grscheller.datastructures 0.7.4.0

Package grscheller.datastructures

Python grscheller.datastructures package

Data structures supporting a functional style of programming, yet still endeavor to be Pythonic.

Overview

The data structures in this package:

• Allow developers to focus on the algorithms the data structures were designed to support.
• Take care of all the "bit fiddling" needed to implement desired behaviors.
• Don't force the raising of gratuitous exceptions upon client code leveraging this package.
• Safely handle mutating contained data by pushing it to a protected inner scope.
• Safely share data between data structure instances by pushing mutation to an outer scope and making shared immutable internal state inaccessible to client code.
• Allow for "lazy" evaluation avoiding race conditions by having iterators process non-mutating copies of data structure internal state.
• Code to the "happy" path & provide FP tools for "exceptional" events.

Sometimes the real power of a data structure comes not from what it enables you to do, but from what it prevents you from doing.

Data structure summary

• Non-Typed Datastructures
• Functional Subpackage
• Iterator Library

Design choices

Type annotations

Type annotations used in this package are extremely useful in helping external tooling work well. See PEP-563 & PEP-649. These features are slated for Python 3.13 but work now in Python 3.11 by including annotations from __future__. This package was developed using Pyright to provide LSP information to Neovim. This allowed the types to guide the design of this package.

None as "non-existence"

As a design choice, Python None is semantically used by this package to indicate the absence of a value. How does one store a "non-existent" value in a very real data structure? Granted, implemented in CPython as a C language data structure, the Python None "singleton" builtin "object" does have a sort of real existence to it. Unless specifically documented otherwise, None values are not stored to these data structures as data. Maybe & Either objects are provided as better ways to handle "missing" data.

Methods which mutate objects don't return anything.

For methods which mutate their data structures, I try to follow the Python convention of not returning anything, like the append method of the Python List builtin object. Most functional programming languages will return a reference to the data structure.