How to Calculate the Mode of NumPy Array?

Last Updated : 29 May, 2025

The goal here is to calculate the mode of a NumPy array, which refers to identifying the most frequent value in the array. For example, given the array [1, 1, 2, 2, 2, 3, 4, 5], the mode is 2, as it appears most frequently. Let's explore different approaches to accomplish this.

Using scipy.stats.mode

scipy.stats.mode function is designed specifically to find the mode of an array or dataset. It handles edge cases and is optimized for performance. This method returns not just the mode but also the count of its occurrences.

Python

from scipy import stats as st
import numpy as np

a = np.array([1, 1, 2, 2, 2, 3, 4, 5])
res = st.mode(a)
print(res.mode)

Output

Explanation: st.mode(a) computes the mode of the array a using scipy.stats.mode. The result is stored in res as an object.

Using np.bincount and np.argmax

np.bincount counts the occurrences of non-negative integers in an array and np.argmax helps in finding the index of the maximum value, which corresponds to the mode.

Python

import numpy as np

a = np.array([1, 1, 2, 2, 2, 3, 4, 5])
c = np.bincount(a)
m = np.argmax(c) 
print(m)

Output

Explanation:

np.bincount(a) counts occurrences of each integer in a, returning [0, 2, 3, 1, 1, 1].
np.argmax(c) returns the index of the highest count, which is 2, the mode.

Using collections.Counter

Counter class from the collections module counts the occurrences of elements in an iterable. By using the most_common method, we can directly retrieve the most frequent element, i.e., the mode.

Python

from collections import Counter
import numpy as np

a = np.array([1, 1, 2, 2, 2, 3, 4, 5])
c = Counter(a) 
m = c.most_common(1)[0][0] 
print(m)

Output

Explanation:

Counter(a) counts the occurrences of each element in a.
c.most_common(1)[0][0] extracts the most frequent element (mode) from the Counter object.

Using np.unique with return_counts

np.unique returns the unique elements in the array along with their counts, which can then be used to find the mode by selecting the element with the highest count.

Python

import numpy as np

a = np.array([1, 1, 2, 2, 2, 3, 4, 5])
u, c = np.unique(a, return_counts=True)
m = u[np.argmax(c)]  
print(m)