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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

An overview of conda

Conda is a command line-tool that is responsible for managing environments and Python packages, rather than using pip. There are ways to query and search the packages, create new environments if necessary, and install and update Python packages into the existing conda environments. This command-line tool also keeps track of dependencies between packages and platform specifics, helping you to create working environments from different combinations of packages. To check the version of conda that is running, you can enter conda --version in Python and it will show, for example, conda 3.18.2 as the version.

A conda environment is a filesystem directory that contains a specific collection of conda packages. To begin using an environment, simply set the PATH variable to point it to its bin directory.

Here is an example of the package installation from the command line using conda:

$ conda install scipy

Fetching package metadata: ....
Solving package specifications: .
Package...
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