Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Data Analysis

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

Arrow left icon
Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image