Pandas and The Hello World of SQL Queries!

Here, you'll take advantage of the power of pandas to write the results of your SQL query to a DataFrame in one swift line of Python code!

You'll first import pandas and create the SQLite 'Chinook.sqlite' engine. Then you'll query the database to select all records from the Album table.

Recall that to select all records from the Orders table in the Northwind database, Hugo executed the following command:

df = pd.read_sql_query("SELECT * FROM Orders", engine)

This exercise is part of the course

Introduction to Importing Data in Python

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Exercise instructions

Import the pandas package using the alias pd.
Using the function create_engine(), create an engine for the SQLite database Chinook.sqlite and assign it to the variable engine.
Use the pandas function read_sql_query() to assign to the variable df the DataFrame of results from the following query: select all records from the table Album.
The remainder of the code is included to confirm that the DataFrame created by this method is equal to that created by the previous method that you learned.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import packages
from sqlalchemy import create_engine
import ____ as ____

# Create engine: engine


# Execute query and store records in DataFrame: df
df = pd.read_sql_query(____, ____)

# Print head of DataFrame
print(df.head())

# Open engine in context manager and store query result in df1
with engine.connect() as con:
    rs = con.execute("SELECT * FROM Album")
    df1 = pd.DataFrame(rs.fetchall())
    df1.columns = rs.keys()

# Confirm that both methods yield the same result
print(df.equals(df1))

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