7 Most Common Python Mistakes Data Scientists Make

7 Most Common Python Mistakes Data Scientists Make | Regardless of whether you’re an expert or beginner in Python, this software development language can be tricky. Mistakes are an inevitable part of the learning process when it comes to software engineering. However, there are some that are certainly avoidable. By avoiding these seven common Python missteps, you can produce higher-quality codes with less bugs.

Giving Variables Vague Names 

Failing to use good variable names is one of the most common mistakes among data scientists. Use descriptive names for your data frames. This ensures your code is as readable as possible. On the other hand, the use of an overly short variable name might confuse coders trying to read your code. Therefore, it’s better to go with longer variable names than short, non-descriptive ones. 

Not Using Type Annotation 

Type annotation or type hint is a good method for assigning types to variables. Hints are essentially extensions of your code that indicate variables. This allows others to read your code more easily. Using type annotations allows other developers to understand your intentions clearly. This reduces any ambiguity and confusion when reading your code. 

Not Following PEP conventions 

PEP is an index of all Python Enhancement Proposals or PEPs. These conventions are the official language style guideline for Python. It’s good practice as a data scientist to follow PEP conventions because it makes it easier for fellow developers to understand your code. 

Failing To Deal With Warnings 

It happens to the best developers. You finish creating your code and run it only to find a warning message. Although warnings are not technically errors, they do point out potential issues in your code. It typically means that your code ran successfully, but not necessarily in the way it was designed. Developing a good understanding of the source code should help reduce warnings and result in fewer potential bugs. 

Overusing Jupyter Notebooks 

Although Jupyter notebooks can be convenient and handy in a pinch, it is not an ideal integrated development environment. Overly relying on Jupyter notebooks means you’re missing out on a good IDE to tackle your tasks and boost your productivity. “Using Jupyter notebooks may give you instant feedback as a data scientist, but it’s generally considered bad practice in Python,” explains Amanda Smithson, a tech blogger at UK Writings and Revieweal. “Overusing Jupyter notebooks can encourage poor coding habits as a data scientist.” 

While Jupyter notebooks are excellent for experimentation due to their instant feedback, they are not recommended for long-term use. For long-term projects and general good practice, it’s best to use a good IDE for your tasks.

Not Writing Tests 

Regardless of your time constraints for a project, it’s important to write tests no matter what. If managers are rushing you to develop faster, insist on writing tests. The last thing you want to produce is a bad code. Ultimately, you are accountable for the quality of the project, not your manager. If they are rushing the speed of your development, reiterate the potential consequences of not writing tests. When writing tests, it’s good practice to have the main functionality of the code covered. 

Not Pinning Your Dependencies 

Many data scientists make the easily avoidable mistake of not pinning their dependencies in a requirements file. Pinning ensures that when the package-management system installs your package, it installs dependency versions as you intended. However, if your package works with all versions of a dependency, pinning is not necessary. This might cause your package to stop working in the future so it’s best practice to pin your dependencies regardless. 

“Including all of your dependencies in a requirements file is common practice in Python,” explains Justin Vaughn, a tech writer at Elite assignment help and UK Top Writers. “However, it’s best to use the penultimate dependency rather than the latest one because there are less unknown bugs.” 

Conclusion 

Python is a highly flexible programming language with various mechanisms to help you boost productivity. It’s important to fully understand its capabilities and nuances to get the most out of this language. Improving your understanding of Python as a data scientist can also help avoid these common errors. While it may not be possible to always avoid missteps in your work, learning about the most common mistakes developers make when structuring code will result in better quality projects and more reliable codes. 

Christina Lee is a content specialist and technology writer at Write my essay and Big Assignments. She writes about the most recent tech updates and news for such services, as Simple Grad, and others.

Search