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Learning pandas

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

Product type Paperback

Published in Jun 2017

Publisher

ISBN-13 9781787123137

Length 446 pages

Edition 2nd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Author (1):

Michael Heydt

View More author details

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. pandas and Data Analysis FREE CHAPTER

2. Up and Running with pandas

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

Performance benefits of stacked data

Finally, we will examine why we would want to stack data. It can be shown that stacked data is more efficient than using lookup through a single level index and then a column lookup, or even compared to an .iloc lookup that specifies the row and column by location. The following demonstrates this:

This can have extreme benefits on an application performance if it is required to repeatedly access a large number of scalar values out of a DataFrame.

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Authors (1)

Michael Heydt

Michael Heydt

Michael Heydt is an independent consultant, programmer, educator, and trainer. He has a passion for learning and sharing his knowledge of new technologies. Michael has worked in multiple industry verticals, including media, finance, energy, and healthcare. Over the last decade, he worked extensively with web, cloud, and mobile technologies and managed user experiences, interface design, and data visualization for major consulting firms and their clients. Michael's current company, Seamless Thingies , focuses on IoT development and connecting everything with everything. Michael is the author of numerous articles, papers, and books, such as D3.js By Example, Instant Lucene. NET, Learning Pandas, and Mastering Pandas for Finance, all by Packt. Michael is also a frequent speaker at .NET user groups and various mobile, cloud, and IoT conferences and delivers webinars on advanced technologies.

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