Tipps and Tricks

Sometimes it is not hard to speed up some simple tasks. This page shows some templates that might help to improve the performance of your code.


This page is more or less a stub right now. If you have any interesting facts to share please open a Pull Request or Issue.


map() can be much faster than list comprehensions or generator expressions if and only if the function is implemented in C without Python attribute lookup.

All Python built-ins are written in C and some (there are exceptions like abs()) that perform really fast with map():

>>> import random
>>> l1 = [random.randint(0, 1000) for _ in range(20000)]
>>> l2 = [random.randint(0, 1000) for _ in range(20000)]
>>> l3 = [random.randint(0, 1000) for _ in range(20000)]
>>> %timeit [min(i) for i in zip(l1, l2, l3)]  
100 loops, best of 3: 4.94 ms per loop

>>> %timeit list(map(min, l1, l2, l3))  
100 loops, best of 3: 3.24 ms per loop

Sometimes it is not possible to use such a function directly with map() but before you use functools.partial() you can always use itertools.repeat()!

>>> from itertools import repeat
>>> lst = [0]*100000
>>> %timeit [isinstance(i, int) for i in lst]  
100 loops, best of 3: 17.4 ms per loop

>>> %timeit list(map(isinstance, lst, repeat(int)))  
100 loops, best of 3: 7.99 ms per loop


Using itertools.repeat() is only faster for very few functions. isinstance() is one of those!

Predicate functions

Sometimes one needs a predicate function or filter out some items. One little (although sometimes impossible!) trick is to use methods as predicate:

>>> import random
>>> from iteration_utilities import consume
>>> lst = [random.random() for _ in range(200000)]
>>> %timeit consume((i for i in lst if i > 0.5), None)  
100 loops, best of 3: 9.51 ms per loop

>>> %timeit consume(filter((0.5).__lt__, lst), None)  
100 loops, best of 3: 8.03 ms per loop

This shows only a slight improvement but it’s not always possible to use a generator expression or list comprehension. If you do the same with operator.lt() and functools.partial() or with a custom function you’ll see the performance increase:

>>> from functools import partial
>>> from operator import lt
>>> partial_gt_05 = partial(lt, 0.5)
>>> %timeit consume(filter(lambda x: x > 0.5, lst), None)  
10 loops, best of 3: 22.3 ms per loop

>>> %timeit consume(filter(partial_gt_05, lst), None)  
100 loops, best of 3: 17 ms per loop


Using the __lt__ and equivalent methods is not always possible, for example this bypasses Pythons data model. For example the following will fail: (5).__lt__(10.2) because integer don’t compare to floats. In that case you need to use: (5.0).__lt__(10.2).

However public methods are always available as well as several special methods like: __len__, __contains__, …