Shuffle DataFrame rows in python

Shuffle DataFrame rows in python

To shuffle the rows of a DataFrame in Python, you can use the sample function from the Pandas library. Here's how you can do it:

import pandas as pd # Create a sample DataFrame data = {'Column1': [1, 2, 3, 4, 5], 'Column2': ['A', 'B', 'C', 'D', 'E']} df = pd.DataFrame(data) # Shuffle the rows shuffled_df = df.sample(frac=1, random_state=42) # frac=1 shuffles all rows, random_state for reproducibility print(shuffled_df)

In the above code, the sample function is used with the frac parameter set to 1, which means that all rows of the DataFrame will be shuffled. The random_state parameter is set to an arbitrary value (42 in this case) to ensure reproducibility of the shuffling.

Keep in mind that shuffling the DataFrame doesn't modify the original DataFrame; instead, it creates a new shuffled DataFrame. If you want to shuffle the DataFrame in place (modify the original DataFrame), you can use the inplace parameter:

df.sample(frac=1, random_state=42, inplace=True)

Replace the example DataFrame and column names with your actual DataFrame and column names to apply the shuffling to your data.

Examples

  1. How to Shuffle Rows in a Pandas DataFrame

    • Description: Learn how to shuffle the rows of a Pandas DataFrame to randomize their order.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5], 'B': ['a', 'b', 'c', 'd', 'e'] }) shuffled_df = df.sample(frac=1).reset_index(drop=True) # Shuffle rows print(shuffled_df) # Output: DataFrame with shuffled rows

  2. Shuffling Rows in a DataFrame with a Random Seed

    • Description: Shuffle DataFrame rows using a random seed to ensure reproducibility.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5], 'B': ['a', 'b', 'c', 'd', 'e'] }) shuffled_df = df.sample(frac=1, random_state=42).reset_index(drop=True) # Shuffle with seed print(shuffled_df) # Output: Reproducible shuffled DataFrame

  3. Shuffling Rows in a DataFrame While Keeping Certain Columns Intact

    • Description: Shuffle DataFrame rows but retain specific column ordering or content.
    • Explanation: This technique allows you to randomize certain parts of a DataFrame while keeping others in a consistent order.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'Group': [1, 1, 2, 2, 3], 'Value': [10, 20, 30, 40, 50] }) # Shuffle only rows within each group shuffled_df = df.groupby('Group').apply(lambda x: x.sample(frac=1)).reset_index(drop=True) print(shuffled_df) # Output: DataFrame with shuffled rows within groups

  4. Shuffling Rows in a DataFrame with Custom Weight

    • Description: Shuffle DataFrame rows with custom weights to bias the randomization process.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5], 'B': ['a', 'b', 'c', 'd', 'e'] }) # Define weights for shuffling weights = np.array([0.1, 0.2, 0.3, 0.4, 0.5]) weights /= weights.sum() # Normalize the weights shuffled_df = df.sample(frac=1, weights=weights).reset_index(drop=True) print(shuffled_df) # Output: DataFrame with rows shuffled with custom weights

  5. Shuffling Rows in a Large DataFrame

    • Description: Shuffle a large DataFrame with a considerable number of rows to ensure a different order.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'A': np.random.randint(1, 100, 100), 'B': np.random.choice(list('abcde'), 100) }) shuffled_df = df.sample(frac=1).reset_index(drop=True) # Shuffle a large DataFrame print(shuffled_df.head()) # Output: Display the first few rows of the shuffled DataFrame

  6. Shuffling Rows with Stratified Sampling in a DataFrame

    • Description: Use stratified sampling to shuffle DataFrame rows while maintaining a certain distribution.
    • Explanation: This approach is useful for shuffling while preserving the relative proportions of a specific feature.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'Category': ['A', 'A', 'B', 'B', 'C'], 'Value': [10, 20, 30, 40, 50] }) # Stratified shuffle to maintain proportion of 'Category' stratified_df = df.groupby('Category').apply(lambda x: x.sample(frac=1)).reset_index(drop=True) print(stratified_df) # Output: DataFrame with shuffled rows within each category

  7. Shuffling Rows with a Reset Index in a DataFrame

    • Description: Shuffle DataFrame rows and reset the index to start from zero after shuffling.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5], 'B': ['a', 'b', 'c', 'd', 'e'] }) shuffled_df = df.sample(frac=1).reset_index(drop=True) # Shuffle and reset index print(shuffled_df) # Output: DataFrame with shuffled rows and reset index

  8. Shuffling Rows and Dropping Duplicates in a DataFrame

    • Description: Shuffle DataFrame rows and remove duplicates to ensure unique records.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5, 2], 'B': ['a', 'b', 'c', 'd', 'e', 'b'] }) shuffled_df = df.sample(frac=1).drop_duplicates().reset_index(drop=True) # Shuffle and drop duplicates print(shuffled_df) # Output: Shuffled DataFrame with duplicates removed

  9. Shuffling Rows with a Condition in a DataFrame

    • Description: Shuffle DataFrame rows based on a specific condition or filter.
    • Code:
      import pandas as pd import numpy as np df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5], 'B': ['a', 'b', 'c', 'd', 'e'] }) # Shuffle rows where 'A' is greater than 2 filtered_df = df[df['A'] > 2].sample(frac=1).reset_index(drop=True) print(filtered_df) # Output: Shuffled DataFrame with rows meeting the condition

  10. Shuffling Rows with a Custom Function in a DataFrame

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