66. List Comprehensions

List Comprehensions: Creating lists in a concise, readable way

List comprehensions provide a powerful and syntactically elegant way to create and manipulate lists in Python. It allows you to build a new list by iterating over an iterable, optionally applying conditions or transformations to each item. Below are various examples showcasing the use of list comprehensions.

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1. Basic List Comprehension

Creating a list of squares from an existing list.

# Traditional for-loop
squares = []
for i in range(5):
    squares.append(i**2)

# List comprehension
squares = [i**2 for i in range(5)]
print(squares)  # Output: [0, 1, 4, 9, 16]

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2. List Comprehension with Conditional Logic

You can add conditions to filter items during the iteration.

# List of even numbers from 0 to 9
evens = [i for i in range(10) if i % 2 == 0]
print(evens)  # Output: [0, 2, 4, 6, 8]

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3. Applying Transformation with List Comprehension

You can apply a transformation to each item in the list.

# Convert a list of strings to uppercase
words = ['hello', 'world', 'python']
uppercase_words = [word.upper() for word in words]
print(uppercase_words)  # Output: ['HELLO', 'WORLD', 'PYTHON']

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4. List Comprehension with Multiple Iterators

You can use multiple for loops in a list comprehension.

# Cartesian product of two lists
a = [1, 2, 3]
b = ['x', 'y']
product = [(i, j) for i in a for j in b]
print(product)  # Output: [(1, 'x'), (1, 'y'), (2, 'x'), (2, 'y'), (3, 'x'), (3, 'y')]

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5. Nested List Comprehensions

List comprehensions can also be nested to create more complex lists.

# Flatten a 2D list into a 1D list
matrix = [[1, 2], [3, 4], [5, 6]]
flattened = [item for sublist in matrix for item in sublist]
print(flattened)  # Output: [1, 2, 3, 4, 5, 6]

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6. List Comprehension with Functions

You can also call functions inside list comprehensions to transform data.

# Applying a function to each item in the list
def square(x):
    return x ** 2

numbers = [1, 2, 3, 4]
squared_numbers = [square(num) for num in numbers]
print(squared_numbers)  # Output: [1, 4, 9, 16]

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7. List Comprehension with Conditional Expressions

You can use conditional expressions to modify the list items during iteration.

# Replace even numbers with 0 and odd numbers with the number itself
numbers = [1, 2, 3, 4, 5]
modified_numbers = [0 if num % 2 == 0 else num for num in numbers]
print(modified_numbers)  # Output: [1, 0, 3, 0, 5]

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8. List Comprehension for Filtering Even Numbers

List comprehensions can be used for filtering and creating a new list based on a condition.

# List of even numbers from 1 to 20
evens = [i for i in range(1, 21) if i % 2 == 0]
print(evens)  # Output: [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]

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9. Creating a List of Tuples

You can create a list of tuples using list comprehensions.

# List of tuples (number, its square)
squares = [(i, i**2) for i in range(5)]
print(squares)  # Output: [(0, 0), (1, 1), (2, 4), (3, 9), (4, 16)]

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10. List Comprehension for Dictionary Keys

You can use list comprehensions to generate keys for dictionaries or lists of dictionary items.

# List of dictionary keys
keys = [f'key{i}' for i in range(5)]
print(keys)  # Output: ['key0', 'key1', 'key2', 'key3', 'key4']

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Summary of Features:

• Basic iteration: [expression for item in iterable]

• Filtering: [expression for item in iterable if condition]

• Multiple iterators: [expression for item1 in iterable1 for item2 in iterable2]

• Nested list comprehensions: Used for flattening or transforming nested data structures.

• Function calls: Functions can be applied within list comprehensions to modify or transform the data.

• Conditional expressions: Apply conditional logic (if-else) within the comprehension to modify list items.

List comprehensions enhance the readability and conciseness of your Python code, making it easier to perform complex iterations, transformations, and filtering on iterables.