Reading from SQL databases in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main Python library used to read data from SQL databases into a DataFrame?

The main Python library used is pandas, specifically the read_sql() or read_sql_query() functions.

beginner

What is a database connection string and why is it important?

A connection string is a text that contains information needed to connect to a database, like server address, database name, username, and password. It allows Python to access the database.

beginner

How do you execute a SQL query in Python to get data from a database?

You use pandas.read_sql_query(sql_query, connection) where sql_query is your SQL command and connection is the database connection object.

intermediate

What is the role of the SQLAlchemy library in reading from SQL databases?

SQLAlchemy helps create a database engine which manages the connection to the database. It works well with pandas to read data easily.

beginner

Why is it important to close a database connection after reading data?

Closing the connection frees up resources and avoids locking the database. It keeps your program and database running smoothly.

Which pandas function is used to read data from a SQL query?

A read_sql_query()

B read_csv()

C read_excel()

D read_json()

The function read_sql_query() reads data from a SQL query into a DataFrame.

What does a database connection string NOT usually include?

A Server address

B Username

C Database name

D Python version

Python version is not part of the connection string; it includes database name, username, password, and server address.

Which library helps create a database engine for connections?

A SQLAlchemy

B Matplotlib

C NumPy

D Seaborn

SQLAlchemy creates database engines to manage connections.

What is the correct order to read data from SQL in Python?

A Write SQL query, create connection, read data

B Create connection, write SQL query, read data

C Read data, write SQL query, create connection

D Create connection, read data, write SQL query

First create the connection, then write the SQL query, then read data using pandas.

Why should you close a database connection after use?

A To restart the computer

B To speed up the SQL query

C To save memory and avoid database locks

D To delete the database

Closing the connection saves resources and prevents locking issues.

Explain the steps to read data from a SQL database into a pandas DataFrame.

Think about connecting, querying, reading, and closing.

Describe the role of SQLAlchemy when working with SQL databases in Python.

Focus on connection management and integration with pandas.

Practice

1. What is the main purpose of using pandas.read_sql_query() in Python when working with SQL databases?

easy

A. To close the database connection

B. To create a new SQL database

C. To write data from a DataFrame back to the database

D. To run an SQL query and get the result as a DataFrame

Solution

1. Step 1: Understand the function purpose

   pandas.read_sql_query() runs an SQL query on a database connection and returns the result as a pandas DataFrame.

2. Step 2: Compare options with function use

   Creating databases, closing connections, or writing data are not done by this function.

3. Final Answer:

   To run an SQL query and get the result as a DataFrame -> Option D

4. Quick Check:

   read_sql_query() = runs query and returns DataFrame [OK]

Hint: Remember: read_sql_query fetches data as DataFrame [OK]

Common Mistakes:

• Thinking it creates or modifies the database
• Confusing it with connection closing functions
• Assuming it writes data back to the database

2. Which of the following is the correct way to open a connection to a SQLite database file named data.db using Python's sqlite3 module?

easy

A. conn = sqlite3.open('data.db')

B. conn = sqlite3.connect('data.db')

C. conn = sqlite3.start('data.db')

D. conn = sqlite3.create('data.db')

Solution

1. Step 1: Recall sqlite3 connection syntax

   The correct method to open a connection is sqlite3.connect() with the database filename as argument.

2. Step 2: Check other options

   Methods like open, start, or create do not exist in sqlite3 for opening connections.

3. Final Answer:

   conn = sqlite3.connect('data.db') -> Option B

4. Quick Check:

   sqlite3.connect() opens DB connection [OK]

Hint: Use connect() to open SQLite DB connections [OK]

Common Mistakes:

• Using non-existent methods like open() or start()
• Confusing connect() with create()
• Forgetting to pass the database filename as a string

3. Given the following code, what will be the output?

import sqlite3
import pandas as pd
conn = sqlite3.connect(':memory:')
cursor = conn.cursor()
cursor.execute('CREATE TABLE users (id INTEGER, name TEXT)')
cursor.execute("INSERT INTO users VALUES (1, 'Alice')")
cursor.execute("INSERT INTO users VALUES (2, 'Bob')")
df = pd.read_sql_query('SELECT * FROM users WHERE id = 2', conn)
print(df)

medium

A. id name
0 2 Bob

B. id name
0 1 Alice

C. Empty DataFrame with columns [id, name]

D. SyntaxError due to SQL query

Solution

1. Step 1: Understand the SQL query

   The query selects rows from 'users' where id equals 2, so only Bob's row matches.

2. Step 2: Check DataFrame output

   The DataFrame will have one row with id=2 and name='Bob'.

3. Final Answer:

   id name 0 2 Bob -> Option A

4. Quick Check:

   SELECT * WHERE id=2 returns Bob [OK]

Hint: Filter by id=2 returns Bob's row only [OK]

Common Mistakes:

• Confusing id=1 with id=2
• Expecting all rows instead of filtered
• Thinking the query has syntax errors

4. What is wrong with this code snippet for reading data from a SQLite database?

import sqlite3
import pandas as pd
conn = sqlite3.connect('mydb.db')
df = pd.read_sql_query('SELECT * FROM sales', conn)
conn.close()
print(df.head())
print(conn.execute('SELECT * FROM sales'))

medium

A. Trying to execute a query after closing the connection causes an error

B. The SQL query syntax is incorrect

C. pandas.read_sql_query() cannot read from SQLite databases

D. The connection is never closed

Solution

1. Step 1: Identify connection usage

   The connection is closed with conn.close() before the last execute call.

2. Step 2: Understand error cause

   After closing, trying to run conn.execute() causes a runtime error because the connection is no longer open.

3. Final Answer:

   Trying to execute a query after closing the connection causes an error -> Option A

4. Quick Check:

   Closed connection cannot execute queries [OK]

Hint: Don't use connection after closing it [OK]

Common Mistakes:

• Assuming queries work after closing connection
• Thinking SQL syntax is wrong here
• Believing pandas can't read SQLite data

5. You want to read data from a SQL database and filter rows where the column age is greater than 30. Which of the following code snippets correctly does this and returns a pandas DataFrame?

hard

A. df = pd.read_sql_query('SELECT * FROM users WHERE age > 30', 'conn')

B. df = pd.read_sql_query('SELECT * FROM users WHERE age => 30', conn)

C. df = pd.read_sql_query('SELECT * FROM users WHERE age > 30', conn)

D. df = pd.read_sql_query('SELECT * FROM users WHERE age > "30"', conn)

Solution

1. Step 1: Check SQL syntax for filtering

   The correct SQL syntax for greater than is age > 30. df = pd.read_sql_query('SELECT * FROM users WHERE age > 30', conn) uses this correctly.

2. Step 2: Identify errors in other options

   df = pd.read_sql_query('SELECT * FROM users WHERE age => 30', conn) uses invalid operator =>. df = pd.read_sql_query('SELECT * FROM users WHERE age > "30"', conn) compares age to a string which may cause type issues. df = pd.read_sql_query('SELECT * FROM users WHERE age > 30', 'conn') passes connection as a string, which is incorrect.

3. Final Answer:

   df = pd.read_sql_query('SELECT * FROM users WHERE age > 30', conn) -> Option C

4. Quick Check:

   Correct SQL and connection object needed [OK]

Hint: Use > operator and pass connection object, not string [OK]

Common Mistakes:

• Using => instead of >
• Comparing numeric column to string
• Passing connection as string instead of object