Unpacking Nested JSON into a Dataframe

Introduction

In recent years, the use of JSON (JavaScript Object Notation) has become increasingly popular for data exchange and storage. One common challenge when working with JSON data is how to unpack nested structures into more readable formats. In this article, we will explore ways to convert nested JSON into a Pandas dataframe.

Background

JSON data can be in various forms, including simple objects, arrays, and nested structures. When dealing with nested JSON, it’s common to encounter complex hierarchical data that needs to be processed and converted into a more suitable format for analysis or visualization.

The Problem

In the given example, we have a large nested JSON file with multiple layers of nesting. The goal is to convert this JSON structure into a Pandas dataframe that can be easily manipulated and analyzed.

We’ve already attempted using pd.json_normalize() to achieve this, but only managed to unpack one layer of the nested structure. We’re now looking for alternative approaches to handle these complex nested JSON structures.

Solution

One effective method to tackle nested JSON data is by leveraging Pandas’ built-in functionality, specifically its ability to handle nested lists and dictionaries. In this section, we’ll explore two strategies to convert nested JSON into a dataframe:

1. Using json_normalize() with Nested Lists

When working with nested lists, we can use the record_path parameter of pd.json_normalize() to specify the nested path as a list of strings.

df2 = pd.json_normalize(response, ['data',['facilities']])

This approach allows us to unpack one layer of nesting from the original JSON structure. By using a nested list as the record_path, we’re telling Pandas to iterate over each element in the list and create separate rows for each item.

2. Exploding Nested Lists into Separate Rows

As an alternative, we can use the explode() method on our dataframe to split the nested lists into individual rows. This approach provides more flexibility when dealing with complex JSON structures.

df2 = pd.json_normalize(response, ['data',['facilities']])

# Splitting nested lists into separate rows
df2['network.regions'] = [[y['code'] for y in x] for x in df2['network.regions']]
df2 = df2.explode('network.regions').reset_index(drop=True)

In the above code snippet, we first create a new column network.regions containing the split lists. Then, we use the explode() method to transform each list into separate rows.

Tips and Variations

When working with nested JSON data, keep in mind that:

• Pandas’ handling of nested structures can be quite flexible; however, it’s essential to understand how these functions work under the hood.
• When dealing with extremely large datasets, consider using chunking or other memory-efficient techniques to avoid performance issues.
• The explode() method may not always produce the desired output. Be sure to inspect your data and adjust the approach as needed.

Example Use Cases

Here’s an example dataset that demonstrates how to convert nested JSON into a dataframe:

import requests
import pandas as pd

# Sample API response from OpenNEM Australia
response = requests.get('https://api.opennem.org.au/station/').json()

df2 = pd.json_normalize(response, ['data',['facilities']])

print(df2.head(3))

Output:

Conclusion

Converting nested JSON structures into a dataframe can be achieved using Pandas’ built-in functionality, including json_normalize() and the explode() method. By understanding how these functions work and applying them to your specific use case, you’ll be able to tackle complex JSON data with ease.

Remember to consider performance issues when working with large datasets and adjust your approach as needed. Happy data exploration!

Last modified on 2024-06-17