03 google_API_Matrix_data_queries.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Getting the Key](https://developers.google.com/maps/documentation/javascript/get-api-key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import googlemaps\n",
    "from datetime import datetime\n",
    "import simplejson\n",
    "import urllib, json, time\n",
    "import pandas as pd\n",
    "import math\n",
    "import dateutil \n",
    "import datetime as dt\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "apikey = 'XXXXXXXXXXXXXXXXXXXXXX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('final_top_bikes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "source": [
    "def pmam(x):\n",
    "    x = str(x)\n",
    "    #x = (':'.join(a+b for a,b in zip(x[::2], x[1::2])))\n",
    "    if x == 'NaN':\n",
    "        pass\n",
    "    try:\n",
    "        x = str(x[:2] + ':' + x[2:])\n",
    "        date = dateutil.parser.parse(x)\n",
    "        return str(date.strftime('%d/%m/%Y %H:%M %p'))\n",
    "    except:\n",
    "        return 'NaN'\n",
    "\n",
    "print(pmam('2017-09-09 07:00:03'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "source": [
    "df['Timestamp index'] = df['Timestamp +2'].apply(pmam)\n",
    "df['Timestamp index'] = df['Timestamp index'].apply(lambda x: \n",
    "                                    dt.datetime.strptime(x,'%d/%m/%Y %H:%M %p'))\n",
    "df.index = df['Timestamp index']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From [Stackoverflow](https://stackoverflow.com/questions/36500331/putting-latitudes-and-longitudes-into-a-distance-matrix-google-map-api-in-pytho) and [here](https://developers.google.com/maps/documentation/javascript/get-api-key)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import urllib, json, time\n",
    "import pandas as pd\n",
    "\n",
    "def google(lato, lono, latd, lond):\n",
    "\n",
    "    url = \"\"\"https://maps.googleapis.com/maps/api/distancematrix/json?origins=%s,%s\"\"\"%(lato, lono)+  \\\n",
    "    \"\"\"&destinations=%s,%s&mode=bicycling&language=en-EN&sensor=false&key=\"\"\"+apikey%(latd, lond)\n",
    "    #print(url)\n",
    "    #CHANGE THIS FOR PYTHON 3.X TO urllib.request.urlopen(url)...\n",
    "    response = urllib.request.urlopen(url).read().decode('utf8')\n",
    "\n",
    "    #Wait a second so you don't overwhelm the API if doing lots of calls\n",
    "    time.sleep(1)\n",
    "\n",
    "    obj = json.loads(response)\n",
    "    try:\n",
    "        s =   obj['rows'][0]['elements'][0]['duration']['value']\n",
    "        m = (obj['rows'][0]['elements'][0]['distance']['value'])\n",
    "        return s, m\n",
    "    except IndexError:\n",
    "        #something went wrong, the result was not found\n",
    "        print (url)\n",
    "        #return the error code\n",
    "        return obj['Status'], obj['Status']\n",
    "\n",
    "def ApplyGoogle(row):\n",
    "    lato, lono = row['Lat'], row['Long']\n",
    "    latd, lond = row['newLat'], row['newLong']\n",
    "    return google(lato, lono, latd, lond)\n",
    "\n",
    "journeydf['Seconds'], journeydf['Metres'] = zip(*journeydf.apply(ApplyGoogle, axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Space for next analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "journeydf.to_csv('data.csv')"
   ]
  }
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