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25.9 timeit -- Measure execution time of small code snippets

25.9 timeit -- Measure execution time of small code snippets

New in version 2.3.

This module provides a simple way to time small bits of Python code. It has both command line as well as callable interfaces. It avoids a number of common traps for measuring execution times. See also Tim Peters' introduction to the ``Algorithms'' chapter in the Python Cookbook, published by O'Reilly.

The module defines the following public class:

class Timer( [stmt='pass' [, setup='pass' [, timer=<timer function>]]])
Class for timing execution speed of small code snippets.

The constructor takes a statement to be timed, an additional statement used for setup, and a timer function. Both statements default to 'pass'; the timer function is platform-dependent (see the module doc string). The statements may contain newlines, as long as they don't contain multi-line string literals.

To measure the execution time of the first statement, use the timeit() method. The repeat() method is a convenience to call timeit() multiple times and return a list of results.

print_exc( [file=None])
Helper to print a traceback from the timed code.

Typical use:

    t = Timer(...)       # outside the try/except
        t.timeit(...)    # or t.repeat(...)

The advantage over the standard traceback is that source lines in the compiled template will be displayed. The optional file argument directs where the traceback is sent; it defaults to sys.stderr.

repeat( [repeat=3 [, number=1000000]])
Call timeit() a few times.

This is a convenience function that calls the timeit() repeatedly, returning a list of results. The first argument specifies how many times to call timeit(). The second argument specifies the number argument for timeit().

Note: It's tempting to calculate mean and standard deviation from the result vector and report these. However, this is not very useful. In a typical case, the lowest value gives a lower bound for how fast your machine can run the given code snippet; higher values in the result vector are typically not caused by variability in Python's speed, but by other processes interfering with your timing accuracy. So the min() of the result is probably the only number you should be interested in. After that, you should look at the entire vector and apply common sense rather than statistics.

timeit( [number=1000000])
Time number executions of the main statement. This executes the setup statement once, and then returns the time it takes to execute the main statement a number of times, measured in seconds as a float. The argument is the number of times through the loop, defaulting to one million. The main statement, the setup statement and the timer function to be used are passed to the constructor.

Note: By default, timeit() temporarily turns off garbage collection during the timing. The advantage of this approach is that it makes independent timings more comparable. This disadvantage is that GC may be an important component of the performance of the function being measured. If so, GC can be re-enabled as the first statement in the setup string. For example:
    timeit.Timer('for i in xrange(10): oct(i)', 'gc.enable()').timeit()

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