[python] List comprehension vs map

I ran a quick test comparing three methods for invoking the method of an object. The time difference, in this case, is negligible and is a matter of the function in question (see @Alex Martelli's response). Here, I looked at the following methods:

# map_lambda
list(map(lambda x: x.add(), vals))

# map_operator
from operator import methodcaller
list(map(methodcaller("add"), vals))

# map_comprehension
[x.add() for x in vals]

I looked at lists (stored in the variable vals) of both integers (Python int) and floating point numbers (Python float) for increasing list sizes. The following dummy class DummyNum is considered:

class DummyNum(object):
    """Dummy class"""
    __slots__ = 'n',

    def __init__(self, n):
        self.n = n

    def add(self):
        self.n += 5

Specifically, the add method. The __slots__ attribute is a simple optimization in Python to define the total memory needed by the class (attributes), reducing memory size. Here are the resulting plots.

Performance of mapping Python object methods

As stated previously, the technique used makes a minimal difference and you should code in a way that is most readable to you, or in the particular circumstance. In this case, the list comprehension (map_comprehension technique) is fastest for both types of additions in an object, especially with shorter lists.

Visit this pastebin for the source used to generate the plot and data.

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