Python is a powerful programming language that's widely used in many industries today. If you're new to Python or are looking to improve your Python skills, here are 10 Python class best practices that you should follow.
Python is an object-oriented language, and as such it has classes which you can define to help structure your code. In this article, we will go through 10 best practices for working with classes in Python. These tips will help you write better, more Pythonic code, and will make your code more maintainable in the long run.
Python 3 is the most up-to-date version of the language, with new features and improvements that are not present in Python 2. Additionally, Python 3 has been around for long enough that all major libraries and frameworks have been ported to it, so you’re not likely to run into compatibility issues.
On the other hand, Python 2 will reach end-of-life on January 1, 2020, which means that it will no longer be supported by the Python community. This means that any security vulnerabilities that are discovered after that date will not be fixed, and users will be left on their own to deal with them.
For these reasons, it’s generally best to use Python 3 for your projects, unless you have a specific reason to use Python 2.
When you use global variables in a class, it’s difficult to know where they came from and what they represent. This can lead to confusion and errors when trying to debug your code.
It’s also difficult to change the value of a global variable without affecting other parts of your code. This can make it hard to refactor your code or add new features.
Instead of using global variables, it’s better to create instance variables that are specific to each object. This way, you can easily access them and change their values without affecting other parts of your code.
When you declare class attributes inside the __init__ method, they are instance attributes. This means that each instance of the class will have its own copy of these attributes, and they will be initialized every time you create a new instance.
On the other hand, if you declare them outside of the __init__ method, they are class attributes. This means that there is only one copy of these attributes, and they are shared by all instances of the class.
There are several advantages to using class attributes over instance attributes.
1. They are more efficient. Since there is only one copy of class attributes, they take up less memory than instance attributes.
2. They are easier to access. You can access class attributes directly from the class, without having to create an instance first.
3. They are easier to modify. If you need to change the value of a class attribute, you can do so directly from the class, without having to modify each instance individually.
4. They are more consistent. Since all instances of a class share the same class attributes, they will all behave in the same way. This is not always the case with instance attributes, which can vary from one instance to another.
When you use self as an instance attribute name, it’s easy to shadow the actual self reference. For example, consider the following code:
class Foo:
def __init__(self):
self.self = 42
This code is perfectly valid, but it’s not what you want. The self.self attribute will shadow the real self reference, and you’ll end up with an infinite recursion error when you try to access it.
To avoid this problem, just pick a different name for your instance attributes.
With inheritance, you’re creating a relationship between two classes where one class is a parent of the other. This can lead to problems down the road because it’s not always clear what methods and attributes are available to the child class. In addition, if the parent class changes, the child class may break.
With composition, on the other hand, you’re simply creating an instance of one class within another. This is much more flexible because you’re not tied to any particular implementation. If the internals of the class change, it won’t affect the rest of your code.
When a class is small, it’s easier to understand what the class does. This is because there are fewer methods and attributes to keep track of. In addition, small classes are also more likely to be reusable.
It can be tempting to put all of the related functionality into one large class. However, this can lead to problems down the road. For example, if you need to make a change to the class, you may have to wade through a lot of code that isn’t relevant to the change you’re making.
Instead, it’s better to break up your functionality into smaller classes. This way, you can make changes to individual classes without affecting the other classes.
Suppose you have a class with two methods, and each method has three lines of code. That means there are six potential code paths (two for each method, times three lines in each method). If you only write tests for five of those six code paths, you’re not testing your entire class.
It’s important to test all code paths because it helps ensure that your class is robust and free of bugs. It also makes it easier to refactor your code later on, since you can be confident that all code paths still work as expected.
To make sure you’re testing all code paths, you can use a tool like coverage.py. This tool will analyze your code and tell you which code paths are being executed by your tests, and which ones are not.
When you don’t return a value from a method, Python automatically returns the special value None. This is fine most of the time, but there are certain situations where it can cause problems.
For example, let’s say you have a method that’s supposed to calculate the average of two numbers. If you forget to return a value, the method will return None instead of the calculated average. This can lead to bugs that are hard to track down.
To avoid this problem, always make sure to return a value from your methods. That way, you’ll never accidentally return None when you meant to return something else.
When you’re working on a project by yourself, it’s easy to remember all the details of your code. But as soon as you start working with others, or even just revisiting your own code after some time, that’s no longer the case. Documentation is essential for keeping everyone on the same page and ensuring that your code is understandable and maintainable.
There are two main ways to document Python classes: docstrings and comments. Docstrings are strings that are assigned to special variables like __doc__, and they can be accessed from outside the class. Comments are regular strings that are not assigned to any special variable, and they can only be accessed from within the class.
Both docstrings and comments are useful, but docstrings are generally preferred because they can be accessed from outside the class. This means that you can use a tool like pydoc to generate documentation for your entire project, without having to look through the source code itself.
To add a docstring to a class, simply assign a string to the __doc__ variable. For example:
class MyClass:
“””This is my class.”””
def __init__(self):
pass
To add a comment to a class, simply prefix the string with a “#” character. For example:
class MyClass:
# This is my class.
def __init__(self):
pass
Comments are important, but they should be used sparingly. The reason for this is that comments can quickly become outdated, and when they do, they can actually do more harm than good.
For example, let’s say you have a comment that says “TODO: Fix this bug.” But then the bug gets fixed and the comment is no longer accurate. Now, anyone who reads the comment will think that the bug is still there, when in fact it’s not. This can lead to confusion and wasted time.
It’s better to use comments sparingly and only when absolutely necessary. When in doubt, it’s usually better to err on the side of using fewer comments rather than more.
