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Python: Real-World Data Science

You're reading from   Python: Real-World Data Science Real-World Data Science

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Product type Course
Published in Jun 2016
Publisher
ISBN-13 9781786465160
Length 1255 pages
Edition 1st Edition
Languages
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Authors (5):
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Fabrizio Romano Fabrizio Romano
Author Profile Icon Fabrizio Romano
Fabrizio Romano
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
Martin Czygan Martin Czygan
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Martin Czygan
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Table of Contents (12) Chapters Close

Table of Contents FREE CHAPTER
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline
1. Course Module 1: Python Fundamentals 2. Course Module 2: Data Analysis 3. Course Module 3: Data Mining 4. Course Module 4: Machine Learning 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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