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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. The Pandas Primer 4. Statistics and Linear Algebra 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources

Three-dimensional plots


Two-dimensional plots are the bread and butter of data visualization. However, if you want to show off, nothing beats a good three-dimensional plot. I was once in charge of a software package that could draw contour plots and three-dimensional plots. The software could even draw plots that, when viewed with special glasses, would pop right in front of you.

The matplotlib API has the Axes3D class for three-dimensional plots. By demonstrating how this class works, we will also show how the object-oriented matplotlib API works. The matplotlib Figure class is a top-level container for chart elements:

  1. Create a figure object as follows:

            fig = plt.figure() 
    
  2. Create an Axes3D object from the figure object:

            ax = Axes3D(fig) 
    
  3. The years and CPU transistor counts will be our X and Y axes. It is necessary for us to create coordinate matrices from the years and CPU transistor counts arrays. Create the coordinate matrices with the NumPy meshgrid() function:

    ...
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