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Learning pandas

You're reading from   Learning pandas High performance data manipulation and analysis using Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787123137
Length 446 pages
Edition 2nd Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (16) Chapters Close

Preface 1. pandas and Data Analysis 2. Up and Running with pandas FREE CHAPTER 3. Representing Univariate Data with the Series 4. Representing Tabular and Multivariate Data with the DataFrame 5. Manipulating DataFrame Structure 6. Indexing Data 7. Categorical Data 8. Numerical and Statistical Methods 9. Accessing Data 10. Tidying Up Your Data 11. Combining, Relating, and Reshaping Data 12. Data Aggregation 13. Time-Series Modelling 14. Visualization 15. Historical Stock Price Analysis

Visualization

One of the most important parts of data analysis is in creating a great visualization to immediately convey the underlying meaning in the data. Data visualization is effective, as we humans are visual creatures and have evolved to be able to discern meaning when information is laid out in a way that our brain can interpret almost immediately when the impulses from the retina hit the brain.

Over the years, there has been significant research that has resulted in many effective visualization techniques to convey specific patterns in data. These patterns have been implemented in visualization libraries, and pandas is designed to utilize these and make their use very simple.

This chapter will cover several of these techniques, primarily focusing on matplotlib, and many of the common visualizations. We will do this in three steps. The first introduces the general concepts...

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