NYU2cellId.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "import s2sphere as s2\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import gmplot\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\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>cellid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.922592</td>\n",
       "      <td>40.754528</td>\n",
       "      <td>b10209a4eff00211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.952751</td>\n",
       "      <td>40.677711</td>\n",
       "      <td>b0fdf62a1bae67bd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-73.843010</td>\n",
       "      <td>40.719055</td>\n",
       "      <td>b1020b8445e54f83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-73.860825</td>\n",
       "      <td>40.757793</td>\n",
       "      <td>b1020bee00453ff5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.945183</td>\n",
       "      <td>40.783321</td>\n",
       "      <td>b1020849176acc75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude   latitude            cellid\n",
       "0 -73.922592  40.754528  b10209a4eff00211\n",
       "1 -73.952751  40.677711  b0fdf62a1bae67bd\n",
       "2 -73.843010  40.719055  b1020b8445e54f83\n",
       "3 -73.860825  40.757793  b1020bee00453ff5\n",
       "4 -73.945183  40.783321  b1020849176acc75"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml\n",
    "\n",
    "# generate cell id\n",
    "green = pd.read_csv('./green_tripdata_2015-01.csv')\n",
    "green.index = range(len(green))\n",
    "drop = ['Payment_type','lpep_pickup_datetime','Lpep_dropoff_datetime','Passenger_count',\n",
    "        'Extra','MTA_tax','Tolls_amount','Trip_distance','Fare_amount','Ehail_fee','Total_amount','improvement_surcharge']\n",
    "green.drop(drop,axis=1)\n",
    "def calculateCellId(row):\n",
    "    p = s2.LatLng.from_degrees(row['Store_and_fwd_flag'],row['RateCodeID'])\n",
    "    return s2.CellId.from_lat_lng(p).to_token()\n",
    "\n",
    "green['CellId'] = green.apply(calculateCellId,axis=1)\n",
    "\n",
    "export_record = green[['Store_and_fwd_flag','RateCodeID','CellId']]\n",
    "export_record.columns = ['longitude','latitude','cellid']\n",
    "export_record.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>cellid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1151893</th>\n",
       "      <td>-73.987610</td>\n",
       "      <td>40.691380</td>\n",
       "      <td>b0fdf7eac62889c3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569567</th>\n",
       "      <td>-73.941528</td>\n",
       "      <td>40.813225</td>\n",
       "      <td>b10208506b40162d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552721</th>\n",
       "      <td>-73.962669</td>\n",
       "      <td>40.804543</td>\n",
       "      <td>b1020815ae278f79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176822</th>\n",
       "      <td>-73.945786</td>\n",
       "      <td>40.807953</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319901</th>\n",
       "      <td>-73.844307</td>\n",
       "      <td>40.721359</td>\n",
       "      <td>b1020b85afed4ffb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851615</th>\n",
       "      <td>-73.949577</td>\n",
       "      <td>40.793461</td>\n",
       "      <td>b10208464a341345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807348</th>\n",
       "      <td>-73.896118</td>\n",
       "      <td>40.866943</td>\n",
       "      <td>b102093a6ba9c217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330251</th>\n",
       "      <td>-73.947212</td>\n",
       "      <td>40.784363</td>\n",
       "      <td>b1020848589ee0b3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295353</th>\n",
       "      <td>-73.944077</td>\n",
       "      <td>40.679329</td>\n",
       "      <td>b10209d9febdc6c7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118459</th>\n",
       "      <td>-73.936531</td>\n",
       "      <td>40.820530</td>\n",
       "      <td>b1020856c50a03b3</td>\n",
       "    </tr>\n",
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       "      <th>386222</th>\n",
       "      <td>-73.993515</td>\n",
       "      <td>40.697292</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276343</th>\n",
       "      <td>-73.922691</td>\n",
       "      <td>40.826603</td>\n",
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       "    <tr>\n",
       "      <th>9184</th>\n",
       "      <td>-73.868355</td>\n",
       "      <td>40.749378</td>\n",
       "      <td>b1020be89587a4ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261014</th>\n",
       "      <td>-73.939117</td>\n",
       "      <td>40.757343</td>\n",
       "      <td>b10209b51eff5ff3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492294</th>\n",
       "      <td>-73.951530</td>\n",
       "      <td>40.713585</td>\n",
       "      <td>b102082ccc6a126f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351883</th>\n",
       "      <td>-73.938393</td>\n",
       "      <td>40.817310</td>\n",
       "      <td>b102085120a1ba4d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622187</th>\n",
       "      <td>-73.954376</td>\n",
       "      <td>40.805744</td>\n",
       "      <td>b1020840e41a15d3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254169</th>\n",
       "      <td>-73.939369</td>\n",
       "      <td>40.807446</td>\n",
       "      <td>b1020851b95847bf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633750</th>\n",
       "      <td>-73.961205</td>\n",
       "      <td>40.714550</td>\n",
       "      <td>b1020829c8ce0899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970164</th>\n",
       "      <td>-73.898720</td>\n",
       "      <td>40.860043</td>\n",
       "      <td>b102093b99184b11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         longitude   latitude            cellid\n",
       "1151893 -73.987610  40.691380  b0fdf7eac62889c3\n",
       "569567  -73.941528  40.813225  b10208506b40162d\n",
       "552721  -73.962669  40.804543  b1020815ae278f79\n",
       "1176822 -73.945786  40.807953  b102084574640e1f\n",
       "1319901 -73.844307  40.721359  b1020b85afed4ffb\n",
       "851615  -73.949577  40.793461  b10208464a341345\n",
       "807348  -73.896118  40.866943  b102093a6ba9c217\n",
       "1330251 -73.947212  40.784363  b1020848589ee0b3\n",
       "1295353 -73.944077  40.679329  b10209d9febdc6c7\n",
       "118459  -73.936531  40.820530  b1020856c50a03b3\n",
       "386222  -73.993515  40.697292  b0fdf793127d56f5\n",
       "1276343 -73.922691  40.826603  b1020902266c724d\n",
       "9184    -73.868355  40.749378  b1020be89587a4ff\n",
       "261014  -73.939117  40.757343  b10209b51eff5ff3\n",
       "492294  -73.951530  40.713585  b102082ccc6a126f\n",
       "351883  -73.938393  40.817310  b102085120a1ba4d\n",
       "622187  -73.954376  40.805744  b1020840e41a15d3\n",
       "254169  -73.939369  40.807446  b1020851b95847bf\n",
       "633750  -73.961205  40.714550  b1020829c8ce0899\n",
       "970164  -73.898720  40.860043  b102093b99184b11"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_record.count()\n",
    "export_record_drop0 = export_record[export_record.longitude != 0]\n",
    "export_record_drop0.count()\n",
    "export_record_drop0.to_csv('cellid.csv')\n",
    "\n",
    "export_record_drop0.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    1506026\n",
       "longtitude    1506026\n",
       "latitude      1506026\n",
       "cellid        1506026\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_with_cellId = pd.read_csv('./cellid.csv')\n",
    "record_with_cellId.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-75.35131072998048, -70.91851043701172) (39.36097717285156, 42.91824722290039)\n"
     ]
    }
   ],
   "source": [
    "x_lim=(record_with_cellId['longtitude'].min(),record_with_cellId['longtitude'].max())\n",
    "y_lim=(record_with_cellId['latitude'].min(),record_with_cellId['latitude'].max())\n",
    "print(x_lim, y_lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3a2a03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=record_with_cellId['longtitude']\n",
    "y=record_with_cellId['latitude']\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
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