This engineering effort invites you to combine what you learned about the basics of Python programming and data analysis to implement a useful program that can summarize a list of floating-point data values stored in a file. However, the first step towards summarizing the data correctly requires the program to transform the input data values from a text-based format to a numerical representation. Along with learning more about how to implement data transformation and summarization routines you will also explore the basics of writing your own test cases. As you enhance your technical skills, you will continue to program with tools such as VS Code and a terminal window and both the Python programming language and the Poetry package manager.
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!
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" button in the data-summarization-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!
This project invites you to implement a data summarization program called
datasummarizer program takes as input a file of floating point values and computes their arithmetic mean. Here is an excerpt from the
input/data.txt file that contains the floating-point values that the
datasummarizer must summarize:
As this example indicates, these numbers are floating-point values. Can you explain why these floating point numbers are written as, for instance,
2.5169521900e+0? After you have studied and understood the contents of this file, you are ready to install the project's dependencies with the command
poetry install and then run it with the command
poetry run datasummarizer --data-file input/data.txt. Specifically, you will know that your
datasummarizer works correctly when it outputs the computed mean as
0.9919614640914002. If you did not get this answer, then please confirm the correctness of your functions for data transformation and summarization.
🔬 The data file contains 100 data values in it! Let's get summarizing!
🧮 The computed mean is 0.9919614640914002!
Don't forget that if you want to run the
datasummarizer you must use your terminal to first go into the GitHub repository containing this project and then go into the
datasummarizer directory that contains the project's code. Finally, remember that before running the program you must run
poetry install to add the dependencies.
If you study the file
datasummarizer/datasummarizer/main.py you will see that it has many
TODO markers that designate the parts of the program that you need to implement before
datasummarizer will produce correct output. If you run the provided test suite with the command
poetry run task test or you try to run the program with the command
poetry run datasummarizer --data-file input/data.txt you will see an error message in your terminal window. This is due to the fact that there are key parts of this program that are missing! In addition to implementing the program's main functions you also need to correctly
import the correct modules and objects, like
datasummarizer program takes as input textual values from an input file, you will need to implement a data transformation function that can take as input a string that contains a numerical value on each line and returns a list of floating-point values suitable for input into a mathematical computation. For your reference, here is the signature of the
def transform_string_to_number_list(data_text: str) -> List[float]
You program also needs to contain a data summarization function that can take as input a list of floating-point values and then return a single floating-point value that corresponds to the arithmetic mean of the values in the list. As you are implementing this function, please ensure that your function can handle without crashing an empty list of numerical values, returning a "not a number" (i.e.,
NaN) designator in this situation. Here is the signature of the
compute_mean function that you must implement:
def compute_mean(numbers: List[float]) -> float
In summary, you must follow all of the instructions next to the
TODO markers in the provided source code to implement a program that can correctly compute the arithmetic mean of the provided data values in the
datasummarizer/input/data.txt file. In addition to ensuring that your program is adequately documented, has the correct industry-standard format, and adheres to the industry best practices Python programming, you must implement functions that pass a provided Pytest test suite.
If you look in the files called
test_summarize.py you will find the test suites for the
summarize modules. As you complete your implementation of
datasummarizer you should run these tests, as explained in the next subsection, to confirm that your program's functions are working correctly. Ultimately, it is important for both your program to produce the correct output and the test suite to pass!
When you are adding functionality to the
datasummarizer program, make sure that you work in an incremental fashion, adding a small feature to the system and then confirming that it works correctly through linting, testing, and running the program. Once you have added this feature and confirmed that it works correctly, you should commit your source code to your GitHub repository and confirm that you have improved the build status of your project. As you are committing your source code, please pay careful attention to the commit message that you write! Specifically, you should make sure that your commit message features a sentence with an active verb and a clear description of the way in which you changed the source code. You can read the article How to Write a Git Commit Message by Chris Beams to learn some suggestions for ways to improve the quality of your Git commit messages.
As you continue to add and confirm the correctness of
datasummarizer's functionality, you should study the source code in the
pyproject.toml file. This file contains the specification of several tasks that will help you to easily run checks on your Python source code. Now, you can run commands like
poetry run task lint to automatically run all of the linters designed to check the Python source code in your program and its test suite. You can also use the command
poetry run task black to confirm that your source code adheres to the industry-standard format defined by the
black tool. If it does not adhere to the standard then you can run the command
poetry run fixformat and it will automatically reformat the source code. By following a tutorial, you can configure VS Code to use the
black tool to automatically reformat the source code when you save a file.
Along with running tasks like
poetry run task lint, 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 the GatorGrade program runs GatorGrader to automatically check your program and technical writing. If your program has all of the anticipated functionality, you can run the command
poetry run task test and see that the test suite produces output like the following. It is important to note that
datasummarizer comes with two test suites, both of which should pass so as to establish a confidence in the correctness of the program.
collected 6 items
Don't forget that when you commit source code or technical writing to your GitHub repository for this project, it will trigger the run of a GitHub Actions workflow. If you are a student at Allegheny College, then running this workflow consumes build minutes for the course's organization! As such, you should only commit to your repository once you have made substantive changes to your project and you are ready to confirm its correctness. Before you commit to your repository, you can should run checks on your own computer by running
gatorgrade --config config/gatorgrade.yml.
Once you have finished both of the previous technical tasks, you can use a text editor to answer all of the questions in the
writing/reflection.md file. For instance, you should provide the output of the Python program in a fenced code block, explain the meaning of the Python source code segments that you implemented, and answer all of the other questions about your experiences in completing this project. The reflection's objective is to invite you to explain the Python functions for data summarization and transformation.
Since this project is an engineering effort, it is aligned with the evaluating and creating 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 every aspect of the project's specification.
Before you finish all of the required deliverables required by this project is worth pausing to remember that the instructor will give advance feedback to any learner who requests it through GitHub and Discord at least 24 hours before the project's due date! Seriously, did you catch that? This policy means that you can have a thorough understanding of ways to improve your project before its final assessment! To learn more about this opportunity, please read the assessment strategy for this site.
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.