How Can I Efficiently Split a Large DataFrame into Individual DataFrames by Participant ID?

Splitting a Massive DataFrame into Individual DataFrames by Participant ID

Consider a scenario where you possess a colossal DataFrame containing data from an experiment involving 60 participants. Your goal is to divide this voluminous DataFrame into 60 distinct DataFrames, each representing an individual participant. An essential variable, 'name,' uniquely identifies each participant within the DataFrame.

An attempt to accomplish this task using a custom function, 'splitframe,' has proven unsuccessful, prompting the question of a more efficient solution.

A Superior Approach: Data Frame Slicing

An alternative strategy involves employing slicing techniques to segregate the DataFrame. Here's how:

1. Generate a distinctive list of participant names ('UniqueNames') using the 'Names' column of the DataFrame.
2. Establish a dictionary to accommodate the individual DataFrames, utilizing the 'UniqueNames' list as keys.
3. Iterate through each participant name and assign the corresponding data to a separate DataFrame within the dictionary.

This approach, utilizing slicing, provides a more straightforward and efficient method for creating individual DataFrames for each participant:

# Create a DataFrame with 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 names
UniqueNames = data['Names'].unique()

# Initialize a dictionary to store individual DataFrames
DataFrameDict = {elem: pd.DataFrame() for elem in UniqueNames}

# Populate the dictionary with individual DataFrames
for key in DataFrameDict.keys():
    DataFrameDict[key] = data[data['Names'] == key]

Accessing Individual DataFrames

To access a specific DataFrame for a particular participant, simply use the dictionary key corresponding to the participant's name, as demonstrated below:

DataFrameDict['Joe']

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