Chapter 5.4 Comprehensions.md

Chapter 5.4: Comprehensions

Table of Contents:

• Chapter 5.4: Comprehensions
  • List Comprehension
  • List Comprehension With if condition
  • Set Comprehension
  • Dictionary Comprehension
  • Generator Comprehension

List Comprehension

List Comprehension creates a new list by applying an expression to each element of an iterable.

Basic Syntax:

iterable = [<expression> for <element> in <iterable>]

Example 1:

# Regular method
new_list = []
for x in range(1, 11):
    new_list.append(x*2)
print(new_list)
# [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]

# List Comprehension
lst_1 = [x*2 for x in range(1, 11)]

# [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]

Example 2:

word = 'Hello World'
ascii_values = [ord(char) for char in word]

# [72, 101, 108, 108, 111, 32, 87, 111, 114, 108, 100]

List Comprehension With if condition

List Comprehension with if condition is used to filter and perform operations if needed from another iterable.

Syntax:

[<expression> for <element> in <iterable> if <condition>]

Example 1:

numbers = [1, 2, 3, 4, 5, 6, 7]

even  = [num for num in numbers if num%2 == 0]
# [2, 4, 6]

Set Comprehension

Set Comprehension is similar to list comprehension, but uses curley brackets and can not contain duplicate values. Syntax

value = {<expression> for <item[s]> in iterable}

Example:

my_set = {value**3 for value in range(5, 12)}
print(my_set)

# {512, 1000, 1331, 343, 216, 729, 125}

Dictionary Comprehension

Syntax:

value = {<key_expression>: <value_expression> for <item[s]> in iterable}

Example 1:

dict_1 = {key: key**2 for key in range(1, 6)}

# {1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Generator Comprehension

As List Comprehension does an eager execution, it will be computationally inefficient and will use more memory. So if we want to optimize our system to use less memory and perform computation whenever required, we can comprehend generators.

A generator comprehension is similar to the list comprehension. Only the difference is the notation () instead of []

Example: Using List Comprehension

# Using List Comprehension to get first 1000 squares
squares = [x ** 2 for x in range(1000)]
print(squares.__sizeof__())     # 8840

# Using List Comprehension to get first 1000000 squares
squares_1 = [x ** 2 for x in range(1000000)]
print(squares_1.__sizeof__())     # 8448712

Example: Using Generator Comprehension

# Using Generator Comprehension to get first 1000 squares
squares = (x ** 2 for x in range(1000))
print(squares.__sizeof__())   # 96

# Using Generator Comprehension to get first 1000000 squares
squares_1 = (x ** 2 for x in range(1000000))
print(squares_1.__sizeof__())   # 96

In above example, we can see the generator comprehension takes only 96 bytes of memory in both cases while the list comprehension takes 8.8 KB of memory by squares whereas 84.4MB of memory by squares_1.