Python Development Blunders: Don’t Make These 10 Common Mistakes and Bug Fixes
Learn how to avoid common mistakes and missteps in Python development with our detailed guide. From inefficient code to poor error handling, we cover the top blunders to help you write cleaner and more effective Python code
Python is a highly prevalent programming language that boasts a simple syntax, user-friendly interface, and versatile range of applications. Its accessibility, code readability, and versatility have made it a top choice among programmers worldwide. Despite its simplicity, programmers tend to commit multiple errors while writing the Python code.
Let’s examine the 10 common mistakes that Python programmers ten common mistakes that Python programmers often make and offer effective solutions to overcome them. Explore free Python courses in the meantime.
Table of Content
- Incorrect Indentation
- Not using Virtual Environments
- Neglecting Exception Handling
- Altering a List while Iterating through it
- Mutable Function Arguments
- Overusing Global Variables
- Module Name Clash
- Not Closing files Properly
- Not using Built-in functions
- Incorrect usage of Class variables
Incorrect Indentation
In Python, indentation refers to the use of whitespace (either spaces or tabs) at the beginning of a line to indicate the level of nesting or grouping of code blocks. Indentation is essential in Python because it helps the interpreter to understand the structure of the code and how it should be executed.
Read on How to Use Indentation in Python to learn more.
Incorrect indentation refers to the incorrect use of whitespace to define code blocks. Incorrect indentation is the most common mistake that can cause syntax errors and affect the program’s behavior. For example, if a line is indented incorrectly within a loop or conditional statement, the program may not execute as expected or even result in a syntax error.
Possible bug fix: To correct an indentation error, it is recommended to follow these guidelines:
- Choose either spaces or tab keys and avoid combining them: It is important to be careful because mixed tabs and spaces can look the same, but cause issues. To address this, you can use a Python IDE or code editor that provides a search and replace feature to convert tabs to spaces or vice versa.
- Maintain consistency in the number of spaces you use, if you choose spaces: Although it is not necessary to adhere to a recommendation of four spaces, it is essential to use the same number of spaces throughout your code.
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Not using Virtual Environments
Virtual environments allow developers to create isolated environments for each project, preventing version conflicts and enabling clean installations. Failure to use virtual environments can lead to conflicts with different versions of Python modules and dependencies, ultimately affecting the project’s stability.
Possible bug fix: To avoid this, create a virtual environment for every project you work on and use it consistently.
Neglecting Exception Handling
Ignoring exception handling is a very common mistake and it can cause programs to crash unexpectedly, resulting in data loss and other issues.
Let’s say you have written the following code that neglects exception handling in Python:
numerator = 10denominator = 0result = numerator / denominatorprint("The result is:", result)
In this code, you’re trying to divide the numerator by denominator, but denominator is set to 0. This will cause a ZeroDivisionError at runtime, which will terminate the program with an error message.
If you neglect to handle this exception, the error message will be displayed to the user, and the program will terminate. This can be problematic if you are running a program that needs to keep running even if it encounters errors.
Possible Bug fix: To handle this exception, you can use a try-except block to catch the ZeroDivisionError and handle it gracefully
numerator = 10denominator = 0try: result = numerator / denominator print("The result is:", result)except ZeroDivisionError: print("Error: Cannot divide by zero")
In this code, we have enclosed the division operation in a try block, and if a ZeroDivisionError occurs, the program will continue to execute the except block, which will print an error message instead of terminating the program.
Altering a List while Iterating through it
It is strongly advised against modifying a list or collection while iterating over it. One of the most common mistakes that programmers tend to make is attempting to remove an item from a list during iteration.
Suppose you’ve written the following code in Python
numbers = [1, 2, 3, 4, 5]for number in numbers: if number % 2 == 0: numbers.remove(number)print(numbers)
In this code, you are attempting to remove even numbers from the list numbers while iterating over it using a for loop. However, modifying a list during iteration can cause unexpected results.
The above code will remove the even numbers 2 and 4 as expected. However, it will skip over the number 3 because when the loop reaches index 2, the list becomes [1, 3, 4, 5]. The loop then continues at index 3, skipping over the number 3.
This can result in errors or bugs in your program, especially if you are iterating over a large list or modifying it multiple times.
Possible Bug fix: To avoid this common mistake, it is recommended to create a new list instead of modifying the original list during iteration:
numbers = [1, 2, 3, 4, 5]new_numbers = []for number in numbers: if number % 2 != 0: new_numbers.append(number)
In this code, you can create a new list called new_numbers and append the odd numbers to it using a for loop.
Mutable Function Arguments
Another common mistake that programmers make is the practice of using mutable function arguments in Python. Since mutable objects are shared between function calls, modifying a mutable default argument can lead to unintentional changes in its value, affecting the output of the function. This can make the code harder to debug and maintain over time.
Let’s say you have written the following code:
def add_item(item, items=[]): items.append(item) return itemsprint(add_item("apple"))print(add_item("banana"))print(new_numbers)
In this code, you defined a function called add_item that takes an argument item and a mutable default argument, which is initially an empty list. The Python interpreter will use the same list object that was created when the function was defined. As a result, appending to the list can lead to unexpected and confusing behavior, as demonstrated in the following code.
Possible Bug fix: To fix the common mistake in this code, you can define a None default value for the items parameter, which is immutable. Inside the function, you can check if items is None, and if so, you can create a new empty list. You can then append the item to the items list and return it.
def add_item(item, items=None): if items is None: items = [] items.append(item) return itemsprint(add_item("apple"))print(add_item("banana"))print(new_numbers)
By doing so, you can avoid the issue of using mutable default arguments, since a new empty list is created each time the function is called without a items argument. This makes the function more predictable and easier to reason about and helps prevent bugs and unexpected behavior.
Overusing Global Variables
Overusing Global variables is a common mistake that Python programmers often make The overuse of global variables in Python can lead to naming conflicts, difficulty in debugging, reduced code maintainability, and limited reusability. These issues can make the code challenging to read, update, and scale for future projects.
Possible Bug fix: It’s best to limit the use of global variables and instead use local variables and function arguments wherever possible to improve code clarity and maintainability.
Module Name Clash
Module name clash in Python can result in errors such as the wrong module being imported, unexpected behavior, or even crashing of the program. It can also make the code difficult to read and understand, especially when working on large projects with multiple modules.
Let’s say you have written the following code:
# module_a.pydef my_func(): print("This is module A")# module_b.pydef my_func(): print("This is module B")# main.pyfrom module_a import my_funcfrom module_b import my_func
In the above code, file1 defines a greeting function that prints a greeting message to the provided name. However, file2 also defines a greeting function that prints a different message. When both modules are imported into the main module, there is a clash of function names, and the program outputs both messages when called.
Possible Bug fix: The possible fix to the common mistake would be to alias the imported function with a different name to avoid ambiguity
Not Closing Files Properly
Not closing files properly in Python is a common mistake that programmers frequently make. It can cause various issues such as consuming system resources, data corruption, and memory leaks. It is important to close the files properly to free up the resources and ensure that any pending write operations are completed. Additionally, if a file is not closed properly, it may result in data loss or data corruption.
Possible bug fix: To avoid these issues, it is recommended to always use a with statement to open and close files in Python.
Not Using Built-in Functions
Python provides a plethora of built-in functions that can help simplify code and improve performance. Neglecting these functions can lead to unnecessarily complex code that is harder to maintain and debug.
Possible bug fix: Always use built-in functions wherever possible to simplify your code and reduce errors.
Incorrect Usage of Class Variables
Incorrect usage of class variables can lead to unexpected behavior in the program. For instance, if a class variable is modified in one instance of the class, it will reflect the change in all other instances of the class. This can result in unintended consequences and make the code harder to debug. Additionally, if a class variable is used without being properly initialized or if it is used inappropriately, it can lead to runtime errors or logical errors in the program.
Let’s say you have written the following code:
class Car: wheels = [] def __init__(self, brand, model): self.brand = brand self.model = model self.wheels = [] def add_wheel(self, wheel): self.wheels.append(wheel) def print_wheels(self): print(f"{self.brand} {self.model} has {len(self.wheels)} wheels: {self.wheels}")
car1 = Car("Toyota", "Corolla")car2 = Car("Honda", "Civic")
car1.add_wheel("front left")car1.add_wheel("front right")car2.add_wheel("rear left")
In this code, the wheels variable is defined as a class variable, which means it is shared by all instances of the Car class. However, in the __init__ method, a new wheels variable is created for each instance, which overrides the class variable.
Possible bug fix: To fix this common mistake, you can remove the class variable and create a new instance variable in the __init__ method for each instance:
class Car: def __init__(self, brand, model): self.brand = brand self.model = model self.wheels = []
def add_wheel(self, wheel): self.wheels.append(wheel)
def print_wheels(self): print(f"{self.brand} {self.model} has {len(self.wheels)} wheels: {self.wheels}")
car1 = Car("Toyota", "Corolla")car2 = Car("Honda", "Civic")
car1.add_wheel("front left")car1.add_wheel("front right")car2.add_wheel("rear left")
car1.print_wheels() # expected output: Toyota Corolla has 2 wheels: ['front left', 'front right']
Endnotes
Hope this article was helpful for you in understanding the Python Development Blunders. As a Python programmers, please Don’t Make These 10 Common Mistakes and Bug Fixes, and take up Python courses!
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