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.