Extracting List of JSON Objects in String Form from Pandas Dataframe Column

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In this article, we will explore the process of extracting list of JSON objects from a pandas DataFrame column. We’ll cover how to handle nested data structures and extract unique genre names for each row.

Introduction

Pandas is a powerful library used for data manipulation and analysis in Python. When working with large datasets, it’s common to encounter nested data structures like lists or dictionaries within the data. In this article, we will focus on extracting JSON objects from a pandas DataFrame column using the ast.literal_eval() function.

Background

To understand the context, let’s briefly review how to load and manipulate JSON data in Python:

• We can use the pd.read_csv() function to load CSV files into DataFrames.
• To access nested JSON objects within a DataFrame cell, we need to evaluate the JSON string as Python code using the ast.literal_eval() function.

Problem Statement

The problem statement is as follows: given a pandas DataFrame with a column containing nested JSON objects, extract a list of unique genre names for each row.

Solution Approach

Our approach involves the following steps:

1. Load Data: Load the dataset into a pandas DataFrame using pd.read_csv().
2. Apply ast.literal_eval: Use ast.literal_eval() to evaluate the JSON string as Python code.
3. Extract Unique Genre Names: Extract unique genre names for each row by converting the evaluated list to a set and then back to a list.

Code

Step 1: Load Data

import pandas as pd

# Load dataset into DataFrame
obj_movies = pd.read_csv('tmdb_5000_movies.csv')

Step 2: Apply ast.literal_eval

We’ll apply ast.literal_eval() to each row in the ‘genres’ column to evaluate it as Python code.

import ast

# Apply ast.literal_eval() to the 'genres' column
obj_movies['uniq'] = [list(set([y['name'] for y in x])) for x in obj_movies['genres'].apply(ast.literal_eval)]

Step 3: Extract Unique Genre Names

We’ll extract unique genre names for each row by converting the evaluated list to a set and then back to a list.

Example Use Case

To test our solution, we can print the first few rows of the DataFrame with the extracted unique genre names:

# Print the first 10 rows with extracted unique genre names
print (obj_movies[['uniq'] ].head(10))

Output:

Conclusion

In this article, we covered the process of extracting list of JSON objects in string form from pandas DataFrame column. We used ast.literal_eval() to evaluate nested JSON objects and extract unique genre names for each row.

By following these steps, you can extract list of JSON objects from a pandas DataFrame column and work with unique genre names for each row in your data analysis tasks.

Step 6: Best Practices

• Use ast.literal_eval(): Always use ast.literal_eval() when working with nested data structures to ensure safe evaluation.
• Validate User Input: When allowing user input, always validate and sanitize it before passing it to functions like ast.literal_eval().
• Handle Edge Cases: Be aware of potential edge cases and handle them accordingly.

Step 7: Further Reading

For more information on working with pandas DataFrames and JSON data in Python, check out the following resources:

• Pandas Documentation
• JSON Documentation
• AST Library Documentation

Last modified on 2023-10-18