Count of specific case per row in matrix

Question

I am fairly new to numpy and scientific computing and I struggle with a problem for several days, so I decided to post it here.

I am trying to get a count for a specific occurence of a condition in a numpy array.

In [233]: import numpy as np

In [234]: a= np.random.random([5,5])

In [235]: a >.7
Out[235]: array([[False,  True,  True, False, False],
   [ True, False, False, False,  True],
   [ True, False,  True,  True, False],
   [False, False, False, False, False],
   [False, False,  True, False, False]], dtype=bool)

What I would like to count the number of occurence of True in each row and keep the rows when this count reach a certain threshold:

ex :

results=[]
threshold = 2

for i,row in enumerate(a>.7):
  if len([value for value in row if value==True]) > threshold:
     results.append(i) # keep ids for each row that have more than 'threshold' times True 

This is the non-optimized version of the code but I would love to achieve the same thing with numpy (I have a very large matrix to process).

I have been trying all sort of things with np.where but I only can get flatten results. I need the row number

Thanks in advance !

Answer 1

To make results reproducible, use some seed:

>>> np.random.seed(100)

Then for a sample matrix

>>> a = np.random.random([5,5])

Count number of occurences along axis with sum:

>>> (a >.7).sum(axis=1)
array([1, 0, 3, 1, 2])

You can get row numbers with np.where:

>>> np.where((a > .7).sum(axis=1) >= 2)
(array([2, 4]),)

To filter result, just use boolean indexing:

>>> a[(a > .7).sum(axis=1) >= 2]
array([[ 0.89041156,  0.98092086,  0.05994199,  0.89054594,  0.5769015 ],
       [ 0.54468488,  0.76911517,  0.25069523,  0.28589569,  0.85239509]])

Answer 2

You can sum over axis with a.sum.
Then you can use where on the resulting vector.

results = np.where(a.sum(axis=0) < threshold))