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Mastering Python Data Visualization

You're reading from   Mastering Python Data Visualization Generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783988327
Length 372 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. A Conceptual Framework for Data Visualization 2. Data Analysis and Visualization FREE CHAPTER 3. Getting Started with the Python IDE 4. Numerical Computing and Interactive Plotting 5. Financial and Statistical Models 6. Statistical and Machine Learning 7. Bioinformatics, Genetics, and Network Models 8. Advanced Visualization A. Go Forth and Explore Visualization Index

Chapter 5. Financial and Statistical Models

Financial and economic models primarily help in the simplification and abstraction of data and make extensive use of probability and statistics. It's always important to take a look at the data; the first step in data analysis should be plotting the data. Problems such as bad data, outliers, and missing data can often be detected by visualizing data. Bad data should be corrected whenever possible or otherwise discarded. However, in some unusual cases, such as in a stock market, outliers are good data and should be retained. To summarize, it is important to detect the bad data and outliers and to understand them so that appropriate action can be taken. The choice of data variables plays an important role in these models.

The selection of variables is important because the nature of a model will often determine the facts that are being looked at. For instance, in order to measure inflation, a model of behavior is required so that you...

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