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Python: Data Analytics and Visualization

You're reading from   Python: Data Analytics and Visualization Perform data processing and analysis with the help of python libraries, gain practical insights into predictive modeling and generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Course
Published in Mar 2017
Publisher Packt
ISBN-13 9781788290098
Length 866 pages
Edition 1st Edition
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Authors (4):
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Martin Czygan Martin Czygan
Author Profile Icon Martin Czygan
Martin Czygan
Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
Kirthi Raman Kirthi Raman
Author Profile Icon Kirthi Raman
Kirthi Raman
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
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Toc

Appendix B. Go Forth and Explore Visualization

Python has been around since 1991 and has gained popularity among the community of scientists and engineers. Among many libraries, numpy, scipy, and matplotlib have been widely used in scientific computing. Sage covers the areas of algebra, combinatorics, numerical mathematics, number theory, and calculus using an easy browser interface via IPython. Another popular package called pandas can be used to store and process complex datasets.

There are multiple tools to run and edit Python programs, and one among them is Anaconda from Continuum. One of the advantages of Anaconda is that it does not cost anything and comes inbuilt with most necessary packages. The underlying command-line tool for managing environments and Python packages is conda, and the editor is Spyder.

In the past, installing Spyder was complicated because it involved downloading and installing it in a multistep process. Installation in the recent versions has been very...

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