Normalize JSON Data into a pandas DataFrame with json_normalize

Discover how to transform complex JSON structures into a pandas DataFrame using json_normalize in this comprehensive guide.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to Normalize JSON data into a pandas dataframe with json_normalize

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Normalize JSON Data into a pandas DataFrame with json_normalize

Working with JSON data can sometimes feel overwhelming, especially when the structure is complex. If you’ve tried to convert such data into a pandas DataFrame but hit a roadblock—like a KeyError—you’re not alone. This post aims to demystify the process of using json_normalize` to flatten your JSON data into a usable DataFrame format.

Understanding the Problem

Let's take a look at a sample complex JSON object that a user attempted to transform:

[[See Video to Reveal this Text or Code Snippet]]

The main issue arises when the user tries to convert this nested structure into a DataFrame and encounters the following error:

[[See Video to Reveal this Text or Code Snippet]]

This error hints that json_normalize is unable to locate the specified record_path. Let's understand how to resolve this issue step by step.

Solution: Using json_normalize Properly

Step 1: Identify the Main Key

The first key in the JSON object is "customers", which contains an array of customer details. To work with this data, we need to pull it out correctly.

Step 2: Use json_normalize

Here's how to properly apply json_normalize on the above data structure to flatten it.

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of the Code

record_path="addresses": This tells json_normalize to look for the addresses key within each customer object.

meta="h": This collects the value associated with the h key for each entry as additional information in the DataFrame.

record_prefix="adr_": It prefixes the column names of the addresses dictionary, helping identify which columns belong to addresses.

Expected Output

After running the code, the resulting DataFrame will have flattened the JSON structure, making it easier to analyze. You’ll notice that the address information is nicely separated alongside the customer’s h value.

Conclusion

By understanding how to properly navigate and reference nested JSON structures, you can effortlessly transform such data into pandas DataFrames using json_normalize. This tool is invaluable for data analysis, allowing for a smooth transition from raw JSON to structured data.

If you encounter a similar issue, remember to check the structure of your JSON and ensure your record_path is appropriately defined. Happy coding!