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In this article, we will learn about Python list comprehensions, and how to use it.

Python List Comprehension
Video: Python List, Set & Dictionary Comprehension
List Comprehension vs For Loop in Python
Example 2: Iterating through a string Using List Comprehension
Syntax of List Comprehension
List Comprehensions vs Lambda functions
Conditionals in List Comprehension
Nested Loops in List Comprehension
Key Points to Remember
Table of Contents

Video: Python List, Set & Dictionary Comprehension
List Comprehension vs For Loop in Python

Suppose, we want to separate the letters of the word human and add the letters as items of a list. The first thing that comes in mind would be using for loop.

Example 1: Iterating through a string Using for Looph_letters = []

for letter in ‘human’:

h_letters.append(letter)

print(h_letters)

When we run the program, the output will be:

[‘h’, ‘u’, ‘m’, ‘a’, ‘n’]

However, Python has an easier way to solve this issue using List Comprehension. List comprehension is an elegant way to define and create lists based on existing lists.

Lets see how the above program can be written using list comprehensions.

Example 2: Iterating through a string Using List Comprehension

h_letters = [ letter for letter in ‘human’ ]

print( h_letters)

When we run the program, the output will be:

[‘h’, ‘u’, ‘m’, ‘a’, ‘n’]

In the above example, a new list is assigned to variable h_letters, and list contains the items of the iterable string ‘human’. We call print() function to receive the output.

Syntax of List Comprehension

[expression for item in list]

We can now identify where list comprehensions are used.

If you noticed, human is a string, not a list. This is the power of list comprehension. It can identify when it receives a string or a tuple and work on it like a list.

You can do that using loops. However, not every loop can be rewritten as list comprehension. But as you learn and get comfortable with list comprehensions, you will find yourself replacing more and more loops with this elegant syntax.

List Comprehensions vs Lambda functions

List comprehensions arent the only way to work on lists. Various built-in functions and lambda functions can create and modify lists in less lines of code.

Example 3: Using Lambda functions inside Listletters = list(map(lambda x: x, ‘human’))

print(letters)

When we run the program, the output will be

[‘h’,’u’,’m’,’a’,’n’]

However, list comprehensions are usually more human readable than lambda functions. It is easier to understand what the programmer was trying to accomplish when list comprehensions are used.

Conditionals in List Comprehension

List comprehensions can utilize conditional statement to modify existing list (or other tuples). We will create list that uses mathematical operators, integers, and range().

Example 4: Using if with List Comprehensionnumber_list = [ x for x in range(20) if x % 2 == 0]

print(number_list)

When we run the above program, the output will be:

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

The list ,number_list, will be populated by the items in range from 0-19 if the item’s value is divisible by 2.

Example 5: Nested IF with List Comprehensionnum_list = [y for y in range(100) if y % 2 == 0 if y % 5 == 0]

print(num_list)

When we run the above program, the output will be:

[0, 10, 20, 30, 40, 50, 60, 70, 80, 90]

Here, list comprehension checks:

Is y divisible by 2 or not?
Is y divisible by 5 or not?

If y satisfies both conditions, y is appended to num_list.

Example 6: if…else With List Comprehensionobj = [“Even” if i%2==0 else “Odd” for i in range(10)]

print(obj)

When we run the above program, the output will be:

[‘Even’, ‘Odd’, ‘Even’, ‘Odd’, ‘Even’, ‘Odd’, ‘Even’, ‘Odd’, ‘Even’, ‘Odd’]

Here, list comprehension will check the 10 numbers from 0 to 9. If i is divisible by 2, then Even is appended to the obj list. If not, Odd is appended.

Nested Loops in List Comprehension

Suppose, we need to compute the transpose of a matrix that requires nested for loop. Lets see how it is done using normal for loop first.

Example 7: Transpose of Matrix using Nested Loopstransposed = []

matrix = [[1, 2, 3, 4], [4, 5, 6, 8]]

for i in range(len(matrix[0])):

transposed_row = []

for row in matrix:

transposed_row.append(row[i])

transposed.append(transposed_row)

print(transposed)

Output

[[1, 4], [2, 5], [3, 6], [4, 8]]

The above code use two for loops to find transpose of the matrix.

We can also perform nested iteration inside a list comprehension. In this section, we will find transpose of a matrix using nested loop inside list comprehension.

Example 8: Transpose of a Matrix using List Comprehensionmatrix = [[1, 2], [3,4], [5,6], [7,8]]

transpose = [[row[i] for row in matrix] for i in range(2)]

print (transpose)

When we run the above program, the output will be:

[[1, 3, 5, 7], [2, 4, 6, 8]]

In above program, we have a variable matrix which have 4 rows and 2 columns.We need to find transpose of the matrix. For that, we used list comprehension.

**Note: The nested loops in list comprehension dont work like normal nested loops. In the above program, for i in range(2) is executed before row[i] for row in matrix. Hence first, a value is assigned to i then item directed by row[i] is appended in the transpose variable.

Key Points to Remember

List comprehension is an elegant way to define and create lists based on existing lists.
List comprehension is generally more compact and faster than normal functions and loops for creating list.
However, we should avoid writing very long list comprehensions in one line to ensure that code is user-friendly.
Remember, every list comprehension can be rewritten in for loop, but every for loop cant be rewritten in the form of list comprehension.

Table of Contents

List Comprehension vs for Loopin Python
List Comprehensions vs Lambda functions
Conditionals in List Comprehension
Nested Loops inList Comprehension
Key Points to Remember

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