So basically I have an array, that consists of 14 rows and 426 Columns, every row represents one property of a dog and every column represents one dog, now I want to know how many dogs are ill, this property is represented by the 14. row. 0 = Healthy and 1 = ill, so how do I count the specific row?
I tried to use the numpy.count_nonzero but this counts the values of the entire array, is there a way to tell it to only count a specific row?
You can simply sum the values of the 14.row and you get the number (count) of ill dogs:
count = A[13,:].sum() # number of ill dogs -- 13 because the index starts with 0
Suppose we have this vector:
>import numpy as np
>arr = np.arange(30).reshape(6,5)
>arr
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]])
With this, you will get the sum of all values for a specific row:
>np.sum(arr[1,:]) #On row 1
35
For your specific case, use:
>np.sum(arr[13,:])
