Ready, Steady, Go —-> Data Science by setting up Conda in your Computer
It has never been easier from the early python data tools invention than now, to set up an user environment in our own computer. Conda brings that easiness with it.
Conda as it defines itself is an “OS-agnostic, system-level binary package manager and ecosystem.”
The guiding principles of python are written by Tim Peters in PEP 20 — The Zen of Python. One of the aphorisms mentioned in it is, “There should be one– and preferably only one –obvious way to do it.” Conda is an effort towards it not only for Python but other languages like R, Ruby, etc.
If you wish to read more about conda, read an excellent blog post written by Travis Oliphant. He’s the creator of Numpy and Scipy.
The best way to install Conda package manager for python is through any one of the two distributions,
- Anaconda
- Miniconda
Choosing Between Anaconda and Miniconda
Miniconda is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on. If you’re ready to allocate more space (around 3 GB), Anaconda is the best option. Anaconda will install a wide range of packages that you might need to deal with the data. Otherwise, Miniconda will serve the purpose and you can install any package as required.
To get an up to date and stable version of the software, installing from the official documentation is the best way to do so. We’ll provide you the links to the docs. The steps mentioned in the docs are very easy to follow like a cakewalk. Also, by the time whenever any of the links are dead, We’ll update them.
Installing Conda through Anaconda:
Installing Conda through Miniconda:
We didn’t provide all the instructions directly here on this page because it’s very easy and clearly mentioned in the docs. The theme of this article is only to provide you with the necessary information to choose between Miniconda and Anaconda.