I have been guilty of all these bad habits. It took me some time to ditch them.
Python is well known for its simplicity and versatility. As one of the most used programming languages, there are a lot of great tutorials out there. Unfortunately, the easy-to-learn syntax can sometimes mislead developers, especially those who are new to the language. Writing clean Python code can be tricky.
In this article, we are going to dive into some of the common mistakes and bad habits that one might have picked up when they first started learning Python. This includes things that I wished someone would tell me earlier.
Don’t worry, I do not intend to bore you with a wall of text. Instead, you’ll be given code snippets that are easy to digest.
- Using an empty list as a default argument
- Avoiding list comprehension
- Abusing list comprehension
- Using bare exception
isthe wrong way
Working with file streams is probably the most common thing that we do as we started learning Python. Most of the tutorials would start off by telling us to open and close files using the example below:
f = open("stocks.txt", "r") data = f.read() f.close() # NOTE: Always remember to close!
In such cases, we would often be reminded to always close the file after using it because:
- It consumes our limited system resource
- It prevents us from moving or deleting the files
Instead, the best practice here is to always use context manager (the
with statement) in Python while dealing with file operations. Here's an example:
with open("stocks.txt", "r"): data = f.read()
Using context manager here also helps us with closing the file during exceptions.
Well, you could argue that we could also use the
try-finally approach here instead. Though, the
with statement simplifies our code to just two lines.
This is probably one of the more confusing gotchas for newcomers in Python.
I must admit, this one caught me off guard when I first started learning Python many years ago. I remember when I was first introduced to the wonders of default arguments when working with functions, I was using it mindlessly like nobody’s business. Here’s an example:
def add_book(book_id, storage=): storage.append(book_id) return storage my_book_list = add_book(1) print(my_book_list) my_other_book_list = add_book(2) print(my_other_book_list) # Expectation: #  #  # Reality: #  # [1, 2] Huh, what? But they are different variables!
When a function is defined, the default arguments (e.g.
storage in the example above) are created (i.e. evaluated) only once.
As a list in Python is mutable, the list will be modified every time you call the
add_book function. Here, the same list is used over and over again instead of creating a new list.
# Do: def add_book(book_id, storage=None): if storage is None: storage =  storage.append(book_id) return storage
The same concept applies while working with other mutable objects, i.e. dictionary in Python.
List comprehension is undoubted one of the most distinct features in Python. Unarguably, it helps to improve code readability and is often considered more elegant and “pythonic”.
Having that said, new Python developers may struggle to fully leverage this feature, or at least get used to using it at the beginning. Here’s an example:
# Don't: def generate_fruit_basket(fruits): """Without list comprehension""" basket =  for fruit in fruits: if fruit != "grapes": basket.append(fruit) # Do: def generate_fruit_basket_lc(fruits): """With list comprehension""" return [fruit for fruit in fruits if fruit != "grapes"]
If you are new to Python, take a breath and spend some time learning list comprehensions. It will help you immensely, I promise.
The same logic applies to using dictionary comprehensions in Python.
We get it. List comprehensions are “pythonic”. However, I have seen way too many instances where the use of list comprehension is being abused in various forms.
Worse, some list comprehensions are written to the extent that the code is not even readable anymore, defeating the original purpose of using list comprehensions.
Here’s an example of what you should NOT do:
# Don't: def get_book_price(inventory): return [(book, price) for book in inventory if book.startswith('A') for price in inventory[book] if price > 10] # Perhaps slightly better...? But please don't. def get_book_price(inventory): return [ (book, price) for book in inventory if book.startswith('A') for price in inventory[book] if price > 10 ]
While list comprehensions are more compact and run faster, we should avoid writing long and complex list comprehensions (especially in a single line) to ensure code readability. Do not get bogged down with list comprehensions.
I was guilty of this. Very guilty. As a beginner, I did not appreciate how some codebases explicitly catch exceptions. Back then, I find them overly verbose and redundant. I guess I was just being lazy.
Here’s an example of what a bare except look like:
# Do NOT ever use bare exception: while True: try: input_string = input("Input something") converted_input = int(input_string) print(converted_input) except: # Can't do Ctrl + C to exit! print("Not a number!")
In short, we should never use bare except as it catches all exceptions, including some that don’t want, i.e. KeyboardInterrupt in this example where we want to exit our application.
On top of that, it makes debugging a nightmare as it can cause our application to fail without us knowing why. So, stop using exceptions like this.
Does it really matter though? I have seen too many in wild Python code. In case you didn’t know, they are different.
isoperator checks if the two items reference the same object (i.e. present in the same memory location). A.k.a reference equality.
==operator checks for value equality. Here’s a brief example of what I mean:
# NOTE: In Python everything is an object, including list listA = [1, 2, 3] listB = [1, 2, 3] listA is listB # False because they are NOT the same actual object listA == listB # True because they are equivalent
listBare different objects.
Here’s a summary of the Do’s and Don’ts of using
# Do: if foo is None: ... # Don't: if foo == None: ... # Do: if not bar: ... # Don't: if bar == False: ... # Do: if baz: ... # Don't: if baz is True: ... # Don't if baz == True: ...
Many would consider this an act of convenience. But this is just plain lazy.
import *, you risk polluting the current namespace as it imports all the functions and classes from the library.
What does “polluting namespace” even mean? Well, in layman terms, it basically means that whatever functions (or classes) that you have imported with
import * may clash with the functions defined by you.
Basically, you run the risk of overriding variables or functions and eventually it becomes incredibly hard to tell which function is from which.
Python is a relatively easy language to get started with programming. It comes with many mechanisms and paradigms which hugely improve our productivity as a developer.
Having that said, one can easily pick up the habits of writing bad code in Python. Upon reflection, this post also serves as a reminder for me (and hopefully you) to stay away from these bad Python coding habits.
There are too many don’ts in this article.
Here’s something you can do — start using tools like autopep8, flake8, and pylint, or even mypy to keep your code quality at the highest standards. They will make you a better developer by helping you to check your code against standards like PEP8 and more.
I hope you find the pointers in this article helpful and happy coding!