You can use the following methods to find the most frequent value in a NumPy array:

**Method 1: Find Most Frequent Value**

**#find frequency of each value
values, counts = np.unique(my_array, return_counts=True)
#display value with highest frequency
values[counts.argmax()]
**

If there are multiple values that occur most frequently in the NumPy array, this method will only return the first value.

**Method 2: Find Each Most Frequent Value**

**#find frequency of each value
values, counts = np.unique(my_array, return_counts=True)
#display all values with highest frequencies
values[counts == counts.max()]
**

If there are multiple values that occur most frequently in the NumPy array, this method will return each of the most frequently occurring values.

The following examples show how to use each method in practice.

**Example 1: Find Most Frequent Value in NumPy Array**

Suppose we have the following NumPy array:

**import numpy as np
#create NumPy array
my_array = np.array([1, 2, 4, 4, 4, 5, 6, 7, 12])
**

Notice that there is only one value that occurs most frequently in this array: **4**.

We can use the **argmax()** function to return the value that occurs most frequently in the array:

**#find frequency of each value
values, counts = np.unique(my_array, return_counts=True)
#display value with highest frequency
values[counts.argmax()]
4**

The function correctly returns the value **4**.

**Example 2: Find Each Most Frequent Value in NumPy Array**

Suppose we have the following NumPy array:

**import numpy as np
#create NumPy array
my_array = np.array([1, 2, 4, 4, 4, 5, 6, 7, 12, 12, 12])
**

Notice that there are two values that occur most frequently in this array: **4** and **12**.

We can use the **max()** function to return each of the values that occur most frequently in the array:

**#find frequency of each value
values, counts = np.unique(my_array, return_counts=True)
#display each value with highest frequency
values[counts == counts.max()]
array([ 4, 12])
**

The function correctly returns the values **4** and **12**.

**Note**: You can find the complete documentation for the NumPy **unique()** function here.

**Additional Resources**

The following tutorials explain how to perform other common tasks in NumPy:

How to Remove Duplicate Elements in NumPy Array

How to Replace Elements in NumPy Array

How to Rank Items in NumPy Array