# Count positive elements in a list with Python list understandings

I have a list of integers and I need to count how many of them are > 0.
I'm currently doing it with a list comprehension that looks like this:

``````sum([1 for x in frequencies if x > 0])
```
```

It seems like a decent comprehension but I don't really like the "1"; it seems like a bit of a magic number. Is there a more Pythonish way to do this?

If you want to reduce the amount of memory, you can avoid generating a temporary list by using a generator:

``````sum(x > 0 for x in frequencies)
```
```

This works because `bool` is a subclass of `int`:

``````>>> isinstance(True,int)
True
```
```

and `True`'s value is 1:

``````>>> True==1
True
```
```

However, as Joe Golton points out in the comments, this solution is not very fast. If you have enough memory to use a intermediate temporary list, then sth's solution may be faster. Here are some timings comparing various solutions:

``````>>> frequencies = [random.randint(0,2) for i in range(10**5)]

>>> %timeit len([x for x in frequencies if x > 0])   # sth
100 loops, best of 3: 3.93 ms per loop

>>> %timeit sum([1 for x in frequencies if x > 0])
100 loops, best of 3: 4.45 ms per loop

>>> %timeit sum(1 for x in frequencies if x > 0)
100 loops, best of 3: 6.17 ms per loop

>>> %timeit sum(x > 0 for x in frequencies)
100 loops, best of 3: 8.57 ms per loop
```
```

Beware that timeit results may vary depending on version of Python, OS, or hardware.

Of course, if you are doing math on a large list of numbers, you should probably be using NumPy:

``````>>> frequencies = np.random.randint(3, size=10**5)
>>> %timeit (frequencies > 0).sum()
1000 loops, best of 3: 669 us per loop
```
```

The NumPy array requires less memory than the equivalent Python list, and the calculation can be performed much faster than any pure Python solution.