Book Image

The Data Wrangling Workshop - Second Edition

By : Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar
Book Image

The Data Wrangling Workshop - Second Edition

By: Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar

Overview of this book

While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you’re a beginner, then The Data Wrangling Workshop will help to break down the process for you. You’ll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you’ll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you’ll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources.
Table of Contents (11 chapters)
Preface

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='[email protected]' \
    """
    rows = cursor.execute(sql)
    for...