Alternative to Scipy mode function in Numpy?

Question

Is there another way in numpy to realize scipy.stats.mode function to get the most frequent values in ndarrays along axis?(without importing other modules) i.e.

import numpy as np
from scipy.stats import mode

a = np.array([[[ 0,  1,  2,  3,  4],
                  [ 5,  6,  7,  8,  9],
                  [10, 11, 12, 13, 14],
                  [15, 16, 17, 18, 19]],

                 [[ 0,  1,  2,  3,  4],
                  [ 5,  6,  7,  8,  9],
                  [10, 11, 12, 13, 14],
                  [15, 16, 17, 18, 19]],

                 [[40, 40, 42, 43, 44],
                  [45, 46, 47, 48, 49],
                  [50, 51, 52, 53, 54],
                  [55, 56, 57, 58, 59]]])

mode= mode(data, axis=0)
mode = mode[0]
print mode
>>>[ 0,  1,  2,  3,  4],
   [ 5,  6,  7,  8,  9],
   [10, 11, 12, 13, 14],
   [15, 16, 17, 18, 19]

Answer 1

The scipy.stats.mode function is defined with this code, which only relies on numpy:

def mode(a, axis=0):
    scores = np.unique(np.ravel(a))       # get ALL unique values
    testshape = list(a.shape)
    testshape[axis] = 1
    oldmostfreq = np.zeros(testshape)
    oldcounts = np.zeros(testshape)

    for score in scores:
        template = (a == score)
        counts = np.expand_dims(np.sum(template, axis),axis)
        mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
        oldcounts = np.maximum(counts, oldcounts)
        oldmostfreq = mostfrequent

    return mostfrequent, oldcounts

Source: https://github.com/scipy/scipy/blob/master/scipy/stats/stats.py#L609