There are several ways to manage Python versions and Python environments and some of these include `Pipenv`, `Anaconda` and `Virtualenv`. This blog will focus on the `Virtualenv`. According to its [online documentation](https://virtualenv.pypa.io/en/latest/) Virtualenv is used to create isolated Python environments. Using a virtual environment helps to keep Python projects isolated and the developer does not have to worry about accidentally changing versions of her libraries, thereby unwittingly breaking her application. Below are some quick steps on how to install and create virtual environments with Virtualenv. You'll need to have Python 3 installed for Virtualenv to work.
Anaconda is a free open source software tool and it comes with programming languages like `Python` and `R`; as well as the package manager [Conda](https://en.wikipedia.org/wiki/Anaconda_(Python_distribution)) which you can read more about [here](https://conda.io/docs/user-guide/install/index.html). According to its [Wikipedia](https://en.wikipedia.org/wiki/Anaconda_(Python_distribution)) page, Anaconda is used by over 6 million people, has over 1400 commonly used data science packages and it can be easily installed on most major operating systems. This tutorial continues from `Episode #17`, and here we learn how to use `Conda` to install and manage our Python environments and packages.
In this screencast, we are going to learn about how to install `Python` on our local machine. Some operating systems such as the `Mac OSX` come preinstalled with Python. To find out if you already have `Python` installed you can fire up your terminal and type in `python`.