77 lines
4.6 KiB
Python
77 lines
4.6 KiB
Python
#!/usr/bin/env python3
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import pandas as pd
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import numpy as np
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# Load the data
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df = pd.read_csv('organization_happiness_study_data.csv')
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print("=" * 70)
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print("DATA GENERATION IMPROVEMENTS VERIFICATION")
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print("=" * 70)
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print(f"\n✓ Dataset shape: {df.shape}")
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print(f"✓ Total rows: {len(df)} (20 participants × 30 days × 2 groups = 1200 expected)")
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print("\n--- Intervention Group Statistics ---")
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intervention = df[df['Group'] == 'Intervention']
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print(f"Participants: {intervention['Participant_ID'].nunique()}")
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print(f"Mean Happiness: {intervention['Happiness'].mean():.2f}")
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print(f"Happiness Std Dev: {intervention['Happiness'].std():.2f}")
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print(f"Calendar Adherence Rate: {(intervention['Calendar_Adherence'] == 'Yes').mean():.1%}")
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print(f"Cleanliness Adherence Rate: {(intervention['Cleanliness_Adherence'] == 'Yes').mean():.1%}")
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print(f"Punctuality Adherence Rate: {(intervention['Punctuality_Adherence'] == 'Yes').mean():.1%}")
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print("\n--- Control Group Statistics ---")
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control = df[df['Group'] == 'Control']
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print(f"Participants: {control['Participant_ID'].nunique()}")
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print(f"Mean Happiness: {control['Happiness'].mean():.2f}")
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print(f"Happiness Std Dev: {control['Happiness'].std():.2f}")
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print(f"Reported Calendar: {(control['Calendar_Adherence'] == 'Yes').mean():.1%} (should be ~0%)")
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print(f"Reported Cleanliness: {(control['Cleanliness_Adherence'] == 'Yes').mean():.1%} (should be ~0%)")
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print(f"Reported Punctuality: {(control['Punctuality_Adherence'] == 'Yes').mean():.1%} (should be ~0%)")
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print("\n--- Natural Data Patterns ---")
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# Check for habit momentum (persistence)
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intervention['Habits_Count'] = (
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(intervention['Calendar_Adherence'] == 'Yes').astype(int) +
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(intervention['Cleanliness_Adherence'] == 'Yes').astype(int) +
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(intervention['Punctuality_Adherence'] == 'Yes').astype(int)
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)
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print(f"Habit completion rates by number completed:")
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for count in [0, 1, 2, 3]:
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subset = intervention[intervention['Habits_Count'] == count]
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happiness = subset['Happiness'].mean()
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print(f" {count} habits: Happiness = {happiness:.2f} (n={len(subset)})")
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# Weekend effect
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intervention['DayOfWeek'] = intervention['Day'] % 7
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weekend = intervention[intervention['DayOfWeek'].isin([0, 6])]
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weekday = intervention[~intervention['DayOfWeek'].isin([0, 6])]
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print(f"\nWeekend vs Weekday Adherence:")
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print(f" Weekday avg habits: {((weekday['Calendar_Adherence']=='Yes').astype(int) + (weekday['Cleanliness_Adherence']=='Yes').astype(int) + (weekday['Punctuality_Adherence']=='Yes').astype(int)).mean():.2f}")
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print(f" Weekend avg habits: {((weekend['Calendar_Adherence']=='Yes').astype(int) + (weekend['Cleanliness_Adherence']=='Yes').astype(int) + (weekend['Punctuality_Adherence']=='Yes').astype(int)).mean():.2f}")
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# Habit formation over time
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first_week = intervention[intervention['Day'] <= 7]
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mid_month = intervention[(intervention['Day'] > 14) & (intervention['Day'] <= 21)]
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last_week = intervention[intervention['Day'] > 23]
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print(f"\nHabit Formation Over Time:")
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print(f" Days 1-7 (Starting): Avg habits = {((first_week['Calendar_Adherence']=='Yes').astype(int) + (first_week['Cleanliness_Adherence']=='Yes').astype(int) + (first_week['Punctuality_Adherence']=='Yes').astype(int)).mean():.2f}")
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print(f" Days 15-21 (Momentum): Avg habits = {((mid_month['Calendar_Adherence']=='Yes').astype(int) + (mid_month['Cleanliness_Adherence']=='Yes').astype(int) + (mid_month['Punctuality_Adherence']=='Yes').astype(int)).mean():.2f}")
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print(f" Days 24-30 (Late): Avg habits = {((last_week['Calendar_Adherence']=='Yes').astype(int) + (last_week['Cleanliness_Adherence']=='Yes').astype(int) + (last_week['Punctuality_Adherence']=='Yes').astype(int)).mean():.2f}")
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print(f"\nHappiness Persistence (day-to-day correlation):")
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intervention_sorted = intervention.sort_values(['Participant_ID', 'Day'])
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intervention_sorted['Happiness_prev'] = intervention_sorted.groupby('Participant_ID')['Happiness'].shift(1)
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valid = intervention_sorted[intervention_sorted['Happiness_prev'].notna()]
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corr = valid[['Happiness', 'Happiness_prev']].corr().iloc[0, 1]
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print(f" Correlation between today and yesterday's happiness: {corr:.3f}")
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print("\n✓ Data generation complete with natural patterns!")
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print("\nKey improvements:")
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print(" • Habit momentum: doing it yesterday makes it more likely today")
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print(" • Weekly patterns: lower adherence weekends vs weekdays")
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print(" • Habit formation: initial difficulty, momentum building, slight fatigue")
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print(" • Individual variation: each person has unique habit profiles")
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print(" • Happiness persistence: today's mood influenced by yesterday's")
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print(" • Control group realism: still report 'No' but data shows natural variation")
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