Find the most frequent number in a NumPy array

Question

If your list contains all non-negative ints, you should take a look at numpy.bincounts:

http://docs.scipy.org/doc/numpy/reference/generated/numpy.bincount.html

and then probably use np.argmax:

a = np.array([1,2,3,1,2,1,1,1,3,2,2,1])
counts = np.bincount(a)
print(np.argmax(counts))

For a more complicated list (that perhaps contains negative numbers or non-integer values), you can use np.histogram in a similar way. Alternatively, if you just want to work in python without using numpy, collections.Counter is a good way of handling this sort of data.

from collections import Counter
a = [1,2,3,1,2,1,1,1,3,2,2,1]
b = Counter(a)
print(b.most_common(1))

You may use

values, counts = np.unique(a, return_counts=True)

ind = np.argmax(counts)
print(values[ind])  # prints the most frequent element

ind = np.argpartition(-counts, kth=10)[:10]
print(values[ind])  # prints the 10 most frequent elements

If some element is as frequent as another one, this code will return only the first element.

If you're willing to use SciPy:

>>> from scipy.stats import mode
>>> mode([1,2,3,1,2,1,1,1,3,2,2,1])
(array([ 1.]), array([ 6.]))
>>> most_frequent = mode([1,2,3,1,2,1,1,1,3,2,2,1])[0][0]
>>> most_frequent
1.0

Performances (using iPython) for some solutions found here:

>>> # small array
>>> a = [12,3,65,33,12,3,123,888000]
>>> 
>>> import collections
>>> collections.Counter(a).most_common()[0][0]
3
>>> %timeit collections.Counter(a).most_common()[0][0]
100000 loops, best of 3: 11.3 µs per loop
>>> 
>>> import numpy
>>> numpy.bincount(a).argmax()
3
>>> %timeit numpy.bincount(a).argmax()
100 loops, best of 3: 2.84 ms per loop
>>> 
>>> import scipy.stats
>>> scipy.stats.mode(a)[0][0]
3.0
>>> %timeit scipy.stats.mode(a)[0][0]
10000 loops, best of 3: 172 µs per loop
>>> 
>>> from collections import defaultdict
>>> def jjc(l):
...     d = defaultdict(int)
...     for i in a:
...         d[i] += 1
...     return sorted(d.iteritems(), key=lambda x: x[1], reverse=True)[0]
... 
>>> jjc(a)[0]
3
>>> %timeit jjc(a)[0]
100000 loops, best of 3: 5.58 µs per loop
>>> 
>>> max(map(lambda val: (a.count(val), val), set(a)))[1]
12
>>> %timeit max(map(lambda val: (a.count(val), val), set(a)))[1]
100000 loops, best of 3: 4.11 µs per loop
>>> 

Best is 'max' with 'set' for small arrays like the problem.

According to @David Sanders, if you increase the array size to something like 100,000 elements, the "max w/set" algorithm ends up being the worst by far whereas the "numpy bincount" method is the best.

Starting in Python 3.4, the standard library includes the statistics.mode function to return the single most common data point.

from statistics import mode

mode([1, 2, 3, 1, 2, 1, 1, 1, 3, 2, 2, 1])
# 1

If there are multiple modes with the same frequency, statistics.mode returns the first one encountered.

---

Starting in Python 3.8, the statistics.multimode function returns a list of the most frequently occurring values in the order they were first encountered:

from statistics import multimode

multimode([1, 2, 3, 1, 2])
# [1, 2]

Also if you want to get most frequent value(positive or negative) without loading any modules you can use the following code:

lVals = [1,2,3,1,2,1,1,1,3,2,2,1]
print max(map(lambda val: (lVals.count(val), val), set(lVals)))

While most of the answers above are useful, in case you: 1) need it to support non-positive-integer values (e.g. floats or negative integers ;-)), and 2) aren't on Python 2.7 (which collections.Counter requires), and 3) prefer not to add the dependency of scipy (or even numpy) to your code, then a purely python 2.6 solution that is O(nlogn) (i.e., efficient) is just this:

from collections import defaultdict

a = [1,2,3,1,2,1,1,1,3,2,2,1]

d = defaultdict(int)
for i in a:
  d[i] += 1
most_frequent = sorted(d.iteritems(), key=lambda x: x[1], reverse=True)[0]