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The Data Wrangling Workshop - Second Edition

You're reading from  The Data Wrangling Workshop - Second Edition

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781839215001
Pages 576 pages
Edition 2nd Edition
Languages
Authors (3):
Brian Lipp Brian Lipp
Profile icon Brian Lipp
Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Profile icon Shubhadeep Roychowdhury
Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Profile icon Dr. Tirthajyoti Sarkar
View More author details

Table of Contents (11) Chapters

Preface
1. Introduction to Data Wrangling with Python 2. Advanced Operations on Built-In Data Structures 3. Introduction to NumPy, Pandas, and Matplotlib 4. A Deep Dive into Data Wrangling with Python 5. Getting Comfortable with Different Kinds of Data Sources 6. Learning the Hidden Secrets of Data Wrangling 7. Advanced Web Scraping and Data Gathering 8. RDBMS and SQL 9. Applications in Business Use Cases and Conclusion of the Course Appendix

Joins

Now, we will learn how to exploit the relationship we just built. This means that if we have the primary key from one table, we can recover all the data needed from that table and also all the linked rows from the child table. To achieve this, we will use something called a join.

A join is basically a way to retrieve linked rows from two tables using any kind of primary key – foreign key relation that they have. There are many types of join, including INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, and CROSS. They are used in different situations. However, most of the time, in simple 1: N relations, we end up using an INNER join. In Chapter 1, Introduction to Data Wrangling with Python, we learned about sets. We can view an INNER join as an intersection of two sets. The following diagram illustrate the concepts:

Figure 8.7: A diagram representing the intersection join

Here, A represents one table, and B represents another. The meaning of having...

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