Insight on Anaconda Python

What is Anaconda for Python?

Python which was devised by Guido Van Rossum and first released in 1991 is a high-level programming language and it’s one of the most popular coding language used by software developers to build, manage, control and testing.

Python has a set of additional libraries which helps in scientific computing and computational modelling. Python is also an interpreter and along with the library packages, Python has a number of distributions and Anaconda is one of them.

No doubt that Python is a powerful programming language and is almost indispensable for modern data science because not only it is powerful it is convenient also. It enables data scientists and developers to connect with an array of tools and useful functions in easy and programmatic ways.

However, at times a few of the tools need a little assembly because Python is a programme to be used generally so it’s not specific to data science. But there are various pre-packages available without the need to assemble from which many Python users can derive benefit.

Anaconda distribution of Continuum Analytic’s is one such repackaging of Python which is aimed at developers who use Python for data science. It has a management Graphical User Interface, a bokeh of scientifically arranged work environments and specific tools to ease the process of using Python for data science.

Anaconda Repackage includes:-

The Python interpreter

The latest version of Python interpreter is included in Anaconda by default. This is a custom built version by Anaconda Inc, created especially for the Anaconda distribution. The interpreter has better performance optimization and more secure compiler for some platforms.

The Anaconda Navigator

The most interesting thing about Anaconda’s distribution of Python is a GUI (Graphical User Interface), it is called the Anaconda Navigator. The navigator is actually an organizational structure for the expanded form of Anaconda.

The Navigator helps you to add and launch high-level program application like R Studio or Jupyterlab. It can also help you to control virtual environments and packages; start a new project; gives you a totally new course to work in Anaconda version 5 and execute a number of administrative tasks.


Anaconda’s developers have found a way to deal with the limitations of Pip packages, they have developed their own package management called Conda. It not only deals with Python packages but also outside dependencies of Python ecosystem.

Anaconda has made working with data easier

Conda environments let the developer use a specific version of packages to place them into it and work with them in isolation.

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