[New Concept Exercise]: Functional Tools in Python

[BethanyG]

This issue describes how to implement the functional tools in Python concept exercise for the Python track. The related concept documents issue can be found here.

✅ Getting started

If you have not yet created or contributed to a concept exercise, this issue will require some upfront reading to give you the needed background knowledge. Some good example exercises to look at in the repo:

💡Example Exercises💡 (click to expand)

1. Little Sister's Vocabulary
2. Meltdown Mitigation
3. Making the Grade
4. Ellen's Alien Game

We also recommend completing one or more of the concept exercises (they're called "learning exercises") on the website.

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 go through the following documents:

General Contributing Docs:

• Contributing to Exercism | Exercism and GitHub | - Contributor Pull Request Guide
• What are those Weird Task Tags about?
• Exercism Formatting and Style Guide
• Exercism Markdown Specification
• Reputation

Documents on Language Tracks and Concept Exercises

• Building Language Tracks: An Overview
• What are Concept Exercises?
• Concept Exercise Specifications
• Concept Exercise Stories

🎯 Goal

The goal of this concept exercise is to teach an understanding/use of core functional tools (e.g, map(), filter(), and functools.reduce() in Python.

💡Learning objectives

• Understand/use the built-in map() function.
  • constructing a lambda or callback function used as the argument.
  • comparisons to set, dict or list comprehensions
  • when and when not to use - considering performance & readability
• Understand/use the built-in filter() function.
  • constructing a lambda or callback function used as the argument.
  • comparisons to set, dict or list comprehensions
  • when and when not to use - considering performance & readability
• Understand/use the functools.reduce() function
  • constructing a lambda or callback function used as the argument.
  • using initializer as a "first call" or to guard against TypeErrors when the passed iterable is empty.
  • comparisons to sum(), min(), max(), any(), all(), math.prod() and to intertools.accumulate()
  • when and when not to use - considering performance & readability

🤔 Concepts

• functional tools (e.g, map(), filter(), and functools.reduce() ) in Python.
• built-ins
  • any() & all()
  • sum()
  • min() & max()
  • map()
  • filter()
• functools.reduce()
• itertools.accumulate()

🚫 Topics that are Out of scope

Concepts & Subjects that are Out of Scope (click to expand)

• comprehensions
• comprehensions in lambdas
• map(), filter() or functools.reduce() within a comprehension
• functools beyond functools.reduce()(this will get its own exercise)
• generators
• map(), filter() or functools.reduce() within a generator expression
• using an assignment expression or "walrus" operator (:=) in a lambda (walrus in general is OK if it is explained, just not inside a lambda)

↩️ Prerequisites

These are the concepts/concept exercises the student should be familiar with before taking on/learning this concept.

Prereqs (click to expand)

• basics
• bools
• comparisons
• dicts
• dict-methods
• functions
• function-arguments
• higher-order-functions
• iteration
• lambdas
• lists
• list-methods
• numbers
• sequences
• sets
• strings
• string-methods
• tuples

📚 Resources for Writing and Reference

Resources (click to open)

• Python Docs: Defining Functions
• Python Docs Tutorial: Lambda Expressions
• Functions as Objects in Python
• Composing Programs: Higher-Order Functions
• built-ins: Python Docs
  • map() & filter()
  • any() & all()
  • min() & max()
  • sum()
• itertools.accumulate()
• functools.reduce()
• Real Python: Functional Programming in Python: When and How to Use it
• Real Python : Python's map(): Processing Iterables Without a Loop
• Real Python: Python's reduce(): From Functional to Pythonic Style

Additional Articles (click to open)

• Learn by Example: Python Lambda Function
• Python Docs: Functional Programming HOWTO

Exercise Ideas & Stories

Should you need inspiration for an exercise story, you can find a collection here. You can also port an exercise from another track, but please make sure to only to include tasks that actually make sense in Python and that add value for a student. Remove/replace/add tasks as needed to make the concept clear/workable.

📁 Exercise Files to Be Created

File Detail for this Exercise (click to collapse)

Exercise introduction.md For more information, see Exercise introduction.md This can summarize/paraphrase the linked concept documents if they have already been created (either the about or the introduction). The summary does need to have enough information and examples for the student to complete all the tasks outlined for this concept exercise. Exercise instructions.md For more information, see instructions.md Instructions for an exercise usually center on a story that sets up the code challenge to be solved. You can create your own story, or fork one from the ones listed here. Please make sure to give credit to the original authors if you use a story or fork an exercise. Exercise Exemplar.py Solution For more information, see exemplar implementation. This file should not use syntax or datas structures not introduced in this exercise or in this exercise's prerequisites. It will be used as an "ideal" solution for the challenge, so make sure it conforms to PEP8 and other formatting conventions, and does not use single letter variable names. It should also include proper module and function-level docstrings. However, it should NOT include typehinting or type aliases. <Exercise>.py (Stub) for Implementation For more information, see stub implementation. This file should provide the expected function names imported for testing, and optionally TODO comments and or docstrings to aid the student in their implementation. TODOs and docstrings are not required. <Exercise>_Test.py Files For more information, see Tests. Additionally, please note that Python associates exercise tasks to tests via a Pytest Marker, and uses unittest subtests as a form of test paramaterization. See the test file for Little Sisters Vocab for examples of how these techniques work. Exercise Hints.md For more information on writing hints see hints.md Hints should provide enough information to get most students "un-stuck" and moving toward a solution. They should not provide a student with a direct solution. You can refer to one or more of the resources linked in this issue 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. 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.

♾️ Exercise Metadata - Track

For more information on concept exercises and formatting for the Python track config.json , please see config.json. 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 : e3ad3c65-e038-46ab-a157-e3d4be638272
• concepts should be filled in from the Concepts section in this issue
• prerequisites should be filled in from the Prerequisites section in this issue

🎶 Implementation Notes

• As a reminder, code in the .meta/examplar.py file should only use syntax & concepts introduced in this exercise or one of its prerequisite exercises. We run all our examplar.py files through PyLint, but do not strictly require module docstrings. We do require function docstrings similar to PEP257. See this concept exercise exemplar.py for an example.

• Please do not use comprehensions, generator expressions, or other syntax not previously covered either in the introduction to this exercise, or to one of its prerequisites. 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.

  • All asserts should contain a "user friendly" failure message (these will display on the webiste to students, so be as clear as you can).
  • We use a PyTest custom mark to link test cases to exercise task numbers.
  • We also use unittest.subtest to parameterize test input where/when needed. Here is an example testfile that shows all three of these in action.

• While we do use PyTest as our test runner and for some implementation tests, please check with a maintainer before using a PyTest-specific 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.

🆘 Next Steps & Getting Help

1. If you'd like to work on this issue, comment saying "I'd like to work on this" (there is no real need to wait for a response, just go ahead, we'll assign you and put a [claimed] label on the issue).
2. If you have any questions while implementing, please post the questions as comments in here, or contact one of the maintainers on our Slack channel.