points_date_parser.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter para obtener intervalos de tiempo de los puntos para poder generar un heatmap "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Termine\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>LAT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDEX</th>\n",
       "      <th>DATE1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2016-10-20 22:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2016-10-20 23:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2016-10-21 01:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2016-10-21 02:10:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>2016-10-21 02:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>2016-10-21 02:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>2016-10-21 02:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>2016-10-21 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>2016-10-21 10:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>2016-10-21 12:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>2016-10-21 16:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>2016-10-21 21:10:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <th>2016-10-21 21:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>2016-10-22 11:10:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <th>2016-10-23 04:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <th>2016-10-24 19:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <th>2016-10-24 21:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <th>2016-10-24 21:10:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <th>2016-10-24 21:10:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <th>2016-10-24 21:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <th>2016-10-24 22:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <th>2016-10-24 22:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <th>2016-10-24 22:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <th>2016-10-24 22:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <th>2016-10-24 22:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <th>2016-10-24 23:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <th>2016-10-24 23:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <th>2016-10-25 01:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <th>2016-10-25 01:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <th>2016-10-25 01:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275678</th>\n",
       "      <th>2017-09-29 17:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275679</th>\n",
       "      <th>2017-09-29 17:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275680</th>\n",
       "      <th>2017-09-29 17:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275681</th>\n",
       "      <th>2017-09-29 17:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275682</th>\n",
       "      <th>2017-09-29 17:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275683</th>\n",
       "      <th>2017-09-29 17:30:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275684</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275685</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275686</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275687</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275688</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275689</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275690</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275691</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275692</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275693</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275694</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275695</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275696</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275697</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275698</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275699</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275700</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275701</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275702</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275703</th>\n",
       "      <th>2017-09-29 17:40:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275704</th>\n",
       "      <th>2017-09-29 17:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275705</th>\n",
       "      <th>2017-09-29 17:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275706</th>\n",
       "      <th>2017-09-29 17:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275707</th>\n",
       "      <th>2017-09-29 17:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>275706 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count  LAT\n",
       "INDEX  DATE1                          \n",
       "1      2016-10-20 22:50:00      1  NaN\n",
       "2      2016-10-20 23:00:00      1  NaN\n",
       "3      2016-10-21 01:50:00      1  NaN\n",
       "4      2016-10-21 02:10:00      1  NaN\n",
       "5      2016-10-21 02:30:00      1  NaN\n",
       "6      2016-10-21 02:40:00      1  NaN\n",
       "7      2016-10-21 02:50:00      1  NaN\n",
       "8      2016-10-21 03:00:00      1  NaN\n",
       "9      2016-10-21 10:20:00      1  NaN\n",
       "10     2016-10-21 12:40:00      1  NaN\n",
       "11     2016-10-21 16:40:00      1  NaN\n",
       "12     2016-10-21 21:10:00      1  NaN\n",
       "13     2016-10-21 21:20:00      1  NaN\n",
       "14     2016-10-22 11:10:00      1  NaN\n",
       "15     2016-10-23 04:30:00      1  NaN\n",
       "16     2016-10-24 19:20:00      1  NaN\n",
       "17     2016-10-24 21:00:00      1  NaN\n",
       "18     2016-10-24 21:10:00      1  NaN\n",
       "19     2016-10-24 21:10:00      1  NaN\n",
       "20     2016-10-24 21:20:00      1  NaN\n",
       "21     2016-10-24 22:20:00      1  NaN\n",
       "22     2016-10-24 22:20:00      1  NaN\n",
       "23     2016-10-24 22:20:00      1  NaN\n",
       "24     2016-10-24 22:30:00      1  NaN\n",
       "25     2016-10-24 22:50:00      1  NaN\n",
       "26     2016-10-24 23:20:00      1  NaN\n",
       "27     2016-10-24 23:40:00      1  NaN\n",
       "28     2016-10-25 01:50:00      1  NaN\n",
       "29     2016-10-25 01:50:00      1  NaN\n",
       "30     2016-10-25 01:50:00      1  NaN\n",
       "...                           ...  ...\n",
       "275678 2017-09-29 17:30:00      1  NaN\n",
       "275679 2017-09-29 17:30:00      1  NaN\n",
       "275680 2017-09-29 17:30:00      1  NaN\n",
       "275681 2017-09-29 17:30:00      1  NaN\n",
       "275682 2017-09-29 17:30:00      1  NaN\n",
       "275683 2017-09-29 17:30:00      1  NaN\n",
       "275684 2017-09-29 17:40:00      1  NaN\n",
       "275685 2017-09-29 17:40:00      1  NaN\n",
       "275686 2017-09-29 17:40:00      1  NaN\n",
       "275687 2017-09-29 17:40:00      1  NaN\n",
       "275688 2017-09-29 17:40:00      1  NaN\n",
       "275689 2017-09-29 17:40:00      1  NaN\n",
       "275690 2017-09-29 17:40:00      1  NaN\n",
       "275691 2017-09-29 17:40:00      1  NaN\n",
       "275692 2017-09-29 17:40:00      1  NaN\n",
       "275693 2017-09-29 17:40:00      1  NaN\n",
       "275694 2017-09-29 17:40:00      1  NaN\n",
       "275695 2017-09-29 17:40:00      1  NaN\n",
       "275696 2017-09-29 17:40:00      1  NaN\n",
       "275697 2017-09-29 17:40:00      1  NaN\n",
       "275698 2017-09-29 17:40:00      1  NaN\n",
       "275699 2017-09-29 17:40:00      1  NaN\n",
       "275700 2017-09-29 17:40:00      1  NaN\n",
       "275701 2017-09-29 17:40:00      1  NaN\n",
       "275702 2017-09-29 17:40:00      1  NaN\n",
       "275703 2017-09-29 17:40:00      1  NaN\n",
       "275704 2017-09-29 17:50:00      1  NaN\n",
       "275705 2017-09-29 17:50:00      1  NaN\n",
       "275706 2017-09-29 17:50:00      1  NaN\n",
       "275707 2017-09-29 17:50:00      1  NaN\n",
       "\n",
       "[275706 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "\n",
    "points = pd.read_csv(\"../data/device_positions.csv\")\n",
    "points['DATE1'] = pd.to_datetime(points['DATE1'])\n",
    "\n",
    "points['DOW'] = points['DATE1'].dt.dayofweek\n",
    "\n",
    "dow = {0: 'Lunes', 1:'Martes', 2:'Miercoles', 3:'Jueves', 4:'Viernes', 5:'Sabado', 6:'Domingo'}\n",
    "file_name = 'data_'\n",
    "\n",
    "fieldnames = (\"INDEX\",\"ID\",\"LAT\",\"LONG\", \"DATE1\", \"DOW\")\n",
    "\n",
    "# print(points.loc[points['DOW'] == 1])\n",
    "del points['DATE2']   \n",
    "\n",
    "def write_csv_dow():\n",
    "    for i in range(7):\n",
    "        rows = points.loc[points['DOW'] == i]\n",
    "        rows.to_csv('../data/' + file_name + '{}.csv'.format(dow[i]))\n",
    "    print('Termine')\n",
    "\n",
    "write_csv_dow()\n",
    "\n",
    "points['count'] = np.zeros_like(points['INDEX'])\n",
    "points_agg_all = points.groupby(['INDEX', pd.Grouper(key='DATE1', freq='10min')]).agg({'count': lambda x: x.count()})\n",
    "\n",
    "points_agg_all['LAT'] = points['LAT']\n",
    "\n",
    "points_agg_all\n",
    "# print(points)\n",
    "\n",
    "# print(points['serie'])\n",
    "\n"
   ]
  },
  {
   "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
}