How to Append Nested JSON Values in a Python Pandas DataFrame

Discover how to effectively extract and append nested JSON values from a WebSocket stream to a Pandas DataFrame in Python.
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Handling Nested JSON in Pandas DataFrame

In the world of data science and programming, efficiently extracting and managing data is crucial. One common challenge developers encounter is appending values from nested JSON structures to a Pandas DataFrame in Python. If you've ever tried to extract a specific value from a complex JSON response and found yourself stuck, you're not alone.

In this guide, we will walk through a typical scenario where you might need to append a nested JSON value to a DataFrame and discuss the solution to overcome this challenge.

The Problem

Let's consider a real-world example where you're working with WebSocket data, particularly from Binance where market prices send updates in JSON format. You might want to extract the closing price of Ethereum (ETH) from a nested JSON structure. Here's a snippet of how the JSON data could look:

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

When you attempt to append the closing price (accessed via json_message['k']['c']), you may face issues because you're trying to append a float rather than a structured object that Pandas DataFrame can manage.

Understanding the Solution

The Core Issue

The main problem with the initial attempt lies in how you're trying to append the data to the DataFrame:

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

In this line, close is a float, and Pandas can only append structurally compatible data like dictionaries or Series. Hence, simply trying to append close will not work as intended.

Proposed Fix

To resolve this issue, you should convert the closing value (close) into a dictionary format that can be appended to the DataFrame. Here's how to do that:

Convert the Close Value to a Dictionary:
Instead of appending the float directly, create a dictionary to hold this value.

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

Append the Dictionary to the DataFrame:
Now use this dictionary when appending to the DataFrame:

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

Updated Code Example

Here’s how the complete on_message function would look after incorporating the above changes:

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

Summary

By following the steps above, you can efficiently extract nested JSON values and append them to a Pandas DataFrame. This not only resolves the immediate issue but also enhances your ability to work with complex data streams, especially in real-time applications like financial market data.

With this knowledge, you can now easily adapt your approach to handle nested JSON values without encountering issues when working with Pandas. Happy coding!