Comprehensions in Python
Comprehensions in Python are concise and expressive ways to create new data structures (such as lists, dictionaries, and sets) by applying an expression to each item in an existing iterable (e.g., lists, tuples, ranges) and collecting the results. Comprehensions provide a more readable and efficient alternative to traditional for loops when you need to generate a new collection based on an existing one.
In the below PDF we discuss about Comprehensions in Python in detail in simple language, Hope this will help in better understanding.
Python supports three types of comprehensions:
- List Comprehensions: These allow you to create lists by applying an expression to each item in an iterable and optionally filtering the items based on a condition. The result is a new list . Examples:
numbers = [1, 2, 3, 4, 5] squares = [x**2 for x in numbers if x % 2 == 0]
- Dictionary Comprehensions: These enable you to create dictionaries by specifying both key and value expressions for each item in an iterable and optionally applying a condition.Examples:
names = ["Alice", "Bob", "Charlie"]
name_lengths = {name: len(name) for name in names}- Set Comprehensions: These allow you to create sets by applying an expression to each item in an iterable and optionally filtering items based on a condition. Example:
numbers = [1, 2, 2, 3, 4, 4, 5]
unique_even_numbers = {x for x in numbers if x % 2 == 0}
Related Question
A comprehension in Python is a concise way to create lists, dictionaries, or sets by applying an expression to each item in an iterable (e.g., a list, tuple, or range) and optionally filtering the items based on a condition.
There are three main types of comprehensions in Python:
List comprehensions: Used to create lists.
Dictionary comprehensions: Used to create dictionaries.
Set comprehensions: Used to create sets.
Comprehensions are a good choice when you need to create lists, dictionaries, or sets based on existing iterables, apply a transformation to the elements, and optionally filter them. They are particularly useful for tasks that involve data transformation, filtering, or generation in a concise manner.
Comprehensions in Python provide a more concise and readable way to create lists, dictionaries, or sets compared to traditional for loops. They make the code shorter and often more efficient.
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