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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

Product type Paperback

Published in Oct 2014

Publisher

ISBN-13 9781783553358

Length 348 pages

Edition 1st Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Author (1):

Ivan Idris

View More author details

Table of Contents (17) Chapters

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

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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Authors (1)

Idris

Idris

Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.

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