Testing Python Performance with timeit
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We'll explore how to objectively measure the speed of Python code using the built-in timeit module. There are often several working solutions to a problem, but which one is faster? I'll show you my working approach to performance testing.
👌 What's inside:
1. Practical use of the timeit module for speed testing.
2. Comparison of two methods for summing a list: the for loop vs. the built-in sum() function.
3. Checking code correctness using assertions and passing variables via globals.
4. How to correctly select the number of test runs.
5. Why you shouldn't use print() in tests and how to test file handling.
Visual comparison of performance in different Python versions (3.9 vs. 3.13).
💁♂️ Who will benefit from this:
1. Python developers who want to write not only functional but also efficient code.
2. Anyone who has encountered slow program performance and wants to learn how to identify bottlenecks.
3. Beginners, who want to develop the habit of testing the performance of their solutions from the very beginning.
#python #timeit #programming
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