As Python programming assignments become increasingly popular in classrooms worldwide, so do the complaints of students who are struggling with them.
If you’re among those who reluctantly tackle their homework every night, then this post is especially for you – here’s everything you need to know about why most people cannot stand Python programming assignments!
For 2019, Google searches reveal Python as the web’s second most sought-after language. Plus, a proficient Python programmer can make up to $118k annually, and it is one of the official languages employed by Google!
Needless to say, this makes learning Python an immensely worthy investment for anyone looking into lucrative programming jobs.
Despite its popularity, this language presents many complex concepts that make it difficult even for veteran programmers, let alone those who have just started out.
From having to debug more often than anticipated to limiting access to support resources, here are eight valid reasons why many cannot enjoy these programs anymore. Let’s take a look!
1. Verbose Syntax
For instance, list comprehensions and lambda functions can help make code more concise. However, they may still be more verbose than equivalent solutions in other languages.
2. Indentation Rules
Python relies on indentation to define code blocks, which can lead to confusion and errors if not managed correctly. Students may dislike Python homework because they must pay close attention to indentation, and a simple mistake can lead to unexpected results or syntax errors.
For instance, a student may be required to create an if statement. Still, they must also ensure the appropriate code is indented correctly, or it won’t run.
Python is an interpreted language, which can result in slower execution times compared to compiled languages like C or Java.
Students working on computationally intensive projects might become frustrated with Python’s performance, especially when dealing with large datasets or complex algorithms.
Studies indicate that C is 2.34 times slower than Java, while Python lags significantly behind at 33.34 times the speed of Java. It makes Python programming homework tedious and time-consuming, especially on larger projects.
4. Limited Multithreading Support
Python’s Global Interpreter Lock (GIL) can be a significant constraint for students working on concurrent or parallel programming assignments.
The GIL prevents multiple native threads from executing Python bytecodes simultaneously, limiting the full utilization of multi-core processors and causing potential bottlenecks in performance.
For instance, if a student is working on a program requiring intensive calculations, they may be unable to take advantage of multiple cores or threads to speed up the process since Python’s GIL prevents this.
5. Lack of Strong Typing
Python is a dynamically typed language, meaning type-checking is done during runtime. While this feature can provide flexibility, it can also lead to unexpected errors and bugs that might not be caught until the program is executed.
Students may prefer strongly typed languages like Java or C++, where type checking occurs during compilation, helping to catch errors early on.
For example, a student may have declared a variable as a string but set it to an integer value. As this isn’t possible in strongly typed languages, the compiler will detect this error during compilation and report it to the student, allowing them to fix the mistake quickly.
In Python, such an error will go unnoticed until runtime, making it difficult to track down and fix.
6. Incompatibility Between Python 2 and Python 3
Python 2 and Python 3 have significant differences in syntax and functionality, creating confusion for students who need to switch between the two versions.
For example, the “print” statement in Python 2 uses a different syntax than the “print” function in Python 3, requiring students to adapt their code accordingly. It can be especially confusing for beginners, leading to frustration and difficulty completing a Python assignment.
Python’s large library of modules also tends to have different versions for Python 2 and Python 3, which can lead to further incompatibility issues if students need to use modules that are only available on one version.
7. Limited Library Support for Certain Domains
While Python has extensive libraries for many domains, there are areas where it may lack comprehensive support compared to other languages.
For example, although Python has libraries like Pygame for game development, they may not be as robust as those available in languages like C# (Unity) or C++ (Unreal Engine) for creating professional-grade games.
Suppose you are a student working on game development projects. In that case, you may need to use other languages to access features that aren’t available in Python.
8. Steeper Learning Curve for Advanced Features
Although Python is generally considered beginner-friendly, it has advanced features and constructs like decorators, context managers, and metaclasses that can be challenging for students to grasp.
The learning curve may become steeper as students progress to more advanced topics, potentially causing frustration with Python programming homework.
For instance, a student may need to understand Python’s asynchronous programming model to build a fast, multi-threaded application. However, the complexity of these features may make such a task seem daunting or unachievable.
It is essential to note that these reasons may not apply to every student or situation. Many students find Python a highly enjoyable and accessible programming language and solve Python problems easily!
However, these points illustrate some potential challenges students might face when working with Python programming homework.