Data Processing Function in Python

Overview

In this document, we will create a Python function that processes a given dataset by filtering rows based on a certain condition, calculating the mean of a specified column, and returning the results in a structured format. This implementation follows best practices in coding, including input validation, comprehensive documentation, and clear comments.

Function Design

Purpose

The function process_data takes in a DataFrame, filters out rows based on a specified threshold of a numeric column, calculates the mean of another specified column, and returns the results.

Imports

We will use the pandas library to manage the DataFrame.

Function Implementation

import pandas as pd

def process_data(df: pd.DataFrame, filter_column: str, threshold: float, mean_column: str) -> dict:
    """
    Process the input DataFrame by filtering and calculating the mean.

    Parameters:
    df (pd.DataFrame): The input DataFrame to process.
    filter_column (str): The name of the column to filter on.
    threshold (float): The threshold value for filtering the DataFrame.
    mean_column (str): The column name for which the mean will be calculated.

    Returns:
    dict: A dictionary containing the filtered DataFrame and the mean of the specified column.

    Raises:
    ValueError: If the specified columns do not exist in the DataFrame or if the 
                threshold is not a number.
    """
    
    # Validate input types
    if not isinstance(df, pd.DataFrame):
        raise ValueError("Input df must be a pandas DataFrame.")

    if not isinstance(threshold, (int, float)):
        raise ValueError("Threshold must be a numeric value.")
    
    # Validate column names
    if filter_column not in df.columns or mean_column not in df.columns:
        raise ValueError("Specified columns must exist in the DataFrame.")
    
    # Filter the DataFrame
    filtered_df = df[df[filter_column] > threshold]
    
    # Calculate the mean of the specified column
    mean_value = filtered_df[mean_column].mean() if not filtered_df.empty else None
    
    # Create result dictionary
    results = {
        "filtered_data": filtered_df,
        "mean_value": mean_value
    }
    
    return results

Explanation

• Imports: The function begins by importing pandas, a necessary library for handling DataFrame operations.
• Function Definition: It takes four parameters: a DataFrame, a column to filter on, a threshold for filtering, and a column for mean calculation.
• Input Validation: Checks are included to ensure that the input DataFrame is valid, the threshold is a number, and specified columns exist in the DataFrame.
• Filtering: The DataFrame is filtered based on the given threshold.
• Mean Calculation: The mean of the specified column is calculated and returned along with the filtered DataFrame.
• Documentation: A comprehensive docstring is provided to document the parameters, return types, and exceptions.

Usage Example

Here’s how you can use the process_data function:

# Sample DataFrame
data = {
    'A': [1, 2, 3, 4, 5],
    'B': [5, 6, 7, 8, 9]
}
df = pd.DataFrame(data)

# Process the DataFrame
result = process_data(df, filter_column='A', threshold=2, mean_column='B')

# Display the results
print("Filtered Data:")
print(result['filtered_data'])
print("Mean Value of Column B: ", result['mean_value'])

Conclusion

This function provides a robust framework for processing data, making it efficient and scalable, while adhering to software engineering best practices. For further data analysis and visualization techniques, consider exploring courses on the Enterprise DNA Platform.