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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

Where does pandas fit in the pipeline?

As discussed in the previous section, pandas can be used to perform Step 4 to Step 6 in the pipeline. And Step 4 to Step 6 are the backbone of any data science process, application, or product:

Where does pandas fit in the data analytics pipeline?

The Step 1 to Step 6 can be performed in pandas by some methods. Those in the Step 4 to Step 6 are the primary tasks while the Step 1 to Step 3 can also be done in some way or other in pandas.

pandas is an indispensable library if you're working with data, and it would be near impossible to find code for data modeling that doesn't import pandas into the working environment. Easy-to-use syntax in Python and the availability of a spreadsheet-like data structure called a dataframe make it amenable even to users who are too comfortable and too unwilling to move away from Excel. At the same...

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