Flatten nested JSONs

A feature of JSON data is that it can be nested: an attribute's value can consist of attribute-value pairs. This nested data is more useful unpacked, or flattened, into its own dataframe columns. The pandas.io.json submodule has a function, json_normalize(), that does exactly this.

The Yelp API response data is nested. Your job is to flatten out the next level of data in the coordinates and location columns.

pandas (as pd) and requests have been imported. The results of the API call are stored as response.

This exercise is part of the course

Streamlined Data Ingestion with pandas

View Course

Exercise instructions

Load the json_normalize() function from pandas' io.json submodule.
Isolate the JSON data from response and assign it to data.
Use json_normalize() to flatten and load the businesses data to a dataframe, cafes. Set the sep argument to use underscores (_), rather than periods.
Show the data head.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load json_normalize()
____

# Isolate the JSON data from the API response
data = ____

# Flatten business data into a dataframe, replace separator
cafes = ____(data["businesses"],
             ____)

# View data
print(cafes.head())

Edit and Run Code