WARNING: As of 2/10/2018, the master branch of this repository is under construction as we reorganize and refactor for the upcoming 2.0 release. The 2.x series of Altair will have full support for VegaLite 1.0/2.0 and Vega 2.0/3.0, We have created a 1.x branch for maintenance of the 1.x series. Altair is a declarative statistical visualization library for Python. Altair is developed by Brian Granger and Jake Vanderplas in close collaboration with the UW Interactive Data Lab.
Data visualization gives many insights that data alone cannot. Python has some of the most interactive data visualisation tools. The most basic plot types are shared between multiple libraries, but others are only available in certain libraries. Data journalist and information designer, David McCandless, talking about the significance of data visualization in his TED talk had said, "By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you're lost in information, an information map is kind of useful."
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Oh no:-/ I haven't written a post in so long! For my second technical post, I had in mind something related to the talk I gave at PyCon Sweden 2017 back in September 2017 (here's a link to my slides on Github). My idea then was to share my experience of trying a new data visualisation tool. This tool called Altair isn't another data visualisation library as such, but a Python API to access the powerful and simple Vega-Lite JSON like grammar for interactive graphics (itself build on top of Vega). While it was fun and interesting to try out Altair (I used it for a personal project), it is still in the process of supporting Vega-Lite 2.0.
Advantage of scalability – Python is highly scalable and is faster than languages like Stata and Matlab. The flexibility with which the code can be designed is the reason why Python is more scalable than R. For quick development of applications this is the language that is most popularly used. Powerful packages – There are many packages a data scientist can choose to develop his applications. SciPy is used for scientific computing, NumPy is used for mathematical computing, Pandas is popular in data manipulation.