More Practice With SQL Queries - Lab

Introduction

In this lesson, we'll run through some practice questions to refresh your knowledge of SQL Queries!

Objectives

You will be able to:

• Use GROUP BY statements in SQL to apply aggregate functions like: COUNT, MAX, MIN, and SUM
• Decide and perform whichever type of join is best for retrieving desired data
• Use the HAVING clause to compare different aggregates
• Write subqueries to decompose complex queries

Getting Started

As in previous labs, we'll make use of the sqlite3 library as well as Pandas. By combining them, we'll be able to write queries as Python strings, and make sure that the results are always returned as a Pandas DataFrame.

We'll start by loading both libraries and connecting to the database we'll be using for this lab, data.sqlite. You may remember this database from a previous lab. As a refresher, here's the ERD diagram for this database:

In the cell below:

• Import the necessary libraries pandas and sqlite3
• Establish a connection to the database data.sqlite
• Get the cursor from the connection and store it in the variable c

Basic Queries

Now, let's review basic SQL queries. In the cell below:

Write a query that gets the first name, last name, phone number, address, and credit limit for all customers in California with a credit limit greater than 25000.00.

# For the first query, the boilerplate for getting 
# the query into a DataFrame has been provided for you
c.execute(""" """)
df = pd.DataFrame(c.fetchall())
df.columns = [x[0] for x in c.description]
df

Expected Output

Aggregate Functions and GROUP BY

Next, write a query that gets the average credit limit per state.

Expected Output

JOINs

Now, write a query that uses JOIN statements to get the customer name, customer number, order number, status, and quantity ordered. Print only the head of this DataFrame.

Expected Output

HAVING and ORDER BY

Now, return the customerName, customerNumber, productName, productCode, and total number ordered for any product a customer has bought 10 or more of cumulatively. Sort the rows in descending order by the quantity ordered.

Hint: For this one, you'll need to make use of HAVING, GROUP BY, and ORDER BY -- make sure you get the order of them correct!

Expected Output

Subqueries

Finally, get the first name, last name, employee number, and office code for employees from offices with less than 5 employees. Print the first five rows of this DataFrame.

Expected Output

Summary

In this lesson, we reviewed all the major concepts and keywords associated with SQL queries!