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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 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

Legends and annotations


Legends and annotations are effective tools to display information required to comprehend a plot in a glance. A typical plot will have the following additional information elements:

  • A legend describing the various data series in the plot. This is provided by invoking the matplotlib legend() function and supplying the labels for each data series.

  • Annotations for important points in the plot. The matplotlib annotate() function can be used for this purpose. A matplotlib annotation consists of a label and an arrow. This function has many parameters describing the label and arrow style and position, so you may need to call help(annotate) for a detailed description.

  • Labels on the horizontal and vertical axes. These labels can be drawn by the xlabel() and ylabel() functions. We need to give these functions the text of the labels as a string and optional parameters such as the font size of the label.

  • A descriptive title for the graph with the matplotlib title() function. Typically...

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