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Writing clean code in Python

Python is a programming language that offers a high level of flexibility. The counterpart is that developers can easily use different tricks that will lead to heterogeneity in the source code, decreasing its readability and maintainability. As with any programming language, it’s important to define best practices in a team to bring consistency to the source code, avoid bugs, and save time during code reviews. At Packmind, we recently ran a webinar with our partner Arolla (the replay is in French) on How to write clean code in Python? We share in this post an extract of the discussed practices. NB 1: Please note that we don’t claim the following practices are always valid, and the “don’t” examples are always bad. Trust yourself 😉

#1 Use Counter to count occurrences

Using the Counter from the collection library is more efficient at run time when you want to count different occurrences of elements in a list, tuple, or another hashable iterable:


#2 Use “in” to simplify if statements

The keyword “in” is an elegant, readable and maintainable way to check the presence of a specific element in a sequence :


#3 Put actual before expected in assertions

Assertions will be easier to read in this order:


#4 Use properties when relevant

Sometimes, when we create a class, we will have a field whose value stems from one or multiple other ones. For example, in a class Person, we can have a full_name field which concatenates the values of first_name and last_name . In such cases, it is important to protect the content of the composite field by defining a property with the annotation @property. Going back to our example with the class Person, this will prevent a user to set the value of the full_name from outsite by writing person.full_name = ... .


#5 Use fully qualified, absolute imports

This makes the code more readable and maintainable, so that when it’s time to modify the code, it is easier to figure where each object in the code comes from. Performance-wise, it is basically the same as importing the full module (ex. import foo ) as Python always loads the full module, whether or not we import just an object of that module. That is to say if when we write from [](<>) import Bar , Python loads the entirety of the module and then proceeds to pick Bar


#6 Use iterators instead of explicit lists

Avoid creating a new list when it’s not relevant.


#7 Use list comprehensions

A list comprehension is a way of creating a new list by transforming elements from an existing iterable (such as a list, tuple, or dictionary), and we want to, filter some elements, and perform operations on each element.


#8 Prefer using keyword-only arguments

Many times, especially when there is no logical order between the parameters of a function or method, it is recommended to call the function or method by specifying the name of the parameters (ex. make_coffee(with_sugar=True, with_milk=True) ). It is possible to force the parameters to be named when the function/method is called. We can do that by using the “*” at the beginning of the parameters. This avoids many possible issues and confusion. However, it is not something to do all the time but rather when it makes sense. Instead of:

We’d prefer:


#9 Use ABCMeta for abstract classes

This practice can be relevant if you work with developers who are not expert in Python, but are more familiar with Java or C#. They’ll be more comfortable with the “abstract” concepts for classes and methods. ABCMeta is a metaclass (a class that creates classes) in Python. It stands for “Abstract Base Class Meta”. Instead of:

We’d prefer:


#10 Use a main() function

Avoid global variables and, in general, source code outside functions. Instead of:

We’d prefer:


#11 Do not use empty lists as default arguments

This can lead to unexpected and very weird behavior. Instead of:

We’d prefer:


#12 Prefer f-strings to string concatenation

F-strings allow writing sentences in a far more natural (and admittedly less annoying) way than what string concatenation can provide. Do:



#13 Prefer enumerate() to range(len()) when you want keep the index of iterable items

This practice contributes to code readability as well as its performance while still keeping the index around. The performance gain is because enumerate() creates an iterator for the collection, which is more efficient than looping through each item Don’t:



Over to you — Share your best coding practices now

These best practices can be defined in Packmind from our IDE and Code reviews plugins. We’re compatible with VSCode, JetBrains suite, and Eclipse. So if you code Python with VSCode or PyCharm, you can go for it! Each practice you create will then be validated as a team during dedicated workshops, and the final result looks like this: You can provide syntactic patterns to provide suggestions while coding or reviewing code and use this practice during onboarding workshops in Packmind. The whole catalog is also available on our public Hub of best practices, where users can share practices on various domains and use them in Packmind. You can get started on creating your practices now for free.