How to Map a Function in NumPy

1. Map a Function in NumPy With the numpy.vectorize() Function
2. Map a Function in NumPy With the lambda Keyword in Python

This tutorial will introduce the methods to map a function over a NumPy array in Python.

Map a Function in NumPy With the numpy.vectorize() Function

The numpy.vectorize() function maps functions on data structures that contain a sequence of objects like arrays in Python. It successively applies the input function on each element of the sequence or array. The return-type of the numpy.vectorize() function is determined by the input function. See the following code example.

import numpy as np

array = np.array([1, 2, 3, 4, 5])


def fun(e):
    return e % 2


vfunc = np.vectorize(fun)

result = vfunc(array)
print(result)

Output:

[1 0 1 0 1]

We first created the array with the np.array() function and declared the function fun. Then we passed the fun function to the np.vectorize() function and stored the result in vfunc. After that, we passed the array to the vfunc and stored the result inside the result array.

Map a Function in NumPy With the lambda Keyword in Python

The lambda keyword creates an anonymous function in Python. Anonymous functions are helpful when we only need a function temporarily in our code. We can also use the lambda functions to map a function over a NumPy array. We can pass an array to the lambda function to apply iteratively over each array element.

import numpy as np

array = np.array([1, 2, 3, 4, 5])


def lfunc(e):
    return e % 2


result = lfunc(array)
print(result)

Output:

[1 0 1 0 1]

We first created the array with the np.array() function and the lambda function lfunc with the lambda keyword. We then mapped the lfunc to the array by passing array to the lfunc function. We saved the result inside the result array and printed the values inside it.