Randomly Select Columns from Pandas DataFrame

Randomly Select Columns from Pandas DataFrame

To randomly select columns from a Pandas DataFrame, you can use the random module from Python's standard library in conjunction with DataFrame indexing. The approach you choose can depend on whether you want to select a fixed number of columns or a fraction of the total columns. Here are two ways to do it:

Select a Fixed Number of Columns

If you want to select a fixed number of columns (e.g., 3 columns), you can use random.sample:

import pandas as pd import random # Example DataFrame df = pd.DataFrame({ 'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9], 'D': [10, 11, 12], 'E': [13, 14, 15] }) # Number of columns to select num_columns = 3 # Randomly select columns selected_columns = random.sample(list(df.columns), num_columns) # New DataFrame with selected columns new_df = df[selected_columns] print(new_df)

Select a Fraction of Columns

If you want to select a fraction of the total columns (e.g., 50% of the columns), you can calculate the number of columns to select based on the total number of columns:

# Fraction of columns to select fraction = 0.5 # Calculate the number of columns to select num_columns = int(len(df.columns) * fraction) # Randomly select columns selected_columns = random.sample(list(df.columns), num_columns) # New DataFrame with selected columns new_df = df[selected_columns] print(new_df)

Notes

  • The random.sample function ensures that the selected columns are unique and no column is selected more than once.
  • Make sure that num_columns does not exceed the total number of columns in the DataFrame to avoid a ValueError.
  • This method can be used for any DataFrame, regardless of its size or the nature of its contents.

Using these methods, you can randomly sample columns from a DataFrame for purposes like feature selection in machine learning, data exploration, or creating subsets of your data for analysis.

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