Sample and shuffle data to provide a fake dataset with the same structure that can be used for developing methods for sensitive data

sample_and_shuffle.py

def sample_and_shuffle(file, frac=0.1, save_to_file = False, **kwargs):
    """
    shuffle each column in a given dataframe and draw n samples
    this is useful for developing methods out of the server for sensitive data
    and testing on a data file that keeps the same structure

    :param file: path to csv file
    :param frac: fraction of data to sample
    :param save_to_file: save the sampled data to a csv file
    :param kwargs: keyword arguments for pandas.read_csv
    :return: sampled dataframe
    """
    
    import pandas as pd
    
    # read data
    df = pd.read_csv(file, **kwargs)

    # sample data
    df_shuffled = df.sample(frac=frac)

    # shuffle each column
    for n, column in enumerate(df.columns):

        df_shuffled[column] = df[column].sample(frac=frac).values
    
    # check for data breach on all sampled records
    breach = False
    for record_id in df_shuffled["record_id"].values:
        print(record_id)
        if (df[df["record_id"]==record_id].values == df_shuffled[df_shuffled["record_id"]==record_id].values).all():
            print("data breach on record:", record_id)
            breach = True
            break 
           
    # save to file
    if not breach and save_to_file:
        df_shuffled.to_csv("fake_data.csv", sep=";", index=False)
        print("Success!")
    
    return df_shuffled