What are some common mistakes in Python benchmarking?
An example of an AI chatbot conversation about common Python benchmarking mistakes.
What are some common mistakes in Python benchmarking?
Some common mistakes include not using a proper benchmarking library, failing to isolate the code being tested, and not accounting for system load.
Can you explain why using a proper benchmarking library is important?
Using a proper benchmarking library ensures accurate measurements, provides useful tools for setup, and helps avoid common pitfalls.
What are some popular benchmarking libraries for Python?
Popular libraries include timeit, pytest-benchmark, and asv (Airspeed Velocity). Each has its own strengths.
How can I isolate the code being tested during benchmarking?
You can isolate the code by using functions or classes to encapsulate the code, and by running benchmarks in a controlled environment.
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