Why Use Anaconda
If you’re looking to get started into data science or data analysis with Python, there is nothing better than to use anaconda.
What is anaconda? Well if you go to their website they say this:
With over 4.5 million users, the open source Anaconda Distribution is the easiest way to do Python data science and machine learning. It includes hundreds of popular data science packages and the conda package and virtual environment manager for Windows, Linux, and MacOS. Conda makes it quick and easy to install, run, and upgrade complex data science and machine learning environments like scikit-learn, TensorFlow, and SciPy. Anaconda Distribution is the foundation of millions of data science projects as well as Amazon Web Services’ Machine Learning AMIs and Anaconda for Microsoft on Azure and Windows.
That’s very broad but let’s unpack it a bit then I’ll get into why I use it and highly recommend it. First it’s open source which means yes it’s free but not without a massive community behind it driving it. It’s important to keep that in mind.
Normally with any kind of python development you have to import different packages to do certain things, I think it’s great that way it keeps python clean and you only import things that you need for certain projects others seem to disagree but that’s for another post. So say you need numpy, and pandas for a project, and later on you find that you need yet another package related to data analysis like matplotlib. You have to either know what to type in the terminal and install it typically with pip or go to the website and download it manually. When you download and install Anaconda you don’t have to worry about that, you get all the packages you need (1000+), and oh yeah you get Python itself too.
But wait there’s more.
That’s just the tip of the proverbial iceberg. Anaconda gives you a nice dashboard to manage everything, it comes with built in editors and IDE’s and Jupyter Notebooks. Also includes the ability to build virtual environments, which once you get into deeper things becomes really handy because it separates your projects from your system. Think of an virtual environment as adding a new room in your house and only having the tools you need in that room for getting a specific job done.
The biggest thing that I found nice about it especially getting started away from tutorial land from places like datacamp and teamtreehouse, it cut down on frustration of importing packages just to find out when you ran them they weren’t where they were supposed to be. With Anaconda everything is right with the python interpreter which makes it so much easier getting started. If you don’t really know what I’m talking about here you’ve dodged a bullet.
For me it’s just so nice to have everything packaged up in one place, easy to find and get in and get started rather than spend all your time trying to link to packages and what not. The Anaconda Navigator also helps with this. It’s my go to, I’ve used it on Linux and Windows and works great. As an aside if you use it on Linux (Ubuntu and PopOS! In my case) you have to start it from the terminal, but if you’re a linux users it’s probably something you’re used to.
Originally published at Russell Comer.