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Mastering pandas

You're reading from   Mastering pandas A complete guide to pandas, from installation to advanced data analysis techniques

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Product type Paperback
Published in Oct 2019
Publisher
ISBN-13 9781789343236
Length 674 pages
Edition 2nd Edition
Languages
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Author (1):
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Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Overview of Data Analysis and pandas FREE CHAPTER
2. Introduction to pandas and Data Analysis 3. Installation of pandas and Supporting Software 4. Section 2: Data Structures and I/O in pandas
5. Using NumPy and Data Structures with pandas 6. I/Os of Different Data Formats with pandas 7. Section 3: Mastering Different Data Operations in pandas
8. Indexing and Selecting in pandas 9. Grouping, Merging, and Reshaping Data in pandas 10. Special Data Operations in pandas 11. Time Series and Plotting Using Matplotlib 12. Section 4: Going a Step Beyond with pandas
13. Making Powerful Reports In Jupyter Using pandas 14. A Tour of Statistics with pandas and NumPy 15. A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates 16. Data Case Studies Using pandas 17. The pandas Library Architecture 18. pandas Compared with Other Tools 19. A Brief Tour of Machine Learning 20. Other Books You May Enjoy

The pandas Library Architecture

In this chapter, we examine the various libraries that are available to pandas users. This chapter is intended to be a short guide to help the user to navigate and find their way around the various modules and libraries that pandas provides. It gives a breakdown of how the library code is organized, and it gives a brief description of the various modules. It will be most valuable to users who are interested in seeing the inner workings of pandas , as well as to those who wish to make contributions to the code base. We will also briefly demonstrate how you can improve performance using Python extensions. The various topics that will be discussed are as follows:

  • Introduction to the pandas library hierarchy
  • Description of pandas modules and files
  • Improving performance using Python extensions
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