Python: count number of elements in list for if condition

Question

Given a list of integers, what is the most Pythonic / best way of counting how many elements are within a certain range?

I researched and found 2 ways of doing it:

>>> x = [10, 60, 20, 66, 79, 5]
>>> len([i for i in x if 60 < i < 70])
1

or:

>>> x = [10, 60, 20, 66, 79, 5]
>>> sum(1 for i in x if 60 < i < 70)
1

Which method uses less time/memory (for larger lists) and why? Or maybe another way is better...

Answer 1

The generator expression is more memory efficient, because you don't have to create an extra list.

Creating a list and getting it's length (the latter being a very fast O(1) operation) seems to be faster than creating a generator and doing n additions for relatively small lists.

In [13]: x = [1]
In [14]: timeit len([i for i in x if 60 < i < 70])
10000000 loops, best of 3: 141 ns per loop
In [15]: timeit sum(1 for i in x if 60 < i < 70)
1000000 loops, best of 3: 355 ns per loop
In [16]: x = range(10)
In [17]: timeit len([i for i in x if 60 < i < 70])
1000000 loops, best of 3: 564 ns per loop
In [18]: timeit sum(1 for i in x if 60 < i < 70)
1000000 loops, best of 3: 781 ns per loop
In [19]: x = range(50)
In [20]: timeit len([i for i in x if 60 < i < 70])
100000 loops, best of 3: 2.4 µs per loop
In [21]: timeit sum(1 for i in x if 60 < i < 70)
100000 loops, best of 3: 2.62 µs per loop
In [22]: x = range(1000)
In [23]: timeit len([i for i in x if 60 < i < 70])
10000 loops, best of 3: 50.9 µs per loop
In [24]: timeit sum(1 for i in x if 60 < i < 70)
10000 loops, best of 3: 51.7 µs per loop

I tried with various lists, for example [65]*n and the trend does not change. For example:

In [1]: x = [65]*1000
In [2]: timeit len([i for i in x if 60 < i < 70])
10000 loops, best of 3: 67.3 µs per loop
In [3]: timeit sum(1 for i in x if 60 < i < 70)
10000 loops, best of 3: 82.3 µs per loop