Python, at first glance, is a simplistic programming language which anyone could master easily, with enough practice and dedication. However, while there are some common mistakes which are especially prevalent with beginner coders, even advanced Python programmers can make some mistakes when they underestimate the power of every character you write in this language, so read on for some of the most common and advanced mistakes.
1. Changing Lists During Iteration
Many programmers make the mistake of trying to edit items in a list in Python during iteration – for example, telling the code to delete an element, but then looping so that the code has to try and delete the same element again, which it can’t do because the element no longer exists. This will pull up an IndexError and can be easily avoided by keeping track of your loops and remembering how powerful they can be.
2. Be Careful With Indentations
Python is a finicky program, since every single formatting choice matters. One of these formatting choices is whether or not to indent your code, which means quite a lot within Python, so, if you indent incorrectly, you could be ruining the program and making it turn into an error-filled mess.
Spaces and tabs for indentation should never be mixed up, but either can be used in a program, and the number of each (especially spaces – around five-ten would be recommended) is up to you, but keep it consistent and make sure you use them at the correct times, to separate code into working parts, especially with loops.
3. Not Using/Understand Python Scope Rules
In Python, you’ll be using the LEGB rule for scope resolution – wondering what that means? Local, Enclosing, Global and Built-in, which describes the rules perfectly. Although this may seem simple, not using or understanding these rules leads to many of the more advanced Python mistakes, which are subtler and more difficult to work out than others. Researching them extensively can help you to avoid mistakes like misusing assignments so that Python confuses them with other, local elements, which can be hard to spot and rectify.
4. Misnaming Libraries
Python has an extensive collection of its own libraries, so, when you are creating, naming and importing your own libraries, you have to take care not to confuse Python by naming your library the same or a very similar name to a library that Python already has. If this happens, it can cause all sorts of problems like Python trying to import new modules or scan through its own libraries for a similarly named library, so avoid these issues by naming everything uniquely and being aware of the names of the Python libraries.
5. Not Noticing Differences Between Python 2 and 3
If you’ve learned and mastered Python 2, then you’ll have found it fairly easy to switch to the newer Python 3, since much of the language is similar (it is, after all, still Python) and the interface, etc. should be familiar, but there are some differences between the two programs which could cause some problems if you’re not aware of them. For example, exceptions are handled much differently and more strictly in Python 3, so some loops which would’ve worked in Python 2 won’t work in Python 3 because they will be caught by the stricter rules.
6. Too-Basic Error Handling
When you are debugging your code and find a singular error, it may be tempting to fix this with an easy if/else command and move on. However, if several similar errors start appearing, then going down this easy route will soon turn out to be an arduous task and clog up your script with unnecessary code. Instead, if you find an error, make sure you strip down the code and start from the beginning; don’t just take shortcuts which might ruin your code or reveal more problems later on.
Conclusion
Python is an incredible coding language, but you have to be careful, since it is very powerful and a simple slip-up can ruin even an advanced programmer’s programs. Keeping all of this information in mind, you can create detailed and highly capable applications and word-based programs – or even programs with graphical elements – within Python, so it is certainly worth the short learning curve and keeping an eye out for avoidable errors.
Nora Mork is a tech journalist at Do my essay online service. She regularly speaks at tech events to share her best practices.