Beginner coders may find it a challenge to get started, especially with so many settings and choices to be made to begin a new project. When working with Python, a good setup is vital to ensure your programme works as intended later on; just like how a data science course is equally beneficial for theory lessons. So, here are some tips for you when you start your first Python project:
Install an IDE
Integrated Development Environment (IDE) is the name for the programme you use to enter and edit code. Some examples are PyCharm, Spyder, and Python’s own IDLE. While coders may suggest beginners to start with simpler code editors like Geany, Notepad++, or Atom, IDE are considered more robust and suitable for serious projects. Different IDEs have different features that you may or may not find useful for your type of project and goals later in your coding journey. One of the more comprehensive and beginner-friendly IDEs is PyCharm, which is used by many developers.
Set up a virtual environment
A virtual environment helps to keep dependencies that are required by different projects separated by creating an isolated python environment for them. Once you create the virtual environment, you may activate and deactivate it anytime to test your programme.
Set your requirements.txt file
This file is used to hold results from pip freeze. It contains a version of every dependency that was installed when pip freeze was previously run.
Black is a python package that helps you format your code to meet PEP8 coding conventions. Adding it to your development environment will require anyone else who works on the project to adhere to the same style guide.
Install and configure pytest
Pytest is a popular and user-friendly library used to test Python code. It runs the code to determine if it has any functional issues. You can install pytest through your requirements.txt file with two commands. If your virtual environment is deactivated, both commands need to be run again.
Use Travis CI
Travis CI is a service used by developers to create and test projects. It helps programmers to check that their code passes the set standards, and only code that is of the standard can be integrated into the project.
Add code coverage
Code coverage produces a report on what percentage of your code has been tested. This helps you check which portions of your code has test coverage, especially useful if you have a big project with a lot of lines of code to handle.
Coveralls helps keep track of your code coverage and publishes it as a track record for others to see. You need to create an account using your GitHub credentials and add coveralls to your code. After which, your subsequent coverage reports will be sent to Coveralls.
PyUp helps to keep your package dependencies up to date by sending you a reminder whenever any dependencies are found to be outdated or vulnerable to security threats. To avoid being bombarded by pull requests at any time, set your update schedule to weekly.
Don’t worry if all these seem confusing at first! If you are keen on learning how to use Python for data analysis, there are many data analytics courses out there that will help you to pick up the necessary Python knowledge for you to begin your own projects.