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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

Array indexing


Elements from NumPy arrays can be selected using four methods: scalar selection, slicing, numerical indexing, and logical (or Boolean) indexing. Scalar selection and slicing are the basic methods to access elements in an array, which has already been discussed here. Numerical indexing and logical indexing are closely related and allows more flexible selection. Numerical indexing uses lists or arrays of locations to select elements, whereas logical indexing uses arrays that contain Boolean values to select elements.

Numerical indexing

Numerical indexing is an alternative to slice notation. The idea in numerical indexing is to use coordinates to select elements. This is similar to slicing. Arrays created using numerical indexing create copies of data, whereas slices are only views of data, and not copies. For performance sake, slicing should be used. Slices are similar to one-dimensional arrays, but the shape of the slice is determined by the slice inputs.

Numerical indexing in...

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