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Mastering Python Data Visualization

You're reading from   Mastering Python Data Visualization Generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783988327
Length 372 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. A Conceptual Framework for Data Visualization 2. Data Analysis and Visualization FREE CHAPTER 3. Getting Started with the Python IDE 4. Numerical Computing and Interactive Plotting 5. Financial and Statistical Models 6. Statistical and Machine Learning 7. Bioinformatics, Genetics, and Network Models 8. Advanced Visualization A. Go Forth and Explore Visualization Index

Packages installed with Anaconda

The following command will display a list of all the packages in the Anaconda environment:

conda list 

The featured packages in Anaconda are Astropy, Cython, h5py, IPython, LLVM, LLVMpy, matplotlib, Mayavi, NetworkX, NLTK, Numexpr, Numba, numpy, pandas, Pytables, scikit-image, scikit-learn, scipy, Spyder, Qt/PySide, and VTK.

In order to check the packages that are installed with Anaconda, navigate to the command line and enter the conda list command to quickly display a list of all the packages installed in the default environment. Alternatively, you can also check Continuum Analytics for details on the list of packages available in the current and latest release.

In addition, you can always install a package with the usual means, for example, using the pip install command or from the source using a setup.py file. Although conda is the preferred packaging tool, there is nothing special about Anaconda that prevents the usage of standard Python packaging tools...

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