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Title: bpo-44019: Implement operator.call(). by anntzer · Pull Request #27888 · python/cpython · GitHub

Open Graph Title: bpo-44019: Implement operator.call(). by anntzer · Pull Request #27888 · python/cpython

X Title: bpo-44019: Implement operator.call(). by anntzer · Pull Request #27888 · python/cpython

Description: Having operator.call(obj, arg) mean type(obj).__call__(obj, arg) is consistent with the other dunder operators. The semantics with *args, **kwargs then follow naturally from the single-arg semantics. I realize that I proposed different semantics in the original bug report, but that didn't even actually match to the proposed use case; the second proposal in the thread is the correct one. The semantics here work for at least two different use cases that I have seen: Using concurrent.futures.Executor.map on a list of callables (executor.map(operator.call, callables)). In that case the performance doesn't actually matter much and a Python helper would in fact be fine. Loading a "typed" csv file (cf. numpy.loadtxt), where each column has a separate type, e.g. converters = [int, float, int, float] lines = [ # Assume that we already `str.split` them. ["1", "2.2", "3", "4.4"], ["5", "6.6", "7", "8.8"], ] [[*map(operator.call, converters, words)] for words in lines] # faster than the list comprehension [[conv(word) for conv, word in zip(converters, words)] for words in lines] Here the performance actually matters (with numpy, one may easily be loading millions of rows). A simple benchmark gives $ ./python -mtimeit -s 'from operator import call; funcs = [int, float, float, int]; inputs = ["1", "2", "3", "4"]' -- '[*map(call, funcs, inputs)]' 500000 loops, best of 5: 761 nsec per loop $ ./python -mtimeit -s 'from operator import call; funcs = [int, float, float, int]; inputs = ["1", "2", "3", "4"]' -- '[func(inp) for func, inp in zip(funcs, inputs)]' 200000 loops, best of 5: 1.07 usec per loop i.e. map(operator.call, ...) gives a significant speedup. https://bugs.python.org/issue44019

Open Graph Description: Having operator.call(obj, arg) mean type(obj).__call__(obj, arg) is consistent with the other dunder operators. The semantics with *args, **kwargs then follow naturally from the single-arg semanti...

X Description: Having operator.call(obj, arg) mean type(obj).__call__(obj, arg) is consistent with the other dunder operators. The semantics with *args, **kwargs then follow naturally from the single-arg semanti...

Opengraph URL: https://github.com/python/cpython/pull/27888

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