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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
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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

Summary

This chapter focused on three main themes: styling and result formatting options in pandas, creating interactive dashboards in Jupyter Notebook, and exploring formatting and typesetting options in Jupyter Notebook to create powerful reports.

Output formatting such as conditional formatting, bold and italics output, highlighting certain sections, and so on can be done by styling options in pandas. Basic interactive dashboards can be created in Jupyter Notebook. LaTex, and MathJax and provide powerful typesetting and markdown options for writing equations and formatting text. Reports can be shared as ipynb files on GitHub, and can be viewed in an online viewer called NbViewer. Jupyter Hub is the multi-user server-based deployment method.

In the next chapter, we will look at how pandas can be used to perform statistical calculations using packages; we will also perform calculations...

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