92 lines
No EOL
2.9 KiB
Python
92 lines
No EOL
2.9 KiB
Python
import pandas as pd
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import numpy as np
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np.random.seed(42) # ensures you get exactly the same data every time
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N_PARTICIPANTS_PER_GROUP = 20
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DAYS = list(range(1, 31))
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def clip_yes_prob(prob, ceiling):
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return min(ceiling, max(0.05, prob))
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def generate_intervention_group(start_participant_id=1):
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rows = []
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for offset in range(N_PARTICIPANTS_PER_GROUP):
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participant_id = start_participant_id + offset
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org_bias = np.random.normal(0.7, 0.15) # each person has their own organization tendency
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for day in DAYS:
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calendar = np.random.choice(['Yes', 'No'], p=[clip_yes_prob(org_bias + 0.1, 0.95), 1 - clip_yes_prob(org_bias + 0.1, 0.95)])
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clean = np.random.choice(['Yes', 'No'], p=[clip_yes_prob(org_bias, 0.90), 1 - clip_yes_prob(org_bias, 0.90)])
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ontime = np.random.choice(['Yes', 'No'], p=[clip_yes_prob(org_bias + 0.05, 0.92), 1 - clip_yes_prob(org_bias + 0.05, 0.92)])
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adherence_count = sum(x == 'Yes' for x in [calendar, clean, ontime])
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baseline_happiness = np.random.normal(5.5, 1.0)
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happiness = int(np.clip(np.random.normal(baseline_happiness + adherence_count * 1.1, 1.2), 1, 10))
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rows.append([
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participant_id,
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'Intervention',
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day,
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calendar,
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clean,
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ontime,
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happiness,
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])
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return rows
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def generate_control_group(start_participant_id):
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rows = []
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for offset in range(N_PARTICIPANTS_PER_GROUP):
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participant_id = start_participant_id + offset
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for day in DAYS:
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# Control group only records happiness; all habit columns are No.
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calendar = 'No'
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clean = 'No'
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ontime = 'No'
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baseline_happiness = np.random.normal(5.5, 1.0)
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control_noise = np.random.normal(0.0, 1.1)
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happiness = int(np.clip(baseline_happiness + control_noise, 1, 10))
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rows.append([
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participant_id,
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'Control',
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day,
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calendar,
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clean,
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ontime,
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happiness,
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])
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return rows
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data = []
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data.extend(generate_intervention_group(start_participant_id=1))
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data.extend(generate_control_group(start_participant_id=N_PARTICIPANTS_PER_GROUP + 1))
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df = pd.DataFrame(
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data,
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columns=[
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'Participant_ID',
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'Group',
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'Day',
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'Calendar_Adherence',
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'Cleanliness_Adherence',
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'Punctuality_Adherence',
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'Happiness',
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],
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)
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# Save the combined dataset
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df.to_csv('organization_happiness_study_data.csv', index=False)
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print("✅ Full dataset saved as 'organization_happiness_study_data.csv' — open it in Excel!")
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print(df.head(10)) # shows first 10 rows |