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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

Special Data Operations in pandas

pandas has an array of special operators for generating, aggregating, transforming, reading, and writing data from and to a variety of data types, such as number, string, date, timestamp, and time series. The basic operators in pandas were introduced in the previous chapter. In this chapter, we will continue that discussion and elaborate on the methods, syntax, and usage of some of these operators.

Reading this chapter will allow you to perform the following tasks with confidence:

  • Writing custom functions and applying them on a column or an entire DataFrame
  • Understanding the nature of missing values and handling them
  • Transforming and performing calculations on series using functions
  • Miscellaneous numeric operations on data

Let's delve into it right away. For the most part, we will generate our own data to demonstrate the methods.

The following...

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