Ditch These 7 Bad Habits in Python

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.



  1. Using open and close instead of with
  2. Using an empty list as a default argument
  3. Avoiding list comprehension
  4. Abusing list comprehension
  5. Using bare exception
  6. Using == and is the wrong way
  7. Using import *

1. Using open & close Instead of the with Statement

Working with file streams is probably the most common task as we started learning Python. Most of the tutorials would start 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 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.

2. Using an Empty List or Dict as a Default Argument

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 them mindlessly like nobody’s business. Here’s an example:

def add_book(book_id, storage=[]):
    return storage

my_book_list = add_book(1)

my_other_book_list = add_book(2)

# Expectation:
# [1]
# [2]

# Reality:
# [1]
# [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.

What you should do instead

# Do:
def add_book(book_id, storage=None):
    if storage is None:
        storage = []
    return storage

The same concept applies while working with other mutable objects, i.e. dictionary in Python.

3. Not Using List Comprehension

List comprehension is undoubtedly one of the most distinct features of 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":

# Do:
def generate_fruit_basket_lc(fruits):
    """With list comprehension"""
    return [fruit for fruit in fruits if fruit != "grapes"]
Learn to use list comprehension

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.

4. Abusing List Comprehension

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
Don’t do these

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.

5. Using Bare Except

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 looks like:

# Do NOT ever use bare exception:
while True:
        input_string = input("Input something")
        converted_input = int(input_string)
    except: # Can't do Ctrl + C to exit!
        print("Not a number!")
Avoid doing this

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.

6. Using == and is the wrong way

Does it really matter though? I have seen too many in wild Python code. In case you didn’t know, they are different.

  • The is operator checks if the two items reference the same object (i.e. present in the same memory location). A.k.a reference equality.
  • The == 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
Remember, listA and listB are different objects

Here’s a summary of the Do’s and Don’ts of using is and == operator:

# 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:
Using if baz and if not baz

7. Using import star (asterisk)

Many would consider this an act of convenience. But this is just plain lazy.

By using 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's terms, it 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.

Closing Thoughts

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 habit 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.

What's next?

There are too many don’ts in this article.

Here’s something you can do — start using tools like autopep8, flake8, 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!

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