Python comprehensions provide a concise way to create lists, sets, dictionaries, and other collections. They allow for the generation of a new collection by applying an expression to each item in an iterable, optionally filtering items using a condition. The result is often more readable and faster than using traditional loops.
Here’s an example of a list comprehension that creates a list of squares from 0 to 9:
squares = [x**2 for x in range(10)]
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?