What are the differences in Pythons time.clock() in Mac vs Windo

ghz 14hours ago ⋅ 12 views

What are the differences in Pythons time.clock() in Mac vs Windows?

I am using Python's time to gauge the time frame of a Selenium process. My script is like this...

start_time = time.clock()
...
#ending with
final_time = '{0:.2f}'.format(time.clock()-start_time)

When ran on a windows OS I will get something like 55.22 but if ran on the Mac it will return something like .14 even though it was about the same time.

Any idea what is happening differently on the Mac? I am actually going to try on Ubuntu as well to see the differences.

Answer

The discrepancy you're seeing between the time.clock() results on Windows and macOS (and possibly on Ubuntu as well) arises from differences in how the time.clock() function behaves on different operating systems. This is a known issue, and here's why:

Explanation:

  1. time.clock() behavior on Windows vs. macOS/Linux:

    • On Windows, time.clock() measures wall-clock time (i.e., the real elapsed time).
    • On macOS and Linux, time.clock() used to measure CPU time (i.e., the amount of time the CPU spent executing your program) until Python 3.3. In Python 3.3+, it was replaced by time.process_time() to measure CPU time consistently across platforms.

  2. Changes in Python 3.3+:

    • Starting from Python 3.3, macOS and Linux use time.process_time() (instead of time.clock()), which measures the CPU time that the process has consumed.
    • On Windows, time.clock() continues to work as wall-clock time.

  3. Your case:

    • On Windows, time.clock() measures real elapsed time, which is what you expect.
    • On macOS and Linux, if you're using Python 3.3+ (which you likely are), time.clock() measures the CPU time, and this is why you're seeing much smaller numbers, as it measures only the CPU time, not the real elapsed time.

Solutions:

  1. Use time.perf_counter() for wall-clock time: If you're looking to measure real elapsed time (wall-clock time) across all platforms (Windows, macOS, and Linux), you should use time.perf_counter() instead of time.clock(). This function is designed to provide the most accurate wall-clock time across different platforms and is consistent in all environments.

    import time

start_time = time.perf_counter()  # Get the wall-clock time at the start
# Your Selenium operations
final_time = time.perf_counter() - start_time  # Calculate elapsed time
print(f"Elapsed time: {final_time:.2f} seconds")

  2. Alternative (use time.process_time() on macOS/Linux): If you're specifically interested in CPU time (e.g., time spent by the CPU in executing your program), you can use time.process_time() on both macOS and Linux. This is also available on Windows, starting from Python 3.3.

    import time

start_time = time.process_time()  # CPU time at the start
# Your Selenium operations
final_time = time.process_time() - start_time  # Calculate CPU time
print(f"CPU time: {final_time:.2f} seconds")

  3. Compatibility:

    • If you want to ensure compatibility and avoid issues where time.clock() behaves differently, always use time.perf_counter() for wall-clock time and time.process_time() for CPU time. These functions provide consistent results on all platforms.

In summary:

The main issue is that time.clock() is not consistent across platforms in its meaning. On Windows, it gives wall-clock time, while on macOS and Linux, it gives CPU time. The best solution is to switch to time.perf_counter() for measuring wall-clock time across all platforms.

#py