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Verifying Code Style Using Linters

Overview

Teaching: 15 min
Exercises: 10 min
Questions
  • What tools can help with maintaining a consistent code style?

  • How can we automate code style checking?

Objectives
  • Use code linting tools to verify a program’s adherence to a Python coding style convention.

Verifying Code Style Using Linters

We’ve seen how we can use PyCharm to help us format our Python code in a consistent style. This aids reusability, since consistent-looking code is easier to modify since it’s easier to read and understand if it’s consistent. We can also use tools to identify consistency issues in a report-style too, using code linters. Linters analyse source code to identify and report on stylistic and even programming errors. Let’s look at a very well used one of these called pylint.

First, let’s ensure we are on the style-fixes branch once again.

$ git checkout style-fixes

Pylint is just a Python package so we can install it in our virtual environment using:

$ pip3 install pylint
$ pylint --version

We should see the version of Pylint, something like:

pylint 2.13.3
...

We should also update our requirements.txt with this new addition:

$ pip3 freeze > requirements.txt

Pylint is a command-line tool that can help our code in many ways:

Pylint can also identify code smells.

How Does Code Smell?

There are many ways that code can exhibit bad design whilst not breaking any rules and working correctly. A code smell is a characteristic that indicates that there is an underlying problem with source code, e.g. large classes or methods, methods with too many parameters, duplicated statements in both if and else blocks of conditionals, etc. They aren’t functional errors in the code, but rather are certain structures that violate principles of good design and impact design quality. They can also indicate that code is in need of maintenance and refactoring.

The phrase has its origins in Chapter 3 “Bad smells in code” by Kent Beck and Martin Fowler in Fowler, Martin (1999). Refactoring. Improving the Design of Existing Code. Addison-Wesley. ISBN 0-201-48567-2.

Pylint recommendations are given as warnings or errors, and Pylint also scores the code with an overall mark. We can look at a specific file (e.g. inflammation-analysis.py), or a module (e.g. inflammation). Let’s look at our inflammation module and code inside it (namely models.py and views.py). From the project root do:

$ pylint inflammation

You should see an output similar to the following:

************* Module inflammation.models
inflammation/models.py:5:82: C0303: Trailing whitespace (trailing-whitespace)
inflammation/models.py:6:66: C0303: Trailing whitespace (trailing-whitespace)
inflammation/models.py:34:0: C0305: Trailing newlines (trailing-newlines)
************* Module inflammation.views
inflammation/views.py:4:0: W0611: Unused numpy imported as np (unused-import)

------------------------------------------------------------------
Your code has been rated at 8.00/10 (previous run: 8.00/10, +0.00)

Your own outputs of the above commands may vary depending on how you have implemented and fixed the code in previous exercises and the coding style you have used.

The five digit codes, such as C0303, are unique identifiers for warnings, with the first character indicating the type of warning. There are five different types of warnings that Pylint looks for, and you can get a summary of them by doing:

$ pylint --long-help

Near the end you’ll see:

  Output:
    Using the default text output, the message format is :
    MESSAGE_TYPE: LINE_NUM:[OBJECT:] MESSAGE
    There are 5 kind of message types :
    * (C) convention, for programming standard violation
    * (R) refactor, for bad code smell
    * (W) warning, for python specific problems
    * (E) error, for probable bugs in the code
    * (F) fatal, if an error occurred which prevented pylint from doing
    further processing.

So for an example of a Pylint Python-specific warning, see the “W0611: Unused numpy imported as np (unused-import)” warning.

It is important to note that while tools such as Pylint are great at giving you a starting point to consider how to improve your code, they won’t find everything that may be wrong with it.

How Does Pylint Calculate the Score?

The Python formula used is (with the variables representing numbers of each type of infraction and statement indicating the total number of statements):

10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)

For example, with a total of 31 statements of models.py and views.py, with a count of the errors shown above, we get a score of 8.00. Note whilst there is a maximum score of 10, given the formula, there is no minimum score - it’s quite possible to get a negative score!

Exercise: Further Improve Code Style of Our Project

Select and fix a few of the issues with our code that Pylint detected. Make sure you do not break the rest of the code in the process and that the code still runs.

Make sure you commit and push requirements.txt and any file with further code style improvements you did and merge onto your development and main branches.

$ git add requirements.txt
$ git commit -m "Added Pylint library"
$ git push origin style-fixes
$ git checkout develop
$ git merge style-fixes
$ git push origin develop
$ git checkout main
$ git merge develop
$ git push origin main

Optional Exercise: Improve Code Style of Your Other Python Projects

If you have a Python project you are working on or you worked on in the past, run it past Pylint to see what issues with your code are detected, if any.

It is possible to automate these kind of code checks with GitHub’s Continuous Integration service GitHub Actions - we will come back to automated linting in the episode on “Diagnosing Issues and Improving Robustness”.

Key Points

  • Use linting tools on the command line (or via continuous integration) to automatically check your code style.