Let’s see how to calculate mode in Python.

## Mode in Python

To calculate the mode, we need to import the statistics module.

Luckily, there is dedicated function in statistics module to calculate mode.

import statistics as s x = [1, 5, 7, 5, 8, 43, 6] mode = s.mode(x) print("Mode equals: " + str(mode))

## Mode in Numpy

It was how to calculate mode in Python. However, calculating the mode directly with NumPy requires a workaround since NumPy does not have a built-in mode function.

import numpy as np my_array = np.array([1, 2, 4, 4, 7, 7, 7, 20]) mode = np.argmax(np.bincount(my_array)) print(f"Mode equals: {mode}")

Thanks to this mode = np.argmax(np.bincount(my_array)) easy trick mode has been calculated.

## How to calculate the mode of an array in NumPy?

In addition to using the statistics module to calculate the mode of a list in Python, you can also use the np.argmax and np.bincount functions in NumPy. The np.argmax function takes an array as a parameter and returns the index of the element with the maximum value. The np.bincount function takes an array as a parameter and returns a count of the number of times each element appears in the array.

To calculate the mode of an array in NumPy, you can use the following code:

import numpy as np my_array = np.array([1, 2, 4, 4, 7, 7, 7, 20]) mode = np.argmax(np.bincount(my_array)) print(f"Mode equals: {mode}")

This code will print the following output:

Mode equals: 7

As you can see, the mode of the array my_array is 7. This is because the element 7 appears more often than any other element in the array.