Which Python Timing Function Should You Use for Accurate Benchmarks?

Accuracy Comparison of Python's Timing Functions

In Python, two primary functions for timing are available: time.clock() and time.time(). The choice between them depends on the desired level of accuracy.

Since Python 3.3, time.clock() has become deprecated, urging developers to use time.process_time() or time.perf_counter() instead.

Historically, time.clock() was recommended for benchmarking in Python 2.7. According to the time module documentation:

• On Unix systems, time.clock() returns the current processor time in seconds. This measure is precision-dependent on the underlying C function and is suitable for algorithm timing.
• On Windows, time.clock() provides wall-clock seconds elapsed as a floating-point number with a resolution of typically one microsecond.

In contrast, time.time() measures wall-clock time in seconds from an arbitrary point in the past. It offers high accuracy but may be influenced by system events like sleep mode or time zone changes.

For benchmarking Python or timing algorithms, time.process_time() or time.perf_counter() are now the more appropriate choices. The former returns CPU time for the current process, while the latter measures a high-resolution system time.

Additionally, the timeit module provides specialized functions specifically designed for benchmarking code snippets, offering precise and standardized timing results.

The above is the detailed content of Which Python Timing Function Should You Use for Accurate Benchmarks?. For more information, please follow other related articles on the PHP Chinese website!