You can use the following basic syntax to map a function over a NumPy array:

#define function my_function = lambda x: x*5 #map function to every element in NumPy array my_function(my_array)

The following examples show how to use this syntax in practice.

**Example 1: Map Function Over 1-Dimensional NumPy Array**

The following code shows how to map a function to a NumPy array that multiplies each value by 2 and then adds 5:

import numpy as np #create NumPy array data = np.array([1, 3, 4, 4, 7, 8, 13, 15]) #define function my_function = lambda x: x*2+5 #apply function to NumPy array my_function(data) array([ 7, 11, 13, 13, 19, 21, 31, 35])

Here is how each value in the new array was calculated:

- First value: 1*2+5 =
**7** - Second value: 3*2+5 =
**11** - Third value: 4*2+5 =
**13**

And so on.

**Example 2: Map Function Over Multi-Dimensional NumPy Array**

The following code shows how to map a function to a multi-dimensional NumPy array that multiplies each value by 2 and then adds 5:

import numpy as np #create NumPy array data = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) #view NumPy array print(data) [[1 2 3 4] [5 6 7 8]] #define function my_function = lambda x: x*2+5 #apply function to NumPy array my_function(data) array([[ 7, 9, 11, 13], [15, 17, 19, 21]])

Notice that this syntax worked with a multi-dimensional array just as well as it worked with a one-dimensional array.

**Additional Resources**

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

How to Add a Column to a NumPy Array

How to Convert NumPy Array to List in Python

How to Export a NumPy Array to a CSV File