Synthetic Data Generation Tool in Python

Last update on December 21 2024 07:18:05 (UTC/GMT +8 hours)

Write a Python program to create a tool for generating synthetic data for testing purposes.

The task involves developing a tool capable of generating synthetic data, primarily for testing purposes. This tool creates artificial datasets with customizable characteristics such as data types, distributions, and sizes. Synthetic data generation aids in various testing scenarios, including software development, quality assurance, and data analysis, by providing controlled datasets that mimic real-world data while ensuring privacy and security.

Sample Solution:

Python Code :

# Import necessary libraries
import pandas as pd
import numpy as np

class SyntheticDataGenerator:
    def __init__(self, num_rows, num_columns):
        self.num_rows = num_rows
        self.num_columns = num_columns
        self.data = None

    def generate_numeric_data(self, min_value=0, max_value=100):
        # Generate random numeric data
        self.data = pd.DataFrame(np.random.randint(min_value, max_value, size=(self.num_rows, self.num_columns)), columns=[f"Column_{i}" for i in range(1, self.num_columns + 1)])
    
    def generate_categorical_data(self, categories=None, weights=None):
        # Generate random categorical data
        if categories is None:
            categories = ['Category_A', 'Category_B', 'Category_C']
        if weights is None:
            weights = [0.5, 0.3, 0.2]
        self.data = pd.DataFrame(np.random.choice(categories, size=(self.num_rows, self.num_columns), p=weights), columns=[f"Column_{i}" for i in range(1, self.num_columns + 1)])
    
    def generate_dates(self, start_date='2020-01-01', end_date='2021-12-31', format='%Y-%m-%d'):
        # Generate date data
        start_date = pd.to_datetime(start_date)
        end_date = pd.to_datetime(end_date)
        self.data = pd.DataFrame({'Date': pd.date_range(start=start_date, end=end_date, periods=self.num_rows)})
    
    def save_data(self, filename='synthetic_data.csv'):
        # Save generated data to a CSV file
        self.data.to_csv(filename, index=False)

# Example usage
if __name__ == "__main__":
    # Initialize data generator
    data_generator = SyntheticDataGenerator(num_rows=1000, num_columns=5)
    # Generate numeric data
    data_generator.generate_numeric_data()
    # Generate categorical data
    data_generator.generate_categorical_data()
    # Generate dates
    data_generator.generate_dates()
    # Save generated data to a CSV file
    data_generator.save_data('synthetic_data.csv')

Output:

synthetic_data.xls

Explanation:

• Imported "pandas" and "numpy" libraries for data manipulation and generation.
• Defined a class "SyntheticDataGenerator" to generate synthetic data.
• Initialize the class with the number of rows and columns for the dataset.
• Created methods to generate numeric, categorical, and date data using random values or predefined categories.
• Implemented a method to save generated data to a CSV file.
• Demonstrated example usage by generating synthetic data with numeric, categorical, and date columns, then saving it to a CSV file.

Python Code Editor :

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