Environment Setup
Before we start, we need to set up our development environment. While general software development often uses Integrated Development Environments (IDE) like VS Code or PyCharm, Data Analytics thrives in interactive environments. We will install Anaconda, the industry-standard distribution for data science. It simplifies package management and provides easy access to tools like JupyterLab, which is essential for any data science professional.
What is Anaconda?
Anaconda is an open-source Python and R distribution containing hundreds of packages, which includes open-source libraries, IDEs like Jupyter and Spyder, along with Conda. Conda is a package and environment manager that makes it easy to download and use popular open-source libraries curated by the Anaconda team. Conda makes it seamless to not only install but also maintain (updates to newer versions when available) all your packages, including Python. Anaconda aims to provide everything you need for data science in one single download.
Installation
Download Anaconda by visiting the Anaconda website: https://www.anaconda.com/download. Choose the Python 3.* (* = whichever is the latest) version. This software requires a minimum of ~2 GB of disk space.
Steps
To educate yourself, scroll through the screen to have a cursory glance at all the packages being installed.
Click Continue to accept the terms and install by selecting Continue on all the remaining steps.
Once installed, Anaconda Navigator can be invoked like so:
- In macOS - by clicking on Anaconda-Navigator, present in the Applications folder.
- In Windows - open the command prompt and type in anaconda-navigator. The command prompt can be opened by pressing Win + R and then typing cmd.
Once you start Anaconda Navigator, you will see the screen below.
- Anaconda Navigator provides quick links to all the main applications, along with references to developer blogs and technical documentation. It is a good idea to explore these and understand the resources at your disposal.
- The two pointers shown in the image above—JupyterLab and Learning—are of high importance. While this book features built-in interactive code blocks, mastering JupyterLab is essential for independent projects, and the Learning tab provides rich online resources to supplement your journey.
- Although the installation is a simple process as described above, in all operating systems, you may sometimes encounter issues. Your first stop could be to visit the Anaconda Navigator documentation page - https://docs.anaconda.com/anaconda/user-guide/troubleshooting.
- In addition, Stack Overflow (https://stackoverflow.com/questions/tagged/anaconda) has a tag for Anaconda, and it should be your second stop, as required, to see if anyone else has encountered issues similar to yours.
If you are seeing an exception or error, then first try entering the exact same exception or error into the search box to get results for possible solutions. - Finally, JupyterLab is a modern, feature-rich interface for Jupyter and is highly stable across all operating systems. If you encounter issues starting JupyterLab, you can work with the classic Jupyter Notebook instead. To invoke it, select the Jupyter Notebook card in Anaconda Navigator. Note that all screenshots in this book are from JupyterLab, so you may see minor variations if you work with the classic interface.