How to Extract Nested JSON Data Using Python Pandas

Learn how to efficiently extract nested JSON data into a DataFrame using Python Pandas! This guide walks you through the steps with clear examples and code snippets.
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How to Extract Nested JSON Data Using Python Pandas

In today's data-driven world, extracting useful information from JSON (JavaScript Object Notation) is crucial for analysis and application development. JSON data is often nested, making it challenging to access specific values effortlessly. This guide will guide you on how to extract nested JSON data using Python's powerful library, Pandas. We'll break down a concrete example to illustrate these concepts in an approachable manner.

Understanding the Problem

Imagine you're working with data sourced from an API that returns a nested JSON response. Below is an example of such a response:

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

The challenge here is to extract specific pieces of information, such as time, high, low, and close from this nested structure, and convert it into a readable format that you can analyze in a Pandas DataFrame.

Solution Overview

To effectively extract and manipulate this data, we will follow these steps:

Format the timestamp into a more human-readable format.

Let's dive into the code to see how this works in practice.

Step-by-Step Breakdown

1. Convert JSON String to Python Data Structure

First, we must convert the JSON string response into a Python dictionary. You will typically receive this string from an API call.

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

2. Normalize the JSON Data

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

Here, we specify record_path='Data' because we're interested in the nested Data key that contains the array we want.

3. Format the Timestamp

Next, we need to convert the time column, which is in Unix timestamp format, into a more readable datetime format:

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

Complete Code Example

Here's the complete code combining all the above steps:

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

Expected Output

Upon running the code, we would expect an output similar to:

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

Conclusion

Working with nested JSON can be daunting, but with Python and Pandas, it becomes manageable and straightforward. By following the steps outlined in this guide, you can efficiently extract relevant data from such structures and prepare them for analysis or visualization.

Feel free to adapt this guide to suit your specific JSON extraction needs. Happy coding!