Python may be installed several different ways, but Anaconda is the most comprehensive out-of-the-box solution for beginning Python programmers. It offers built-in support for Jupyter Notebooks, which are one of the most common formats for data science projects because they allow code to be presented elegantly alongside text and visualizations.
For our programming environment, we will be using JupyterLab, a flexible and extensible interface for working in Jupyter Notebooks. Jupyter is installable within Anaconda. We will also cover the basics of setting up virtual environments. While this may seem like extra overhead for the installation process, using virtual environments will help you keep your Python workspace tidy, reproducible, and relatively free from conflicts between Python modules and packages.
Let's get started!
To install Anaconda, go to the Anaconda download page.
If you are on a Mac, you can choose between graphical and command-line installers. If you are not yet comfortable with using the command line, select the graphical installer.
Once you have downloaded the installer, open it and follow the instructions on the Anaconda docs page for Windows, Mac, or Linux, depending on your operating system.
Other options for using Python include installing it from python.org.
Installing Python by downloading it from python.org is the option that gives you the most control over your installation, but also the option that requires the most work.
Alternatively, instead of installing Python on a local computer, you could use hosted notebooks in Google Colab. Using Colab is a fairly easy option for getting started and learning the basics of the language, but in order to use files from your local drive, you will have to upload them to Google Drive. There are also limits to your session time when using Colab. Colab is a free service as of now, but may not always be.
Please note: key commands work differently in Google Colab from how they work in Jupyter. If you work with notebooks across both platforms, you will have to learn two sets of shortcuts.
