Скачать или смотреть Normalizing Nested JSON to a Pandas DataFrame

Learn how to efficiently normalize nested JSON into a Pandas DataFrame and manage unique IDs for smooth data handling.
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Normalizing Nested JSON to a Pandas DataFrame: A Step-by-Step Guide

In today's data-driven world, working with JSON data is an essential skill for many programmers and data analysts alike. Often, we encounter situations where we need to flatten nested JSON structures for further analysis or integration into SQL tables. In this guide, we will specifically address the challenge of normalizing nested JSON to create a Pandas DataFrame that includes unique identifiers.

The Problem

The challenge arises when we have a complex JSON structure, like the following sample:

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This JSON has unique identifiers (e.g., 138082239, 138082238) nested within it. When we try to flatten this using pd.json_normalize(), we may end up with output columns like data.138082239, data.138082238, etc. This isn't ideal for our analysis where we want a structured DataFrame that includes these IDs directly.

Desired DataFrame Output

We want the DataFrame to resemble the following structure:

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The Solution

To efficiently normalize this nested JSON into a DataFrame, we can use a combination of list comprehension and the pandas.DataFrame.assign() method. Let's break it down step-by-step.

Step 1: Import Required Libraries

Make sure to have the pandas library installed in your Python environment. If not, you can install it using pip:

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Then, import the necessary library:

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Step 2: Pull the JSON Data

Using the requests library, you can retrieve your JSON data from the API:

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Step 3: Normalize the JSON Data

To normalize the JSON data and create the DataFrame, you can employ the following code snippet:

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Step 4: Select Desired Columns

Finally, select the columns you wish to display in your DataFrame:

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Output

Upon execution, the output will be:

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Conclusion

By following the above steps, you can successfully normalize nested JSON into a structured Pandas DataFrame, allowing for efficient data handling. With this approach, you also maintain unique identifiers, ensuring you can easily reference or query your data later on.

If you run into complications or require further assistance, feel free to leave a comment below!