Update Fedora state: 2026-04-29 11:50
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Breadway
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dot_config/private_Code/User/History/7da6e0fb/yfjL.py
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dot_config/private_Code/User/History/7da6e0fb/yfjL.py
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import argparse
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import os
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from pathlib import Path
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import logging
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from scipy import stats
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import statsmodels.api as sm
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import statsmodels.formula.api as smf
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logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
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def load_data(path):
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df = pd.read_csv(path)
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logging.info("Loaded %d rows from %s", len(df), path)
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return df
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def prepare_data(df):
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# Ensure required columns exist
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required = {'Participant_ID', 'Happiness', 'Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence'}
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missing = required - set(df.columns)
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if missing:
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raise KeyError(f"Missing required columns: {missing}")
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if 'Group' not in df.columns:
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df['Group'] = 'Intervention'
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df['Group'] = df['Group'].astype(str).str.strip().str.title()
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# Normalize adherence to boolean (Yes/No or True/False)
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for col in ['Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence']:
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df[col] = df[col].astype(str).str.strip().str.lower().map({'yes': True, 'no': False, 'true': True, 'false': False})
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# Count habits per row
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df['Habits_Count'] = (
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df[['Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence']].fillna(False).astype(int).sum(axis=1)
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)
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# Coerce Happiness to numeric and drop rows without Happiness
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df['Happiness'] = pd.to_numeric(df['Happiness'], errors='coerce')
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before = len(df)
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df = df.dropna(subset=['Happiness'])
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logging.info('Dropped %d rows without numeric Happiness', before - len(df))
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return df
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def descriptive_stats(df):
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print('Dataset shape:', df.shape)
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print('\nOverall summary:')
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print(df['Happiness'].describe())
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if 'Group' in df.columns:
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print('\nRows by group:')
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print(df['Group'].value_counts())
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print('\nAverage happiness by group:')
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print(df.groupby('Group')['Happiness'].agg(['mean', 'count', 'std']).round(3))
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print('\nAverage happiness by number of habits completed:')
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print(df.groupby('Habits_Count')['Happiness'].agg(['mean', 'count', 'std']).round(3))
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print('\nMedian happiness by habits:')
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print(df.groupby('Habits_Count')['Happiness'].median())
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# Correlations
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print('\nPearson correlation between Habits_Count and Happiness:')
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print(df[['Habits_Count', 'Happiness']].corr().round(3))
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print('\nPoint-biserial correlation (each habit vs happiness, intervention group only):')
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habit_df = df[df['Group'] == 'Intervention'] if 'Group' in df.columns else df
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for habit in ['Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence']:
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mask = ~habit_df[habit].isna()
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if mask.sum() == 0:
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print(f'{habit:22} (no data)')
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continue
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r, p = stats.pointbiserialr(habit_df.loc[mask, habit].astype(int), habit_df.loc[mask, 'Happiness'])
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print(f"{habit:22} r = {r:.3f} p = {p:.4f}")
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def cohen_d(x, y):
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# Cohen's d for two independent samples
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nx, ny = len(x), len(y)
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dof = nx + ny - 2
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pooled_sd = np.sqrt(((nx - 1) * x.std(ddof=1) ** 2 + (ny - 1) * y.std(ddof=1) ** 2) / dof)
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return (x.mean() - y.mean()) / pooled_sd
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def run_ols(df):
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if 'Group' in df.columns:
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model = smf.ols('Happiness ~ Habits_Count + C(Group)', data=df).fit()
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print('\nOLS regression: Happiness ~ Habits_Count + Group')
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else:
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X = sm.add_constant(df['Habits_Count'])
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y = df['Happiness']
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model = sm.OLS(y, X).fit()
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print('\nSimple OLS regression: Happiness ~ Habits_Count')
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print(model.summary())
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return model
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def run_mixedlm(df):
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# Random intercept for Participant_ID
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try:
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md = smf.mixedlm('Happiness ~ Habits_Count', data=df, groups=df['Participant_ID'])
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mdf = md.fit(reml=False)
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print('\nMixed-effects model (random intercept by Participant_ID):')
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print(mdf.summary())
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return mdf
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except Exception as e:
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logging.warning('MixedLM failed: %s', e)
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return None
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def make_plots(df, outdir, show_plots=False):
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outdir = Path(outdir)
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outdir.mkdir(parents=True, exist_ok=True)
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sns.set_style('whitegrid')
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# Boxplot by Habits_Count
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plt.figure(figsize=(9, 6))
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sns.boxplot(data=df, x='Habits_Count', y='Happiness', color='#4C72B0')
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plt.title('Daily Happiness by Number of Habits Completed')
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plt.xlabel('Number of habits followed (0–3)')
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plt.ylabel('Happiness (1–10)')
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f1 = outdir / 'happiness_by_habits_box.png'
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plt.tight_layout()
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plt.savefig(f1)
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if show_plots:
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plt.show()
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plt.close()
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# Violin / jitter + regression
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plt.figure(figsize=(9, 6))
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sns.violinplot(data=df, x='Habits_Count', y='Happiness', inner=None, color='#55A868')
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sns.stripplot(x='Habits_Count', y='Happiness', data=df, color='k', alpha=0.3, jitter=0.15)
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plt.title('Happiness distribution by Habits Completed')
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f2 = outdir / 'happiness_by_habits_violin.png'
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plt.tight_layout()
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plt.savefig(f2)
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if show_plots:
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plt.show()
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plt.close()
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# Participant average bar
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participant_avg = df.groupby('Participant_ID')['Happiness'].mean().sort_values()
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plt.figure(figsize=(12, 5))
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sns.barplot(x=range(len(participant_avg)), y=participant_avg.values, color='#C44E52')
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plt.axhline(df['Happiness'].mean(), color='black', linestyle='--', alpha=0.6)
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plt.xticks(range(len(participant_avg)), participant_avg.index, rotation=45)
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plt.title('Average Happiness per Participant (sorted)')
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f3 = outdir / 'participant_avg_happiness.png'
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plt.tight_layout()
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plt.savefig(f3)
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if show_plots:
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plt.show()
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plt.close()
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if 'Group' in df.columns:
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plt.figure(figsize=(7, 5))
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sns.barplot(data=df, x='Group', y='Happiness', estimator='mean', errorbar='sd', color='#8172B2')
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plt.title('Mean Happiness by Group')
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plt.ylabel('Average happiness')
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f_group = outdir / 'happiness_by_group.png'
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plt.tight_layout()
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plt.savefig(f_group)
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if show_plots:
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plt.show()
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plt.close()
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# Scatter with linear fit
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plt.figure(figsize=(9, 6))
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if 'Group' in df.columns:
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sns.scatterplot(data=df, x='Habits_Count', y='Happiness', hue='Group', alpha=0.35)
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else:
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sns.regplot(x='Habits_Count', y='Happiness', data=df, x_jitter=0.18, scatter_kws={'alpha': 0.4})
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plt.title('Happiness vs Number of Habits Completed (with linear fit)')
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f4 = outdir / 'happiness_vs_habits_regression.png'
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plt.tight_layout()
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plt.savefig(f4)
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if show_plots:
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plt.show()
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plt.close()
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logging.info('Saved plots to %s', outdir)
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def main(args):
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df = load_data(args.data)
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df = prepare_data(df)
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descriptive_stats(df)
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# Effect sizes
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group0 = df[df['Habits_Count'] == 0]['Happiness']
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group3 = df[df['Habits_Count'] == 3]['Happiness']
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if len(group0) > 1 and len(group3) > 1:
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d = cohen_d(group3, group0)
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print(f"\nCohen's d (3 habits vs 0 habits) = {d:.3f}")
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if 'Group' in df.columns:
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control = df[df['Group'] == 'Control']['Happiness']
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intervention = df[df['Group'] == 'Intervention']['Happiness']
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if len(control) > 1 and len(intervention) > 1:
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d_group = cohen_d(intervention, control)
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print(f"Cohen's d (Intervention vs Control happiness) = {d_group:.3f}")
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# Models
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run_ols(df)
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run_mixedlm(df)
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# Plots
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make_plots(df, args.outdir, show_plots=args.show)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Improved data analysis for organization_happiness_study_data.csv')
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parser.add_argument('--data', type=str, default='organization_happiness_study_data.csv', help='CSV data path')
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parser.add_argument('--outdir', type=str, default='plots', help='Directory to save plots')
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parser.add_argument('--show', action='store_true', help='Show plots interactively')
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args = parser.parse_args()
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main(args)
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