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  • 10 Common Mistakes to Avoid in Python Assignments

    May 09, 2023
    Sarah Johnson
    Sarah Johnson
    Sarah Johnson is a highly experienced and talented computer scientist with over 5 years of Python programming experience in data science and machine learning.

    Python has grown to become one of the world's most popular programming languages. Its ease of use, readability, and versatility have made it a favorite of developers of all skill levels. As a result, Python assignments are becoming more popular, particularly in academia. Despite its ease of use, students frequently struggle with doing their Python assignments due to some typical errors. In this blog post, we will go through ten typical Python assignment mistakes to avoid.

    Mistake #1: Failure to Recognize the Requirements

    The first mistake that students make when working on Python projects is failing to comprehend the problem. Before digging into the code, it is critical to thoroughly study and comprehend the problem statement. Check that you understand what the program is supposed to do, what input it should receive, and what output it should produce. It will be difficult to write code that solves a problem if you do not understand it.

    Mistake #2: Not Using Comments

    Comments are an important component of any programming language, including Python. They aid in explaining the objective of the code and making it easier for others to comprehend. As a result, using comments in your Python code to describe what each line performs is critical. This is especially useful when working on huge projects involving numerous people. It will be easier for others to understand and adjust your code if it is well-documented.

    Mistake #3: Failure to Follow Naming Conventions

    Python has various naming standards that must be followed. Variable names, for example, should be lowercase, and words should be separated by underscores. Lowercase letters and underscores should also be used to name functions. Following these guidelines will make your code more readable and understandable. If you do not adhere to the naming conventions, your code will be more difficult to read and understand for others.

    Mistake #4: Not Testing the Code

    Testing code is one of the most important tasks in the development process. Before submitting your Python assignment, you should extensively test your code to confirm that it works properly. This entails executing it with various input values and comparing the output to the desired result. If you do not test your code, you risk submitting an assignment that does not function properly, which may result in a worse grade.

    Mistake #5: Incorrect Indentation

    Python employs indentation to indicate the structure of the code. As a result, correct indentation is critical while creating Python code. Your code will not function properly if a suitable indentation is not used. It will also be more difficult for others to read and comprehend. As a result, correct indentation is critical for making your code easier to read and understand.

    Mistake #6: Not Using Libraries

    Python is a strong programming language with a plethora of libraries and modules to help you simplify and expedite your work. Students working on Python assignments frequently make the mistake of not using libraries. This can result in inefficient, time-consuming, and difficult-to-maintain code.

    Python libraries are pre-built code packages that include functions and classes that are designed to execute specific tasks. Libraries can be downloaded and imported into your code to execute certain tasks using pre-built functions and classes. Using libraries can save you time and effort while also ensuring the efficiency and effectiveness of your work.

    Python offers a large library that may be used for a variety of tasks such as scientific computing, data analysis, web development, and more. NumPy, Pandas, Matplotlib, SciPy, Django, Flask, and TensorFlow are some of the most popular Python libraries.

    Mistake #7: Failure to divide code into functions.

    Writing Python code may be difficult, especially when working on huge programs. Code that is not broken up into functions is a common mistake that can make it difficult to read, understand, and maintain. It can also make the code more difficult to reuse and test.

    Python programming requires the use of functions. They enable you to divide code into reusable chunks that may be accessed from anywhere in the program. Functions are versatile and adaptable because they can take input arguments and return output values. Using functions can help to modularize the code, making it easier to read, understand, and maintain.

    Breaking code up into functions can also help to enhance code performance. Functions can be optimized independently, which can help to lower the program's overall execution time. Furthermore, functions can be reused throughout the program, reducing the amount of duplicate code and improving code maintainability.

    It is critical to follow best practices for function design when breaking up code into functions. Functions should be named clearly and descriptively and built to accomplish a specific purpose. Functions should also have a clear input and output interface so that it is clear how to utilize the function.

    Mistake #8: Failure to Handle Exceptions

    Exceptions are errors that occur during software execution. Exceptions are raised in Python when an error occurs and the program is unable to continue. If an exception occurs, the program will terminate unless it is handled. Exception handling is an important component of programming, and failing to handle them is a common error made by students working on Python assignments.

    If exceptions are not handled properly, the application may end unexpectedly or deliver inaccurate results. This can make it difficult to diagnose and correct code issues. It can also cause unanticipated behavior that is difficult to replicate.

    In Python, try-except blocks can be used to handle exceptions. A try-except block allows you to catch and manage exceptions that occur while the code is being executed. If an exception occurs within the try block, the except block's code is executed. This allows you to handle the exception graciously and prevent the program from terminating unexpectedly.

    Students working on Python projects frequently make mistakes when handling exceptions. It has the potential to cause the software to crash or generate inaccurate results. To manage exceptions, try-except blocks can be used to catch and handle exceptions that occur during code execution. Handling exceptions is an important component of programming that can help your code become more resilient and reliable.

    Mistake #9: Making Use of Global Variables

    The use of global variables can make your code more difficult to read and understand. Furthermore, global variables can cause issues if multiple functions attempt to modify the same variable at the same time. As a result, if possible, global variables should be avoided. Instead, utilize local variables or send variables to functions as parameters.

    The use of global variables can make your code more difficult to read and understand. Furthermore, global variables can cause issues if multiple functions attempt to modify the same variable at the same time. This can result in unpredictable behavior and faults in your code.

    A global variable exists outside of any function or class. Any part of the code can access and modify global variables. While using global variables may appear to be more convenient, it can make the code more difficult to read and maintain. It can also cause issues when many functions edit the same global variable at the same time, resulting in race situations and unexpected behavior.

    To prevent utilizing global variables, local variables or variables passed as parameters to functions must be used. A variable that is defined within a function or class and may only be accessed within that scope is referred to as a local variable. Using local variables might help to keep the code tidy and understandable.

    Variables passed as parameters to functions can also help to eliminate the use of global variables. Variables can be supplied as arguments to a function when it is called. After that, the function can adjust the variables and return the results. This can help to avoid directly altering global variables and make the code more modular.

    If you must use global variables, you must follow best practices for doing so. This involves avoiding modifying global variables from several functions at the same time, utilizing descriptive variable names, and using global variables as little as possible.

    The use of global variables might make the code more difficult to read and maintain. It can also cause issues when many functions edit the same global variable at the same time. To prevent these problems, utilize local variables or send variables as parameters to functions. If you must utilize global variables, it is critical to follow recommended practices to keep your code orderly and maintainable.

    Mistake #10: Failure to Refactor Code

    The technique of altering the design of existing code without compromising its functionality is known as refactoring. Refactoring is the process of changing code to make it more efficient, readable, and easier to maintain. Code refactoring can assist in identifying and eliminating code smells, which are symptoms of poor code design. Duplicated code, long methods or classes, and a huge number of parameters are all examples of code smells.

    Many students make the mistake of not refactoring code when working on Python assignments. They frequently produce code that is functional but difficult to understand or maintain. As the codebase grows in size, it becomes more difficult to make changes. Refactoring the code can assist to prevent these issues in the future and make it easier to add new features or fix faults.

    There are various advantages of refactoring code:

    1. Refactoring can improve code efficiency. You can lower the total size of the codebase by finding and removing duplicated code. This allows the code to execute faster and consume fewer resources. Furthermore, by dividing large functions or classes into smaller, more modular components, you can improve code efficiency.
    2. Refactoring code can improve readability. You may make the code easier to comprehend by deleting extraneous code, commenting on it, and using proper naming conventions. This will be useful to those who may need to read or edit the code in the future. Readable code is also easier to debug, which saves time and effort over time.
    3. Code refactoring can make it easier to maintain. You can make future code modifications easier by breaking up huge functions or classes into smaller, more manageable components. When adding new features or fixing bugs, this can save time and effort. Furthermore, by adhering to best practices for code design, you can make the code more robust and error-prone.

    Refactoring code is an important step in producing high-quality Python code. It can improve the efficiency, readability, and maintainability of the code. You can increase the quality of your Python assignments and become a better programmer overall by avoiding the pitfall of not reworking code.


    Python is a well-known programming language that is simple to learn and apply. However, when working on Python assignments, students frequently make mistakes. In this blog post, we highlighted ten typical Python assignment mistakes to avoid. These errors include failing to comprehend the problem, failing to use comments, failing to follow naming conventions, failing to test the code, failing to use proper indentation, failing to use libraries, failing to break up code into functions, failing to handle exceptions, failing to use global variables, and failing to rework code. By avoiding these blunders, you can develop better Python code and enhance your Python assignment grades.

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