Learn how to create virtual environments for your Python data science projects using Pyenv, Virtualenv, and Pip on Mac OS Big Sur — If you’ve used Python before, you may have encountered a situation known as “dependency hell”. Some packages depend on other packages to work. Sometimes, the package that another package depends on must be a certain version. Later on, you might need to use a different version of that package because…

Learn the Python basics so that you can use it for your data science projects. — I. About Python 🐍 Created by Dutch programmer Guido van Rossum at Centrum Wiskunde & Informatica, Python made its debut in 1991. Over thirty years it has gained popularity earned a reputation of being the “Swiss army knife of programming languages.” Here are a few reasons why: A robust community has cultivated a wealth…

In Pandas II, we began to clean up the Metal Bands by Nation data set. We eliminated an unneeded column and filled some missing values. Now, we are going to examine the other columns, assess their data types and value, and take action as needed. — I. Examining Data Types Open your project notebook, then click Kernel > Restart and Run All to re-execute our previous work.

Learn how to deal with missing values in Python data science projects with Pandas using drop(), drop_duplicates(), and isna(). — In Pandas Essentials I, I described how to begin analyzing and manipulating data in Pandas with Python. In this tutorial, I will explain how to continue working with the Metal Bands by Nation data set by addressing missing values. In the first section, Dealing with Missing Values, I provide some…

Begin learning the essential Pandas methods to begin creating data science projects with Python, head(), info(), sum(), and describe(). — I. About Pandas for Data Science in Python Pandas is a data analysis library that is built on top of Python. This flexible library is useful for manipulating and analyzing data in a variety of structures, however it is especially useful for tabular data, like SQL tables and Excel spreadsheets. …

Learn how to use GitHub repositories to share and collaborate on data science projects — I. What is Git? Git is a version control system — a software tool that allows developers to track and manage changes to the code in a special kind of database. Since it was developed in 2005 by Linus Torvalds, Git has grown to be one of the most widely used version control systems…

Learn how to install a kernelspec to access your Python data science virtual environments within Jupyter Notebook — In the Creating Virtual Environments for Python Data Science Projects, I explained how to install Pyenv and Virtualenv to manage your Python versions and virtual environments on mac OS Big Sur. With that scaffolding in place, the next step will be to create a project directory, activate a new environment…

In Virtual Environments I, I explained how to install package managers, the latest release of Python, and Pyenv and Virtualenv. Now that those items are installed, we can set up a virtual environment for a Python project. — I. Create a Unique Virtual Environment for a Data Science Project with Pyenv-Virtualenv It’s a good idea to create a new virtual environment for each of your project. So we will start by making a directory for our project.

Before you begin your first data science project using Python, it’s helpful to make sure that your computer is properly set up for the task. I broke this process down into manageable steps to help you get get started. — Note to readers: Since the internet is full of outdated advice, it’s wise to check when directions or solutions have been written. It’s also helpful to know what system and specifications the author was working on. I’m using an Apple MacBook Air, Early 2015 that is running macOS Catalina Version…