erikrose/more-itertools

Features

• Iterable Transformation Utilities - Provides a comprehensive set of functions to map, filter, group, and combine iterables with a functional style.
• Memory-Efficient Data Streaming - Offers a toolkit for chunking, interleaving, and flattening sequences to process large datasets with minimal memory overhead.
• Predicate-Based Element Filtering - Implements predicate-based element filtering to extract specific values or remove padding from collections.
• Iterator State Wrappers - Wraps basic iterators in classes that maintain internal buffers for peeking or seeking within a sequence.
• Iterator Consumption Control - Enables peeking at upcoming elements or seeking within a stream to control how data is consumed.
• Iterator State Control - Ships utilities for peeking at elements and seeking within streams to precisely control sequence consumption.
• Iterable Chunking - Dividing long sequences into smaller chunks or windows to process data in manageable parts without loading everything into memory.
• Iterator Peek and Seek - Implements stateful wrappers that allow users to peek at future elements or seek within a sequence.
• Sequence Transformations - Offers algorithms for mapping and transforming sequences, including padding and repeating values.
• Combinatorial Generation - Implements algorithms for generating all possible permutations, subsets, and combinations through iterative and recursive exploration.
• Python Functional Programming Libraries - Provides an advanced collection of functional primitives and iterable transformation tools that extend the standard Python itertools module.
• Iterable Flatteners - Ships utilities that collapse nested sequences or iterators into a single flat stream of elements.
• Generator-Based Iterators - Uses Python generator functions to yield values on demand for memory-efficient stream processing.
• Combinatorial Generators - Provides generators for producing all possible permutations, combinations, and powersets from a set of input elements.
• Combinatorial Sets - Provides algorithms for deriving all possible combinations of subsets, permutations, and powersets.
• Lazy Sequence Processing - Processes data sequences using generators to avoid full memory allocation via lazy evaluation.
• Iterable Grouping - Partitions standard iterables into groups based on size or key functions to process large sequences in parts.
• Windowed Stream Splitting - Provides utilities to divide continuous data streams into discrete windows or batches using slice-based logic.
• Stream Combinators - Provides operators for merging, interleaving, and zipping multiple data sources into a single unified stream.
• Iterable Property Analyzers - Computes summaries including uniqueness, running statistics, and element counts to analyze data sequence characteristics.
• Mathematical Sequences - Provides implementations for calculating recursive patterns and numerical progressions through number theory routines.
• Number Theory Algorithms - Implements algorithms for solving problems in integer arithmetic and number theory, such as totients and primes.
• Numerical Computation Routines - Runs numeric routines such as matrix multiplications and polynomial evaluations for high-level mathematical computations.
• Functional Utility Libraries - Extends standard library iteration tools with additional routines.

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