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Generates a synthetic dataset with 200 samples containing group-based measurements, quality scores, environmental data, and temporal information, then saves it to a CSV file.

Purpose

Creates demonstration data for testing statistical analysis workflows. The function generates a structured dataset with three groups (Group_A, Group_B, Group_C) that have different baseline characteristics, along with associated measurements, quality scores, temperature, humidity, dates, and pass/fail outcomes. This is useful for testing data analysis pipelines, statistical models, and visualization tools without requiring real data.

Source Code

def create_sample_data():
    """Create sample dataset for demonstration"""
    np.random.seed(42)
    
    # Generate sample data
    n_samples = 200
    
    # Groups
    groups = ['Group_A', 'Group_B', 'Group_C'] * (n_samples // 3) + ['Group_A'] * (n_samples % 3)
    
    # Measurements with group effects
    measurements = []
    quality_scores = []
    
    for group in groups:
        if group == 'Group_A':
            base_value = 100
            quality_base = 85
        elif group == 'Group_B':
            base_value = 105
            quality_base = 90
        else:  # Group_C
            base_value = 95
            quality_base = 80
        
        measurements.append(base_value + np.random.normal(0, 10))
        quality_scores.append(quality_base + np.random.normal(0, 5))
    
    # Create DataFrame
    df = pd.DataFrame({
        'Group': groups,
        'Measurement': measurements,
        'Quality_Score': quality_scores,
        'Temperature': np.random.normal(25, 3, n_samples),
        'Humidity': np.random.normal(60, 8, n_samples),
        'Date': pd.date_range('2024-01-01', periods=n_samples, freq='D'),
        'Pass_Fail': np.random.choice(['Pass', 'Fail'], n_samples, p=[0.85, 0.15])
    })
    
    # Save to CSV
    csv_path = '/tf/active/smartstat/demo_data.csv'
    df.to_csv(csv_path, index=False)
    print(f"✅ Sample data created: {csv_path}")
    print(f"   - {len(df)} rows, {len(df.columns)} columns")
    print(f"   - Groups: {df['Group'].value_counts().to_dict()}")
    
    return csv_path, df

Return Value

Returns a tuple containing two elements: (1) a string representing the file path where the CSV was saved ('/tf/active/smartstat/demo_data.csv'), and (2) a pandas DataFrame containing the generated sample data with columns: Group, Measurement, Quality_Score, Temperature, Humidity, Date, and Pass_Fail. The DataFrame has 200 rows with specific group distributions and normally distributed values.

Dependencies

• numpy
• pandas

Required Imports

import numpy as np
import pandas as pd

Usage Example

import numpy as np
import pandas as pd

def create_sample_data():
    """Create sample dataset for demonstration"""
    np.random.seed(42)
    n_samples = 200
    groups = ['Group_A', 'Group_B', 'Group_C'] * (n_samples // 3) + ['Group_A'] * (n_samples % 3)
    measurements = []
    quality_scores = []
    for group in groups:
        if group == 'Group_A':
            base_value = 100
            quality_base = 85
        elif group == 'Group_B':
            base_value = 105
            quality_base = 90
        else:
            base_value = 95
            quality_base = 80
        measurements.append(base_value + np.random.normal(0, 10))
        quality_scores.append(quality_base + np.random.normal(0, 5))
    df = pd.DataFrame({
        'Group': groups,
        'Measurement': measurements,
        'Quality_Score': quality_scores,
        'Temperature': np.random.normal(25, 3, n_samples),
        'Humidity': np.random.normal(60, 8, n_samples),
        'Date': pd.date_range('2024-01-01', periods=n_samples, freq='D'),
        'Pass_Fail': np.random.choice(['Pass', 'Fail'], n_samples, p=[0.85, 0.15])
    })
    csv_path = '/tf/active/smartstat/demo_data.csv'
    df.to_csv(csv_path, index=False)
    return csv_path, df

# Usage
file_path, data = create_sample_data()
print(f"Data saved to: {file_path}")
print(data.head())

Best Practices

• The function uses a fixed random seed (42) for reproducibility, ensuring the same data is generated on each run
• Ensure the target directory '/tf/active/smartstat/' exists before calling this function, or modify the csv_path to a valid location
• The function creates 200 samples with specific group distributions: approximately 67 samples each for Group_A and Group_B, and 66 for Group_C
• Group_A has base measurement of 100 and quality score of 85, Group_B has 105/90, and Group_C has 95/80
• All numeric measurements use normal distributions with specified means and standard deviations
• The Pass_Fail column has an 85% pass rate, simulating realistic quality control scenarios
• Consider parameterizing the file path, number of samples, and group characteristics for more flexible reuse
• The function prints status information to stdout, which may need to be suppressed in production environments

Tags

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