Data science is one of the fastest growing field in Technology. What makes it interesting is the things it comprises and things that make data science feasible. We all have came across dozens of computer program till now. Many of them involve packages. One such computer program is “Anaconda”. It is a free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. It is Advantageous, when working on a data science project. we may need additional packages (numpy, scikit-learn, pandas) which comes pre installed with Anaconda, but also we can use Anaconda’s package manager, conda, or pip to install those.
PIP — “Prefered Installed Program” or “Pip install packages”. What it is? It is a package manager for python packages, or modules(included in python version 3.4 or more). What is package? It contains all the files one need for a module(python code libraries).
Conda is an open source and package management system and environment management system. It easily creates, saves, loads, switches between environment on your local computer. Within seconds you can set up a totally separate environment to run different version of python. It can be combined with continuous integration systems to provide frequent, automated listing of your code.
Conda and pip are often considered to be identical. Some functionality of these two tools overlap. But it’s created to be used for different purpose. The differences they both hold are that conda is a cross platform package and environment manager which installs and manages conda packages(they are binary), whereas pip is recommended for installing packages from the python package index(PyPI). PIP installs python software packaged as wheels or source distributors, whereas conda is not limited to python, it downloads software packages be it of any programming language. PIP has no built in support for environment but rather depends on tools to create environment, whereas conda has ability to create isolated environment that contain different version of python. They can be combined to create data science environment.
