Time Analysis in List Comprehensions and Loop

The list comprehensions in Python 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 loops and list comprehension based on performance.

Python

# 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 taken 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: 0.39
Time taken for list_comprehension: 0.35

From the above program, we can see list comprehensions are quite faster than for loop.

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.

Example:

Python

numbers = [12, 13, 14,] 
doubled = [x *2  for x in numbers] 
print(doubled)

Output

[24, 26, 28]

Time Analysis in List Comprehensions and Loop

Python

Python – List Comprehension

Python

Similar Reads