The itertools.compress function in Python’s itertools module filters elements from an iterable based on the values of a selector iterable. It is useful for selecting elements from one iterable when the corresponding elements in another iterable are true.
The itertools.chain function in Python’s itertools module returns an iterator that iterates over multiple iterables in sequence, as if they were a single iterable. It is useful for chaining together multiple sequences to be treated as one continuous sequence.
The itertools.accumulate function in Python’s itertools module returns an iterator that yields accumulated sums, or accumulated results of other binary functions, specified via the func parameter. It is useful for performing cumulative operations on iterable data.
The itertools.repeat function in Python’s itertools module returns an iterator that repeats a single value infinitely or a specified number of times. It is useful for generating constant sequences or combining with other iterators.
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.count function in Python’s itertools module returns an iterator that generates consecutive integers, starting from a specified number. It is often used for looping over a sequence of numbers in an efficient way.
The collections.UserString class in Python’s collections module provides a wrapper around string objects. It is a useful base class for creating custom string-like objects by subclassing and modifying its behavior.
The collections.UserList class in Python’s collections module provides a wrapper around list objects. It is a useful base class for creating custom list-like objects by subclassing and modifying its behavior.
The collections.UserDict class in Python’s collections module acts as a wrapper around dictionary objects. It is a useful base class for creating custom dictionary-like objects by subclassing and modifying its behavior.
The collections.defaultdict class in Python’s collections module is a subclass of the built-in dict class. It overrides one method to provide a default value for a nonexistent key, simplifying the process of dealing with missing keys in a dictionary.
The collections.OrderedDict class in Python’s collections module provides a dictionary subclass that remembers the order in which items were inserted. This is useful when the order of elements is important for your application.
The collections.Counter class in Python’s collections module provides a dictionary subclass designed for counting hashable objects. It is used for counting items in a collection and provides additional functionality to easily manipulate and retrieve counts.
The collections.ChainMap class in Python’s collections module provides a way to manage multiple dictionaries as a single view. It allows you to search through a sequence of dictionaries as if they were one, making it easier to work with multiple contexts or scopes.
The collections.deque class in Python’s collections module provides a double-ended queue with efficient appends and pops from both ends. It is a useful data structure for tasks that require fast access and modification of elements at both ends of the queue.
The collections.namedtuple function in Python’s collections module is a factory function that creates tuple subclasses with named fields. This function is useful for creating simple classes to bundle together a few attributes and provide named access to them.
The json.JSONEncoder class in Python’s json module provides a way to encode Python objects into JSON strings. This class is useful for customizing the JSON serialization process, allowing you to handle specific types or structures in your Python data.
The json.JSONDecoder class in Python’s json module provides a way to decode JSON strings into Python objects. This class is useful for customizing the JSON deserialization process, allowing you to handle specific types or structures in your JSON data.
The json.loads function in Python’s json module deserializes a JSON-formatted string into a Python object. This function is useful for converting JSON data received as a string into a Python object that can be manipulated within your code.
The json.load function in Python’s json module deserializes a JSON-formatted stream into a Python object. This function is useful for reading JSON data from a file and converting it into a Python object.
The json.load function in Python’s json module deserializes a JSON-formatted stream into a Python object. This function is useful for reading JSON data from a file and converting it into a Python object.
The json.dump function in Python’s json module is used to convert a Python object into a JSON string and write it to a file. This function is helpful when you want to save data in a file in JSON format.
The re.purge function in Python’s re module clears the regular expression cache. This function is useful for freeing up memory used by compiled regular expressions that are no longer needed.
The re.escape function in Python’s re module escapes all non-alphanumeric characters in a string. This function is useful when you need to treat a string as a literal value in a regular expression, ensuring that any special characters are not interpreted as regex metacharacters.
The re.subn function in Python’s re module performs the same task as re.sub, but also returns the number of substitutions made. This function is useful when you need to know both the result of the substitution and how many substitutions were made.
The re.sub function in Python’s re module replaces occurrences of a pattern in a string with a specified replacement string. This function is useful for performing substitutions in text based on regular expression patterns.