Determine sum of numpy array while excluding certain values

Question

I would like to determine the sum of a two dimensional numpy array. However, elements with a certain value I want to exclude from this summation. What is the most efficient way to do this?

For example, here I initialize a two dimensional numpy array of 1s and replace several of them by 2:

import numpy

data_set = numpy.ones((10, 10))

data_set[4][4] = 2
data_set[5][5] = 2
data_set[6][6] = 2

How can I sum over the elements in my two dimensional array while excluding all of the 2s? Note that with the 10 by 10 array the correct answer should be 97 as I replaced three elements with the value 2.

I know I can do this with nested for loops. For example:

elements = []
for idx_x in range(data_set.shape[0]):
  for idx_y in range(data_set.shape[1]):
    if data_set[idx_x][idx_y] != 2:
      elements.append(data_set[idx_x][idx_y])

data_set_sum = numpy.sum(elements)

However on my actual data (which is very large) this is too slow. What is the correct way of doing this?

Answer 1

Use numpy's capability of indexing with boolean arrays. In the below example data_set!=2 evaluates to a boolean array which is True whenever the element is not 2 (and has the correct shape). So data_set[data_set!=2] is a fast and convenient way to get an array which doesn't contain a certain value. Of course, the boolean expression can be more complex.

In [1]: import numpy as np
In [2]: data_set = np.ones((10, 10))
In [4]: data_set[4,4] = 2
In [5]: data_set[5,5] = 2
In [6]: data_set[6,6] = 2
In [7]: data_set[data_set != 2].sum()
Out[7]: 97.0
In [8]: data_set != 2
Out[8]: 
array([[ True,  True,  True,  True,  True,  True,  True,  True,  True,
         True],
       [ True,  True,  True,  True,  True,  True,  True,  True,  True,
         True],
       ...
       [ True,  True,  True,  True,  True,  True,  True,  True,  True,
         True]], dtype=bool)