Project Structure and Planning
When beginning a capstone project, it’s important to plan out the overall structure and organization of the project. Here are some things to consider during the planning phase:
Define the goal or purpose of the project. What problem are you trying to solve? What questions are you trying to answer? Having a clear goal will help guide your approach.
Break the project into individual tasks or features that need to be completed to achieve the overall goal. This helps identify the major components and scope of work.
Create user stories to define how specific users will interact with and benefit from different features. User stories help guide the user experience aspects.
Create visualizations like flow charts or diagrams to map out how different components or classes will relate and interact with each other. This helps define the software architecture.
Determine dependencies between tasks. This helps establish a logical order for implementing features and helps avoid issues down the road.
Define a timeline with target dates for completing each task and feature. This helps stay on track to finish the project.
Consider things like data sources, external APIs, package or library requirements during the planning phase.
Project Structure in Code
Once you’ve planned out your overall structure, you need to establish an organized directory and code structure to implement your project:
Create a main.py file to serve as the entry point for your application. This will tie everything together.
Establish a base package/directory for your project with sub-directories like models, views, controllers etc to group logical components.
Define classes and modules to match your planned components – data models, business logic, views, utilities etc. Split code into logical units of work.
Consider following established Python design patterns like MVC (models, views, controllers), MVT (models, views, templates) depending on the type of project.
Handle configuration through a settings/config.py module rather than hardcoding values.
Create a requirements.txt file to track package dependencies that need to be installed.
Include documentation files like a README explaining how to set up, run and use your project. Consider Sphinx for extensive documentation.
Set up logging, error handling and debugging tools to more easily troubleshoot issues.
For larger projects, establish a test directory with unit tests for different modules using a framework like pytest. Tests help refactor safely.
Version Control and Collaboration
Use version control from the beginning with Git to track changes, enable collaboration and avoid integration issues.
Create a public GitHub repo from the start to share your work and get feedback from others.
Break work into small, atomic commits that each address one change or issue at a time for easier reviews.
Consider feature branching for larger new features to isolate work before merging back.
For collaborative work, use pull requests on GitHub to review changes before merging back to the main branch.
Automate tests to run on pull requests to catch regressions before merging code.
Use issue tracking for tracking bugs, enhancements or projects as a team. Link commits and PRs back to issues.
Writechangelog and releasenotesdocuments to keep users informed of changes between versions.
This covers some key best practices and considerations for planning and structuring a well-organized Python capstone project to make development smoother and ensure your codebase stays maintainable as features are added over time. Proper structure and tooling is important for larger solo or collaborative projects.
Andrew Stroud