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Pandas Cookbook

You're reading from   Pandas Cookbook Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781784393878
Length 532 pages
Edition 1st Edition
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Author (1):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
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Table of Contents (12) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Beginning Data Analysis 4. Selecting Subsets of Data 5. Boolean Indexing 6. Index Alignment 7. Grouping for Aggregation, Filtration, and Transformation 8. Restructuring Data into a Tidy Form 9. Combining Pandas Objects 10. Time Series Analysis 11. Visualization with Matplotlib, Pandas, and Seaborn

Translating SQL WHERE clauses

Many pandas users will have a background processing data directly from databases using the ubiquitous Structured Query Language (SQL). SQL is a standardized language to define, manipulate, and control data stored in a database. The SELECT statement is the most common way to use SQL to select, filter, aggregate, and order data. Pandas has the ability to connect to databases and send SQL statements to them.

SQL is a very important language to know for data scientists. Much of the world's data is stored in databases that necessitate SQL to retrieve, manipulate, and perform analyses on. SQL syntax is fairly simple and easy to learn. There are many different SQL implementations from companies such as Oracle, Microsoft, IBM, and more. Although the syntax is not compatible between the different implementations, the core of it will look very much the...
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