Auto-Extracting Columns from Nested Dictionaries in Pandas

As a data analyst, working with nested dictionaries can be challenging, especially when dealing with complex datasets. In this article, we will explore how to extract specific columns from nested dictionaries in pandas.

Introduction

The problem at hand involves extracting certain columns (e.g., text and type) from nested multiple dictionaries stored in a jsonl file column. We have a pandas DataFrame (df) that contains the data, but it’s not directly accessible due to its nested structure. The task is to create a function that automatically extracts these specific columns for the entire DataFrame.

Understanding the Issue

The issue lies in how pandas handles mixed data types when using .apply() method. In this case, we’re trying to extract values from nested dictionaries stored as Series (lists), which can lead to ambiguous ordering errors.

To resolve this, we need to restructure our approach to avoid using .apply() and instead use vectorized operations that take advantage of pandas’ optimized data structures.

Alternative Approach

Instead of creating a new DataFrame with the extracted columns, we can directly manipulate the existing data in the referenced_tweets column. This requires iterating over each row in the list and then extracting the desired values.

Here’s an alternative approach:

# Extracting specific columns from nested dictionaries
refs = df[df['referenced_tweets'].notnull()]['referenced_tweets']

dict_hold_list = []
for ref in refs:
    for r in ref:
        # Extract 'text' and 'type'
        dict_hold_list.append({'text': r.get('text'), 'type': r.get('type')})
df_ref_tweets = pd.DataFrame(dict_hold_list)

Key Takeaways

• The .apply() method can lead to ambiguous ordering errors when dealing with mixed data types.
• Directly manipulating the existing data in a pandas Series (list) is often more efficient than creating new DataFrames or using .apply().
• Pandas provides optimized data structures for vectorized operations, making it easier to work with complex datasets.

Example Use Case

Suppose we have a DataFrame df that contains user information, including a nested dictionary column called 'referenced_tweets'. We want to extract the values from this column and perform further analysis on them. The approach outlined above would allow us to achieve this without using .apply():

import pandas as pd

# Sample data
data = {
    'id': [1, 2, 3],
    'referenced_tweets': [
        {'id': '1392893055112400898', 'text': '...'},
        {'id': '1234567890', 'type': 'some_type', 'text': '...'},
        {'id': '2345678901', 'referenced_tweets': [{'id': '3456789012', 'text': '...'}]}
    ]
}

# Create DataFrame
df = pd.DataFrame(data)

# Extract values from nested dictionaries
refs = df[df['referenced_tweets'].notnull()]['referenced_tweets']

dict_hold_list = []
for ref in refs:
    for r in ref:
        dict_hold_list.append({'text': r.get('text'), 'type': r.get('type')})

df_ref_tweets = pd.DataFrame(dict_hold_list)

# Print extracted data
print(df_ref_tweets)

This approach demonstrates how to efficiently extract values from nested dictionaries stored in a pandas DataFrame, avoiding the use of .apply() and leveraging vectorized operations for optimal performance.

Last modified on 2023-06-27