Nested List Comprehensions in Python
List Comprehension are one of the most amazing features of Python. It is a smart and concise way of creating lists by iterating over an iterable object. Nested List Comprehensions are nothing but a list comprehension within another list comprehension which is quite similar to nested for loops.
Nested List Comprehension in Python Syntax
Below is the syntax of nested list comprehension:
Syntax: new_list = [[expression for item in list] for item in list]
Parameters:
- Expression: Expression that is used to modify each item in the statement
- Item: The element in the iterable
- List: An iterable object
Python Nested List Comprehensions Examples
Below are some examples of nested list comprehension:
Example 1: Creating a Matrix
In this example, we will compare how we can create a matrix when we are creating it with
Without List Comprehension
In this example, a 5×5 matrix is created using a nested loop structure. An outer loop iterates five times, appending empty sublists to the matrix
, while an inner loop populates each sublist with values ranging from 0 to 4, resulting in a matrix with consecutive integer values.
Python3
matrix = [] for i in range ( 5 ): # Append an empty sublist inside the list matrix.append([]) for j in range ( 5 ): matrix[i].append(j) print (matrix) |
[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
Using List Comprehension
The same output can be achieved using nested list comprehension in just one line. In this example, a 5×5 matrix is generated using a nested list comprehension. The outer comprehension iterates five times, representing the rows, while the inner comprehension populates each row with values ranging from 0 to 4, resulting in a matrix with consecutive integer values.
Python3
# Nested list comprehension matrix = [[j for j in range ( 5 )] for i in range ( 5 )] print (matrix) |
[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
Example 2: Filtering a Nested List Using List Comprehension
Here, we will see how we can filter a list with and without using list comprehension.
Without Using List Comprehension
In this example, a nested loop traverses a 2D matrix, extracting odd numbers from Python list within list and appending them to the list odd_numbers
. The resulting list contains all odd elements from the matrix.
Python3
matrix = [[ 1 , 2 , 3 ], [ 4 , 5 , 6 ], [ 7 , 8 , 9 ]] odd_numbers = [] for row in matrix: for element in row: if element % 2 ! = 0 : odd_numbers.append(element) print (odd_numbers) |
[1, 3, 5, 7, 9]
Using List Comprehension
In this example, a list comprehension is used to succinctly generate the list odd_numbers
by iterating through the elements of a 2D matrix. Only odd elements are included in the resulting list, providing a concise and readable alternative to the equivalent nested loop structure.
Python3
matrix = [[ 1 , 2 , 3 ], [ 4 , 5 , 6 ], [ 7 , 8 , 9 ]] odd_numbers = [ element for row in matrix for element in row if element % 2 ! = 0 ] print (odd_numbers) |
[1, 3, 5, 7, 9]
Example 3: Flattening Nested Sub-Lists
Without List Comprehension
In this example, a 2D list named matrix
with varying sublist lengths is flattened using nested loops. The elements from each sublist are sequentially appended to the list flatten_matrix
, resulting in a flattened representation of the original matrix.
Python3
# 2-D List matrix = [[ 1 , 2 , 3 ], [ 4 , 5 ], [ 6 , 7 , 8 , 9 ]] flatten_matrix = [] for sublist in matrix: for val in sublist: flatten_matrix.append(val) print (flatten_matrix) |
[1, 2, 3, 4, 5, 6, 7, 8, 9]
With List Comprehension
Again this can be done using nested list comprehension which has been shown below. In this example, a 2D list named matrix
with varying sublist lengths is flattened using nested list comprehension. The expression [val for sublist in matrix for val in sublist]
succinctly generates a flattened list by sequentially including each element from the sublists.
Python3
# 2-D List matrix = [[ 1 , 2 , 3 ], [ 4 , 5 ], [ 6 , 7 , 8 , 9 ]] # Nested List Comprehension to flatten a given 2-D matrix flatten_matrix = [val for sublist in matrix for val in sublist] print (flatten_matrix) |
[1, 2, 3, 4, 5, 6, 7, 8, 9]
Example 4: Manipulate String Using List Comprehension
Without List Comprehension
In this example, a 2D list named matrix
containing strings is modified using nested loops. The inner loop capitalizes the first letter of each fruit, and the outer loop constructs a new 2D list, modified_matrix
, with the capitalized fruits, resulting in a matrix of strings with initial capital letters.
Python3
matrix = [[ "apple" , "banana" , "cherry" ], [ "date" , "fig" , "grape" ], [ "kiwi" , "lemon" , "mango" ]] modified_matrix = [] for row in matrix: modified_row = [] for fruit in row: modified_row.append(fruit.capitalize()) modified_matrix.append(modified_row) print (modified_matrix) |
[['Apple', 'Banana', 'Cherry'], ['Date', 'Fig', 'Grape'], ['Kiwi', 'Lemon', 'Mango']]
With List Comprehension
In this example, a 2D list named matrix
containing strings is transformed using nested list comprehension. The expression [[fruit.capitalize() for fruit in row] for row in matrix]
efficiently generates a modified matrix where the first letter of each fruit is capitalized, resulting in a new matrix of strings with initial capital letters.
Python3
matrix = [[ "apple" , "banana" , "cherry" ], [ "date" , "fig" , "grape" ], [ "kiwi" , "lemon" , "mango" ]] modified_matrix = [[fruit.capitalize() for fruit in row] for row in matrix] print (modified_matrix) |
[['Apple', 'Banana', 'Cherry'], ['Date', 'Fig', 'Grape'], ['Kiwi', 'Lemon', 'Mango']]