This engineering effort invites you to extend your knowledge about the basics of data analysis to implement a program that can statistically analyze a data set of real-world population records. After you finish the
dataanalysis program it will compute the summary statistics (e.g., mean, median, and standard deviation) of population data from from 1970 until 2019. As you enhance your technical skills, you will continue to program with tools such as VS Code and both the Python programming language and the Poetry package manager. Ultimately, your goal for this project is to create a program that can efficiently process real-world data about human population size.
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-analysis-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
dataanalysis program takes as input a file of floating point values and computes summary statistics about the numbers. Before you continue to work on this assignment, please make sure that you understand the meaning of the data in this file. To accomplish this task, you should examine the discussion of this data set, including its visualization from 1970 until 2019, from the Residential Population in Crawford County, PA from the Federal Reserve Bank of St. Louis. The main goal for this program is that it should summarize the population data for Crawford County, the county in which Allegheny College is located. Here is an excerpt from the
input/data.txt file that contains the real-world population data values that the
dataanalysis program must summarize:
1970-01-01,81.342 1971-01-01,83.300 1972-01-01,84.700 1973-01-01,85.500 1974-01-01,86.100 1975-01-01,87.000 1976-01-01,87.600 1977-01-01,87.600 1978-01-01,88.000 1979-01-01,88.100 1980-01-01,88.869
As this example indicates, the numbers in this file are either strings, that should be interpreted as a date, or a floating-point value, that is a recording of a population estimate of people living in Crawford County. After you have studied and understood the structure of this file's contents, you are ready to install the project's dependencies with the command
poetry install and then run it with the command
poetry run dataanalysis --data-file input/data.txt. After you running the program you can use its output and the data visualization available from the Federal Reserve Bank of St. Louis to better understand the population trends for Crawford County. Finally, it is worth noting that the numerical output from the
dataanalysis program contains four properly indented floating-point values that are always rounded to two decimal places.
📦 The data file contains 50 data values in it! 🚀 Let's do some sophisticated data analysis! 🧮 Here are the results of the data analysis: The computed mean is 87.80! The computed median is 88.05! The computed variance is 3.69! The computed standard deviation is 1.92! 💡 What does this tell you about the population of this city?
Don't forget that if you want to run the
dataanalysis you must use your terminal to first go into the GitHub repository containing this project and then go into the
dataanalysis 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
dataanalysis/dataanalysis/main.py you will see that it has many
TODO markers that designate the parts of the program that you need to implement before
dataanalysis will produce the 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 dataanalysis --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 function you also need to correctly
import the correct modules and objects, like
typer. Along with adding command-line features to the
main function in the
main module, you need to provide an implementation of the following functions in other provided Python files:
def compute_mean(numbers: List[float]) -> float
def compute_median(numbers: List[float]) -> float
def compute_difference(numbers: List[float]) -> List[float]
def compute_variance(numbers: List[float]) -> float
def compute_standard_deviation(numbers: List[float]) -> float
It is worth noting that, when appropriate, one of the aforementioned functions can call another function. For instance, the
compute_standard_deviation can call the
compute_variance, thereby reusing its code and avoiding unnecessary code duplication. 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
dataanalysis/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
dataanalysis you should repeatedly run these tests, as explained in the next subsection, to confirm that your program's functions are working correctly. Your program should both produce the correct output and the pass the test suite!
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
dataanalysis comes with two test suites, both of which should pass so as to establish a confidence in the correctness of the program.
collected 13 items tests/test_summarize.py ........... tests/test_transform.py ..
When you are adding functionality to the
dataanalysis 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.
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. As part of this project's reflection you should also consider what technical skills taught in the field of computer science will continue to be the most relevant in the future.
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.