Python is a sophisticated, widely used coding language that, for developers, acts as an artist’s paint brush to a canvas. In fact, it’s one of the most popular languages today.

But what happens when there are missing fundamentals, structure, or other components from a long string of language? It begins to distort the message. Having a strong understanding of the following 8 Python best practices will help you produce stunning works of art, translated through code.

Why Is It Important to Use Best Practices When Coding with Python?

Python programming is incredibly versatile. It can be used to help build everything from a simple command prompt to a complex game development, AI, and more. 

Learn More: What is Python Used For?

Following Python coding best practices ensures that your code is consistently clean and readable. It encourages code reusability, reduces the likelihood of bugs (not to mention, makes it easier to discover and repair bugs), and makes it easier to maintain and restructure. 

As Python’s philosophy revolves around readability and simplicity, adhering to these standards and practices can help developers harness the full potential of Python’s elegant syntax.

4 Basic Best Practices for Python Development

Before digging into Python development, there are four fundamental pillars you should focus on: formatting and syntax, documentation, testing, and naming conventions. Each of these Python coding standards and best practices supports the development process by laying the foundation for more advanced concepts.

1. Formatting & Syntax Best Practices

One of the easiest ways to ensure code quality is by following the PEP8 Python coding style guide for formatting and syntax. Consistent code formatting makes your code easy to read and understand, easy to maintain, reduces errors, and makes it easier to collaborate. This involves best practices around stylistic choices such as indentation, line spacing, use of white space, and comments.

Indentation Guidelines

Python best practices for code quality include using four spaces for each indentation level. It also outlines that tabs and white spaces should not be intermixed (spaces should be used, not tabs). And lastly, indent consistently from start to finish.

Line Spacing

Line spacing guidelines for Python include using blank lines sparingly. Top-level functions and class definitions should be separated with two blank lines while method definitions should be separated by one. 

These guidelines also indicate that lines should be kept reasonably short, around 79 characters (though some coders prefer 99-119). Lastly, line breaks should be used with care and incorporate things like parentheses, brackets, backslashes, and braces to break up longer strings of code.

White Space Usage

PEP 8 indicates to avoid trailing whitespace on all lines, except those that are otherwise blank. It also directs to use spaces around binary operators, such as equal signs, brackets, etc. You should also avoid extra whitespace, ensuring not to place them inside brackets or before commas.

Comments

Comments are another important part of basic Python best practices, as they provide insights into each piece of code. Keeping comments up-to-date is crucial, as outdated comments can be worse than having none at all. 

Comments should be used to explain why the code is the way it is or the problem it’s solving, rather than detailing what it does. Comments should be written using docstrings, when applicable, as they can tie the code to a module, function, or class. And lastly, inline comments should be used sparingly and separated by at least two spaces from the statement, starting with a hashtag and a single space and indented to the same level for each line.

Tools to Enforce These Guidelines

Various tools can help enforce these standards, such as PEP8 checkers and formatters, linters, and integrated development environments (IDEs). Some popular PEP8 checkers include Pylint and Flake8. Popular auto formatters include Black and YAPF. Common IDEs include PyCharm, Sublime Text, and Visual Studio Code.

2. Documentation Best Practices

Clear, current, and comprehensive documentation is a cornerstone of maintainable code. This extends beyond simple comments within your codebase, including module-level docstrings, function, and method explanations, plus context for more complex blocks of code. Here are some Python coding best practices when it comes to documentation:

1. Write docstrings for all public modules, functions, classes, and methods.
2. Follow docstring conventions as outlined by PEP 257.
3. Keep docstrings current to the code.
4. Don’t be redundant when documenting simple code.
5. Use type hints with Python 3.5.
6. Include examples when documenting more complex functions or methods.
7. Use Automated documentation generators, like Sphinx and Doxygen.

When to Use Automated Documentation Generators

These handy tools create documentation automatically using your source code. This can save developers considerable time and effort. Some examples of when Python automated documentation generators come in handy may include when you’re using large or complex code bases, working on a collaborative project, using public APIs or libraries, working on open-source projects, and more.

Why Good Documentation Is Important

Documentation is a critical component of Python coding standards and best practices because it helps explain what the code does, how it does it, and why it was written that way. This is most helpful for complex sections of code where the functionality might not be immediately apparent, or when code can be done multiple ways but a certain way was selected. It supports code understandability, maintenance, collaboration, debugging, and long-term sustainability.

3. Testing Best Practices

Testing your code is yet another essential component of Python best practices for code quality. It ensures a quality completed code, can help catch bugs early, and ensures the code behaves as expected. You can utilize automated testing libraries and frameworks as well as best practices to ensure you’re testing effectively.

• Write Clear, Simple, and Small Tests Early and Frequently. Try writing your tests before writing your code, using the test-driven development (TDD) method and ensure each test is designed to verify a single behavior.
• Use Consistent Testing Methodology. Choose a testing method that works best for your unique project and stick with that testing module for the entire project.
• Automate Your Testing. Automated tests help you catch bugs early, ensure that your code works as expected, and protect against regressions. To write effective tests, you can use one of the many libraries or frameworks available. Unitest and Pytest are popular choices.
• Test Edge Cases and Failure Modes. Testing all aspects of the code, including failure points, will help ensure your code can handle all situations.
• Perform Isolated Testing. Use mock and stub objects to mimic the behavior of more complex objects to help isolate the component that needs testing.
• Aim for High Test Coverage. Tools like coverage.py can help you measure how much of your code is being tested. The more you test, the better.

4. Naming Conventions Best Practices

Naming conventions will also help keep your code consistent and easy to understand and maintain. Python outlines established conventions for naming variables, functions, classes, and modules in their PEP8 guidelines. Names should be self-explanatory, and describe the information in a descriptive way. Some of the Python coding standards and best practices for naming conventions include:

• Variables & Functions: use all lowercase letters and separate words with underscores.
• Classes: use CapWords/CamelCase, using a capital letter for each new word but not separating the words by spaces or underscores.
• Constants: use all caps.
• Modules: use all lowercase. Add underscores between words if it improves readability.
• Methods: use the function naming rules but indicate internal use methods by adding an underscore before the name.
• Single-Character Names: avoid using these altogether.
• Built-In Names: avoid using the Python built-in names, such as naming a variable list.”

4 More Advanced Best Practices for Python Development

Once you’ve mastered the basics