It can be difficult to deal with unexpected "NaN" results in Python assignments, particularly when working with files that contain data variances. In order to guarantee error-free computations, this guide offers a way for computing distinct averages for positive and negative numbers, managing missing values with float('NaN'). It also discusses the necessary formatting steps to guarantee that the output satisfies the requirements of automated grading. Program dependability is increased by using Python's try...except for error handling and with open for file reading, which makes it helpful for assignments and real-world data analysis.