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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
Tools
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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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Toc

Table of Contents (21) Chapters Close

Preface 1. Section 1: Overview of Data Analysis and pandas
2. Introduction to pandas and Data Analysis FREE CHAPTER 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

Before we delve into the awesomeness of pandas, it is mission critical that we install Python and pandas correctly, choose the right IDEs, and set the right options. In this chapter, we discussed these and more. Here is a summary of key takeaways from the chapter:

  • Python 3.x is available, but many users still prefer to use version 2.7 as it is more stable and scientific-computation friendly.
  • The support and bug fixing for version 2.7 has now been stopped.
  • Translating code from one version to other is a breeze. One can also use both versions together using the virtualenv package, which comes pre-installed with Anaconda.
  • Anaconda is a popular Python distribution that comes with 700+ libraries/packages and several popular IDEs, such as Jupyter and Spyder.
  • Python codes are callable from, and usable in, other tools, like R, Azure ML Studio, H20.ai, and Julia.
  • Some of the day...
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