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Debugging is only a part of the important process in software development. You have to identify and correct an error, or a "bug," in your code to make it work as you wish. Well-done debugging saves much of your time and, at least, allows for some of your nerves to be saved and makes the entire development process smoother. Whether you are a beginner or a professional developer; the right practice in debugging could dramatically boost your productivity. This article will cover a guide on how to debug Python code and some of the more efficient techniques of debugging Python so that you can move on and squash errors with confidence.
It's important to know what are the general or most common error messages that debug Python code your way so that you can debug your code effectively. You'll then be able to easily identify where the problem may be and apply the proper fix accordingly.
SyntaxError
SyntaxError occurs when the interpreter encounters something with which it does not think valid syntax should contain. For example, not putting colons after an if statement or not closing our parentheses. For example:
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if x == 10
print("x is 10")
The missing colon after 10 would raise a SyntaxError.
IndentationError
Python uses indentation to specify the extent of code blocks. An IndentationError is raised when the code isn't indented consistently. This can be because you have mixed both spaces and tabs or perhaps your indentation is not consistent throughout the codebase.
NameError
A NameError is raised when you are using a variable or a function that has not yet been defined. For example:
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print(y)
If y has not been defined earlier in the code, then Python will set off NameError.
AttributeError
This exception is raised when you are working with an object and request an attribute or a method that does not exist. It typically occurs when you are working with modules or objects and refer to some attribute that doesn't exist in these.
FileNotFoundError
When you want to open a file that doesn't exist in the place you have specified, it happens. For example:
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open('non_existent_file.txt')
If the file non_existent_file.txt does not exist in the directory, a FileNotFoundError will be raised.
Only through the knowledge of such common mistakes, is the first step toward debugging. Knowing these kinds of errors will let you identify the problem more quickly and take steps toward a solution.
One of the simplest, yet most effective, while you are looking at how to debug Python code is through the use of print statements. That is, you can set print statements at various places in your code to trace the values of variables and the flow of execution.
Example:
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def sum_numbers(a, b):
print(f'a = {a}, b = {b}') # Print values of a and b
c = a + b
print(f'c = {c}') # print value of c
return c
result = sum_numbers(2, 3)
print(result)
The print statements clearly show what happens inside the sum_numbers function. This technique helps you to check whether your variables hold expected values and whether the logic flows as intended.
However, while useful to do quick checks, print statements are not the best solution in the long run. Remember to remove/comment on these statements once you have identified and fixed the issue so your code stays clean.
For more complex debugging tasks, Python Debugger (pdb) is a very efficient tool that enables line-by-line execution of your code, and at each step, you can look at the variables. This tool will help when you go deep inside the logic of your code.
How to Use pdb:
Put the following line in your code, where you want to start with pdb debugging:
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import pdb; pdb.set_trace()
This is a line when the execution of your code reaches it; it will suspend, and you will be in the pdb interactive mode. There, you can use several commands to go through your code:
n (next): Go to the next line of code.
s (step): Go into a call.
c (continue): Execute until the next breakpoint hits.
l (list): Show source code around the current line.
It will let you step through your code and track all the changes to variables to understand exactly where things might be going wrong. This can be quite useful in case you have very insidious bugs that don't become apparent upon looking.
Proactive debugging starts with writing code test cases. Unit tests are a crucial part of software development. They help you catch your mistakes early on when they are still relatively easy to fix before they have time to compound and sprout into bigger problems. Python's unit-test framework provides a robust environment for writing and running tests.
Example:
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import unittest
def sum_numbers(a, b):
return a + b
class TestSumNumbers(unittest.TestCase):
def test_sum(self):
self.assertEqual(sum_numbers(2,3), 5)
self.assertEqual(sum_numbers(-1, 1), 0)
if __name__ == '__main__':
unittest.main()
TestSumNumbers class implemented with a test method to verify that function sum_numbers works as expected. Running these tests will help ensure any changes made to your code don't add any new bugs.
Test-driven development (TDD) is a process where tests are created first to be later followed by the actual implementation of a certain function. It's a development process that not only guarantees debugging but also ensures that your code does what it is supposed to.
While the print statements are very useful for quick-and-dirty debugging, logging is a more flexible and powerful method for following the flow of your code.
The Python logging module lets you send messages to a log with severity levels of DEBUG, INFO, WARNING, ERROR, and even CRITICAL.
Example:
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import logging
logging.basicConfig(level=logging.DEBUG)
def sum_numbers(a, b):
logging.debug(f'a = {a}, b = {b}')
c = a + b
logging.debug(f'c = {c}')
return c
result = sum_numbers(2, 3)
logging.info(f'Result: {result}')
In the above example, logging gives details about the execution of the code, especially in debugging. Unlike print statements, logging can be configured to send the message to different destinations like console or file; this makes it easy to analyze the behavior of your code over time.
A properly cleaned and arranged codebase is of so much importance in reducing the time taken during debugging. The key things in a clean codebase include using meaningful variable names, writing modular code, and following an agreed-upon coding style.
Best Practices for a Clean Codebase
Outlining the steps for how to debug Python code, along with the standards for maintaining clean code, is necessary. Apply meaningful names to variables: No doubt, variable names like x or y are fast to type. However, they do not give any idea as to what the variable is. Use descriptive names that tell the purpose of the variable.
Modular code: Every large function needs to be broken down into many small modules often referred to as functions which can also be reused. This makes your code a lot more readable and also helps in debugging.
Establish a consistent coding style: Coding standards can be enforced using tools like flake8 or black, and they also make your code more readable.
Another advantage of a clean codebase is that it is easier to debug because it puts a lesser cognitive load on the person who is trying to understand the code. Adhering to such best practices can prevent many of these bugs in the first place.
Good documentation is also part of debugging, which is usually left out. Good documentation would otherwise provide context and comments which could be of essential aid in understanding and, consequently, debugging. Use docstrings to document the purpose and functions of your functions, classes, and modules.
How to Write a Good Docstring. Example:
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def sum_numbers(a, b):
"""
This function adds two numbers and returns the result.
Parameters
----------
a (int): The first number.
b (int): The second number.
Returns:
int: The sum of the two numbers.
"""
return a + b
This example is a good practice to help you debug your code but also any other developer who, in the near or far future, will work with your code.
It need not be an individual process to debug. Cooperation with other developers can give you a fresh perspective, or some others might point out something that you may have missed and thereby resolve your bug.
Ways to Collaborate:
Code reviews: It is a practice that is commonplace to catch potential bugs early in the process and improve code quality. Somebody else going through your code sometimes identifies errors that you might have overlooked.
Pair programming: You can more quickly troubleshoot, or even debug, with another developer in real time. This also spreads knowledge.
Online forums: Sites such as Stack Overflow have a community of peers who can advise or provide solutions for your debugging challenges. Do not be afraid to ask for help if you are stuck. Sometimes telling someone about the problem leads to the solution.
Version control systems are tools without which debugging would have been impossible. They enable the tracking of changes made to the code over time, going back in time, and collaborating with other developers.
Best Practices for Version Control:
Commit often: Make it easy to track changes by frequently committing with meaningful messages so that the history of the code can be easily traced.
Branching: You can keep different branches for new features or bug fixing. This way, you experiment as much as you want without affecting your main codebase.
Revert if needed: In case you introduced a bug, no problem. You'll just revert to the previous commit where your code was working fine.
Not only that, version control proves a safety net for your development process in general.
Modern integrated development environments, or IDEs, come with very powerful debugging tools. PyCharm, Visual Studio Code, and Jupyter Notebooks provide features in their debugging tools that include setting breakpoints, inspecting variables, and stepping through lines of code in execution, which enhance efficiency and make the process less time-consuming.
IDE Debugging Features:
Breakpoints: This allows you to place breakpoints in your code, at which the execution of your code will be halted. It can inform you about what has occurred in the program up to that line of code.
Inspect variables: One can get the values taken by different variables at different instances of program execution. This can help a person realize where things could be going wrong.
Step-by-Step Execution: Run line by line and see the flow of your program and the changes in variables.
Getting familiar with the debugging options of your favorite IDE will therefore greatly enhance your productivity and allow you more control while debugging.
One of the basic skills any developer should have is to debug effectively. Some of the available tools and techniques that will help in making the process of debugging easier and increasing the quality of your code include knowledge of common error messages, print statements and logging, Python Debugger, writing test cases, keeping your codebase clean, documenting your code, seeking help from others, using version control, and IDE features. Debugging isn't about just fixing errors; it involves getting a better understanding of your code to make it robust. Apply these best practices and you will be well on your way to tackle any bugs that come your way.
