How Can I Efficiently Split a Pandas DataFrame into Participant-Specific DataFrames?

Splitting Dataframe into Participant-Specific Dataframes

You have a large dataframe with data from 60 respondents and are seeking a way to divide it into individual dataframes for each participant. The unique code for each participant is stored in a variable called 'name.'

Initially, you attempted to use a custom function to append dataframes based on the 'name' variable, but the execution took an unusually long time.

A more efficient approach is to utilize slicing in Pandas DataFrame. The following code provides a solution:

import pandas as pd
import numpy as np

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

# Create a unique list of names
UniqueNames = data.Names.unique()

# Create a dictionary to store the split dataframes
DataFrameDict = {elem: pd.DataFrame() for elem in UniqueNames}

# Iterate through UniqueNames and slice the original data
for key in DataFrameDict.keys():
    DataFrameDict[key] = data[data.Names == key]

# Access a specific dataframe using its name
specific_dataframe = DataFrameDict['Joe']

This approach swiftly creates individual dataframes for each participant, with the 'Names' column used for slicing. The resulting dataframes are organized within a dictionary, DataFrameDict, allowing for easy access.

The above is the detailed content of How Can I Efficiently Split a Pandas DataFrame into Participant-Specific DataFrames?. For more information, please follow other related articles on the PHP Chinese website!