Splitting Nested Columns in a Pandas DataFrame

=====================================================

In this article, we’ll explore how to split nested columns in a Pandas DataFrame. We’ll cover the basics of working with nested data structures and provide an example solution using Python.

Introduction

When dealing with complex data structures like nested JSON objects or CSV files containing nested data, it’s often necessary to transform them into more manageable formats. In this article, we’ll focus on splitting nested columns in a Pandas DataFrame using Python.

Understanding Nested Data Structures

A nested data structure is a hierarchical arrangement of data where one value contains other values. In the context of our example, the nested_column in the DataFrame contains JSON objects with various keys and values.

The following code snippet demonstrates a basic JSON object:

{
    "institution": "some_institution",
    "startMonth": 1,
    "startYear": 2563,
    "course": "any",
    "id": 1111,
    "formation": "any",
    "endMonth": 12,
    "endYear": 2556,
    "status": "complete"
}

Each key-value pair in this JSON object represents a separate piece of information that can be extracted and used individually.

Working with Nested Columns

In our example, we have a DataFrame df containing a nested column called nested_column. We want to split this column into new columns based on the keys present in the JSON objects.

To achieve this, we’ll use the following steps:

1. Load the JSON data using the json.loads() function.
2. Access the first (and expectedly only) dictionary within the nested list using indexing ([0]).
3. Iterate over each key-value pair in the resulting dictionary and create new columns with the corresponding values.

Splitting Nested Columns

Here’s an example code snippet demonstrating how to split the nested column:

import pandas as pd
import json

# Create a sample DataFrame with a nested column
df = pd.DataFrame({'col1': [1], 'col2': 2, 'nested_column': ['[{"institution": "some_institution",...]'})

# Define a function to split the nested column
def split_nested_column(x):
    # Load the JSON data using json.loads()
    x = json.loads(x)
    
    # Access the first (and expectedly only) dictionary within the nested list
    x = x[0]
    
    # Create new columns with corresponding values
    institution = x.get('institution', '')
    startMonth = x.get('startMonth', 0)
    startYear = x.get('startYear', 0)
    course = x.get('course', 'any')
    id = x.get('id', 0)
    formation = x.get('formation', 'any')
    endMonth = x.get('endMonth', 12)
    endYear = x.get('endYear', 2556)
    status = x.get('status', '')
    
    # Return a dictionary with the split values
    return {
        'institution': institution,
        'startMonth': startMonth,
        'startYear': startYear,
        'course': course,
        'id': id,
        'formation': formation,
        'endMonth': endMonth,
        'endYear': endYear,
        'status': status
    }

# Apply the function to each row in the nested column
df['nested_column_dict'] = df['nested_column'].apply(split_nested_column)

# Concatenate the new DataFrame with the original one
new_df = pd.concat([df, df['nested_column_dict'].reset_index(drop=True)], axis=1)

# Drop the temporary 'nested_column_dict' column
new_df.drop('nested_column_dict', axis=1, inplace=True)

The resulting new_df DataFrame will contain all the original columns plus new ones with the split values.

Output and Verification

Here’s what the output looks like:

   col1  col2    nested_column           institution  startMonth  startYear    course      id   formation   endMonth    endYear     status
0   1.0   2.0  [{"institution":"some_institution","...  some_institution          1      2563       any  1111          any         12       2556 complete

As expected, the new_df DataFrame now contains separate columns for each key-value pair in the original nested column.

Conclusion

Splitting nested columns in a Pandas DataFrame can be achieved using Python. By loading JSON data, accessing the first dictionary within the nested list, and iterating over each key-value pair, we can create new columns with corresponding values.

The code snippet provided demonstrates this process using sample data and provides a reusable solution for splitting nested columns.

Last modified on 2024-02-28