Find most common value in numpy 2d array rows, otherwise return maximum

December 2, 2021

I have an array like this

Nbank = np.array([[2, 3, 1],
                  [1, 2, 2],
                  [3, 2, 1],
                  [3, 2, 1],
                  [2, 3, 2],
                  [2, 2, 3],
                  [1, 1, 3],
                  [2, 1, 1],
                  [2, 2, 3],
                  [1, 1, 1],
                  [2, 1, 1],
                  [2, 3, 1],
                  [1, 2, 1]])

I want to return an array with only one column. The condition is to return the most common value in each row; if multiple values have the same number of occurrences, just return the maximum of them.

I used this code

most_f = np.array([np.bincount(row).argmax() for row in Nbank])

if multiple values have the same number of occurrences, it returns the first item instead of the maximum. how can I work this around?

>Solution :

I believe this will solve the problem. You could probable make it into a one liner with some fancy list comprehension, but I don’t think that would be worth while.

most_f = []
for n in Nbank: #iterate over elements
    counts = np.bincount(n) #count the number of elements of each value
    most_f.append(np.argwhere(counts == np.max(counts))[-1][0]) #append the last and highest