Скачать или смотреть Flattening Nested JSON in Pandas with Parent Sequence IDs

Learn how to flatten complex nested JSON objects in Pandas and add sequence IDs for parent products. This step-by-step guide simplifies the process.
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Introduction to Normalizing Nested JSON in Pandas

Handling nested JSON data can be a daunting task for data analysts and developers alike, especially when aiming to extract meaningful insights. If you have a DataFrame containing a column with complex nested JSON objects, you might find yourself needing to normalize this data while maintaining parent-child relationships.

In this guide, we will explore how to flatten nested JSON structures using Pandas and how to assign a unique sequence identifier for each product alongside its parent sequence. Whether you are working with subscription data or any other hierarchical dataset, this walkthrough will simplify the process for you.

The Challenge: Nested JSON Structure

Consider a JSON structure that represents subscriptions with nested product details, such as the following:

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In this example, the JSON is deeply nested. What we want is to flatten this hierarchy into a more accessible table format where we can also track product sequencing and the relationship to their parent products.

Desired Output

The goal is to achieve a DataFrame that resembles the following format:

subscription_boosubscription_ref_prefixno_of_productsproduct_idproduct_seqparent_product_seqTrueS_1.01.0010TrueS_1.01.1021TrueS_1.01.2031TrueS_1.01.1142TrueB_1.02.0050TrueB_1.02.1065TrueB_1.02.2075TrueB_1.02.1186TrueB_1.02.1196Step-by-Step Solution

Let’s walk through how to accomplish this with Pandas using a recursive flattening function.

Step 1: Import Necessary Libraries

First, we need to make sure we have the required libraries. We’ll be using pandas and json.

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Step 2: Load the JSON Data

Next, load your JSON data. For demonstration, we will be using a simple string representation of the JSON structure.

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Step 3: Define the Flattening Function

We will define a function that will flatten the nested product list recursively:

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Step 4: Normalize the JSON Data

Next, we flatten the products for each subscription and use the json_normalize method to create a DataFrame:

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Step 5: Display the Output

Finally, let’s display our normalized DataFrame:

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This will yield the DataFrame in the desired format, complete with product sequences and their corresponding parent product sequences.

Conclusion

In this guide, we covered an effective method for flattening nested JSON in Pandas and maintaining sequences for parent-child product relationships. By employing a recursive flattening function combined with Pandas' powerful json_normalize, you can efficiently transform complex data structures into simpler, analyzable formats.

Feel free to adapt the code examples provided to fit your specific needs as you work with nested JSON objects in your data projects!