Python – List Comprehension
A Python list comprehension consists of brackets containing the expression, which is executed for each element along with the for loop to iterate over each element in the Python list.
Python List comprehension provides a much more short syntax for creating a new list based on the values of an existing list.
Advantages of List Comprehension
- More time-efficient and space-efficient than loops.
- Require fewer lines of code.
- Transforms iterative statement into a formula.
Syntax of List Comprehension
newList = [ expression(element) for element in oldList if condition ]
Example of List Comprehension in Python
Example 1: Iteration with List comprehension
Python3
# Using list comprehension to iterate through loop List = [character for character in [ 1 , 2 , 3 ]] # Displaying list print ( List ) |
Output:
[1, 2, 3]
Example 2: Even list using list comprehension
Python3
list = [i for i in range ( 11 ) if i % 2 = = 0 ] print ( list ) |
Output:
[0, 2, 4, 6, 8, 10]
Example 3: Matrix using List comprehension
Python3
matrix = [[j for j in range ( 3 )] for i in range ( 3 )] print (matrix) |
Output:
[[0, 1, 2], [0, 1, 2], [0, 1, 2]]
List Comprehensions vs For Loop
There are various ways to iterate through a list. However, the most common approach is to use the for loop. Let us look at the below example:
Python3
# Empty list List = [] # Traditional approach of iterating for character in 'Geeks 4 Geeks!' : List .append(character) # Display list print ( List ) |
Output:
['G', 'e', 'e', 'k', 's', ' ', '4', ' ', 'G', 'e', 'e', 'k', 's', '!']
Above is the implementation of the traditional approach to iterate through a list, string, tuple, etc. Now list comprehension does the same task and also makes the program more simple.
List Comprehensions translate the traditional iteration approach using for loop into a simple formula hence making them easy to use. Below is the approach to iterate through a list, string, tuple, etc. using list comprehension.
Python3
# Using list comprehension to iterate through loop List = [character for character in 'Geeks 4 Geeks!' ] # Displaying list print ( List ) |
Output:
['G', 'e', 'e', 'k', 's', ' ', '4', ' ', 'G', 'e', 'e', 'k', 's', '!']
Time Analysis in List Comprehensions and Loop
The list comprehensions are more efficient both computationally and in terms of coding space and time than a for a loop. Typically, they are written in a single line of code. The below program depicts the difference between for loops and list comprehension based on performance.
Python3
# Import required module import time # define function to implement for loop def for_loop(n): result = [] for i in range (n): result.append(i * * 2 ) return result # define function to implement list comprehension def list_comprehension(n): return [i * * 2 for i in range (n)] # Driver Code # Calculate time takens by for_loop() begin = time.time() for_loop( 10 * * 6 ) end = time.time() # Display time taken by for_loop() print ( 'Time taken for_loop:' , round (end - begin, 2 )) # Calculate time takens by list_comprehension() begin = time.time() list_comprehension( 10 * * 6 ) end = time.time() # Display time taken by for_loop() print ( 'Time taken for list_comprehension:' , round (end - begin, 2 )) |
Output:
Time taken for_loop: 0.56 Time taken for list_comprehension: 0.47
From the above program, we can see list comprehensions are quite faster than for loop.
Nested List Comprehensions
Nested List Comprehensions are nothing but a list comprehension within another list comprehension which is quite similar to nested for loops. Below is the program which implements nested loop:
Python3
matrix = [] for i in range ( 3 ): # Append an empty sublist inside the list matrix.append([]) for j in range ( 5 ): matrix[i].append(j) print (matrix) |
Output
[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
Now by using nested list comprehensions same output can be generated in fewer lines of code.
Python3
# Nested list comprehension matrix = [[j for j in range ( 5 )] for i in range ( 3 )] print (matrix) |
[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
List Comprehensions and Lambda
Lambda Expressions are nothing but shorthand representations of Python functions. Using list comprehensions with lambda creates an efficient combination. Let us look at the below examples:
Python3
# using lambda to print table of 10 numbers = [] for i in range ( 1 , 6 ): numbers.append(i * 10 ) print (numbers) |
[10, 20, 30, 40, 50]
Here, we have used for loop to print a table of 10.
Python3
numbers = [i * 10 for i in range ( 1 , 6 )] print (numbers) |
Output:
[10, 20, 30, 40, 50]
Now here, we have used only list comprehension to display a table of 10.
Python3
# using lambda to print table of 10 numbers = list ( map ( lambda i: i * 10 , [i for i in range ( 1 , 6 )])) print (numbers) |
Output:
[10, 20, 30, 40, 50]
Finally, we use lambda + list comprehension to display the table of 10. This combination is very useful to get efficient solutions in fewer lines of code for complex problems.
Conditionals in List Comprehension
We can also add conditional statements to the list comprehension. We can create a list using range(), operators, etc. and cal also apply some conditions to the list using the if statement.
Key Points
- Comprehension of the list is an effective means of describing and constructing lists based on current lists.
- Generally, list comprehension is more lightweight and simpler than standard list formation functions and loops.
- We should not write long codes for list comprehensions in order to ensure user-friendly code.
- Every comprehension of the list can be rewritten in for loop, but in the context of list interpretation, every for loop can not be rewritten.
Below are some examples which depict the use of list comprehensions rather than the traditional approach to iterate through iterables:
Example 1: Python List Comprehension Using If-else
Python3
lis = [ "Even number" if i % 2 = = 0 else "Odd number" for i in range ( 8 )] print (lis) |
Output:
['Even number', 'Odd number', 'Even number', 'Odd number', 'Even number', 'Odd number', 'Even number', 'Odd number']
Example 2: Nested IF with List Comprehension
Python3
lis = [num for num in range ( 100 ) if num % 5 = = 0 if num % 10 = = 0 ] print (lis) |
Output:
[0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
Example 3: Display square of numbers from 1 to 10.
Python3
# Getting square of from 1 to 10 squares = [n * * 2 for n in range ( 1 , 11 )] # Display square of even numbers print (squares) |
Output
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
Example 4: Display Transpose of 2D- Matrix.
Python3
# Assign matrix twoDMatrix = [[ 10 , 20 , 30 ], [ 40 , 50 , 60 ], [ 70 , 80 , 90 ]] # Generate transpose trans = [[i[j] for i in twoDMatrix] for j in range ( len (twoDMatrix[ 0 ]))] print (trans) |
Output:
[[10, 40, 70], [20, 50, 80], [30, 60, 90]]
Example 5: Toggle the case of each character in a string.
Python3
# Initializing string string = 'Geeks4Geeks' # Toggle case of each character List = list ( map ( lambda i: chr ( ord (i) ^ 32 ), string)) # Display list print ( List ) |
Output
['g', 'E', 'E', 'K', 'S', '\x14', 'g', 'E', 'E', 'K', 'S']
Example 6: Reverse each string in a tuple.
Python3
# Reverse each string in tuple List = [string[:: - 1 ] for string in ( 'Geeks' , 'for' , 'Geeks' )] # Display list print ( List ) |
Output:
['skeeG', 'rof', 'skeeG']
Example 7: Display the sum of digits of all the odd elements in a list.
Python3
# Explicit function def digitSum(n): dsum = 0 for ele in str (n): dsum + = int (ele) return dsum # Initializing list List = [ 367 , 111 , 562 , 945 , 6726 , 873 ] # Using the function on odd elements of the list newList = [digitSum(i) for i in List if i & 1 ] # Displaying new list print (newList) |
Output:
[16, 3, 18, 18]
Please Login to comment...