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

Converting JSON Elevation Data to Pandas DataFrame

Objective: Extract elevation data from Google Maps API and organize it in a Pandas DataFrame.

Problem:

A JSON data obtained from the Google Maps API elevation service contains nested information in the format:

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

Importing this JSON into a Pandas DataFrame directly leads to a scattered structure.

Solution:

Using nested list extraction:

To manually separate the elevation, latitude, and longitude data:

data = json.loads(elevations)
lat, lng, el = [], [], []
for result in data['results']:
    lat.append(result[u'location'][u'lat'])
    lng.append(result[u'location'][u'lng'])
    el.append(result[u'elevation'])
df = pd.DataFrame([lat, lng, el]).T

This creates a DataFrame with columns latitude, longitude, and elevation.

Using json_normalize (Pandas v1.01 ):

A simpler approach using Pandas' json_normalize:

df = pd.json_normalize(data['results'])

This flattens the JSON data into a DataFrame with columns for each key in the nested structure.

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