Troubleshooting NaN Output in Python: Fixing Errors in File-Based Calculations

Calculating Separate Averages of Positive and Negative Numbers from a File

Python backend script to read file data, calculate averages, and handle missing values robustly.

def calculate_averages(file_name):
    """Calculate and print average of negative and positive numbers from a file.
    Args:
        file_name (str): Name of the file containing numbers, one per line.
    Returns:
        None (prints averages directly).
    """
    negatives = []
    positives = []
    # Read the file and categorize numbers
    with open(file_name, 'r') as file:
        for line in file:
            try:
                num = float(line.strip())
                if num < 0:
                    negatives.append(num)
                elif num > 0:
                    positives.append(num)
            except ValueError:
                # Ignore lines that aren't valid numbers
                continue
    # Calculate averages with NaN fallback
    neg_avg = sum(negatives) / len(negatives) if negatives else float('NaN')
    pos_avg = sum(positives) / len(positives) if positives else float('NaN')
    # Print averages to match Pearson's expected format
    print(f"{neg_avg:.1f}".replace('nan', 'NaN'))
    print(f"{pos_avg:.1f}".replace('nan', 'NaN'))

# Call the function with test file
calculate_averages('numbers.txt')

Handling Different Data Formats with Modular and Reusable Code

Python backend script with improved modular structure and error handling for various data formats.

import math
def calculate_average(numbers):
    """Helper function to calculate average, returning NaN if list is empty."""
    return sum(numbers) / len(numbers) if numbers else float('NaN')

def parse_numbers(file_name):
    """Parse numbers from file, categorize them into positives and negatives."""
    negatives, positives = [], []
    with open(file_name, 'r') as file:
        for line in file:
            try:
                num = float(line.strip())
                if num < 0:
                    negatives.append(num)
                elif num > 0:
                    positives.append(num)
            except ValueError:
                continue
    return negatives, positives

def display_averages(neg_avg, pos_avg):
    """Prints averages in a specific format."""
    neg_output = str(neg_avg) if not math.isnan(neg_avg) else "NaN"
    pos_output = str(pos_avg) if not math.isnan(pos_avg) else "NaN"
    print(neg_output)
    print(pos_output)

# Main function to tie all parts together
def main(file_name):
    negatives, positives = parse_numbers(file_name)
    neg_avg = calculate_average(negatives)
    pos_avg = calculate_average(positives)
    display_averages(neg_avg, pos_avg)

# Execute main function with file input
main('numbers.txt')

Unit Testing for File-Based Average Calculation Program

Python unit tests for ensuring correct average calculation for different input scenarios.

import unittest
from io import StringIO
import sys

class TestCalculateAverages(unittest.TestCase):
    def setUp(self):
        self.file_name = 'test_numbers.txt'

    def test_both_positives_and_negatives(self):
        with open(self.file_name, 'w') as f:
            f.write("-5\n-10\n15\n20\n")
        output = StringIO()
        sys.stdout = output
        main(self.file_name)
        sys.stdout = sys.__stdout__
        self.assertEqual(output.getvalue().strip(), "-7.5\n17.5")

    def test_no_negatives(self):
        with open(self.file_name, 'w') as f:
            f.write("10\n20\n30\n")
        output = StringIO()
        sys.stdout = output
        main(self.file_name)
        sys.stdout = sys.__stdout__
        self.assertEqual(output.getvalue().strip(), "NaN\n20.0")

    def test_no_positives(self):
        with open(self.file_name, 'w') as f:
            f.write("-10\n-20\n-30\n")
        output = StringIO()
        sys.stdout = output
        main(self.file_name)
        sys.stdout = sys.__stdout__
        self.assertEqual(output.getvalue().strip(), "-20.0\nNaN")

    def tearDown(self):
        import os
        os.remove(self.file_name)

# Run the tests
unittest.main()

Common Questions About Python NaN and Error Handling

1. What does float('NaN') do in Python?
2. This command creates a special float value recognized as “NaN” (Not a Number). It’s useful for handling cases where a calculation is undefined or when you need to flag missing data in your program.
3. How can I ensure my output matches specific formatting requirements?
4. Using methods like replace() allows you to control how your output appears. For example, replace('nan', 'NaN') can ensure your “NaN” values appear in the correct case, as required in certain testing systems.
5. Why is try...except important in file-based programs?
6. The try...except block is crucial for error handling in cases where lines may contain invalid data. It prevents the program from crashing if a line can’t be converted to a float, making the code more reliable.
7. What is an inline conditional, and why use it?
8. An inline conditional like sum(numbers) / len(numbers) if numbers else float('NaN') allows you to perform an operation only when certain conditions are met, such as when a list has values. This is ideal for avoiding errors like division by zero.
9. How does the with open(file_name, 'r') command work?
10. This command opens a file in read mode and automatically closes it afterward. Using “with” ensures the file closes properly, which helps in resource management and avoids errors from accidentally leaving files open.
11. Can I test if a value is “NaN” in Python?
12. Yes, you can use math.isnan() to check if a value is “NaN.” This is particularly helpful when you want to format or exclude “NaN” values in calculations or output.
13. Why is formatting consistency important in automated grading?
14. Automated systems rely on exact formatting, so minor differences (like “nan” instead of “NaN”) can cause errors. Using consistent methods like replace() for formatting prevents these issues.
15. How does using lists simplify categorizing data in Python?
16. Lists allow you to separate data into categories like positives and negatives, which makes calculating separate statistics for each category straightforward. Appending values to lists based on conditions is efficient and keeps the code organized.
17. What are inline conditionals, and when should they be used?
18. Inline conditionals allow for concise one-line statements that execute code only if a condition is met. For example, calculating an average only if values exist in a list, preventing errors.
19. How can I redirect print output for testing?
20. By using StringIO and sys.stdout redirection, you can capture output in tests to verify that it matches expected results. This is a common practice in unit testing where you want to validate program output.
21. What is the purpose of tearDown in unit tests?
22. In unittest frameworks, tearDown() is used to clean up after tests, like removing temporary files. This ensures each test starts with a fresh environment, preventing data interference between tests.

Wrapping Up the Solution

This assignment demonstrates the importance of handling special cases, like missing positive or negative values, when calculating averages in Python. By using conditional statements and formatting adjustments, you ensure that “NaN” is returned when needed, preventing any errors from empty data lists.

Python’s tools like try...except and float('NaN') allow for flexible error management, making it easier to handle unexpected data. Such practices are invaluable for programmers tackling assignments, automated tests, and any situation requiring precise output formatting. 🚀