{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json('../etl/json_data_20190117.js', orient='split', convert_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha_hora</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018/01/18 00:00:00.000000000</td>\n",
       "      <td>68.6513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018/01/18 01:00:00.000000000</td>\n",
       "      <td>55.8892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018/01/18 02:00:00.000000000</td>\n",
       "      <td>49.9876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018/01/18 03:00:00.000000000</td>\n",
       "      <td>48.7169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018/01/18 04:00:00.000000000</td>\n",
       "      <td>50.1882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018/01/18 05:00:00.000000000</td>\n",
       "      <td>58.2931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018/01/18 06:00:00.000000000</td>\n",
       "      <td>71.9269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018/01/18 07:00:00.000000000</td>\n",
       "      <td>81.8295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018/01/18 08:00:00.000000000</td>\n",
       "      <td>86.2269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018/01/18 09:00:00.000000000</td>\n",
       "      <td>82.5609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018/01/18 10:00:00.000000000</td>\n",
       "      <td>83.5881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018/01/18 11:00:00.000000000</td>\n",
       "      <td>85.4896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018/01/18 12:00:00.000000000</td>\n",
       "      <td>84.9621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018/01/18 13:00:00.000000000</td>\n",
       "      <td>86.2296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018/01/18 14:00:00.000000000</td>\n",
       "      <td>85.3197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018/01/18 15:00:00.000000000</td>\n",
       "      <td>85.7774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018/01/18 16:00:00.000000000</td>\n",
       "      <td>83.7897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018/01/18 17:00:00.000000000</td>\n",
       "      <td>84.6174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018/01/18 18:00:00.000000000</td>\n",
       "      <td>84.4254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018/01/18 19:00:00.000000000</td>\n",
       "      <td>84.3614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018/01/18 20:00:00.000000000</td>\n",
       "      <td>88.3781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018/01/18 21:00:00.000000000</td>\n",
       "      <td>85.5268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018/01/18 22:00:00.000000000</td>\n",
       "      <td>83.5063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018/01/18 23:00:00.000000000</td>\n",
       "      <td>85.2686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018/01/19 00:00:00.000000000</td>\n",
       "      <td>68.4506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018/01/19 01:00:00.000000000</td>\n",
       "      <td>55.7259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018/01/19 02:00:00.000000000</td>\n",
       "      <td>49.8416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018/01/19 03:00:00.000000000</td>\n",
       "      <td>48.5745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018/01/19 04:00:00.000000000</td>\n",
       "      <td>50.0416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018/01/19 05:00:00.000000000</td>\n",
       "      <td>58.1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8730</th>\n",
       "      <td>2019/01/16 18:00:00.000000000</td>\n",
       "      <td>148.2487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8731</th>\n",
       "      <td>2019/01/16 19:00:00.000000000</td>\n",
       "      <td>145.6626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8732</th>\n",
       "      <td>2019/01/16 20:00:00.000000000</td>\n",
       "      <td>147.2481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8733</th>\n",
       "      <td>2019/01/16 21:00:00.000000000</td>\n",
       "      <td>144.9166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8734</th>\n",
       "      <td>2019/01/16 22:00:00.000000000</td>\n",
       "      <td>94.0870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8735</th>\n",
       "      <td>2019/01/16 23:00:00.000000000</td>\n",
       "      <td>79.4634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8736</th>\n",
       "      <td>2019/01/17 00:00:00.000000000</td>\n",
       "      <td>63.5035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8737</th>\n",
       "      <td>2019/01/17 01:00:00.000000000</td>\n",
       "      <td>58.1534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8738</th>\n",
       "      <td>2019/01/17 02:00:00.000000000</td>\n",
       "      <td>52.7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8739</th>\n",
       "      <td>2019/01/17 03:00:00.000000000</td>\n",
       "      <td>52.5754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8740</th>\n",
       "      <td>2019/01/17 04:00:00.000000000</td>\n",
       "      <td>52.5791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8741</th>\n",
       "      <td>2019/01/17 05:00:00.000000000</td>\n",
       "      <td>58.1534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8742</th>\n",
       "      <td>2019/01/17 06:00:00.000000000</td>\n",
       "      <td>71.0592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8743</th>\n",
       "      <td>2019/01/17 07:00:00.000000000</td>\n",
       "      <td>79.9598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8744</th>\n",
       "      <td>2019/01/17 08:00:00.000000000</td>\n",
       "      <td>110.0056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8745</th>\n",
       "      <td>2019/01/17 09:00:00.000000000</td>\n",
       "      <td>130.0251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8746</th>\n",
       "      <td>2019/01/17 10:00:00.000000000</td>\n",
       "      <td>143.3443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8747</th>\n",
       "      <td>2019/01/17 11:00:00.000000000</td>\n",
       "      <td>143.1487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8748</th>\n",
       "      <td>2019/01/17 12:00:00.000000000</td>\n",
       "      <td>139.2731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8749</th>\n",
       "      <td>2019/01/17 13:00:00.000000000</td>\n",
       "      <td>143.6774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8750</th>\n",
       "      <td>2019/01/17 14:00:00.000000000</td>\n",
       "      <td>149.5276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8751</th>\n",
       "      <td>2019/01/17 15:00:00.000000000</td>\n",
       "      <td>142.5799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>2019/01/17 16:00:00.000000000</td>\n",
       "      <td>143.8328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8753</th>\n",
       "      <td>2019/01/17 17:00:00.000000000</td>\n",
       "      <td>145.8053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8754</th>\n",
       "      <td>2019/01/17 18:00:00.000000000</td>\n",
       "      <td>146.1780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>2019/01/17 19:00:00.000000000</td>\n",
       "      <td>153.7697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>2019/01/17 20:00:00.000000000</td>\n",
       "      <td>164.5391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>2019/01/17 21:00:00.000000000</td>\n",
       "      <td>149.2263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>2019/01/17 22:00:00.000000000</td>\n",
       "      <td>128.0421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>2019/01/17 23:00:00.000000000</td>\n",
       "      <td>78.4568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         fecha_hora     price\n",
       "0     2018/01/18 00:00:00.000000000   68.6513\n",
       "1     2018/01/18 01:00:00.000000000   55.8892\n",
       "2     2018/01/18 02:00:00.000000000   49.9876\n",
       "3     2018/01/18 03:00:00.000000000   48.7169\n",
       "4     2018/01/18 04:00:00.000000000   50.1882\n",
       "5     2018/01/18 05:00:00.000000000   58.2931\n",
       "6     2018/01/18 06:00:00.000000000   71.9269\n",
       "7     2018/01/18 07:00:00.000000000   81.8295\n",
       "8     2018/01/18 08:00:00.000000000   86.2269\n",
       "9     2018/01/18 09:00:00.000000000   82.5609\n",
       "10    2018/01/18 10:00:00.000000000   83.5881\n",
       "11    2018/01/18 11:00:00.000000000   85.4896\n",
       "12    2018/01/18 12:00:00.000000000   84.9621\n",
       "13    2018/01/18 13:00:00.000000000   86.2296\n",
       "14    2018/01/18 14:00:00.000000000   85.3197\n",
       "15    2018/01/18 15:00:00.000000000   85.7774\n",
       "16    2018/01/18 16:00:00.000000000   83.7897\n",
       "17    2018/01/18 17:00:00.000000000   84.6174\n",
       "18    2018/01/18 18:00:00.000000000   84.4254\n",
       "19    2018/01/18 19:00:00.000000000   84.3614\n",
       "20    2018/01/18 20:00:00.000000000   88.3781\n",
       "21    2018/01/18 21:00:00.000000000   85.5268\n",
       "22    2018/01/18 22:00:00.000000000   83.5063\n",
       "23    2018/01/18 23:00:00.000000000   85.2686\n",
       "24    2018/01/19 00:00:00.000000000   68.4506\n",
       "25    2018/01/19 01:00:00.000000000   55.7259\n",
       "26    2018/01/19 02:00:00.000000000   49.8416\n",
       "27    2018/01/19 03:00:00.000000000   48.5745\n",
       "28    2018/01/19 04:00:00.000000000   50.0416\n",
       "29    2018/01/19 05:00:00.000000000   58.1227\n",
       "...                             ...       ...\n",
       "8730  2019/01/16 18:00:00.000000000  148.2487\n",
       "8731  2019/01/16 19:00:00.000000000  145.6626\n",
       "8732  2019/01/16 20:00:00.000000000  147.2481\n",
       "8733  2019/01/16 21:00:00.000000000  144.9166\n",
       "8734  2019/01/16 22:00:00.000000000   94.0870\n",
       "8735  2019/01/16 23:00:00.000000000   79.4634\n",
       "8736  2019/01/17 00:00:00.000000000   63.5035\n",
       "8737  2019/01/17 01:00:00.000000000   58.1534\n",
       "8738  2019/01/17 02:00:00.000000000   52.7610\n",
       "8739  2019/01/17 03:00:00.000000000   52.5754\n",
       "8740  2019/01/17 04:00:00.000000000   52.5791\n",
       "8741  2019/01/17 05:00:00.000000000   58.1534\n",
       "8742  2019/01/17 06:00:00.000000000   71.0592\n",
       "8743  2019/01/17 07:00:00.000000000   79.9598\n",
       "8744  2019/01/17 08:00:00.000000000  110.0056\n",
       "8745  2019/01/17 09:00:00.000000000  130.0251\n",
       "8746  2019/01/17 10:00:00.000000000  143.3443\n",
       "8747  2019/01/17 11:00:00.000000000  143.1487\n",
       "8748  2019/01/17 12:00:00.000000000  139.2731\n",
       "8749  2019/01/17 13:00:00.000000000  143.6774\n",
       "8750  2019/01/17 14:00:00.000000000  149.5276\n",
       "8751  2019/01/17 15:00:00.000000000  142.5799\n",
       "8752  2019/01/17 16:00:00.000000000  143.8328\n",
       "8753  2019/01/17 17:00:00.000000000  145.8053\n",
       "8754  2019/01/17 18:00:00.000000000  146.1780\n",
       "8755  2019/01/17 19:00:00.000000000  153.7697\n",
       "8756  2019/01/17 20:00:00.000000000  164.5391\n",
       "8757  2019/01/17 21:00:00.000000000  149.2263\n",
       "8758  2019/01/17 22:00:00.000000000  128.0421\n",
       "8759  2019/01/17 23:00:00.000000000   78.4568\n",
       "\n",
       "[8760 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fecha_hora']  = pd.to_datetime(df['fecha_hora'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('fecha_hora', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29f2e588240>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29f2ea98630>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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