How Can I Efficiently Split a Large DataFrame into Smaller Subsets Based on a Unique Identifier?

Splitting Large Dataframes into Smaller Subsets Based on a Unique Identifier Column

When working with large datasets, it can be advantageous to divide them into smaller, manageable subsets for more efficient processing and analysis. This article addresses the specific task of splitting a large dataframe with millions of rows into multiple dataframes, one for each unique code assigned to a participant.

The provided code snippet attempts to split the dataframe using a for loop to iterate through each row and check if the participant code matches the currently assigned code. While this approach is conceptually correct, its execution is inefficient and can lead to excessive runtime for large datasets.

Instead, a more efficient solution can be achieved through data manipulation techniques. By using the unique() function to identify distinct codes and then applying the filter() method to isolate rows associated with each code, we can create separate dataframes seamlessly.

In the improved code below, a dictionary is initialized to store the resulting dataframes, with each unique code serving as the dictionary key. The filter() method is used to extract rows based on the participant code, and the resulting dataframes are appended to the dictionary:

import pandas as pd
import numpy as np

# Create a dataframe with random data and a 'Names' column
data = pd.DataFrame({'Names': ['Joe', 'John', 'Jasper', 'Jez'] * 4, 'Ob1': np.random.rand(16), 'Ob2': np.random.rand(16)})

# Extract unique participant codes
participant_codes = data.Names.unique()

# Initialize a dictionary to store dataframes
participant_dataframes = {code: pd.DataFrame() for code in participant_codes}

# Iterate through unique codes and create dataframes for each participant
for code in participant_codes:
    participant_dataframes[code] = data[data.Names == code]

# Print dictionary keys to verify participant dataframes
print(participant_dataframes.keys())

By utilizing data manipulation techniques instead of explicit loops, this code provides a more efficient and scalable solution for splitting large dataframes based on a unique identifier column.

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