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The Data Wrangling Workshop

You're reading from   The Data Wrangling Workshop Create your own actionable insights using data from multiple raw sources

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839215001
Length 576 pages
Edition 2nd Edition
Languages
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Authors (3):
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Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Brian Lipp Brian Lipp
Author Profile Icon Brian Lipp
Brian Lipp
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Toc

Table of Contents (11) Chapters Close

Preface
1. Introduction to Data Wrangling with Python 2. Advanced Operations on Built-In Data Structures FREE CHAPTER 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

Retrieving Specific Columns from a JOIN Query

In the previous exercise, we saw that we can use a JOIN to fetch the related rows from two tables. However, if we look at the results, we will see that it returned all the columns, thus combining both tables. This is not very concise. What about if we only want to see the emails and the related comments, and not all the data?

There is some nice shorthand code that lets us do this:

import sqlite3
with sqlite3.connect("../lesson.db") as conn:
    cursor = conn.cursor()
    cursor.execute("PRAGMA foreign_keys = 1")
    sql = """
    SELECT comments.* FROM comments \
    JOIN user ON comments.user_id = user.email \
    WHERE user.email='bob@example.com' \
    """
    rows = cursor.execute(sql)
    for...
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