Clusters (K-means).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlite3\n",
    "import itertools\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_clusters(locations, nb_clusters):\n",
    "    plt.figure(figsize=(8,5))\n",
    "    km = KMeans(n_clusters=nb_clusters,\n",
    "           init='k-means++',\n",
    "           n_init=10,\n",
    "           max_iter=1000,\n",
    "           tol=1e-04,\n",
    "           random_state=0)\n",
    "    y_km = km.fit_predict(locations)\n",
    "    \n",
    "    color = itertools.cycle(('lightblue', 'hotpink', 'orange', 'mediumseagreen', 'dodgerblue', 'pink', 'maroon', 'gray', 'yellow', 'gold','blue'))\n",
    "    marker = itertools.cycle((',', '+', '.', 'o', '*', 'v', 's', 'p', 'D', '|'))\n",
    "    for i in range(0, nb_clusters):\n",
    "        plt.scatter(locations[y_km==i,0],\n",
    "                   locations[y_km==i,1],\n",
    "                   color=color.next(),\n",
    "                   s=50,\n",
    "                   marker=marker.next(),\n",
    "                   label='Cluster '+str(i+1))\n",
    "\n",
    "    plt.scatter(km.cluster_centers_[:,0],\n",
    "               km.cluster_centers_[:,1],\n",
    "               color='red',\n",
    "               s=250,\n",
    "               marker='*',\n",
    "               label='Centroids')\n",
    "\n",
    "    plt.title('Clusters')\n",
    "    plt.xlabel('Latitude')\n",
    "    plt.ylabel('Longitude')\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    return y_km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RQjyKI0HMqWvAbw4Ce4rEd90197XHei979+gsj/TAmIeAX24F4u6zn//oo0BR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11df0d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('neelabh_data.csv')\n",
    "data['datetime'] = pd.to_datetime(data['datetime'])\n",
    "data = data.set_index('datetime')\n",
    "df = data['2017-04-27':'2017-01-23']\n",
    "\n",
    "coordinates = df[['LATITUDE', 'LONGITUDE']]\n",
    "coordinates_matrix = coordinates.as_matrix()\n",
    "clusters = plot_clusters(coordinates_matrix, 10)\n",
    "clusters = clusters + 1 #Because originally cluster id start from 0, to make it interactive we add 1 for cluster 0 to be cluster 1and so on...\n",
    "\n",
    "df2 = pd.DataFrame(index=df.index)\n",
    "df2['clusterID'] = clusters\n",
    "df3 = pd.concat([df, df2], axis=1) #Data Frame that contains datetime, coordinates and cluster id\n",
    "df3.to_csv('neelabh_data_with_clusters.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>clusterID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-27 18:01:54</th>\n",
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       "    <tr>\n",
       "      <th>2017-04-27 17:58:30</th>\n",
       "      <td>32.733626</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-27 09:54:03</th>\n",
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       "      <th>2017-04-27 09:49:10</th>\n",
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       "      <th>2017-04-26 22:55:31</th>\n",
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       "      <th>2017-04-26 22:49:20</th>\n",
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       "      <th>2017-04-26 22:34:11</th>\n",
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       "      <th>2017-04-26 22:22:54</th>\n",
       "      <td>32.733663</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 19:17:54</th>\n",
       "      <td>32.735955</td>\n",
       "      <td>-97.112759</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 14:41:42</th>\n",
       "      <td>32.732438</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 14:40:37</th>\n",
       "      <td>32.732451</td>\n",
       "      <td>-97.108499</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 14:36:21</th>\n",
       "      <td>32.732487</td>\n",
       "      <td>-97.109597</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 14:35:17</th>\n",
       "      <td>32.732426</td>\n",
       "      <td>-97.109130</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 14:33:44</th>\n",
       "      <td>32.732677</td>\n",
       "      <td>-97.112059</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 14:31:49</th>\n",
       "      <td>32.733309</td>\n",
       "      <td>-97.113568</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 13:27:28</th>\n",
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       "      <th>2017-04-26 10:05:07</th>\n",
       "      <td>32.733311</td>\n",
       "      <td>-97.113847</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 10:01:23</th>\n",
       "      <td>32.733485</td>\n",
       "      <td>-97.114532</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 09:59:45</th>\n",
       "      <td>32.733379</td>\n",
       "      <td>-97.113927</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 09:56:52</th>\n",
       "      <td>32.733662</td>\n",
       "      <td>-97.110179</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 09:52:16</th>\n",
       "      <td>32.733706</td>\n",
       "      <td>-97.106464</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 09:48:13</th>\n",
       "      <td>32.733346</td>\n",
       "      <td>-97.103301</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-26 06:00:51</th>\n",
       "      <td>32.733331</td>\n",
       "      <td>-97.103181</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-25 22:05:30</th>\n",
       "      <td>32.733362</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-25 22:01:52</th>\n",
       "      <td>32.733364</td>\n",
       "      <td>-97.103136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 21:57:54</th>\n",
       "      <td>32.733339</td>\n",
       "      <td>-97.103218</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 20:56:52</th>\n",
       "      <td>32.733296</td>\n",
       "      <td>-97.103319</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 20:48:16</th>\n",
       "      <td>32.733419</td>\n",
       "      <td>-97.103444</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-25 20:46:30</th>\n",
       "      <td>32.733654</td>\n",
       "      <td>-97.105120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-24 19:33:34</th>\n",
       "      <td>32.733526</td>\n",
       "      <td>-97.104260</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 19:30:10</th>\n",
       "      <td>32.733131</td>\n",
       "      <td>-97.108656</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 19:27:20</th>\n",
       "      <td>32.732364</td>\n",
       "      <td>-97.110111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 19:26:44</th>\n",
       "      <td>32.732438</td>\n",
       "      <td>-97.108753</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 19:24:12</th>\n",
       "      <td>32.732389</td>\n",
       "      <td>-97.108700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 19:19:49</th>\n",
       "      <td>32.732339</td>\n",
       "      <td>-97.110645</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-24 19:18:31</th>\n",
       "      <td>32.732342</td>\n",
       "      <td>-97.110251</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-24 19:17:45</th>\n",
       "      <td>32.732059</td>\n",
       "      <td>-97.112123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 19:17:43</th>\n",
       "      <td>32.731297</td>\n",
       "      <td>-97.112553</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:36:32</th>\n",
       "      <td>32.731357</td>\n",
       "      <td>-97.112578</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:33:54</th>\n",
       "      <td>32.731320</td>\n",
       "      <td>-97.112992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:32:11</th>\n",
       "      <td>32.731369</td>\n",
       "      <td>-97.112680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:30:45</th>\n",
       "      <td>32.733389</td>\n",
       "      <td>-97.113300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:24:27</th>\n",
       "      <td>32.733566</td>\n",
       "      <td>-97.113205</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:20:41</th>\n",
       "      <td>32.733446</td>\n",
       "      <td>-97.113238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:20:39</th>\n",
       "      <td>32.732847</td>\n",
       "      <td>-97.113300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:19:37</th>\n",
       "      <td>32.730940</td>\n",
       "      <td>-97.113125</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 17:18:08</th>\n",
       "      <td>32.731094</td>\n",
       "      <td>-97.113253</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 15:59:20</th>\n",
       "      <td>32.730873</td>\n",
       "      <td>-97.113029</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 15:54:57</th>\n",
       "      <td>32.730902</td>\n",
       "      <td>-97.113038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 15:54:54</th>\n",
       "      <td>32.731021</td>\n",
       "      <td>-97.113217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 15:53:03</th>\n",
       "      <td>32.733310</td>\n",
       "      <td>-97.113749</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 13:32:18</th>\n",
       "      <td>32.733286</td>\n",
       "      <td>-97.113781</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 13:05:09</th>\n",
       "      <td>32.733277</td>\n",
       "      <td>-97.112923</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 11:43:41</th>\n",
       "      <td>32.733289</td>\n",
       "      <td>-97.113863</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 10:27:04</th>\n",
       "      <td>32.733299</td>\n",
       "      <td>-97.113724</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 10:20:10</th>\n",
       "      <td>32.733283</td>\n",
       "      <td>-97.113682</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 10:16:38</th>\n",
       "      <td>32.733626</td>\n",
       "      <td>-97.110444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 10:12:58</th>\n",
       "      <td>32.733756</td>\n",
       "      <td>-97.107007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24 10:10:09</th>\n",
       "      <td>32.733624</td>\n",
       "      <td>-97.104642</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      LATITUDE  LONGITUDE  clusterID\n",
       "datetime                                            \n",
       "2017-04-27 18:01:54  32.733402 -97.113254          1\n",
       "2017-04-27 17:58:30  32.733626 -97.109255          1\n",
       "2017-04-27 09:54:03  32.733423 -97.103172          1\n",
       "2017-04-27 09:49:10  32.733399 -97.103327          1\n",
       "2017-04-26 22:55:31  32.733014 -97.103329          1\n",
       "2017-04-26 22:49:26  32.733418 -97.103237          1\n",
       "2017-04-26 22:49:20  32.733371 -97.103218          1\n",
       "2017-04-26 22:34:11  32.730247 -97.104818          1\n",
       "2017-04-26 22:22:54  32.733663 -97.112438          1\n",
       "2017-04-26 19:17:54  32.735955 -97.112759          1\n",
       "2017-04-26 14:41:42  32.732438 -97.108952          1\n",
       "2017-04-26 14:40:37  32.732451 -97.108499          1\n",
       "2017-04-26 14:36:21  32.732487 -97.109597          1\n",
       "2017-04-26 14:35:17  32.732426 -97.109130          1\n",
       "2017-04-26 14:33:44  32.732677 -97.112059          1\n",
       "2017-04-26 14:31:49  32.733309 -97.113568          1\n",
       "2017-04-26 13:27:28  32.733250 -97.112943          1\n",
       "2017-04-26 10:05:07  32.733311 -97.113847          1\n",
       "2017-04-26 10:01:23  32.733485 -97.114532          1\n",
       "2017-04-26 09:59:45  32.733379 -97.113927          1\n",
       "2017-04-26 09:56:52  32.733662 -97.110179          1\n",
       "2017-04-26 09:52:16  32.733706 -97.106464          1\n",
       "2017-04-26 09:48:13  32.733346 -97.103301          1\n",
       "2017-04-26 06:00:51  32.733331 -97.103181          1\n",
       "2017-04-25 22:05:30  32.733362 -97.103084          1\n",
       "2017-04-25 22:01:52  32.733364 -97.103136          1\n",
       "2017-04-25 21:57:54  32.733339 -97.103218          1\n",
       "2017-04-25 20:56:52  32.733296 -97.103319          1\n",
       "2017-04-25 20:48:16  32.733419 -97.103444          1\n",
       "2017-04-25 20:46:30  32.733654 -97.105120          1\n",
       "...                        ...        ...        ...\n",
       "2017-04-24 19:33:34  32.733526 -97.104260          1\n",
       "2017-04-24 19:30:10  32.733131 -97.108656          1\n",
       "2017-04-24 19:27:20  32.732364 -97.110111          1\n",
       "2017-04-24 19:26:44  32.732438 -97.108753          1\n",
       "2017-04-24 19:24:12  32.732389 -97.108700          1\n",
       "2017-04-24 19:19:49  32.732339 -97.110645          1\n",
       "2017-04-24 19:18:31  32.732342 -97.110251          1\n",
       "2017-04-24 19:17:45  32.732059 -97.112123          1\n",
       "2017-04-24 19:17:43  32.731297 -97.112553          1\n",
       "2017-04-24 17:36:32  32.731357 -97.112578          1\n",
       "2017-04-24 17:33:54  32.731320 -97.112992          1\n",
       "2017-04-24 17:32:11  32.731369 -97.112680          1\n",
       "2017-04-24 17:30:45  32.733389 -97.113300          1\n",
       "2017-04-24 17:24:27  32.733566 -97.113205          1\n",
       "2017-04-24 17:20:41  32.733446 -97.113238          1\n",
       "2017-04-24 17:20:39  32.732847 -97.113300          1\n",
       "2017-04-24 17:19:37  32.730940 -97.113125          1\n",
       "2017-04-24 17:18:08  32.731094 -97.113253          1\n",
       "2017-04-24 15:59:20  32.730873 -97.113029          1\n",
       "2017-04-24 15:54:57  32.730902 -97.113038          1\n",
       "2017-04-24 15:54:54  32.731021 -97.113217          1\n",
       "2017-04-24 15:53:03  32.733310 -97.113749          1\n",
       "2017-04-24 13:32:18  32.733286 -97.113781          1\n",
       "2017-04-24 13:05:09  32.733277 -97.112923          1\n",
       "2017-04-24 11:43:41  32.733289 -97.113863          1\n",
       "2017-04-24 10:27:04  32.733299 -97.113724          1\n",
       "2017-04-24 10:20:10  32.733283 -97.113682          1\n",
       "2017-04-24 10:16:38  32.733626 -97.110444          1\n",
       "2017-04-24 10:12:58  32.733756 -97.107007          1\n",
       "2017-04-24 10:10:09  32.733624 -97.104642          1\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('neelabh_data.csv')\n",
    "data['datetime'] = pd.to_datetime(data['datetime'])\n",
    "data = data.set_index('datetime')\n",
    "df = data['2017-04-27':'2017-01-23']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-22 11:44:39</th>\n",
       "      <td>32.733305</td>\n",
       "      <td>-97.113762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-22 10:17:28</th>\n",
       "      <td>32.733303</td>\n",
       "      <td>-97.113548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-22 10:16:12</th>\n",
       "      <td>32.733736</td>\n",
       "      <td>-97.112267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-22 10:13:47</th>\n",
       "      <td>32.733708</td>\n",
       "      <td>-97.110273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-22 10:10:07</th>\n",
       "      <td>32.733605</td>\n",
       "      <td>-97.107041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      LATITUDE  LONGITUDE\n",
       "datetime                                 \n",
       "2017-05-22 11:44:39  32.733305 -97.113762\n",
       "2017-05-22 10:17:28  32.733303 -97.113548\n",
       "2017-05-22 10:16:12  32.733736 -97.112267\n",
       "2017-05-22 10:13:47  32.733708 -97.110273\n",
       "2017-05-22 10:10:07  32.733605 -97.107041"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>clusterID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-27 18:01:54</th>\n",
       "      <td>32.733402</td>\n",
       "      <td>-97.113254</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27 17:58:30</th>\n",
       "      <td>32.733626</td>\n",
       "      <td>-97.109255</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27 09:54:03</th>\n",
       "      <td>32.733423</td>\n",
       "      <td>-97.103172</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27 09:49:10</th>\n",
       "      <td>32.733399</td>\n",
       "      <td>-97.103327</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 22:55:31</th>\n",
       "      <td>32.733014</td>\n",
       "      <td>-97.103329</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      LATITUDE  LONGITUDE  clusterID\n",
       "datetime                                            \n",
       "2017-04-27 18:01:54  32.733402 -97.113254          8\n",
       "2017-04-27 17:58:30  32.733626 -97.109255          8\n",
       "2017-04-27 09:54:03  32.733423 -97.103172          1\n",
       "2017-04-27 09:49:10  32.733399 -97.103327          1\n",
       "2017-04-26 22:55:31  32.733014 -97.103329          1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XJUvQbqurgROAdQCq+jpwWEoyGRXQTjZcbR+yYdQ3gQfMVXV9mVtr0sIYHbEitnGw3hSj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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113098150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timediff = []\n",
    "for datetime in range(0, len(df3)-1):\n",
    "    out = pd.Timedelta(df3.index[datetime] - df3.index[datetime+1]).seconds / 60.0\n",
    "    timediff.append(out)\n",
    "timediff = np.array(timediff)\n",
    "timeStats = stats.describe(timediff)\n",
    "plt.plot(timediff)\n",
    "plt.xlabel('Observation (i)')\n",
    "plt.ylabel('Time Difference in Minutes (i-(i+1))')\n",
    "plt.title('Time Difference')\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xSRJwPPCVTsYwlCG+Txr7/CWp7Giw4WB65gtc0nTgcOAnpeidpUni/G41LxUG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e172d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(timediff, 50, color='green', label='Time Difference', alpha=0.5)\n",
    "plt.xlim([-10, 200])\n",
    "plt.xlabel('Time Difference in Minutes (i-(i+1))')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Time Difference Histograms')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qq6e3uc+NgZcAX7e9G3A/VTfWX5Wrw1q+Qsz2yban2Z42YcKENsOLiIhWNJtI1tp+qG9G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\naRFB9cU3RdXYW5N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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141683d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(timediff, 1000, color='green', label='Time Difference', alpha=0.5)\n",
    "plt.xlim([-10, 25])\n",
    "plt.xlabel('Time Difference in Minutes (i-(i+1))')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Time Difference Histograms')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>clusterID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-27 18:01:54</th>\n",
       "      <td>32.733402</td>\n",
       "      <td>-97.113254</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27 17:58:30</th>\n",
       "      <td>32.733626</td>\n",
       "      <td>-97.109255</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27 09:54:03</th>\n",
       "      <td>32.733423</td>\n",
       "      <td>-97.103172</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27 09:49:10</th>\n",
       "      <td>32.733399</td>\n",
       "      <td>-97.103327</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 22:55:31</th>\n",
       "      <td>32.733014</td>\n",
       "      <td>-97.103329</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 22:49:26</th>\n",
       "      <td>32.733418</td>\n",
       "      <td>-97.103237</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 22:49:20</th>\n",
       "      <td>32.733371</td>\n",
       "      <td>-97.103218</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 22:34:11</th>\n",
       "      <td>32.730247</td>\n",
       "      <td>-97.104818</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 22:22:54</th>\n",
       "      <td>32.733663</td>\n",
       "      <td>-97.112438</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 19:17:54</th>\n",
       "      <td>32.735955</td>\n",
       "      <td>-97.112759</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 14:41:42</th>\n",
       "      <td>32.732438</td>\n",
       "      <td>-97.108952</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 14:40:37</th>\n",
       "      <td>32.732451</td>\n",
       "      <td>-97.108499</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 14:36:21</th>\n",
       "      <td>32.732487</td>\n",
       "      <td>-97.109597</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 14:35:17</th>\n",
       "      <td>32.732426</td>\n",
       "      <td>-97.109130</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 14:33:44</th>\n",
       "      <td>32.732677</td>\n",
       "      <td>-97.112059</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 14:31:49</th>\n",
       "      <td>32.733309</td>\n",
       "      <td>-97.113568</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 13:27:28</th>\n",
       "      <td>32.733250</td>\n",
       "      <td>-97.112943</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 10:05:07</th>\n",
       "      <td>32.733311</td>\n",
       "      <td>-97.113847</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 10:01:23</th>\n",
       "      <td>32.733485</td>\n",
       "      <td>-97.114532</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 09:59:45</th>\n",
       "      <td>32.733379</td>\n",
       "      <td>-97.113927</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 09:56:52</th>\n",
       "      <td>32.733662</td>\n",
       "      <td>-97.110179</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 09:52:16</th>\n",
       "      <td>32.733706</td>\n",
       "      <td>-97.106464</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 09:48:13</th>\n",
       "      <td>32.733346</td>\n",
       "      <td>-97.103301</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 06:00:51</th>\n",
       "      <td>32.733331</td>\n",
       "      <td>-97.103181</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 22:05:30</th>\n",
       "      <td>32.733362</td>\n",
       "      <td>-97.103084</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 22:01:52</th>\n",
       "      <td>32.733364</td>\n",
       "      <td>-97.103136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 21:57:54</th>\n",
       "      <td>32.733339</td>\n",
       "      <td>-97.103218</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 20:56:52</th>\n",
       "      <td>32.733296</td>\n",
       "      <td>-97.103319</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 20:48:16</th>\n",
       "      <td>32.733419</td>\n",
       "      <td>-97.103444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 20:46:30</th>\n",
       "      <td>32.733654</td>\n",
       "      <td>-97.105120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 15:13:31</th>\n",
       "      <td>32.733454</td>\n",
       "      <td>-97.112277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 15:12:25</th>\n",
       "      <td>32.731590</td>\n",
       "      <td>-97.111489</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:43:23</th>\n",
       "      <td>32.731663</td>\n",
       "      <td>-97.111545</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:39:52</th>\n",
       "      <td>32.731523</td>\n",
       "      <td>-97.111594</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:37:32</th>\n",
       "      <td>32.731764</td>\n",
       "      <td>-97.111486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:35:37</th>\n",
       "      <td>32.733531</td>\n",
       "      <td>-97.111486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:33:21</th>\n",
       "      <td>32.733388</td>\n",
       "      <td>-97.113254</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:33:15</th>\n",
       "      <td>32.733627</td>\n",
       "      <td>-97.112261</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:28:31</th>\n",
       "      <td>32.733330</td>\n",
       "      <td>-97.113392</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:20:24</th>\n",
       "      <td>32.732971</td>\n",
       "      <td>-97.112169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 14:01:25</th>\n",
       "      <td>32.733410</td>\n",
       "      <td>-97.113295</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 13:26:32</th>\n",
       "      <td>32.733383</td>\n",
       "      <td>-97.113260</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 13:20:25</th>\n",
       "      <td>32.733741</td>\n",
       "      <td>-97.112531</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 13:01:55</th>\n",
       "      <td>32.733370</td>\n",
       "      <td>-97.113735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 12:26:37</th>\n",
       "      <td>32.733331</td>\n",
       "      <td>-97.113729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 12:14:00</th>\n",
       "      <td>32.733341</td>\n",
       "      <td>-97.113773</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 12:11:56</th>\n",
       "      <td>32.733377</td>\n",
       "      <td>-97.113407</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 12:11:50</th>\n",
       "      <td>32.733348</td>\n",
       "      <td>-97.113508</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 12:08:09</th>\n",
       "      <td>32.733559</td>\n",
       "      <td>-97.109413</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 12:07:18</th>\n",
       "      <td>32.733557</td>\n",
       "      <td>-97.109930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 12:05:14</th>\n",
       "      <td>32.733304</td>\n",
       "      <td>-97.109049</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 12:00:07</th>\n",
       "      <td>32.733625</td>\n",
       "      <td>-97.109056</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 11:58:43</th>\n",
       "      <td>32.733643</td>\n",
       "      <td>-97.107253</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 11:56:22</th>\n",
       "      <td>32.733652</td>\n",
       "      <td>-97.105222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 11:54:56</th>\n",
       "      <td>32.733380</td>\n",
       "      <td>-97.103315</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 11:53:56</th>\n",
       "      <td>32.733640</td>\n",
       "      <td>-97.103041</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 09:33:24</th>\n",
       "      <td>32.733294</td>\n",
       "      <td>-97.103535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 08:51:17</th>\n",
       "      <td>32.733407</td>\n",
       "      <td>-97.103026</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 08:48:17</th>\n",
       "      <td>32.733277</td>\n",
       "      <td>-97.103162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23 08:45:46</th>\n",
       "      <td>32.733330</td>\n",
       "      <td>-97.103257</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4466 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      LATITUDE  LONGITUDE  clusterID\n",
       "datetime                                            \n",
       "2017-04-27 18:01:54  32.733402 -97.113254          1\n",
       "2017-04-27 17:58:30  32.733626 -97.109255          1\n",
       "2017-04-27 09:54:03  32.733423 -97.103172          1\n",
       "2017-04-27 09:49:10  32.733399 -97.103327          1\n",
       "2017-04-26 22:55:31  32.733014 -97.103329          1\n",
       "2017-04-26 22:49:26  32.733418 -97.103237          1\n",
       "2017-04-26 22:49:20  32.733371 -97.103218          1\n",
       "2017-04-26 22:34:11  32.730247 -97.104818          1\n",
       "2017-04-26 22:22:54  32.733663 -97.112438          1\n",
       "2017-04-26 19:17:54  32.735955 -97.112759          1\n",
       "2017-04-26 14:41:42  32.732438 -97.108952          1\n",
       "2017-04-26 14:40:37  32.732451 -97.108499          1\n",
       "2017-04-26 14:36:21  32.732487 -97.109597          1\n",
       "2017-04-26 14:35:17  32.732426 -97.109130          1\n",
       "2017-04-26 14:33:44  32.732677 -97.112059          1\n",
       "2017-04-26 14:31:49  32.733309 -97.113568          1\n",
       "2017-04-26 13:27:28  32.733250 -97.112943          1\n",
       "2017-04-26 10:05:07  32.733311 -97.113847          1\n",
       "2017-04-26 10:01:23  32.733485 -97.114532          1\n",
       "2017-04-26 09:59:45  32.733379 -97.113927          1\n",
       "2017-04-26 09:56:52  32.733662 -97.110179          1\n",
       "2017-04-26 09:52:16  32.733706 -97.106464          1\n",
       "2017-04-26 09:48:13  32.733346 -97.103301          1\n",
       "2017-04-26 06:00:51  32.733331 -97.103181          1\n",
       "2017-04-25 22:05:30  32.733362 -97.103084          1\n",
       "2017-04-25 22:01:52  32.733364 -97.103136          1\n",
       "2017-04-25 21:57:54  32.733339 -97.103218          1\n",
       "2017-04-25 20:56:52  32.733296 -97.103319          1\n",
       "2017-04-25 20:48:16  32.733419 -97.103444          1\n",
       "2017-04-25 20:46:30  32.733654 -97.105120          1\n",
       "...                        ...        ...        ...\n",
       "2017-01-23 15:13:31  32.733454 -97.112277          1\n",
       "2017-01-23 15:12:25  32.731590 -97.111489          1\n",
       "2017-01-23 14:43:23  32.731663 -97.111545          1\n",
       "2017-01-23 14:39:52  32.731523 -97.111594          1\n",
       "2017-01-23 14:37:32  32.731764 -97.111486          1\n",
       "2017-01-23 14:35:37  32.733531 -97.111486          1\n",
       "2017-01-23 14:33:21  32.733388 -97.113254          1\n",
       "2017-01-23 14:33:15  32.733627 -97.112261          1\n",
       "2017-01-23 14:28:31  32.733330 -97.113392          1\n",
       "2017-01-23 14:20:24  32.732971 -97.112169          1\n",
       "2017-01-23 14:01:25  32.733410 -97.113295          1\n",
       "2017-01-23 13:26:32  32.733383 -97.113260          1\n",
       "2017-01-23 13:20:25  32.733741 -97.112531          1\n",
       "2017-01-23 13:01:55  32.733370 -97.113735          1\n",
       "2017-01-23 12:26:37  32.733331 -97.113729          1\n",
       "2017-01-23 12:14:00  32.733341 -97.113773          1\n",
       "2017-01-23 12:11:56  32.733377 -97.113407          1\n",
       "2017-01-23 12:11:50  32.733348 -97.113508          1\n",
       "2017-01-23 12:08:09  32.733559 -97.109413          1\n",
       "2017-01-23 12:07:18  32.733557 -97.109930          1\n",
       "2017-01-23 12:05:14  32.733304 -97.109049          1\n",
       "2017-01-23 12:00:07  32.733625 -97.109056          1\n",
       "2017-01-23 11:58:43  32.733643 -97.107253          1\n",
       "2017-01-23 11:56:22  32.733652 -97.105222          1\n",
       "2017-01-23 11:54:56  32.733380 -97.103315          1\n",
       "2017-01-23 11:53:56  32.733640 -97.103041          1\n",
       "2017-01-23 09:33:24  32.733294 -97.103535          1\n",
       "2017-01-23 08:51:17  32.733407 -97.103026          1\n",
       "2017-01-23 08:48:17  32.733277 -97.103162          1\n",
       "2017-01-23 08:45:46  32.733330 -97.103257          1\n",
       "\n",
       "[4466 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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