Python package of data structures which support the use and implementation of algorithms.
- Functional & imperative programming styles supported
- FP supported but project endeavors to remain Pythonic
- Methods which mutate objects don't return anything
- grscheller.datastructures project on PyPI
- Detailed API documentation on GH-Pages
- Source code on GitHub
Data structures allowing developers to focus on the algorithms they are using instead of all the "bit fiddling" required to implement behaviors, perform memory management, handle coding edge cases, and dealing with exceptional events. These data structures allow iterators to leisurely iterate over inaccessible copies of internal state while the data stuctures themselves are free to safely mutate. They are designed to be reasonably "atomic" without introducing inordinate complexity. Some of these data structures allow data to be safely shared between multiple data structure instances by making shared data immutable and inaccessible to client code.
This package does not force functional programming paradigms on client code, but provide functional tools to opt into. It also does not force unnecessary exception driven code paths upon client code. Purity is important, but not at the expense of practicality. Sometimes the real power of a data structure comes not from what it empowers you to do, but from what it prevents you from doing to yourself.
As a design choice, Python None is semantically used by this package
to indicate the absence of a value. While still freely used as an
implementation detail, None values are not stored in any of these data
structures. Functional Maybe and Either classes are provided in the
core.fp sub-module as better ways to handle "missing" data.