{"id":128916,"date":"2022-10-15T05:42:58","date_gmt":"2022-10-15T05:42:58","guid":{"rendered":"https:\/\/blog.finxter.com\/?p=791161"},"modified":"2022-10-15T05:42:58","modified_gmt":"2022-10-15T05:42:58","slug":"python-return-numpy-array-from-function","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2022\/10\/15\/python-return-numpy-array-from-function\/","title":{"rendered":"Python \u2013 Return NumPy Array From Function"},"content":{"rendered":"\n
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5\/5 – (1 vote) <\/div>\n<\/div>\n

Do you need to create a function that returns a NumPy array<\/strong> but you don’t know how? No worries, in sixty seconds, you’ll know! Go! \"\ud83d\ude80\"<\/p>\n

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 arr<\/code>, and return it to the caller of the function using the keyword <\/a>operation “return arr<\/code>“. <\/p>\n

\"\ud83d\udc49\" Recommended Tutorial<\/strong>: How to Initialize a NumPy Array? 6 Easy Ways<\/a><\/p>\n

Create and Return 1D Array<\/h2>\n

For example, the following code creates a function create_array()<\/code> of numbers 0, 1, 2, …, 9 using the np.arange()<\/a><\/code> function and returns the array to the caller of the function:<\/p>\n

import numpy as np def create_array(): ''' Function to return array ''' return np.arange(10) numbers = create_array()\nprint(numbers)\n# [0 1 2 3 4 5 6 7 8 9]\n<\/pre>\n
The np.arange([start,] stop[, step])<\/code> function creates a new NumPy array with evenly-spaced integers between start<\/code> (inclusive) and stop<\/code> (exclusive). <\/p>\n
The step<\/code> size defines the difference between subsequent values. For example, np.arange(1, 6, 2)<\/code> creates the NumPy array [1, 3, 5]<\/code>.<\/p>\n
To better understand the function, have a look at this video:<\/p>\n
\"YouTube<\/a>
<\/figcaption><\/figure>\n
I also created this figure to demonstrate how NumPy’s arange()<\/code> function works on three examples:<\/p>\n
\"\"<\/figure>\n
In the code example, we used np.arange(10)<\/code> with default start=0<\/code> and step=1<\/code> only specifying the stop=10<\/code> argument.<\/p>\n
If you need an even deeper understanding, I’d recommend you check out our full guide on the Finxter blog.<\/p>\n
\"\ud83d\udc49\" Recommended Tutorial<\/strong>: NumPy Arange Function — A Helpful Illustrated Guide<\/a><\/p>\n
Create and Return 2D NumPy Array<\/h2>\n
You can also create a 2D (or multi-dimensional) array in a Python function by first creating a 2D or (xD) nested list<\/a> and converting the nested list to a NumPy array by passing it into the np.array()<\/a><\/code> function.<\/p>\n
The following code snippet uses nested list comprehension<\/a> to create a 2D NumPy array following a more complicated creation pattern:<\/p>\n
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)\nprint(arr)\n<\/pre>\n
Output:<\/p>\n
[[ 0 1 4 9] [ 1 4 9 16] [ 4 9 16 25]]<\/pre>\n
I definitely recommend reading the following tutorial to understand nested list comprehension in Python:<\/p>\n
\"\ud83d\udc49\" Recommended Tutorial<\/strong>: Nested List Comprehension in Python<\/a><\/p>\n<\/p>\n
More Ways<\/h2>\n
There are many other ways to return an array in Python. <\/p>\n
For example, you can use either of those methods inside the function body to create and initialize a NumPy array:<\/p>\n