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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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Tools
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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

Basic matplotlib plots


We installed matplotlib and IPython in Chapter 1, Getting Started with Python Libraries. Please go back to that chapter if you need to. The procedural MATLAB-like matplotlib API is considered by many as simpler to use than the object-oriented API, so we will demonstrate this procedural API first. To create a very basic plot in matplotlib, we need to invoke the plot() function in the matplotlib.pyplot subpackage. This function produces a two-dimensional plot for a single list or multiple lists of points with known x and y coordinates.

Optionally, we can pass a format parameter, for instance, to specify a dashed line style. The list of format options and parameters for the plot() function is pretty long, but easy to look up with the following commands:

$ ipython -pylab
In [1]: help(plot)

In this example, we will plot two lines: one with a solid line style (the default) and the other with a dashed line style.

The following demo code is in the basic_plot.py file in this...

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