Tired of sifting through cluttered websites to figure out that git command? In this post I try out ChatGPT, a preview release by OpenAI. Not only does it provide help with git commands, I also found it useful for various tasks such as remembering how to use Python’s functools.reduce function, enhancing documentation, creating clear docstrings, optimising functions, and even finding edge case tests. Join me in discovering how AI can boost productivity!
Author: David
Writing Great Docstrings in Python
Have you ever tried to understand a new project by looking at the source code only to find that the code isn’t clear on its own and is lacking documentation, such as docstrings? I have had that experience a few times which slowed down being able to fix bugs and add new features and frequently also meant that the code wasn’t well structured. In this post we’ll look at best practices for documenting in code and its numerous benefits such as helping you be clear on what you actually need to code and reminding yourself and others about how the code works when you come back from an extended holiday. We will also look at a linter that checks your docstrings to make sure they are complete. Your future self will then never be frustrated about a lack of documentation in code again!
Continue reading “Writing Great Docstrings in Python”Enforce All GitHub Actions Status Checks to Pass in PRs
I have been using GitHub actions for a while and they are great! With them you can define your CI/CD along your code and review any changes to CI/CD through your normal PR processes. I’m intending to write more about great workflows and best practices, especially for Python packages, although that will be in a future post. This post addresses a pain point I have had and you may have had as well. Specifically, in PRs, it is possible on GitHub to enforce things like approvals from reviewers before being able to merge. GitHub also has a setting to require status checks to pass before being able to merge, although you have to define exactly which status checks you want to pass. I usually find the names of my status checks change a lot, especially if I’m using matrix strategies for multiple Python versions or package versions. Below we discuss a simple technique to reduce how often you have to update the required status checks!
Continue reading “Enforce All GitHub Actions Status Checks to Pass in PRs”Giving and Receiving Great Feedback through PRs
Do you struggle with PRs? Have you ever had to change code even though you disagreed with the change just to land the PR? Have you ever given feedback that would have improved the code only to get into a comment war? We’ll discuss how to give and receive feedback to extract maximum value from it and avoid all the communication problems that come with PRs. We’ll start with some thoughts about what PRs are intended to achieve and then first discuss how to give feedback that will be well received and result in improvements to the code followed by how to extract maximum value from feedback you receive without agreeing to suboptimal changes. Finally, we will look at a checklist for giving and receiving feedback you can use as you go through reviews both as an author and reviewer.
Continue reading “Giving and Receiving Great Feedback through PRs”Writing Great Test Documentation
Have you ever needed to understand a new project and started reading the tests only to find that you have no idea what the tests are doing? Writing great test documentation as you are writing tests will improve your tests and help you and others reading the tests later. We will first look at why test documentation is important both when writing tests and for future readers and then look at a framework that helps give some structure to your test documentation. Next, we will look at a showcase of the flake8-test-docs tool that automates test documentation checks to ensure your documentation is great! Finally, we briefly discuss how this framework would apply in more advanced cases, such as when you are using fixtures or parametrising tests.
Help your Users fix your Errors
Have you ever encountered an error when using a package and then gone to Google to find out how to solve the error only not to find any clear answers? Wouldn’t you have preferred to go directly to documentation that tells you exactly what went wrong and how to resolve that error? A lot of us can tell similar stories, especially when we try something new. I have also abandoned an otherwise promising package after encountering an error that wasn’t clear or when I didn’t know how to solve the error.
Continue reading “Help your Users fix your Errors”Reduce Duplication in Pytest Parametrised Tests using the Walrus Operator
Do you find yourself having to repeat literal values (like strings and integers) in parametrised tests? I often find myself in this situation and have been looking for ways of reducing this duplication. To show an example, consider this trivial function:
def get_second_word(text: str) -> str | None:
"""Get the second word from text."""
words = text.split()
return words[1] if len(words) >= 2 else None
Python TypedDict Arbitrary Key Names with Totality
In PEP 589 Python has introduced type hints for dictionaries. This helps with defining the structure of dictionaries where the keys and the types of the values are well known. TypedDict also supports keys that may not be present using the concept of totality. Using inheritance a dictionary where some keys are not required can be built. The default syntax for defining TypedDict is a class based syntax (example below). Whilst this is easy to understand, it does limit the names of the keys of the dictionary to valid python variable names. If a dictionary needs to include keys with, for example, a dash, the class based syntax no longer is appropriate for defining the TypedDict. There is an alternative syntax (similar to named tuples) that allows for arbitrary key names (example below). However, this syntax does not support inheritance which means that a dictionary with mixed totality cannot be constructed using this syntax alone.
Continue reading “Python TypedDict Arbitrary Key Names with Totality”Inheritance for SQLAlchemy Models
In software engineering one of the key principles of object oriented software is the concept of inheritance. It can be used to increase code re-use which reduces the volume of tests and speeds up development. You can use inheritance in SQLAlchemy as described here. However, this inheritance is mainly used to describe relationships between tables and not for the purpose of re-using certain pieces of models defined elsewhere.
The openapi specification allows for inheritance using the allOf statement. This means that you could, for example, define a schema for id properties once and re-use that schema for any number of objects where you can customise things like the description that may differ object by object. You can also use allOf to combine objects, which is a powerful way of reducing duplication. You could, for example, define a base object with an id and name property that you then use repeatedly for other objects so that you don’t have to keep giving objects an id and a name.
If this feature could be brought to SQLAlchemy models, you would have a much shorter models.py files which is easier to maintain and understand. The plan for the openapi-SQLAlchemy package is to do just that. The first step has been completed with the addition of support for allOf for column definitions. If you aren’t familiar with the package, the Reducing API Code Duplication article describes the aims of the package.
To start using the column inheritance feature, read the documentation for the feature which describes it in detail and gives and example specification that makes use of it.
openapi-SQLAlchemy Now Supports $ref for Columns
A new version of openapi-SQLAlchemy has been release which adds support for $ref for columns. The package now supports openapi schemas such as the following:
components:
schemas:
Id:
type: integer
description: Unique identifier for the employee.
example: 0
Employee:
description: Person that works for a company.
type: object
properties:
id:
$ref: "#/components/schemas/Id"
name:
type: string
description: The name of the employee.
example: David Andersson.
If you are interested in how this was accomplished with decorators and recursive programming, the following article describes the implementation: Python Recursive Decorators.
David Andersson 