pandas json normalize specific columns

Title: How to Use Pandas to Normalize JSON Data with Specific Columns
Introduction:
Pandas is a powerful data manipulation library in Python, widely used for handling and analyzing structured data. One common task is normalizing JSON data, especially when dealing with nested structures. In this tutorial, we'll explore how to use Pandas to normalize JSON data, focusing on specific columns.
Requirements:
Make sure you have Pandas installed before running the code. If you haven't installed it yet, you can do so using:
Code Example:
Suppose you have a JSON file with nested structures, and you want to normalize it using Pandas while extracting information from specific columns. Here's a step-by-step guide:
Explanation:
Import the necessary libraries, including Pandas and the json_normalize function.
Create a sample JSON data structure. Replace it with your actual JSON data.
Display the original DataFrame.
Specify the columns that need to be normalized (columns_to_normalize).
Iterate through the specified columns, applying pd.Series to extract and expand nested structures.
Concatenate the expanded columns with the original DataFrame, dropping the original nested columns.
Display the normalized DataFrame.
This example demonstrates the process of normalizing JSON data while selectively extracting information from specific columns. Adjust the code according to your actual JSON structure and column requirements.
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