Python collections

The Python ‘in’ Operator

The Python in operator checks for the inclusion of an item within a collection object. It returns True if the item is included and False otherwise. The in operator is versatile and can be used with many data types such as strings, lists, tuples, dictionaries, and sets.

The Python filter() Function

The Python filter() function provides a concise way to filter elements from an iterable based on a specified condition, returning an iterator of the true elements. Its syntax involves passing a function and an iterable object. The function can be a lambda expression or a defined function. The function tests each element and returns true or false based on the specified condition. If the function argument is set to None, filter() returns truthy elements of the iterable. Using filter() provides a more readable and concise approach for filtering elements compared to list comprehensions or loops.

The Python reduce() Function

The Python reduce() function aggregates elements in an iterable iteratively, accumulating a single result. It simplifies code involving iterative aggregation, providing a concise alternative to traditional loops. The basic syntax involves passing a function and an iterable, with an optional initial value. It’s powerful but not always necessary due to built-in functions like sum() and multiply().

The Python map() Function

The Python map() function transforms each item of an iterable and returns a new iterable object. It accepts a function and an iterable, creating an iterable map object. Unlike list comprehensions, map() uses functions and returns an iterable. With map(), multiple iterables can be handled in parallel.