gps_data

logistic_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldf = pd.read_excel('LogiNext_Location_Analytics_Data (1).xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldf = ldf.rename(columns={'Tracking Date':'trackdt','Battery %': 'battery'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldf.trackdt = pd.to_datetime(ldf.trackdt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ldf = ldf.sort_values(by='trackdt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>battery</th>\n",
       "      <th>Speed</th>\n",
       "      <th>trackdt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>72.860698</td>\n",
       "      <td>19.111690</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-08-12 15:10:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>72.860148</td>\n",
       "      <td>19.111601</td>\n",
       "      <td>85.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-08-12 19:55:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>72.873396</td>\n",
       "      <td>19.126335</td>\n",
       "      <td>84.55</td>\n",
       "      <td>24.82</td>\n",
       "      <td>2014-08-12 20:04:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>72.926755</td>\n",
       "      <td>19.125281</td>\n",
       "      <td>83.64</td>\n",
       "      <td>18.71</td>\n",
       "      <td>2014-08-12 20:26:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Longitude   Latitude  battery  Speed             trackdt\n",
       "20  72.860698  19.111690   100.00   0.00 2014-08-12 15:10:16\n",
       "19  72.860148  19.111601    85.45   0.00 2014-08-12 19:55:10\n",
       "29  72.873396  19.126335    84.55  24.82 2014-08-12 20:04:29\n",
       "28  72.926755  19.125281    83.64  18.71 2014-08-12 20:26:05"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf.head(4)2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa96c38a990>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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YU13XasgXcbQfJHeg/RR4LXAOMCEt/yLwV1WOPwZ4mv1/sFcBH0yffwm4On1+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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa96c0f7810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldf.plot(x='Longitude',y='Latitude',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldf['rolling_long'] = ldf.Longitude.rolling(window=2).mean()\n",
    "ldf['rolling_lat'] = ldf.Latitude.rolling(window=2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa95bfd52d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VW5+sZxVld4NVViwWKRaLuZ6jmjGVNRFxtKR7gQXAC8CaiDhoAHVbgTt7xlR6fXYI8MOI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa96c1012d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldf.plot(x='rolling_long',y='rolling_lat',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldf['dist'] = (ldf.Latitude.diff(1).apply(np.square) + ldf.Longitude.diff(1).apply(np.square)).apply(np.sqrt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldf['actual_speed'] = ldf.dist / (ldf.trackdt.diff(1).dt.seconds / 3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldf['actual_speed_norm'] = (ldf.actual_speed - ldf.actual_speed.mean()) / ldf.actual_speed.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa95bf5ba10>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DRu4hmAPBrKQ6GQjz5qW9jNav78z7WTk5EMxKqpOBMGkSHHEELFky8rLWuxwIZiXVyUAA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\ncSCYmRngQDAzs4wDwczMAAeCmZll/j8KUMsxKOYAowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa95bdf0f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldf.set_index('trackdt').actual_speed.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa95ba67dd0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y/dXXWH179edffXv1OXv2XKqnnuqq0+nouuv26vjxhVPO37PnUh048LnnfL0GfT055pjj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa95b9a28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldf.actual_speed_norm.hist(bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>battery</th>\n",
       "      <th>Speed</th>\n",
       "      <th>trackdt</th>\n",
       "      <th>rolling_long</th>\n",
       "      <th>rolling_lat</th>\n",
       "      <th>dist</th>\n",
       "      <th>actual_speed</th>\n",
       "      <th>actual_speed_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>72.860148</td>\n",
       "      <td>19.111601</td>\n",
       "      <td>85.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-08-12 19:55:10</td>\n",
       "      <td>72.860423</td>\n",
       "      <td>19.111646</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>-0.631233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>72.971236</td>\n",
       "      <td>19.206873</td>\n",
       "      <td>79.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-08-12 22:47:49</td>\n",
       "      <td>72.966981</td>\n",
       "      <td>19.202444</td>\n",
       "      <td>0.012283</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>-0.602910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>72.961535</td>\n",
       "      <td>19.169745</td>\n",
       "      <td>57.27</td>\n",
       "      <td>36.67</td>\n",
       "      <td>2014-08-13 10:15:06</td>\n",
       "      <td>72.964054</td>\n",
       "      <td>19.169372</td>\n",
       "      <td>0.005093</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>-0.629854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>72.859800</td>\n",
       "      <td>19.091486</td>\n",
       "      <td>54.55</td>\n",
       "      <td>43.71</td>\n",
       "      <td>2014-08-15 18:38:48</td>\n",
       "      <td>72.856920</td>\n",
       "      <td>19.100409</td>\n",
       "      <td>0.018752</td>\n",
       "      <td>0.002445</td>\n",
       "      <td>-0.622497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>72.839350</td>\n",
       "      <td>19.105030</td>\n",
       "      <td>100.00</td>\n",
       "      <td>43.71</td>\n",
       "      <td>2014-08-15 20:06:03</td>\n",
       "      <td>72.849575</td>\n",
       "      <td>19.098258</td>\n",
       "      <td>0.024528</td>\n",
       "      <td>0.016868</td>\n",
       "      <td>-0.568357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>72.851480</td>\n",
       "      <td>19.156466</td>\n",
       "      <td>65.45</td>\n",
       "      <td>43.71</td>\n",
       "      <td>2014-08-15 21:52:54</td>\n",
       "      <td>72.845415</td>\n",
       "      <td>19.130748</td>\n",
       "      <td>0.052847</td>\n",
       "      <td>0.029675</td>\n",
       "      <td>-0.520280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>72.842956</td>\n",
       "      <td>19.115235</td>\n",
       "      <td>50.00</td>\n",
       "      <td>43.71</td>\n",
       "      <td>2014-08-18 09:55:47</td>\n",
       "      <td>72.839648</td>\n",
       "      <td>19.110813</td>\n",
       "      <td>0.011046</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>-0.629451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>72.842923</td>\n",
       "      <td>19.115111</td>\n",
       "      <td>50.00</td>\n",
       "      <td>43.71</td>\n",
       "      <td>2014-08-18 10:10:57</td>\n",
       "      <td>72.842940</td>\n",
       "      <td>19.115173</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000508</td>\n",
       "      <td>-0.629768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>72.841900</td>\n",
       "      <td>19.116240</td>\n",
       "      <td>50.00</td>\n",
       "      <td>43.71</td>\n",
       "      <td>2014-08-18 10:28:11</td>\n",
       "      <td>72.842412</td>\n",
       "      <td>19.115676</td>\n",
       "      <td>0.001524</td>\n",
       "      <td>0.005304</td>\n",
       "      <td>-0.611762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>72.846876</td>\n",
       "      <td>19.115668</td>\n",
       "      <td>49.09</td>\n",
       "      <td>43.71</td>\n",
       "      <td>2014-08-18 11:15:20</td>\n",
       "      <td>72.844388</td>\n",
       "      <td>19.115954</td>\n",
       "      <td>0.005009</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>-0.607748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Longitude   Latitude  battery  Speed             trackdt  rolling_long  \\\n",
       "19  72.860148  19.111601    85.45   0.00 2014-08-12 19:55:10     72.860423   \n",
       "37  72.971236  19.206873    79.09   0.00 2014-08-12 22:47:49     72.966981   \n",
       "34  72.961535  19.169745    57.27  36.67 2014-08-13 10:15:06     72.964054   \n",
       "15  72.859800  19.091486    54.55  43.71 2014-08-15 18:38:48     72.856920   \n",
       "16  72.839350  19.105030   100.00  43.71 2014-08-15 20:06:03     72.849575   \n",
       "31  72.851480  19.156466    65.45  43.71 2014-08-15 21:52:54     72.845415   \n",
       "22  72.842956  19.115235    50.00  43.71 2014-08-18 09:55:47     72.839648   \n",
       "21  72.842923  19.115111    50.00  43.71 2014-08-18 10:10:57     72.842940   \n",
       "24  72.841900  19.116240    50.00  43.71 2014-08-18 10:28:11     72.842412   \n",
       "23  72.846876  19.115668    49.09  43.71 2014-08-18 11:15:20     72.844388   \n",
       "\n",
       "    rolling_lat      dist  actual_speed  actual_speed_norm  \n",
       "19    19.111646  0.000557      0.000117          -0.631233  \n",
       "37    19.202444  0.012283      0.007663          -0.602910  \n",
       "34    19.169372  0.005093      0.000485          -0.629854  \n",
       "15    19.100409  0.018752      0.002445          -0.622497  \n",
       "16    19.098258  0.024528      0.016868          -0.568357  \n",
       "31    19.130748  0.052847      0.029675          -0.520280  \n",
       "22    19.110813  0.011046      0.000592          -0.629451  \n",
       "21    19.115173  0.000128      0.000508          -0.629768  \n",
       "24    19.115676  0.001524      0.005304          -0.611762  \n",
       "23    19.115954  0.005009      0.006374          -0.607748  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf[ldf.actual_speed_norm < -0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa95b91bfd0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zbCRJxRk2kqTiDBtJUnGGjSSpOMNGklScYSNJKs6wkSQVZ9hIkoozbCRJxRk2kqTiDBtJ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\nSSrOsJEkFWfYSJKK+3/bbfaof1oCUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa95bb11a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldf[ldf.actual_speed_norm < -0.5].plot(x='Longitude',y='Latitude', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa95b853510>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VW5+sZxVld4NVViwWKRaLuZ6jmjGVNRFxtKR7gQXAC8CaiDhoAHVbgTt7xlR6fXYI8MOI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa95b6650d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldf.plot(x='rolling_long',y='rolling_lat',kind='scatter')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}