Most efficient way to find mode in numpy array

Update

The scipy.stats.mode function has been significantly optimized since this post, and would be the recommended method

Old answer

This is a tricky problem, since there is not much out there to calculate mode along an axis. The solution is straight forward for 1-D arrays, where numpy.bincount is handy, along with numpy.unique with the return_counts arg as True. The most common n-dimensional function I see is scipy.stats.mode, although it is prohibitively slow- especially for large arrays with many unique values. As a solution, I’ve developed this function, and use it heavily:

import numpy

def mode(ndarray, axis=0):
    # Check inputs
    ndarray = numpy.asarray(ndarray)
    ndim = ndarray.ndim
    if ndarray.size == 1:
        return (ndarray[0], 1)
    elif ndarray.size == 0:
        raise Exception('Cannot compute mode on empty array')
    try:
        axis = range(ndarray.ndim)[axis]
    except:
        raise Exception('Axis "{}" incompatible with the {}-dimension array'.format(axis, ndim))

    # If array is 1-D and numpy version is > 1.9 numpy.unique will suffice
    if all([ndim == 1,
            int(numpy.__version__.split('.')[0]) >= 1,
            int(numpy.__version__.split('.')[1]) >= 9]):
        modals, counts = numpy.unique(ndarray, return_counts=True)
        index = numpy.argmax(counts)
        return modals[index], counts[index]

    # Sort array
    sort = numpy.sort(ndarray, axis=axis)
    # Create array to transpose along the axis and get padding shape
    transpose = numpy.roll(numpy.arange(ndim)[::-1], axis)
    shape = list(sort.shape)
    shape[axis] = 1
    # Create a boolean array along strides of unique values
    strides = numpy.concatenate([numpy.zeros(shape=shape, dtype='bool'),
                                 numpy.diff(sort, axis=axis) == 0,
                                 numpy.zeros(shape=shape, dtype='bool')],
                                axis=axis).transpose(transpose).ravel()
    # Count the stride lengths
    counts = numpy.cumsum(strides)
    counts[~strides] = numpy.concatenate([[0], numpy.diff(counts[~strides])])
    counts[strides] = 0
    # Get shape of padded counts and slice to return to the original shape
    shape = numpy.array(sort.shape)
    shape[axis] += 1
    shape = shape[transpose]
    slices = [slice(None)] * ndim
    slices[axis] = slice(1, None)
    # Reshape and compute final counts
    counts = counts.reshape(shape).transpose(transpose)[slices] + 1

    # Find maximum counts and return modals/counts
    slices = [slice(None, i) for i in sort.shape]
    del slices[axis]
    index = numpy.ogrid[slices]
    index.insert(axis, numpy.argmax(counts, axis=axis))
    return sort[index], counts[index]

Result:

In [2]: a = numpy.array([[1, 3, 4, 2, 2, 7],
                         [5, 2, 2, 1, 4, 1],
                         [3, 3, 2, 2, 1, 1]])

In [3]: mode(a)
Out[3]: (array([1, 3, 2, 2, 1, 1]), array([1, 2, 2, 2, 1, 2]))

Some benchmarks:

In [4]: import scipy.stats

In [5]: a = numpy.random.randint(1,10,(1000,1000))

In [6]: %timeit scipy.stats.mode(a)
10 loops, best of 3: 41.6 ms per loop

In [7]: %timeit mode(a)
10 loops, best of 3: 46.7 ms per loop

In [8]: a = numpy.random.randint(1,500,(1000,1000))

In [9]: %timeit scipy.stats.mode(a)
1 loops, best of 3: 1.01 s per loop

In [10]: %timeit mode(a)
10 loops, best of 3: 80 ms per loop

In [11]: a = numpy.random.random((200,200))

In [12]: %timeit scipy.stats.mode(a)
1 loops, best of 3: 3.26 s per loop

In [13]: %timeit mode(a)
1000 loops, best of 3: 1.75 ms per loop

EDIT: Provided more of a background and modified the approach to be more memory-efficient

Expanding on this method, applied to finding the mode of the data where you may need the index of the actual array to see how far away the value is from the center of the distribution.

(_, idx, counts) = np.unique(a, return_index=True, return_counts=True)
index = idx[np.argmax(counts)]
mode = a[index]

Remember to discard the mode when len(np.argmax(counts)) > 1, also to validate if it is actually representative of the central distribution of your data you may check whether it falls inside your standard deviation interval.

I think a very simple way would be to use the Counter class. You can then use the most_common() function of the Counter instance as mentioned here.

For 1-d arrays:

import numpy as np
from collections import Counter

nparr = np.arange(10) 
nparr[2] = 6 
nparr[3] = 6 #6 is now the mode
mode = Counter(nparr).most_common(1)
# mode will be [(6,3)] to give the count of the most occurring value, so ->
print(mode[0][0])    

For multiple dimensional arrays (little difference):

import numpy as np
from collections import Counter

nparr = np.arange(10) 
nparr[2] = 6 
nparr[3] = 6 
nparr = nparr.reshape((10,2,5))     #same thing but we add this to reshape into ndarray
mode = Counter(nparr.flatten()).most_common(1)  # just use .flatten() method

# mode will be [(6,3)] to give the count of the most occurring value, so ->
print(mode[0][0])

This may or may not be an efficient implementation, but it is convenient.

A neat solution that only uses numpy (not scipy nor the Counter class):

A = np.array([[1,3,4,2,2,7], [5,2,2,1,4,1], [3,3,2,2,1,1]])

np.apply_along_axis(lambda x: np.bincount(x).argmax(), axis=0, arr=A)

array([1, 3, 2, 2, 1, 1])

from collections import Counter

n = int(input())
data = sorted([int(i) for i in input().split()])

sorted(sorted(Counter(data).items()), key = lambda x: x[1], reverse = True)[0][0]

print(Mean)

The Counter(data) counts the frequency and returns a defaultdict. sorted(Counter(data).items()) sorts using the keys, not the frequency. Finally, need to sorted the frequency using another sorted with key = lambda x: x[1]. The reverse tells Python to sort the frequency from the largest to the smallest.

simplest way in Python to get the mode of an list or array a

import statistics
a=[7,4,4,4,4,25,25,6,7,4867,5,6,56,52,32,44,4,4,44,4,44,4]
print(f"{statistics.mode(a)} is the mode (most frequently occurring number)")

That’s it

If you want to use numpy only:

x = [-1, 2, 1, 3, 3]
vals,counts = np.unique(x, return_counts=True)

gives

(array([-1,  1,  2,  3]), array([1, 1, 1, 2]))

And extract it:

index = np.argmax(counts)
return vals[index]

if you want to find mode as int Value here is the easiest way
I was trying to find out mode of Array using Scipy Stats but the problem is that output of the code look like:

ModeResult(mode=array(2), count=array([[1, 2, 2, 2, 1, 2]])) , I only want the Integer output so if you want the same just try this

import numpy as np
from scipy import stats
numbers = list(map(int, input().split())) 
print(int(stats.mode(numbers)[0]))

Last line is enough to print Mode Value in Python: print(int(stats.mode(numbers)[0]))

If you wish to use only numpy and do it without using the index of the array, the following implementation combining dictionaries with numpy can be used.

x = np.array([1, 1, 2, 3])
val, count = np.unique(x,return_counts=True)
freq = {}
for v, c in zip(val, count):
  freq[v] = c
mode = sorted(freq.items(),key =lambda kv :kv[1])[-1] # (1, 2)
print(mode[0]) # prints 1 (most frequent item, mode)

Finding Mode using dict in python

def mode(x):
  d={}
  k=0
  v=0
  for i in x:
    d[i]=d.get(i,0)+1
    if d[i]>v:
      k=i
      v=d[i]
  print(d)
  return k

print(mode(x))