List comprehension in Python

List comprehension in Python

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List comprehensions are a powerful and concise way to create new lists from existing ones in Python. They're a type of syntactic sugar that simplifies your code by allowing you to write expressions directly inside the square brackets [] used for creating lists.

Here's the basic syntax:

[expression for variable in iterable]

Let's break it down:

Here are some examples to illustrate how list comprehensions work:

Example 1: Squaring numbers

numbers = [1, 2, 3, 4, 5]
squared_numbers = [n ** 2 for n in numbers]
print(squared_numbers)  # Output: [1, 4, 9, 16, 25]

In this example, we create a new list squared_numbers containing the squares of each number in the original list numbers.

Example 2: Filtering values

fruits = ['apple', 'banana', 'cherry', 'date', 'elderberry']
tasty_fruits = [fruit for fruit in fruits if fruit.startswith('a')]
print(tasty_fruits)  # Output: ['apple', 'elderberry']

Here, we create a new list tasty_fruits containing only the fruits that start with the letter "A" from the original list fruits.

Example 3: Mapping values

colors = ['red', 'green', 'blue', 'yellow', 'purple']
uppercase_colors = [color.upper() for color in colors]
print(uppercase_colors)  # Output: ['RED', 'GREEN', 'BLUE', 'YELLOW', 'PURPLE']

In this case, we create a new list uppercase_colors containing the uppercase versions of each color from the original list colors.

Example 4: Nested comprehensions

students = [('John', 'A'), ('Jane', 'B'), ('Jim', 'A')]
grades = {student[0]: student[1] for student in students}
print(grades)  # Output: {'John': 'A', 'Jane': 'B', 'Jim': 'A'}

Here, we create a dictionary grades where the keys are the students' names and the values are their corresponding grades. This example demonstrates nested comprehensions, where you can use another comprehension inside an expression.

These are just a few examples of what list comprehensions can do in Python. With practice, you'll become proficient in using them to write concise and efficient code!

Happy coding!

Dictionary comprehension in Python

Here's an overview of dictionary comprehension in Python:

In Python, dictionaries are mutable data structures that map keys to values. A dictionary comprehension is a concise way to create a new dictionary from an existing one or multiple iterables. It's similar to list comprehensions, but for dictionaries.

Basic Syntax

A basic dictionary comprehension has the following syntax:

{key: value for variable in iterable}

Where:

Examples

Here are some examples of using dictionary comprehensions:

# Create a new dictionary with all the same key-value pairs
numbers = {'one': 1, 'two': 2, 'three': 3}
double_numbers = {k: v * 2 for k, v in numbers.items()}
print(double_numbers)  # {'one': 2, 'two': 4, 'three': 6}
Create a new dictionary with only certain key-value pairs
fruits = {'apple': 1, 'banana': 2, 'cherry': 3}
juicy_fruits = {k: v for k, v in fruits.items() if v > 1}
print(juicy_fruits)  # {'banana': 2, 'cherry': 3}
Create a new dictionary with transformed values
words = {'hello': 'Hello', 'world': 'World'}
uppercase_words = {k: v.upper() for k, v in words.items()}
print(uppercase_words)  # {'hello': 'HELLO', 'world': 'WORLD'}
Create a new dictionary by combining multiple iterables
people = [('John', 25), ('Alice', 30)]
places = ['Home', 'Work']
addresses = {name: place for name, age in people for place in places}
print(addresses)  # {'John': 'Home', 'John': 'Work', 'Alice': 'Home', 'Alice': 'Work'}

Advantages

Dictionary comprehensions offer several advantages over traditional dictionary creation methods:

Common Use Cases

Here are some common scenarios where dictionary comprehensions are particularly useful:

I hope this helps! Let me know if you have any questions or need further clarification.