Python has evolved over time since its inception and transformed into a modern framework that builds high-performance, robust, and scalable applications. The latest Python version has introduced several security fixes and the Python web development community is constantly pushing newer updates.
When it comes to adopting Python best practices, developers can do so many things right and wrong. Here is an overview of everything you need to know, including how to use best practices.
Follow the python best practices for the best project outcomes and deliverables. We will provide you guidance on different project structure types, Python Enhancement Proposals, coding styles, and much more. Let’s get started:
Pipx can be installed via homebrew or the system package manager on Linux. It’s better than pip and ensures that all your applications use the correct libraries and have no mismatches or missing packages.
Get familiar with the pyOpenSci project documentation for creating pyproject.toml files. It introduces developers to the new file format and the pyproject.toml file is used to store configuration information at a central location.
There are modern tools too like Poetry or Hatch that automatically create the structure and lets developers start using pyproject.toml files by default. Try them out.
The src layout structure is a popular file layout choice used by modern developers. It creates editable installs of your project packages.
Python virtual environments enable developers to control dependencies in the code. They ensure that the right package and library versions are used for software programs. Virtual environments offer a way to rerun the code and make results reproducible.
Python virtual environments eliminate any conflicts that arise when different packages are used between different projects.
Don’t forget to leave comments in your code and keep it simple.
It helps other developers understand context and makes reading the project more accessible. Good code commentary also as a reminder to what you were doing throughout. Update your comments often and ensure that comments don’t contradict what the code is doing. The first letter of every comment should be capitalized and comments be written clear, concise, and complete.
You can use inline comments or block comments to annotate your code.
Automated testing will help you catch bugs early on and prevent many vulnerabilities. It saves time and avoids pitfalls down the line.
Some of the best Python automated testing tips are:
Black is a popular code formatting tool being used by developers these days. It is run with the CI system and automatically rejects code that does not match the required project format. If you need both code formatting and quality checks, you may consider using Ruff.
For critical applications, consider using type hinting. Refer to PEP 484 - Type Hints and PEP 526 - Syntax for Variable Annotations for the required type hinting notation.
Format strings using f-strings instead of str.format(), % formatting, and str.Template()
Keep in mind that f-strings is a feature available to only Python version 3.6 and up. To get a complete overview of how f-strings work, refer to this guide.
You can use one-liners or multiline docstrings to document script functions, environment variables, command line syntax, and files. A complete explanation on how DocStrings work and their use cases can be found in the PEP 257 - DocStrings Conventions guide.
The Zen of Python consists of a collection of 20 poetic lines that describe how to write clean, concise, and quality code. It is considered to be the Bible of Python code style.
The Python Enhancement Proposal is a set of standards laid down for writing clean, high-quality, and well-structured code. Developers need the PEP 8 style guide to improve code readability, consistency, and ensure easy maintenance.
The three main types of Python Enhancement Proposal are:
Modular programming can be used to break up complex Python projects into simpler and more manageable programs. Following the best Python project structure building practices ensures smooth collaboration, reduces duplication, and makes the code reusable. It also helps avoid potential namespace conflicts as namespaces can be defined for each module.
Here is a list of some of the best practices for structuring your Python project:
Follow good naming conventions like using underscores for variables and functions and capital letters for classes. Use lowercase for instance variable names and avoid choosing names that are too wordy or hard to remember.
The CapWords naming convention scheme is best suited for defining class names in Python. All global variables should be in lowercase and underscores should join the words. Non-public instance variables should use a single underscore.
All instance methods should have ‘self’ for the first argument. Class methods will have the first argument named ‘cls.’
Make separate folders for different parts of your project. An example of this is creating a folder for the code, one for testing, one for project documentation, and so on. A good folder structure will make it convenient to troubleshoot issues; it makes locating files easier for other developers during code navigation.
Git is a popular version control tool that can help you keep track of code changes and suggests actionable recommendations. Developers can backup their work in cloud repositories and most cloud-based Git solutions come with a free tier for solo users.
Pip is a great package manager and is especially useful when working with large projects that have multiple dependencies. It tracks all different pieces of software involved in a project that are required to run it.
Following these Python coding best practices will ensure that you stay on the right track and prevent project delays. They increase productivity, improve team performance, and make the code highly readable. You will learn how to double-check the code you’re writing and weed out any errors in the process. It will make building new projects much more efficient and cost-effective.
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