The above code prodcues a graph of the cohen's d values of the column mentioned. for Max Speed. it give a Cohen's d for 7days, 14days, 21days, and 2days. I need a fuction to that will create a df for all those columns with there cohen's d values for 7, 14,21 and 28days.

def two_sample_data_setup_for_cohens_d(mean_1, mean_2, sample_std_dev_1, sample_std_dev_2): Creates two normal distributions of data values. Calculates differences in values and standard deviation of list of differences. Calculates Cohen's D - equation used in dependent t-tests param mean_1: mean of group one param mean_2: mean of group two :param sample_std_dev_1: sample standard deviation of group one param sample_std_dev_2: sample standard deviation of group two :returns: values_1, values_2, cohens_d: values for group one, values for group two, Cohen's d = values_1 np.random.normal(loc=mean_1, scale=sample_std_dev_1, size=3000) values_2 np.random.normal(loc=mean_2, scale=sample_std_dev_2, size=3000) differences_group_values = np. subtract(values_1, values_2) std_dev_differences_group_values np.std(differences_group_values, ddof=1) cohens_d = round(abs((mean_1 mean_2)/std_dev_differences_group_values), 2) return values_1, values_2, cohens_d def interpret_cohens_d(cohens_d): Determines text interpretation of effect size given Cohen's d value :param cohens_d: float of Cohen's d value :returns: effect_size_interpretation: adjective to describe magnitude of effect size if = 0.9: effect_size_interpretation "Very Large" return effect_size_interpretation def visualize_distributions (values_1, values_2, cohens_d, cohens_d_interpretation): Plots KDE plots of values from values_1 and values_2. Inserts Cohen's d value and interpretation in title param values_1: values in group one param values_2: values in group two :param cohens_d: Cohen's d value :param cohens_d_interpretation: text to describe magnitude of effect size :returns: plot figure plt.figure(figsize=(10, 8)) sns.distplot(values_1, hist=False) sns.distplot(values_2, hist=False) plt.xlabel("value", labelpad=13) plt.ylabel("frequency", labelpad=13) plt.title("Distribution of Two Groups with {0} Cohen's d of {1}".format(cohens_d_interpretation, cohens_d), y=1.015, fontsiz two_groups_close_means_small_std_dev = two_sample_data_setup_for_cohens_d(0, 1.2, 3, 3) values_1 = two_groups_close_means_small_std_dev[@] values_2 two_groups_close_means_small_std_dev [1] cohens_d = two_groups_close_means_small_std_dev[2] cohens_d_interpretation = interpret_cohens_d(cohens_d) visualize_distributions (values_1, values_2, cohens_d, cohens_d_interpretation) = columns ['Accelerations Zone 5', 'Decelerations Zone 5', 'Max Speed','Sprints'] # Need to check which more columns we need to add? for column in columns: for i in [7,14,21,28]: df_new = extract_records_based_on_days (data, data_match_day, 'Session Date', column,i) train_match_compare(df_new, data_match_day, 'Session Date',column, i) V_1,v_2,v_3,v_4 = cacl_mean_sd(df_new[column], data_match_day[column]) visualize_distributions(df_new[column], data_match_day[column], two_sample_data_setup_for_cohens_d(v_1,v_2,v_3,v_4), interpr