Handle deeply nested data
Last exercise, you flattened data nested down one level. Here, you'll unpack more deeply nested data.
The categories attribute in the Yelp API response contains lists of objects. To flatten this data, you'll employ json_normalize() arguments to specify the path to categories and pick other attributes to include in the dataframe. You should also change the separator to facilitate column selection and prefix the other attributes to prevent column name collisions. We'll work through this in steps.
pandas (as pd) and json_normalize() have been imported. JSON-formatted Yelp data on cafes in NYC is stored as data.
This exercise is part of the course
Streamlined Data Ingestion with pandas
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Flatten businesses records and set underscore separators
flat_cafes = ____(data["businesses"],
____)
# View the data
print(flat_cafes.head())