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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
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

A Tour of Statistics with pandas and NumPy

In this chapter, we'll take a brief tour of classical statistics (also called the frequentist approach) and show you how we can use pandas together with the numpy and stats packages, such as scipy.stats and statsmodels, to conduct statistical analysis. We will also learn how to write the calculations behind these statistics from scratch in Python. This chapter and the following ones are not intended to be primers on statistics; they just serve as an illustration of using pandas along with the stats and numpy packages. In the next chapter, we will examine an alternative approach to the classical view—that is, Bayesian statistics.

In this chapter, we will cover the following topics:

  • Descriptive statistics versus inferential statistics
  • Measures of central tendency and variability
  • Hypothesis testing – the null and alternative...
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