Convert JSON with Dictionaries into a Pandas DataFrame on AWS

Learn how to transform JSON data from AWS Lambda into a structured `Pandas DataFrame` for better data handling and analysis.
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Transforming JSON Data into a Pandas DataFrame on AWS

If you're working with Amazon Web Services (AWS), you might find yourself querying a serverless Aurora SQL database and receiving data in the form of nested JSON structures. This can pose a challenge, especially if you need to manipulate or analyze this data effectively in Python using the Pandas library.

In this post, we will walk through the process of converting JSON data, which contains dictionaries, into a structured Pandas DataFrame using Python. This will allow you to easily work with your data without the hassle of nested complexities.

Understanding the Problem

When you execute a query on your Aurora SQL database via AWS Lambda, the response you receive is a JSON object that often contains deeply nested dictionaries. Here’s an example of a response structure you might encounter:

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

In this case, the "records" field contains a list of lists, where each inner list holds dictionaries with attributes like stringValue, longValue, and booleanValue. The challenge is extracting just the values from this complex structure into a flat DataFrame that can be easily manipulated.

The Solution

To solve this problem, we can take the following steps:

Parse the Records: Convert the nested structure to a more manageable format.

Extract Values: Focus on getting the actual values instead of the keys.

Create the DataFrame: Use Pandas to create a DataFrame from the flat structure.

Step 1: Parse the Records

Assuming you already have the records portion of your JSON parsed into a standard Python object, it might look something like this:

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

Step 2: Extract Values

The next step is to define a function to extract the first value from each dictionary in your records. This can be achieved using a simple comprehension:

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

Step 3: Creating the DataFrame

Now that we have a list of values, we can convert that into a Pandas DataFrame:

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

Example Output

The output of the DataFrame will neatly present the data extracted from your JSON. It will look similar to this:

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

Each row corresponds to a record, and each column corresponds to a field in your dataset.

Keeping the Corresponding Value Types

If you want to maintain the original data types (like differentiating between longValue and stringValue), you can extract the types based on the first record in your data:

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

This gives you insight into what kind of values you have so you can perform any necessary type-casting afterward in your DataFrame.

Conclusion

Transforming nested JSON data into a Pandas DataFrame allows for smoother data manipulation and analysis, especially when dealing with AWS. By parsing the records, extracting their values, and feeding them into Pandas, you get to work with a clean and organized dataset.

By following the steps outlined in this post, you can easily convert complex JSON responses into a clear DataFrame format, making your data-driven decisions more profound and informed. Happy coding!