Скачать или смотреть How to Fix a Misleading Timer in Your Python Code

Discover how to efficiently fix and optimize your Python timer code for better accuracy and performance. Learn simple debugging steps to enhance your coding skills!
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This video is based on the question https://stackoverflow.com/q/66013724/ asked by the user 'lemongrass' ( https://stackoverflow.com/u/12263836/ ) and on the answer https://stackoverflow.com/a/66014363/ provided by the user 'DapperDuck' ( https://stackoverflow.com/u/12602208/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Fix a Misleading Timer in Your Python Code: A Simple Guide

When running benchmarks or measuring time in a Python program, you might find that the results seem off or inaccurate. This is a common issue that can arise due to certain mistakes in your code logic. In this guide, we’ll address a specific example where a user encountered an unexpected behavior with a timer in their benchmarking script. Let’s break down the problem and explore the solution step-by-step.

Understanding the Problem

The user’s original code snippet was intended to benchmark how long it takes to add up a million random numbers from 0 to 9. However, the output results displayed timings that were inconsistent and appeared to have discrepancies. The user reported timings like:

[[See Video to Reveal this Text or Code Snippet]]

Notably, the time difference between each iteration seemed to be roughly half a second, and increasing the number of calculations did not yield expected results.

Root Cause of the Issue

After analyzing the code, it became clear that two main issues were affecting the timer’s accuracy:

Placement of the start variable: The start time was being recorded inside the loop, causing it to measure only the time taken for the last addition of a random number.

Incorrect modulo check: The stress_report % 10000000 == 0 condition was leading to an excessive burden on the system without proper adjustment in how many iterations were performed.

Implementing the Solution

To resolve these issues, we need to make the following changes to the code:

Move the start variable declaration to before the addition loop, so the total time for all calculations is captured.

Update the modulo condition to check for 1000000 to get a clearer read on performance over a more manageable number of iterations.

Here’s the revised code:

[[See Video to Reveal this Text or Code Snippet]]

Output Explanation

After implementing the adjustments, the output may look like this:

[[See Video to Reveal this Text or Code Snippet]]

Now, each iteration reflects the actual time taken to add up a million random numbers, which stabilizes around 1.3 to 1.6 seconds. This approach not only fixes the previous inaccuracies but also provides a clearer benchmark for future analyses.

Conclusion

Getting accurate timings in Python requires careful attention to where variables are declared and how iterations are conducted. By moving the start time outside the inner loop and adjusting the counting condition, you can achieve a more reliable benchmark.

Errors can happen to anyone, but learning from them is key to becoming a better programmer. Happy coding!