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multiply all lists in list of lists

I have a list of masks and I want to obtain the resulting mask by multiplying all of them. My Donkey Kong approach is the following:

a = [[1, 1], [1, 0], [1, 0]]
b = a[0]
for i in range(1, len(a)):
    b = b * np.array(a[i])

which I think it works as returns [1,0] as value of b .

Is there a nicer way of doing this?


EDIT: I am looking for common ranges in the mask. To find all the non zero ranges I do the following: label = 0

for i in range(1, len(labels)):
   label = label + np.array(labels[i])
label = [1 if x > 0 else 0 for x in label]

Take a look at np.prod , which returns the product of array elements over a given axis:

import numpy as np
a = [[1, 1], [1, 0], [1, 0]]
np.prod(a, axis=0)

I see that you are already using numpy so it can be used like other answers suggested. But still, a nice built-in solution only using reduce can be:

from functools import reduce

a = [[1, 1], [1, 0], [1, 0]]

def element_wise_multiply(list1, list2):
    return [x*y for x,y in zip(list1, list2)]

b = reduce(element_wise_multiply, a)

# Or as a lambda:
b = reduce(lambda list1, list2: [x*y for x,y in zip(list1, list2)], a)

This takes every two sub-lists and reduces them to one by multiplying all index-matching elements by using zip . It also gives:

[1, 0]

Use np.prod :

>>> np.prod(a, axis=0)
array([1, 0])
>>> 

Or you could use np.cumprod and get the last value:

>>> np.cumprod(a, axis=1)[-1]
array([1, 0], dtype=int32)
>>> 

what about

>>> np.array(a).prod(axis=0)
array([1, 0])

which multiplies elements within eeach column.

or with all to get a boolean result

>>> np.array(a).all(axis=0)
array([ True, False])

A built-in way with all that short circuits (no other answer short circuits now):

>>> [all(column) for column in zip(*a)]
[True, False]

where zip(*a) kindof transposes to produce "columns" as sublists here and we check if all entries are truthful. all has a short circuting behaviour.

You can int(all(sub)) to cast results to integer after all 'in the sublists.

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