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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

Legends and annotations


Legends and annotations are effective tools to display information required to comprehend a plot at 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 both style and position of the label and arrow, 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, as well as optional parameters, such as the font size of the label.

  • A descriptive title for the graph with the matplotlib title...

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