[Tut] Python – Return NumPy Array From Function

[xSicKxBot]

Python – Return NumPy Array From Function

5/5 – (1 vote)

Do you need to create a function that returns a NumPy array but you don’t know how? No worries, in sixty seconds, you’ll know! Go!

A Python function can return any object such as a NumPy Array. To return an array, first create the array object within the function body, assign it to a variable

Code:

arr

, and return it to the caller of the function using the keywordoperation “

Code:

return arr

“.

Recommended Tutorial: How to Initialize a NumPy Array? 6 Easy Ways

Create and Return 1D Array

For example, the following code creates a function

Code:

create_array()

of numbers 0, 1, 2, …, 9 using the

Code:

np.arange()

function and returns the array to the caller of the function:

Code:

import numpy as np def create_array(): ''' Function to return array ''' return np.arange(10) numbers = create_array() print(numbers) # [0 1 2 3 4 5 6 7 8 9]

The

Code:

np.arange([start,] stop[, step])

function creates a new NumPy array with evenly-spaced integers between

Code:

start

(inclusive) and

Code:

stop

(exclusive).

The

Code:

step

size defines the difference between subsequent values. For example,

Code:

np.arange(1, 6, 2)

creates the NumPy array

Code:

[1, 3, 5]

.

To better understand the function, have a look at this video:



I also created this figure to demonstrate how NumPy’s

Code:

arange()

function works on three examples:



In the code example, we used

Code:

np.arange(10)

with default

Code:

start=0

and

Code:

step=1

only specifying the

Code:

stop=10

argument.

If you need an even deeper understanding, I’d recommend you check out our full guide on the Finxter blog.

Recommended Tutorial: NumPy Arange Function — A Helpful Illustrated Guide

Create and Return 2D NumPy Array

You can also create a 2D (or multi-dimensional) array in a Python function by first creating a 2D or (xD) nested list and converting the nested list to a NumPy array by passing it into the

Code:

np.array()

function.

The following code snippet uses nested list comprehension to create a 2D NumPy array following a more complicated creation pattern:

Code:

import numpy as np def create_array(a,b): ''' Function to return array ''' lst = [[(i+j)**2 for i in range(a)] for j in range(b)] return np.array(lst) arr = create_array(4,3) print(arr)

Output:

Code:

[[ 0 1 4 9] [ 1 4 9 16] [ 4 9 16 25]]

I definitely recommend reading the following tutorial to understand nested list comprehension in Python:

Recommended Tutorial: Nested List Comprehension in Python

More Ways

There are many other ways to return an array in Python.

For example, you can use either of those methods inside the function body to create and initialize a NumPy array:

• Method 1: Use
  Code:

  np.array()
  
  
  
• Method 2: Use
  Code:

  np.zeros()
  
  
  
• Method 3: Use
  Code:

  np.ones()
  
  
  
• Method 4: Use
  Code:

  np.full()
  
  
  
• Method 5: Use
  Code:

  np.empty()
  
  
  
• Method 6: Use
  Code:

  np.arange()
  
  
  
• Bonus: Initialize a NumPy array with CSV data
  

To get a quick overview what to put into the function and how these methods work, I’d recommend you check out our full tutorial.

Recommended Tutorial: How to Initialize a NumPy Array? 6 Easy Ways

Related Tutorials

• Python Return String From Function
  
  
• Python Return Dict From Function
  
  
• Python Return Set From Function
  

Programmer Humor

Code:

Q: How do you tell an introverted computer scientist from an extroverted computer scientist? A: An extroverted computer scientist looks at your shoes when he talks to you.