How to Convert Google Maps Elevation JSON Data to a Pandas DataFrame?

JSON to Pandas DataFrame: Converting Elevation Data

This article addresses the issue of transforming JSON elevation data obtained from Google Maps API into a tabular Pandas DataFrame.

The JSON data resembles the following format:

{
   "results" : [
      {
         "elevation" : 243.3462677001953,',
         "location" : {
            "lat" : 42.974049,',
            "lng" : -81.205203',
         },
         "resolution" : 19.08790397644043',
      },
      {
         "elevation" : 244.1318664550781,',
         "location" : {
            "lat" : 42.974298,',
            "lng" : -81.19575500000001',
         },
         "resolution" : 19.08790397644043',
      }',
   ],
   "status" : "OK"',
}

The goal is to extract the elevation, latitude, and longitude data into a DataFrame with columns titled "Elevation," "Latitude," and "Longitude."

The initially attempted method of pd.read_json(elevations) fails to produce the desired result. However, an improved approach utilizes the json_normalize() function from Pandas, which flattens the JSON data into a DataFrame. The code is as follows:

from urllib2 import Request, urlopen
import json

import pandas as pd    

path1 = '42.974049,-81.205203|42.974298,-81.195755'
request=Request('http://maps.googleapis.com/maps/api/elevation/json?locations='+path1+'&sensor=false')
response = urlopen(request)
elevations = response.read()
data = json.loads(elevations)
df = pd.json_normalize(data['results'])

This operation generates the desired DataFrame format with three columns: "Elevation," "Latitude," and "Longitude."

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