Comprehensive Function Development in Python

Problem Definition

Develop a Python function that calculates the mean, median, and mode of a list of numbers. This function should handle exceptions related to invalid input and provide useful error messages.

Requirements

• Input: A list of numerical values.
• Output: A dictionary containing the mean, median, and mode.
• Handle exceptions for empty lists and non-numeric values.

Implementation Steps

1. Import Necessary Libraries: We will use statistics for calculating the median and mode, and numpy for the mean.
2. Define the Function: Create a function called calculate_statistics.
3. Input Validation: Validate the input for:
   • Non-numeric values.
   • Empty lists.
4. Calculations: Compute mean, median, and mode.
5. Return Output: Format the results in a dictionary.

Code Implementation

import statistics
import numpy as np

def calculate_statistics(data):
    """
    Calculates the mean, median, and mode of a list of numbers.

    Parameters:
    data (list): A list of numerical values.

    Returns:
    dict: A dictionary containing the mean, median, and mode.

    Raises:
    ValueError: If the input list is empty or contains non-numeric values.
    """
    # Validate the input list
    if not data:
        raise ValueError("Input list is empty.")
    
    # Check for non-numeric values
    for item in data:
        if not isinstance(item, (int, float)):
            raise ValueError(f"Non-numeric value found: {item}")
    
    # Calculate statistics
    mean_value = np.mean(data)
    median_value = statistics.median(data)
    
    # Mode calculation may return multiple values; handle accordingly
    try:
        mode_value = statistics.mode(data)
    except statistics.StatisticsError:
        mode_value = "No unique mode"
    
    # Prepare results
    return {
        'mean': mean_value,
        'median': median_value,
        'mode': mode_value
    }

Explanation of the Code

1. Imports: The statistics and numpy libraries are imported for statistical calculations.
2. Function Definition: The function calculate_statistics is defined, which accepts a list of numbers.
3. Input Validation:
   • Checks if the input list is empty and raises a ValueError if so.
   • Iterates through the list to ensure all elements are numeric, raising an error if any non-numeric is found.
4. Calculations:
   • Computes the mean using numpy.mean.
   • Computes the median using statistics.median.
   • Computes the mode using statistics.mode, with exception handling if there’s no unique mode.
5. Returning Results: The function returns a dictionary containing the statistics.

Usage Example

data = [1, 2, 2, 3, 4]
results = calculate_statistics(data)
print(results)  # Output: {'mean': 2.4, 'median': 2, 'mode': 2}

Error Handling Example

try:
    invalid_data = [1, 'two', 3]
    calculate_statistics(invalid_data)
except ValueError as e:
    print(e)  # Output: Non-numeric value found: two

Conclusion

This function is designed to be efficient and user-friendly, providing robust statistical calculations with proper error handling. It is a solid foundation for further statistical analysis and can be easily extended for additional functionality.

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