OLD `functools` Module Concept Exercise

[BethanyG]

This issue describes how to implement the functools concept exercise for the python track.

Getting started

Please please please read the docs before starting. Posting PRs without reading these docs will be a lot more frustrating for you during the review cycle, and exhaust Exercism's maintainers' time. So, before diving into the implementation, please read up on the following documents:

• Contributing to Exercism | Exercism and GitHub | Contributor Pull Request Guide
• What are those Weird Task Tags about?
• Building Language Tracks: An Overview
• What are Concepts?
• Concept Exercise Specifications
• Concept Specifications
• Exercism Formatting and Style Guide
• Exercism Markdown Specification
• Reputation

Goal

This concept exercise is meant to teach an understanding/use of functools (e.g, the functools module) in Python.

Learning objectives

Learn more about the functional tools the Python Standard Library provides through the functools module.
Build and understanding of and use the following methods and decorators from the module:

• functools.partial()
  • partial.func
  • partial.args
  • partial.keywords
• functools.update_wrapper()
• functools.cache()
• @functools.cached_property() (this seems better in class_customization)
• @functools.lru_cache()
• @functools.total_ordering (this method seems more appropriate in the rich comparisons exercise)
• @functools.singledispatch
  • @<function>.register()
• @functools.wraps()
• class functools.partialmethod()
• class functools.singledispatchmethod()

Out of scope

• classes & class customization beyond the direct use of the class methods in this module.
• comprehensions
• comprehensions in lambdas
• decorators (these have their own exercise. See issue OLD decorators concept exercise #2356 )
• map(), filter() or functools.reduce() in a comprehension
• functools.reduce()(this was already covered with map() and filter())
• generators
• using an assignment expression or "walrus" operator (:=) in a lambda
• class decorators beyond the ones described in this module.
• enums

Concepts

• functional tools in python
• functools module
• generic functions
• decorators
• higher-order functions
• partial objects in python/partial evaluation in python
• single dispatch

Prerequisites

These are the concepts/concept exercises the student needs to complete/understand before solving this concept exercise.

• basics
• bools
• classes
• class-customization
• class-components
• comparisons
• rich-comparisons
• decorators
• descriptors
• dicts
• dict-methods
• functions
• function-arguments
• higher-order-functions
• iteration
• lists
• list-methods
• numbers
• sequences
• sets
• strings
• string-methods
• tuples

Resources to refer to

• Python Docs: Defining Functions
• Python Docs: functional programming HOWTO
• Python Docs: functools module
• Pthon Module of the Week: functools - Tools for Maniputlating Fuctions
• Florian Dahlitz: Introduction to Python's Functools Module
• PyDanny: Python Partials are Fun!
• Composing Programs: Higher-Order Functions
• Trey Hunner: Ist it a Class or a Function? It's a callable!
• built-ins: Python Docs
  • map() & filter()
  • any() & all()
  • min() & max()
  • sum()
• Real Python: Functional Programming in Python: When and How to Use it

• Hints

  For more information on writing hints see hints

  • You can refer to one or more of the resources linked above, or analogous resources from a trusted source. We prefer using links within the Python Docs as the primary go-to, but other resources listed above are also good. Please try to avoid paid or subscription-based links if possible.

• links.json

  For more information, see concept links file

  • The same resources listed in this issue can be used as a starting point for the concepts/links.json file, if it doesn't already exist.
  • If there are particularly good/interesting information sources for this concept that extend or supplement the concept exercise material & the resources already listed -- please add them to the links.json document.

Concept Description

Please see the following for more details on these files: concepts & concept exercises

• Concept about.md

  Concept file/issue: The working copy of these files can be found here: functools docs. These may need editing to match the exercise writers needs/style.

  For more information, see Concept about.md

  • This file provides information about this concept for a student who has completed the corresponding concept exercise. It is intended as a reference for continued learning.

• Concept introduction.md

  For more information, see Concept introduction.md

  • This can also be a summary/paraphrase of the document listed above, and will provide a brief introduction of the concept for a student who has not yet completed the concept exercise. It should contain a good summation of the concept, but not go into lots of detail.

• Exercise introduction.md

  For more information, see Exercise introduction.md

  • This should also summarize/paraphrase the above document, but with enough information and examples for the student to complete the tasks outlined in this concept exercise.

Exercise Metadata - Track

For more information on concept exercises and formatting for the Python track config.json , please see concept exercise metadata. The track config.json file can be found in the root of the Python repo.

You can use the below for the exercise UUID. You can also generate a new one via exercism configlet, uuidgenerator.net, or any other favorite method. The UUID must be a valid V4 UUID.

• Exercise UUID : ``
• concepts should be filled in from the Concepts section in this issue
• prerequisites should be filled in from the Prerequisites section in this issue

Exercise Metadata Files Under .meta/config.json

For more information on exercise .meta/ files and formatting, see concept exercise metadata files

• .meta/config.json - see this link for the fields and formatting of this file.
• .meta/design.md - see this link for the formatting of this file. Please use the Goal, Learning Objectives,Concepts, Prerequisites and , Out of Scope sections from this issue.

Implementation Notes

• Code in the .meta/examplar.py file should only use syntax & concepts introduced in this exercise or one of its prerequisite exercises.
• Please do not use comprehensions, generator expressions, or other syntax not previously covered. Please also follow PEP8 guidelines.
• In General, tests should be written using unittest.TestCase and the test file should be named <EXERCISE-NAME>_test.py.
• While we do use PyTest as our test runner and for some implementation tests, please check with a maintainer before using a PyTest test method, fixture, or feature.
• Our markdown and JSON files are checked against prettier . We recommend setting prettier up locally and running it prior to submitting your PR to avoid any CI errors.

Help

If you have any questions while implementing the exercise, please post the questions as comments in this issue, or contact one of the maintainers on our Slack channel.