List comprehension and Lambda Function in Python

Last Updated : 13 Oct, 2024

List comprehension is an elegant way to define and create a list in Python. We can create lists just like mathematical statements and in one line only. The syntax of list comprehension is easier to grasp.

A list comprehension generally consists of these parts :

Output expression,
Input sequence,
A variable representing a member of the input sequence and
An optional predicate part.

Syntax of list comprehension

List = [expression(i) for i in another_list if filter(i)]

Example:

Python

lst = [x ** 2 for x in range(1, 11) if x % 2 == 1]
print(lst)

Output:

[1, 9, 25, 49, 81]

In the above example,

x ** 2 is the expression.
range (1, 11) is an input sequence or another list.
x is the variable.
if x % 2 == 1 is predicate part.

Lambda Function in Python

In Python, an anonymous function means that a function is without a name. As we already know the def keyword is used to define the normal functions and the lambda keyword is used to create anonymous functions. It has the following syntax:

Syntax of lambda

lambda arguments : expression

Example:

Python

lst = list(map(lambda x: x**2, range(1, 5)))
print(lst)

Output:

[1, 4, 9, 16]

Lambda and List Comprehension – Difference

List Comprehension is used to create lists, Lambda is function that can process like other functions and thus return values or lists.

Example:

Python

# list from range 0 to 10
list_ = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
print(list_)
 
# lambda function
lambda_list = list(map(lambda x: x * 2, list_))

# Map basically iterates every element
# in the list_ and returns the lambda 
# function result
print(lambda_list)
 
# list comprehension
list_comp = [x * 2 for x in list_]
print(list_comp)

Output:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

List comprehension vs Lambda function – Performance

As we can see from the graph that overall list comprehension is much faster than the filter function. The filter is faster for a small list only.

Python

import numpy as np
import matplotlib.pyplot as plt
import time


# Compare runtime of both methods
sizes = [i * 10000 for i in range(100)]

filter_runtimes = []
list_comp_runtimes = []

for lis_size in sizes:

    lst = list(range(lis_size))

    # Get time stamps
    time_A = time.time()
    list(filter(lambda x: x % 2, lst))
    time_B = time.time()
    [x for x in lst if x % 2]
    time_C = time.time()

    # Calculate runtimes
    filter_runtimes.append((lis_size, time_B - time_A))
    list_comp_runtimes.append((lis_size, time_C - time_B))


# list comprehension vs. lambda + filter using Matplotlib

filt = np.array(filter_runtimes)
lis = np.array(list_comp_runtimes)

plt.plot(filt[:, 0], filt[:, 1], label='filter')
plt.plot(lis[:, 0], lis[:, 1], label='list comprehension')

plt.xlabel('list size')
plt.ylabel('runtime in seconds)')

plt.legend()
plt.show()