",
    "station_data = data_y[data_y.columns[choice]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3d2d566908>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G4OuvgaFDs49/8UVumaA3DkEWu/vvBy67zH1ZAcAuenbf3T9/0M0d5necxoOx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\nNGIXYoIRAklL6QMOplpQGBzAp0615rShH1BjioRUGxA7S2OHgyeeQ6y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X5/EpY8tLQsGStauJLcrKiYHNQZnEdiDEGIvwCFROxiNXbYt5kz5YGTlmkksauiscPr6xrc581z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\nhBBCupz/D6Q3UOzFUY7ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2d566470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_data.plot(figsize = (15, 7))\n",
    "\n",
    "plt.axhline(station_data.mean(), \n",
    "            linewidth = 3, \n",
    "            color = 'r', \n",
    "            linestyle = 'dashed' )\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f3d2d3a2da0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4Uy6/AEZDkQjAeea9Tc8mmQ2dCmyQ/e9zOKeABNZHd1PW4qhd2cbaiW7zYo81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\nRAAAABYUiQAAACz8/x1lv+HOp7uXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2d5dbba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_data.plot(figsize = (15, 7), \n",
    "                  kind = 'hist', \n",
    "                  bins = 100)\n",
    "\n",
    "plt.axvline(station_data.mean(), \n",
    "            linewidth = 3, \n",
    "            color = 'r', \n",
    "            linestyle = 'dashed' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3d2d0fd5c0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ifvyx8fMlNa9ii8enQwi/AhYKIfQA7gUeLF0sqWl8/DE89RT07Jk7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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3d2d106710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "station_data.plot(kind = 'kde',  \n",
    "                  figsize = (15, 7), \n",
    "                  xlim = (station_data.min(), station_data.max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}