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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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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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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 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
Index

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 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 required to create coordinate matrices from the years and CPU transistor counts arrays. Create the coordinate matrices with the NumPy meshgrid() function:

    X, Y = np.meshgrid(X, Y)
  4. Plot the data with the...

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