If You Are A Python Programmer, Avoid These Mistakes!
Python‘s rise to become one of the world‘s most popular programming languages has been nothing short of meteoric. It ranked as the 2nd most loved language in Stack Overflow‘s 2022 Developer Survey [1], and has been the fastest-growing major programming language over the past 5 years according to GitHub‘s State of the Octoverse report [2].
Much of Python‘s appeal lies in its simple, clean syntax, versatility, and rich ecosystem of libraries – especially for data science and machine learning. As an AI/ML researcher and practitioner, Python is my go-to language for nearly all projects.
However, Python‘s simplicity and flexibility can sometimes be a double-edged sword. Many programmers, especially those coming from other languages, can misunderstand Python‘s unique features and idioms, leading to subtle bugs, performance issues, and maintenance headaches.
In this article, we‘ll dive deep into the most common mistakes Python programmers make, backed by real-world statistics and examples. I‘ll share hard-earned lessons from my experience, along with expert tips and best practices to help you write cleaner, faster, and more Pythonic code. Let‘s get started!
Syntax Stumbles and Silly Slipups
Even for experienced programmers, it‘s easy to trip over Python‘s syntax at first. In a 2021 study of Python errors by data scientists, syntax errors were the 3rd most common type, accounting for nearly 13.7% of all errors [3].
The biggest culprit? Indentation. Python uses whitespace indentation to delimit code blocks, unlike curly braces or keywords in other languages. Forgetting to indent, mixing tabs and spaces, or indenting inconsistently can all lead to IndentationErrors. A 2019 analysis of GitHub issues found that indentation errors accounted for nearly 9% of all Python bugs reported [4].
Forgetting colons at the end of if, for, while, and def statements is another common slip-up. In the same GitHub analysis, missing colons caused about 3% of reported Python issues [4].
Other syntax snafus include:
- Case sensitivity: Variables like
myVarandmyvarare distinct - Using keywords like
if,for,whileas variable names - Misspellings creating accidental new variables instead of raising an error
Best practices to prevent syntax slip-ups:
- Use a linter: Tools like Pylint and Flake8 can catch syntax errors and potential bugs
- Enable editor highlighting: Most IDEs can highlight unmatched brackets, indentation issues, etc.
- Be consistent: Follow the PEP 8 style guide for naming, indentation, and more
Type Troubles and Data Disasters
Python is dynamically and strongly typed, which gives great flexibility but can sometimes lead to unexpected behavior, especially with mutable types. In a survey of Python users, issues related to mutability and copying were the most common source of bugs, affecting over 45% of respondents [5].
One classic pitfall is using mutable default arguments for function parameters. Default values are evaluated only once when the function is defined, so using a mutable default like [] or {} means the same object will be shared across all calls!
def append_to(element, to=[]):
to.append(element)
return to
>> append_to(12)
[12]
>> append_to(42)
[12, 42] To avoid this, always use None as the default and create the mutable object inside the function:
def append_to(element, to=None):
if to is None:
to = []
to.append(element)
return toOther data-related gotchas include:
- Mixing data types, like trying to add an
intand astr - Off-by-one errors with zero-based indexing and slice bounds
- Assuming precision of floats for decimal calculations
- Mismatched % string formatting and f-string placeholders
Data disasters by the digits:
- A 2020 study of StackOverflow Python questions found that 21% related to data type issues [6]
- In the 2021 data science error analysis, 12.4% of errors were
TypeErrors from mixing incompatible types [3] - Misusing or misunderstanding mutability caused bugs for 45% of surveyed Python users [5]
Taming type troubles:
- Use type hints to document expected types
- Convert between types explicitly with
str(),int()etc. - Use
isfor singletons likeNone,is notfor negation - For decimals, use
Decimalobjects instead of floats - Be careful with mutable default arguments
Loopy Logic and Branching Blunders
Loops and conditionals are the backbone of programming logic, but even a small mistake can derail your program‘s flow. Infinite loops, off-by-one errors, and unexpected fall-throughs are just a few of the ways things can go awry.
In a 2018 analysis of Python bugs on GitHub, logical errors in loops and conditionals accounted for nearly a third of all reported issues [7]. Infinite loops were the most common, caused by forgetting to update a counter or condition variable.
# Spot the infinite loop bug!
counter = 0
while counter < 10:
print(counter)
# Oops, forgot to increment counterMixing up comparison operators like = vs. == or misusing and/or in conditions is another frequent flub. Python evaluates conditions from left to right, so without careful parentheses, you might not get the logic you expect:
if x < 10 and x > 20 or y < 5:
# This is equivalent to:
# if (x < 10 and x > 20) or y < 5:
# Probably not what was intended!Looping over lists while also modifying them can lead to unexpected behavior like skipped or repeated items. Imagine trying to remove all negative numbers from a list:
nums = [4, -2, 1, -3, 7, 8]
for num in nums:
if num < 0:
nums.remove(num)
print(nums)
# [4, 1, 7, 8] - Oops, missed the -3! The right way is to iterate over a copy, use a list comprehension, or build a new list:
nums = [num for num in nums if num >= 0]
# [4, 1, 7, 8] - Got them all this timeBranching blunders by the numbers:
- 31% of reported Python bugs on GitHub related to logic errors [7]
- Infinite loops were the #1 most common logical bug [7]
- Errors in conditionals accounted for 8% of all reported issues [4]
Best practices to prevent loopy logic:
- Use descriptive variable names and comments to clarify logic
- Avoid modifying lists while looping; use comprehensions or build new lists
- Double-check comparison operators and parentheses in conditions
- Constrain loop iterations with
breakor by bounding the range - Use
forloops instead ofwhilewhen possible to avoid infinite loops
Classy Quandaries and Scoping Snafus
Object-oriented programming in Python is powerful and convenient, but not without its share of pitfalls. Misunderstanding class variables, botching inheritance, or mixing up scope can lead to bizarre bugs.
One common class quandary is misusing class variables, which are shared among all instances. Modifying a mutable class variable in one instance will unexpectedly change it for all others:
class Oops:
all_instances = []
def __init__(self, value):
self.value = value
self.all_instances.append(self)
>> Oops(1)
>> Oops(2)
>> Oops(3)
>> [instance.value for instance in Oops.all_instances]
[1, 2, 3] # Looks good so far...
>> Oops.all_instances.clear() # Uh oh
>> [instance.value for instance in Oops.all_instances]
[] # Oops, all gone! In Python, methods must explicitly take self as their first argument to receive the instance. Forgetting self is an easy mistake that will raise an error about missing positional arguments.
Circular inheritance, or having two classes inherit from each other, will also raise an error about inconsistent method resolution order. Instead, use composition or refactor the common functionality into a mixin class.
Scoping mistakes, especially with nested functions and lambdas, can introduce incredibly subtle bugs. Variables used in a nested function are resolved when the function is called (late binding), not when it‘s defined:
funcs = []
for i in range(5):
funcs.append(lambda: i)
>> [func() for func in funcs]
[4, 4, 4, 4, 4] # Huh? Shouldn‘t this be [0, 1, 2, 3, 4]?To capture i at each iteration, use a default argument:
funcs = []
for i in range(5):
funcs.append(lambda i=i: i)
>> [func() for func in funcs]
[0, 1, 2, 3, 4] # Ahh, that‘s better!OOP oopses in data:
- 26% of Python users reported bugs related to classes and OOP [5]
- Misusing
selfcaused issues for 6% of users [5] - In a 2019 study, scoping errors accounted for 4% of all reported Python bugs on GitHub [4]
Keeping your classes classy:
- Use instance variables for per-instance state, class variables for shared state
- Don‘t forget
selffor instance methods! - Watch out for circular inheritance; use composition instead
- Be careful with mutable class variables
- Avoid scope surprises with default arguments or factory functions
- Use
globalsparingly and never in functions
Conclusion: Mistake-Proofing Your Python
We‘ve covered a lot of ground in this deep dive into Python pitfalls – from simple syntax slips to mind-bending scope surprises. But don‘t despair! Even the most seasoned Pythonistas make mistakes. The key is to learn from them and develop habits to minimize their occurrence and impact.
To recap, some of the most impactful practices you can adopt are:
- Follow PEP 8 style and conventions
- Use linters and static analyzers like Pylint, Flake8, and mypy
- Write tests, especially for edge cases and failure scenarios
- Use descriptive names and comments to clarify code intent
- Prefer built-ins, standard libraries, and popular third-party packages over rolling your own
- Keep up with new Python features that help avoid common mistakes, like the
walrusoperator:=,strictargument forzip, and@dataclassdecorator
Above all, embrace the Pythonic way of thinking. Python is designed to be readable, explicit, and straightforward. When you find yourself fighting the language or tying yourself in knots, take a step back and ask, "What would Guido do?" Chances are, there‘s a simpler, clearer, more idiomatic approach.
As an AI/ML practitioner, I find Python‘s simplicity and expressiveness invaluable for research and development of intelligent systems. By writing clean, mistake-free Python, you can focus on the real challenges of AI/ML, like model design, training, and deployment.
In future articles, we‘ll explore more advanced Python concepts, design patterns, and best practices, as well as dive into AI/ML-specific libraries and frameworks. Stay tuned, and happy Pythoning!
