Reading from SQL databases in Data Analysis Python

Introduction

We read data from SQL databases to use it for analysis and decision making. It helps us get organized data stored in tables easily.

You want to analyze sales data stored in a company database.

You need to get customer information from a database for a report.

You want to combine data from different tables to find trends.

You have data in a database and want to use Python to explore it.

You want to update or check data stored in a database using code.

Syntax

Data Analysis Python

import sqlite3
import pandas as pd

# Connect to database
conn = sqlite3.connect('database_name.db')

# Read SQL query into DataFrame
df = pd.read_sql_query('SELECT * FROM table_name', conn)

# Close connection
conn.close()

Use sqlite3.connect() to connect to your database file.

pd.read_sql_query() runs your SQL command and loads data into a DataFrame.

Examples

This reads all data from the 'customers' table in the 'sales.db' database.

Data Analysis Python

import sqlite3
import pandas as pd

conn = sqlite3.connect('sales.db')
df = pd.read_sql_query('SELECT * FROM customers', conn)
conn.close()

This reads only the name and age of employees older than 30.

Data Analysis Python

import sqlite3
import pandas as pd

conn = sqlite3.connect('company.db')
query = 'SELECT name, age FROM employees WHERE age > 30'
df = pd.read_sql_query(query, conn)
conn.close()

This counts how many orders are in the 'orders' table.

Data Analysis Python

import sqlite3
import pandas as pd

conn = sqlite3.connect('data.db')
query = 'SELECT COUNT(*) AS total FROM orders'
df = pd.read_sql_query(query, conn)
conn.close()

Sample Program

This program creates a small table of fruits in memory, inserts some data, reads it into a DataFrame, and prints it.

Data Analysis Python

import sqlite3
import pandas as pd

# Create a sample database and table
conn = sqlite3.connect(':memory:')
cursor = conn.cursor()
cursor.execute('CREATE TABLE fruits (id INTEGER, name TEXT, quantity INTEGER)')
cursor.execute('INSERT INTO fruits VALUES (1, "Apple", 10)')
cursor.execute('INSERT INTO fruits VALUES (2, "Banana", 20)')
cursor.execute('INSERT INTO fruits VALUES (3, "Cherry", 15)')
conn.commit()

# Read data from the table
query = 'SELECT * FROM fruits'
df = pd.read_sql_query(query, conn)

# Close connection
conn.close()

print(df)

Important Notes

Always close the database connection after reading data to free resources.

You can use other database connectors like psycopg2 for PostgreSQL or mysql-connector-python for MySQL.

SQL queries can be customized to select only the data you need.

Summary

Reading from SQL databases lets you get data into Python for analysis.

Use sqlite3 to connect and pandas.read_sql_query() to run SQL and get a DataFrame.

Always close your connection when done.

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