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Getting Started with Python Data Analysis

You're reading from   Getting Started with Python Data Analysis Learn to use powerful Python libraries for effective data processing and analysis

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781785285110
Length 188 pages
Edition 1st Edition
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introducing Data Analysis and Libraries FREE CHAPTER 2. NumPy Arrays and Vectorized Computation 3. Data Analysis with Pandas 4. Data Visualization 5. Time Series 6. Interacting with Databases 7. Data Analysis Application Examples 8. Machine Learning Models with scikit-learn Index

Chapter 4. Data Visualization

Data visualization is concerned with the presentation of data in a pictorial or graphical form. It is one of the most important tasks in data analysis, since it enables us to see analytical results, detect outliers, and make decisions for model building. There are many Python libraries for visualization, of which matplotlib, seaborn, bokeh, and ggplot are among the most popular. However, in this chapter, we mainly focus on the matplotlib library that is used by many people in many different contexts.

Matplotlib produces publication-quality figures in a variety of formats, and interactive environments across Python platforms. Another advantage is that Pandas comes equipped with useful wrappers around several matplotlib plotting routines, allowing for quick and handy plotting of Series and DataFrame objects.

The IPython package started as an alternative to the standard interactive Python shell, but has since evolved into an indispensable tool for data...

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