Book Image

SQL for Data Analytics

By : Upom Malik, Matt Goldwasser, Benjamin Johnston
3 (1)
Book Image

SQL for Data Analytics

3 (1)
By: Upom Malik, Matt Goldwasser, Benjamin Johnston

Overview of this book

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you. SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation. By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
Table of Contents (11 chapters)
9. Using SQL to Uncover the Truth – a Case Study

Deleting Data and Tables

We often discover that data in a table is incorrect, and therefore can no longer be used. At such times, we need to delete data from a table.

Deleting Values from a Row

Often, we will be interested in deleting a value in a row. The easiest way to accomplish this task is to use the UPDATE structure we already discussed and to set the column value to NULL like so:

UPDATE {table_name}
SET {column_1} = NULL,
    {column_2} = NULL,
    {column_last} = NULL

Here, {table_name} is the name of the table with the data that needs to be changed, {column_1}, {column_2},… {column_last} is the columns whose values you want to delete, and {WHERE} is a conditional statement like one you would find in a SQL query.

Let's say, for instance, that we have the wrong email on file for the customer with the customer ID equal to 3. To fix that, we can use the following...