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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

Chapter 6. Data Visualization

One of the first steps in data analysis is visualization. Even when looking at a table of values, we can form a mental image of what the data might look like when graphed. Data visualization calls for the conception and analysis of the visual representation of information, signifying data that has been abstracted in some formal pattern, including properties or quantities for units of measurements of the data. Data visualization is tightly associated with scientific visualization and statistical graphics. The Python matplotlib (all lowercase) library is a well-known plotting library based on NumPy, which we will be using in this chapter. It has an object-oriented and a procedural MATLAB-like API, which can be used in parallel. A gallery with matplotlib examples can be found at http://matplotlib.org/gallery.html. The following is a list of topics that will be covered in this chapter:

  • Basic matplotlib plots
  • Logarithmic plots
  • Scatter plots
  • Legends and annotations...
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