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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 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

Comparison with SQL

Pandas is similar to SQL in many ways in the sense that it is used for data selection, data filtering, data aggregation, data generation, and data modification. SQL does to the database tables what pandas does to the DataFrames. In this section, we will compare the features in SQL with their equivalents in pandas.

SELECT

SELECT is used to select or subset data in certain columns of the tables. Suppose you have a table/DataFrame called DallasData. This data would be attached in your book packet or could be accessed from the cloud drive of the book. To select five rows from the three given columns, you write the following

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