How to Efficiently Convert Google Maps Elevation API JSON to a Pandas DataFrame?

JSON to pandas DataFrame: Handling Elevation Data from Google Maps API

When dealing with JSON data, converting it into a structured format like a pandas DataFrame can be essential for further analysis. This is often encountered when working with data obtained from APIs, such as Google Maps Elevation API.

In your case, the JSON data you received includes information on elevation, latitude, and longitude coordinates. Your goal is to transform this data into a structured DataFrame.

To achieve this, one approach involves manually extracting the required fields from the JSON response and constructing a DataFrame accordingly. While this method works, it can be tedious and error-prone.

Fortunately, pandas provides a more convenient solution through its json_normalize() function. This function allows you to convert nested JSON structures into a DataFrame. It automatically flattens the nested data, converting it into a tabular format.

Here's a simplified example demonstrating how to use json_normalize() with your elevation data:

import pandas as pd

# Sample JSON response
data = {
    "results": [
        {"elevation": 243.3462677001953, "location": {"lat": 42.974049, "lng": -81.205203}},
        {"elevation": 244.1318664550781, "location": {"lat": 42.974298, "lng": -81.19575500000001}},
    ],
    "status": "OK",
}

# Convert JSON data to DataFrame using json_normalize()
df = pd.json_normalize(data["results"])

This code will create a DataFrame with the following columns:

• elevation
• location.lat
• location.lng

Using json_normalize(), you can efficiently convert your complex JSON response into a structured DataFrame, making it easier to analyze and manipulate the data.

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