Connect to Relational Databases with Python

In this step, you’ll learn how to connect Python to relational databases using SQLAlchemy and pyodbc. Establishing a connection between Python and databases allows you to execute SQL queries, fetch data, and manipulate query results efficiently. You’ll work with secure database connections, handle credentials safely, and retrieve structured data for further processing.

🟦 Note:
Python’s database libraries provide a seamless way to interact with relational databases like MySQL, PostgreSQL, and SQL Server. Understanding how to connect and interact with these databases is essential for data engineers, analysts, and developers.

Why It Matters

Connecting Python to relational databases is a crucial skill for working with structured data. By mastering this, you will be able to:

• Establish secure connections to databases and manage credentials properly.
• Execute SQL queries directly from Python to fetch, insert, update, or delete records.
• Process query results using Pandas for further analysis and transformations.

Key Concepts

Database Connectivity

• Use SQLAlchemy and pyodbc to connect Python to a relational database.
• Handle database credentials securely using configuration files or environment variables.

Executing SQL Queries

• Perform basic operations such as SELECT, INSERT, UPDATE, and DELETE.
• Fetch query results and process them using Python.

Query Results Processing

• Load query results into Pandas for further manipulation and analysis.
• Transform and export data back to the database for persistence.

✅ Important:
Mastering these concepts allows you to build scalable data pipelines, automate data retrieval, and integrate Python seamlessly with relational databases.

Learning Objectives

• Learn how to establish and configure a secure database connection in Python.
• Execute SQL queries and fetch results programmatically.
• Process, transform, and analyze query results using Pandas.
• Export transformed data back to a relational database.

Now that you understand the fundamentals of database connectivity, let’s move on to establishing a connection and writing SQL queries in the next step!

⚠️ Important Note About Python Indentation

Python relies on correct spacing and indentation for code execution. Even minor inconsistencies in spaces or tabs can cause errors or validation failures.

🔹 Best Practices:

• Ensure consistent indentation (use 4 spaces per level).
• Avoid mixing tabs and spaces in the same script.
• If your answer is correct but validation is failing, try adjusting the spacing and indentation.

✅ Tip: If your script isn't running or validation is failing unexpectedly, double-check your indentation!

In this step, you will learn how to install and configure Python database connectors,
establish a connection to MySQL using SQLAlchemy, and securely manage database credentials using config.ini.

🟦 Why It Matters

• SQLAlchemy simplifies database interactions, making it easier to connect and execute queries.
• Secure credential management using config.ini prevents hardcoding sensitive information.
• Establishing a reliable database connection is essential for executing queries and managing data.

By the end of this step, you will have a fully functional and secure database connection,
laying the foundation for future database operations.

In this step, you will learn how to insert, retrieve, and display records from a MySQL database using SQLAlchemy in Python. You will execute SQL statements to store employee data, fetch records, and format the output for easy readability.

🟦 Why It Matters

• Executing SQL queries from Python allows seamless database interactions.
• Retrieving stored data ensures that records are correctly inserted and available for processing.
• Displaying results in a structured format helps in analyzing and verifying stored information.

By the end of this step, you will have a fully functional script that inserts data into the database, retrieves employee records, and formats the output for display.

In this step, you will load query results from the employees table into a Pandas DataFrame using SQLAlchemy.
You will replace the placeholder with the correct implementation to fetch SQL query results and store them in a Pandas DataFrame.

Why It Matters

• Brings SQL data into Pandas for further manipulation and analysis.
• Enables data transformations such as filtering, categorization, and visualization.
• **Prepares data for advanced analytics and machine learning workflows.

By the end of this step, you will have successfully retrieved and stored SQL data into a Pandas DataFrame for further processing. # Congratulations on Completing the Lab! 🎉

You have successfully completed the lab on Connecting to Relational Databases with Python. In this module, you learned:

You have successfully completed the lab on Connecting to Relational Databases with Python. In this module, you learned:

• How to set up and configure database connections using SQLAlchemy.
• How to securely handle database credentials using environment variables or configuration files.
• How to execute SQL queries in Python
• How to fetch and process query results efficiently using various fetch functions.
• How to handle errors gracefully to prevent database failures.
• How to load SQL query results into Pandas, transform the data, and export it back to the database.

Key Takeaways

• Database Connectivity – You established a secure and efficient connection to a relational database using Python.
• Executing SQL Queries – You successfully wrote and executed SQL queries to manipulate and retrieve data.
• Error Handling & Security – You implemented error handling and secured database credentials properly.
• Data Analysis with Pandas – You transformed and analyzed query results using Pandas functions.
• Data Export – You learned how to insert and update records from Pandas into a relational database.

Thank you for completing the lab! Keep practicing and refining your SQL and Python database integration skills! 🚀