This assignment invites you to run and observe two Python programs called
perform-apply-to-each. Instead of using the Poetry tool for managing dependencies and packaging these programs, which the technical skills advise as a best practice, these programs are scripts, without any dependencies on other Python packages, that you can run through the Python interpreter. As you continue to practice a different way to run a Python program, this project offers you the opportunity to improve your understanding of how to compute the intersection (or elements in common) between two tuples that can contain an arbitrary number of values each of an arbitrary type. You will also learn more about high-order functions as you implement a program that can apply an arbitrary function to the contents of an arbitrary length list of
If you are a student enrolled in a Computer Science class at Allegheny College, you can access this assignment by clicking the link provided to you in Discord. Once you click this link it will create a GitHub repository that you can clone to your computer by following the general-purpose instructions in the description of the technical skills. Specifically, you will need to use the
git clone command to download the project from GitHub to your computer. Now you are ready to add source code and documentation to the project, enabling you to understand more about tuples and higher-order functions.
If you are an emerging proactive programmer who is not enrolled in a Computer Science class at Allegheny College, you can still work on this assignment! To get started, you should click the "Use this template" icon in the structured-types-starter GitHub repository and create your own version of this project's source code. After creating your GitHub repository, you can follow all of the other steps!
If you change into the
source/ directory of your GitHub repository, you will see two Python files called
perform-apply-to-each.py. You can run the
compute-tuple-intersection.py program by typing
python compute-tuple-intersection.py in your terminal window. This program currently has several
TODO markers asking you to add source code from the text book to provide an implementation of a function with the following signature:
def compute_intersection(tuple_one: Tuple[Any, ...], tuple_two: Tuple[Any, ...]) -> Tuple[Any, ...]. Once you have added the required source code your program should produce the following output. Can you explain why different calls to
compute_intersection yield output with the same elements but in a different order?
The first tuple: (1, 'a', 2) The second tuple: ('b', 2, 'a') The first intersection tuple: ('a', 2) The second intersection tuple: (2, 'a')
The second program in the
source/ directory is called
perform-apply-to-each. Again, this program has several
TODO markers that invite you to add source code from the text book to finish the implementation of the function with the signature
def apply_to_each(values: List[int], function: Callable) -> None. After you have added the required source code your program should produce the following output. One interesting aspect of the
apply_to_each function is that it does not return any values, as indicated by the return type annotation of
None. If the function does not return a value, then how can it modify the
values input parameter of type
List[int] as shown in the output? Finally, you will note that
apply_to_each accepts a
function parameter of type
Callable, making it a higher-order function. What are the benefits of using high-order functions in Python programs? How does
apply_to_each use the
Values before transformations: [1, -2, 3.33] Values after applying abs: [1, 2, 3.33] Values after applying int: [1, 2, 3] Values after applying squaring: [1, 4, 9]
Since this project does not use Poetry to manage project dependencies and virtual environments, it does not support the use of commands like
poetry run task test. However, you can leverage the relevant instructions in the technical skills to run the command
gatorgrade --config config/gatorgrade.yml to check your work. If your work meets the baseline requirements and adheres to the best practices that proactive programmers adopt you will see that all the checks pass when you run
gatorgrade. You can study the
config/gatorgrade.yml file in your repository to learn how GatorGrade runs GatorGrader to automatically check your program and technical writing.
Did you know that GatorGrade and GatorGrader are open-source Python programs implemented by Proactive Programmers? If you finish this source code survey and have extra time, please brainstorm some new features that you think these two tools should have, explain your idea by raising an issue in the relevant project's GitHub repository, and take the first step towards implementing and testing your idea. If the maintainers of these tools accept your new feature then you will have helped to improve the experience of other people who use GatorGrade and GatorGrader!
Once you have finished all of the previous technical tasks, you can use a text editor to answer all of the questions in the
writing/reflection.md file. Since this is a source code survey, you should provide output from running each of the provided Python programs on your own laptop and then explain how the program's source code produced that output. A specific goal for this project is to ensure that you can explain each component of a function's type signature, including details about its inputs and outputs.
Since this project is source code survey, it is aligned with the remembering and understanding levels of Bloom's taxonomy. You can learn more about how a proactive programming expert will assess your work by examining the assessment strategy. From the start to the end of this project you may make an unlimited number of reattempts at submitting source code and technical writing that meet the project's specification.
Emerging proactive programmers who have questions about this project are invited to ask them in either the GitHub discussions forum or the Proactive Programmers Discord server. Before you ask your question, please read the advice concerning how to best participate in the Proactive Programmers community. If you find a mistake in this project, please describe it and propose a solution by creating an issue in the GitHub Issue Tracker.