How to Export JSON Data to a DataFrame in Python: Solving the Nested JSON Problem

Discover how to efficiently load a nested `JSON` file into a `DataFrame` using Python. Learn step-by-step methods to extract all columns seamlessly!
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How to Export JSON Data to a DataFrame in Python: Solving the Nested JSON Problem

Importing data into a pandas DataFrame can sometimes be tricky, especially when dealing with nested structures in JSON files. If you've ever encountered a situation where not all columns are loaded correctly, you're not alone. This guide will walk you through a common issue when importing JSON data into a DataFrame and provide a robust solution to ensure that you extract all relevant information.

The Problem

You may find yourself in a situation where you try to load a JSON file using the following simple code:

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

However, upon printing the DataFrame, you may notice that it only contains a few columns:

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

In this example, only the utcTime, config, and sourceID fields are loaded. The issue likely arises because the config field contains nested JSON data, specifically the toothSettings list, leading to information loss.

The Solution

To effectively load all the data, including the nested JSON structure into separate DataFrame columns, follow these steps:

Step 1: Load the JSON File

First, load your JSON file into a DataFrame as you normally would:

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

Step 2: Expand the config Column

Next, you need to expand the config column to separate the nested data into its own columns. Use the following code:

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

Step 3: Explode the toothSettings

Since toothSettings is a list, you'll want to 'explode' this feature to create a separate row for each entry. Execute the following:

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

Step 4: Expand the toothSettings Column

Now you can expand the toothSettings column into its own columns:

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

Step 5: Convert Time Format

Finally, convert the utcTime column from seconds to a datetime format for easier interpretation:

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

Putting It All Together

Here’s how your complete code will look:

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

Sample Output

The final DataFrame will showcase an expanded view of your data, which should look similar to this:

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

Conclusion

Handling nested JSON data doesn't have to be intimidating. By breaking down your process into manageable steps, you can easily convert complex JSON structures into user-friendly DataFrames. This ensures you extract every piece of information available, turning data into actionable insights. Happy coding!