What is Anaconda and Why should I bother about it?
In this Article we will be installing Anaconda, managing python packages, creating individual conda environments and sharing them via conda YAML file. We will be covering most of these topics in following order:
- What is Anaconda & Why Should I bother about it?
- Installing Anaconda
- Creating Environments via Conda
- Managing Packages via Conda
- Saving & Loading Environments
- Listing, Sharing & Removing Environments
- Best Practices
First of All, What is Anaconda & Why Should I bother about it?
You probably already have Python installed and will be wondering why you need this at all. Firstly, since Anaconda comes with a bunch of data science packages, you’ll be all set to start working with data. Secondly, using conda to manage your packages and environments will reduce future issues dealing with the various libraries you’ll be using.
In most of the real world Data Science projects, conda based package and environments are widely used and I personally preferred conda based package installation and maintenance of project then installing and maintaining directly PIP based packages.
So, Why Anaconda?
Anaconda is a distribution of packages built for data science. It comes with conda, a package, and environment manager. We usually used conda to create environments for isolating our projects that use different versions of Python and/or different version of packages. We also use it to install, uninstall, and update packages in our project environments. When you download Anaconda first time it comes with conda, Python, and over 150 scientific packages and their dependencies. Anaconda is a fairly large download (~500 MB) because it comes with the most common data science packages in Python, for people who are conservative about disk space, there is also Miniconda, a smaller distribution that includes only conda and Python. You can still install any of the available packages with conda, that comes by default with the standard version. Conda is a program we will be using exclusively from the command line, so if you aren’t comfortable using it, check out these learn by doing videos on Lynda.com command prompt tutorial for Windows and Linux Command Line Basics for Mac OSX/Linux