Workflow Practice

This project introduces you to working with a relational database using SQLite and Python. You will use Pandas to connect to a database, create tables from CSV files, and write SQL queries to explore data—all inside a Jupyter Notebook.

The dataset used in this project comes from the Bike Store Sample Database by Dillon Myrick. It models a fictional bicycle retail business with multiple stores, customers, products, and staff members. Each table connects through foreign keys such as customer_id, store_id, and product_id.

Learning Outcomes

By completing this project, you will be able to:

• Connect to a SQLite database using Python
• Load and write tables efficiently using Pandas
• Execute SQL queries inside a Jupyter Notebook
• Interpret query results returned as DataFrames
• Work with a multi-table relational dataset
• Follow a complete data workflow from setup to submission

Project Structure

data/  
 |--customers.csv  
 |--orders.csv  
 |--order_items.csv  
 |--products.csv  
 |--categories.csv  
 |--stores.csv  
 |--staffs.csv  
 |--brands.csv  
workflow_practice.ipynb  
README.md  

Instructions

1. Fork and clone this repository to your computer.
2. Open the workflow_practice.ipynb notebook.
3. Follow the step-by-step directions inside the notebook.
   You will:
   • Connect to a SQLite database using Python
   • Load each CSV file into the database as a table using to_sql()
   • Explore the tables and run SQL queries using pd.read_sql()
   • Complete several SQL practice prompts directly in the notebook
4. When you finish, save your completed notebook.
5. Commit and push your work to your GitHub repository.
6. Submit the link to this repository in OpenClass

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Parts of this project, including portions of the lesson text and code examples, were developed with the assistance of AI tools to improve clarity, structure, and formatting.
All content was reviewed, edited, and verified by the instructor for accuracy and educational alignment before inclusion in this project.