Random Sample of a subset of a dataframe in Pandas

Random Sample of a subset of a dataframe in Pandas

You can easily create a random sample of a subset of a DataFrame in pandas using the sample() function. This function allows you to specify the number of samples you want to extract from the DataFrame. 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) # Specify the number of samples you want sample_size = 2 # Create a random sample of a subset of the DataFrame random_sample = df.sample(n=sample_size) print(random_sample)

In this example, df.sample(n=sample_size) creates a random sample of sample_size rows from the DataFrame df. The resulting random_sample DataFrame will contain two random rows from the original DataFrame.

You can also use the frac parameter instead of n to specify the fraction of rows you want to sample:

sample_fraction = 0.5 # Sample 50% of the rows random_sample = df.sample(frac=sample_fraction)

You can adjust the sample_size or sample_fraction to your needs to get the desired subset of the DataFrame.

Examples

  1. How to generate a random sample of rows from a Pandas DataFrame?

    • Description: Users often need to extract a random subset of rows from a DataFrame.
    import pandas as pd # Assuming df is your DataFrame random_sample = df.sample(frac=0.5) # Selects 50% of the rows randomly print("Random sample of DataFrame:\n", random_sample)

  2. Selecting a random sample of rows from a Pandas DataFrame with replacement

    • Description: Sampling with replacement allows rows to be selected more than once.
    import pandas as pd # Assuming df is your DataFrame random_sample = df.sample(n=5, replace=True) # Selects 5 rows randomly with replacement print("Random sample of DataFrame with replacement:\n", random_sample)

  3. How to get a random sample of columns from a Pandas DataFrame?

    • Description: Users may want to sample columns randomly instead of rows.
    import pandas as pd # Assuming df is your DataFrame random_columns = df.sample(axis=1, n=2) # Selects 2 columns randomly print("Random sample of DataFrame columns:\n", random_columns)

  4. Generate a random sample of rows from a Pandas DataFrame based on a condition

    • Description: Users may want to sample rows that satisfy certain conditions.
    import pandas as pd # Assuming df is your DataFrame condition = df['column_name'] == 'desired_value' random_sample = df[condition].sample(frac=0.5) # Selects 50% of rows that satisfy the condition randomly print("Random sample of DataFrame based on condition:\n", random_sample)

  5. Randomly sample rows from a Pandas DataFrame with specific probabilities

    • Description: Users may need to sample rows with different probabilities.
    import pandas as pd # Assuming df is your DataFrame probabilities = [0.1, 0.2, 0.3, 0.2, 0.2] # Probabilities for each row random_sample = df.sample(n=3, weights=probabilities) # Selects 3 rows with given probabilities print("Random sample of DataFrame with probabilities:\n", random_sample)

  6. How to randomly select a subset of rows from a Pandas DataFrame and keep the original index?

    • Description: Retaining the original index after sampling can be useful for further analysis.
    import pandas as pd # Assuming df is your DataFrame random_sample = df.sample(frac=0.5, replace=True).reset_index(drop=True) # Resets index after sampling print("Random sample of DataFrame with original index retained:\n", random_sample)

  7. Randomly select a sample of rows from a Pandas DataFrame and ignore index

    • Description: Ignoring the index can be helpful if you don't need it in the sampled DataFrame.
    import pandas as pd # Assuming df is your DataFrame random_sample = df.sample(frac=0.5, ignore_index=True) # Ignores the index after sampling print("Random sample of DataFrame with index ignored:\n", random_sample)

  8. Generate a random sample of rows from a Pandas DataFrame with a fixed seed for reproducibility

    • Description: Setting a seed ensures that the random sample is reproducible.
    import pandas as pd import numpy as np np.random.seed(42) # Setting a seed for reproducibility random_sample = df.sample(frac=0.5) print("Random sample of DataFrame with seed 42:\n", random_sample)

  9. How to randomly select a sample of rows from a Pandas DataFrame and maintain row order?

    • Description: Maintaining row order after sampling can be important in certain scenarios.
    import pandas as pd # Assuming df is your DataFrame random_sample = df.sample(frac=0.5).sort_index() print("Random sample of DataFrame with maintained row order:\n", random_sample)

  10. Selecting a random sample of rows from a Pandas DataFrame and excluding specific columns

    • Description: Sometimes, users may want to exclude certain columns while sampling rows.
    import pandas as pd # Assuming df is your DataFrame columns_to_exclude = ['column1', 'column2'] random_sample = df.drop(columns=columns_to_exclude).sample(frac=0.5) print("Random sample of DataFrame with specific columns excluded:\n", random_sample)

More Tags

spiral notnull module jframe passwordbox geoserver pandoc android-contentprovider scipy-spatial cloud-foundry

More Python Questions

More Electrochemistry Calculators

More Chemical thermodynamics Calculators

More Internet Calculators

More Date and Time Calculators