Python List Comprehension Beginner's Guide

Today you’re going to learn about python list comprehension, a very powerful feature of python to use when creating a python List based on certain constraints.

let’s get started

Imagine you want to create a list of cubics of numbers ranging from 1 to 100, Generating the list cubic of numbers without using list comprehension would normally look like this.

List cubics without list comprehension

>>> cubics = []
>>> for number in range(1, 101):
...     cubic = number**3
...     cubics.append(cubic)
... 
>>> cubics
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 
 2197, 2744, 3375, 4096, 4913, 5832, 6859, 8000, 9261, 10648,
 ......
]

List of cubics with list comprehension

>>> cubics = [number**3 for number in range(1, 101)]
>>> print(cubics)
[
  1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728, 2197, 2744,
 3375, 4096, 4913, 5832, 6859, 8000, 9261, 10648, 12167, 13824, 15625,
 ....
]

As we can see, we did the same thing with list comprehension but instead of doing it in 4 lines of code, we did it in just one. is it cool?

If you’re a fan of one-liners then list comprehension should be your friend, it can do amazing stuff in just one line.

List comprehension Syntax

List = [element for element in iterable]

More examples: Cartesian Product using list comprehension

If A and B are two non-empty sets, then their cartesian product A × B is the set of all ordered pairs of elements from A and B, with list comprehension generate a list of all pairs in just one line.

>>> a = [1 , 3 , 5, 7]
>>> b = [2, 4, 6, 8]
>>> product = [(i , j) for i in a for j in b]
>>> print(product)
[(1, 2), (1, 4), (1, 6), (1, 8), (3, 2),
 (3, 4), (3, 6), (3, 8), (5, 2), (5, 4),
 (5, 6), (5, 8), (7, 2), (7, 4), (7, 6), 
 (7, 8)
]

Conditional blocks within a list comprehension

Apart from just iterating over sequences, you can also put conditional statements such as if and else within comprehension logic to select the elements based on certain conditions.

Even number using list comprehension

Let’s say we want to make a list of even numbers from 203 to 289 using list comprehension, it can also be done in one-line as shown below;

>>> print(even_numbers)
[
  204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230,
 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258,
 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286,
 288
]

Filtering iterable using list comprehension

list comprehension can also be employed to filter out a given list based on certain parameters. For instance, let's use to it to filter out all candidates name that is above 18

>>> candidates = [
...     ('John', 15), 
...     ('Silyvia', 34), 
...     ('Hamis', 17), 
...     ('Alphonce', 22), 
...     ('Grace', 27)
... ]
>>> 
>>> above_18 = [candidate for candidate in candidates if candidate[1]>=18]
>>> print(above_18)
[('Silyvia', 34), ('Alphonce', 22), ('Grace', 27)]

Loading certain files using list comprehension

>>> import os
>>> images = [img for img in os.listdir() if img.endswith('.jpg')]
>>> print(images)
['AI gallery.jpg']

Flattening 2D array using list comprehension

You can also use list comprehension to flatten the 2D array to a single 1D array by iterating on all elements on a list.

>>> data =[(1, 2), (3, 4), (5, 6)]
>>> flattened = [n for in_data in data for n in in_data]
>>> print(flattened)
[1, 2, 3, 4, 5, 6]

Scalar Product using List Comprehension

List comprehension can also be used to find the scalar of an iterable by multiplying by a given number on each element in an iterable independently as shown below;

>>> vector = [10, 25, 32]
>>> vector3 = [n * 3 for n in vector]
>>> print(vector)
[10, 25, 32]