The threading.Condition class in Python’s threading module provides a mechanism for threads to wait until they are notified. This is useful for scenarios where threads need to coordinate their actions and wait for certain conditions to be met before continuing execution.
The threading.RLock class in Python’s threading module provides a reentrant lock, which allows a thread to acquire the lock multiple times before releasing it. This is useful for more complex synchronization scenarios where a thread might need to re-acquire the lock it already holds.
The threading.Lock class in Python’s threading module provides a simple mechanism for mutual exclusion, allowing only one thread to access a resource at a time. This is useful for preventing race conditions in multithreaded programs.
The threading.Thread class in Python’s threading module allows you to create and manage threads. This is useful for concurrent execution of tasks, making it possible to run multiple operations simultaneously.
The functools.wraps function in Python’s functools module is a decorator that updates the wrapper function to look more like the wrapped function. This is particularly useful when writing decorators, as it helps preserve the metadata of the original function.
The functools.update_wrapper function in Python’s functools module is used to update a wrapper function to look more like the wrapped function. This is particularly useful when writing decorators, as it helps preserve the metadata of the original function.
The functools.reduce function in Python’s functools module applies a binary function cumulatively to the items of an iterable, from left to right, so as to reduce the iterable to a single value. This is useful for performing some computation on a list and returning the result.
The functools.singledispatch function in Python’s functools module is a decorator that transforms a function into a single-dispatch generic function. This allows you to define different behaviors for a function based on the type of its first argument.
The functools.partialmethod function in Python’s functools module allows you to fix a certain number of arguments of a method and generate a new method. This is useful for creating methods with some preset arguments, particularly in object-oriented programming.
The functools.partial function in Python’s functools module allows you to fix a certain number of arguments of a function and generate a new function. This is useful for creating specialized versions of general-purpose functions.
The functools.lru_cache function in Python’s functools module provides a decorator for caching the results of function calls using a Least Recently Used (LRU) strategy. This can help improve performance by storing results of expensive or frequently called functions and reusing them when the same inputs occur.
The functools.cmp_to_key function in Python’s functools module converts a comparison function into a key function. This can be useful when you need to sort data using a comparison function, which was the method used in Python 2, and need to adapt it to Python 3’s key-based sorting.
The functools.cached_property function in Python’s functools module provides a decorator for caching the result of an instance method as a property. This can help improve performance by avoiding repeated calculations for properties that are expensive to compute.
The functools.cache function in Python’s functools module provides a decorator for memoizing function results. This can help improve performance by caching the results of expensive or frequently called functions.
The itertools.cycle function in Python’s itertools module returns an iterator that repeats the items in the given iterable indefinitely. This is useful for cycling through a sequence of items continuously.
The itertools.pairwise function in Python’s itertools module returns an iterator that yields consecutive overlapping pairs of elements from the input iterable. It is useful for processing elements in pairs, such as finding differences or computing rolling statistics.
The itertools.product function in Python’s itertools module returns the Cartesian product of input iterables. It is useful for generating all possible combinations of elements from multiple iterables.
The itertools.combinations function in Python’s itertools module returns an iterator that produces r-length tuples of elements from the input iterable, without regard to the order. It is useful for generating combinations of elements where the order does not matter.
The itertools.permutations function in Python’s itertools module returns successive r-length permutations of elements from the input iterable. This is useful for generating all possible arrangements of a set of elements.
The itertools.tee function in Python’s itertools module returns multiple independent iterators (or "tees") from a single input iterable. It is useful when you need to iterate over the same iterable in parallel, without consuming it.
The itertools.takewhile function in Python’s itertools module returns elements from an iterable as long as the specified predicate function is true. Once the predicate returns false, the iteration stops. This function is useful for extracting a leading sequence from an iterable based on a condition.
The itertools.starmap function in Python’s itertools module returns an iterator that computes the specified function using arguments obtained from the iterable. It is particularly useful for applying a function to the elements of a list of tuples or other iterables where the elements themselves are iterables.
The itertools.islice function in Python’s itertools module returns an iterator that produces selected elements from the input iterable. It is useful for slicing iterables in a memory-efficient way.
The itertools.groupby function in Python’s itertools module returns consecutive keys and groups from the input iterable. The elements are grouped based on the value of a specified key function. It is useful for grouping elements in an iterable that share a common property.
The itertools.filterfalse function in Python’s itertools module returns an iterator that filters elements from an iterable, returning only those for which a specified function returns false. It is useful for excluding elements based on a condition.