A PHP library providing Python-inspired iteration tools for efficient data processing with loops and streams.
IterTools PHP is a library that provides a comprehensive set of iteration utilities inspired by Python's itertools. It solves the problem of verbose and repetitive loop code by offering tools for zipping, chaining, filtering, mapping, and performing mathematical operations on collections. It works with any iterable data type, including arrays, generators, and iterators.
PHP developers who work extensively with data collections and seek more expressive, efficient, and readable ways to process data in loops or streams.
Developers choose IterTools PHP because it brings the elegance and power of Python's itertools to PHP, reducing boilerplate code and enabling complex data transformations with clean, chainable operations. Its dual approach of loop tools and stream tools offers flexibility for different coding styles.
PHP Iteration Tools Library
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Offers over 100 functions including zip, chain, map, filter, and mathematical operations, as shown in the extensive tables in the README covering multi-iteration, single-iteration, and more.
Provides both loop-based tools (e.g., Multi::zip) and fluent stream interfaces (e.g., Stream::of()->map()->filter()), mimicking Python's itertools and Java Streams, as demonstrated in the code examples.
Works seamlessly with any iterable type—arrays, generators, iterators, and Traversable—ensuring versatility across different data structures, as stated in the 'Features' section.
Includes full documentation with examples, supports multiple languages (e.g., Russian), and adheres to PSR-1, PSR-4, and PSR-12 standards, as noted in the 'Documentation' and 'Standards' sections.
Requires PHP 8.2+, with older versions (7.4-8.1) limited to an outdated v1.9, potentially excluding legacy projects or teams slow to upgrade, as mentioned in the 'Minimum Requirements'.
The abstraction layers for streams and iterators may introduce memory and CPU costs compared to optimized native loops, especially for large datasets, though not explicitly benchmarked in the README.
With such a vast array of functions, developers need time to master all features, and the lack of integration guides for popular frameworks might increase adoption friction.