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Specification for Project 5 SQL Module

Overview

Project 5 integrates Python and SQL, focusing on database interactions using SQLite. This project introduces logging, a useful tool for debugging and monitoring projects, and involves creating and managing a database, building a schema, and performing various SQL operations, including queries with joins, filters, and aggregations.

Deliverable Names

  • GitHub Repository: datafun-05-sql or datafun-05-sql-project [see Note 1 below]
  • Documentation: README.md
  • Initialize script: db_initialize_yourname.py
  • Operations script: db_operations_yourname.py

Note 1: You may continue to use the practice repo - or create a new one for this project. You may use other names for your scripts as you like.

Note 2: An example repo that starts on this project is available at datafun-05-sql.

Start a New Project

Follow this common workflow to start a new project.

  1. In your browser, create a GitHub project repository with a default README.md. Name the repo as specified above.
  2. Clone your new repository down to your machine into your Documents folder.
  3. Open your new project repository folder in the Documents folder of your machine in VS Code (if you haven't already).
  4. In VS Code, add a useful .gitignore file with a line for .vsode/ and .venv/ and whatever else doesn't need to be tracked in the repository.
  5. In VS Code, edit your README.md to record your commands, process, and notes so far.
  6. In VS Code, open a terminal - PowerShell if Windows, zsh or bash if Mac/Linux.
  7. Use the terminal to git add your files and folders to source control, and git commit your changes with a useful message (e.g. "initial commit"), and git push the changes up to GitHub.
  8. Verify your GitHub repository.

External Dependencies

This project requires at least the following external modules, so a local project virtual environment is recommended.

  • pandas
  • pyarrow (required when using pandas)

Objective

Create a Python script that demonstrates the ability to interact with a SQL database, including creating a database, defining a schema, and executing various SQL commands. Incorporate logging to document the process and provide user feedback.

Requirements

1. Create and Manage Project Virtual Environment

This project uses external packages, which are not included in the Python Standard Library - we must install them. To keep our project separate from all other Python projects, we will create and manage a local project virtual environment. We'll install our packages into the local project virtual environment. For the recommended process with detailed steps and commands, see PROJECT_VIRTUAL_ENV.md.

2. Project Start

In your Python file, create a docstring with a brief introduction to your project.

3. Import Dependencies (At the Top, After the Introduction)

Organize your project imports near the top of the file, following conventions. For example, standard library imports first, then external library imports, then local module imports. Continue to practice importing your own modules and reuse your prior code when building your project folders. Follow conventional package import organization and aliasing. Import each package just once near the top of the file. Be sure you have INSTALLED any external packages (those not in the Python Standard Library) into your active project virtual environment first.

Note: if we use "import pathlib", we must use "pathlib.Path" when working with a Path. If you use "from pathlib import Path", you omit the initial pathlib in pathlib.Path, and just use Path.

4. Logging

Logging is recommended for most professional projects. Implement logging to enhance debugging and maintain a record of program execution.

  1. Configure logging to write to a file named log.txt.
  2. Log the start of the program using logging.info().
  3. Log the end of the program using logging.info().
  4. Log exceptions using logging.exception().
  5. Log other major events using logging.info().
  6. Log the start and end of major functions using logging.debug().

For example:

import logging

# Configure logging to write to a file, appending new logs to the existing file
logging.basicConfig(filename='log.txt', level=logging.DEBUG, filemode='a', format='%(asctime)s - %(levelname)s - %(message)s')

logging.info("Program started") # add this at the beginning of the main method
logging.info("Program ended")  # add this at the end of the main method

5. Schema Design and Database Creation

Design a schema with at least two related tables, including foreign key constraints. Document the schema design in your README.md.

Implement a Python script to create the database, create the tables, and populate the tables. Keep each SQL statement in a separate file.

For example:

import sqlite3
import pathlib
import pandas as pd

# Your code here....
# Define paths...
# Define functions...

# Define the main function that will call your functions
def main():
    paths_to_verify = [sql_file_path, author_data_path, book_data_path]
    verify_and_create_folders(paths_to_verify)
    
    create_database(db_file_path)
    create_tables(db_file_path, sql_file_path)
    insert_data_from_csv(db_file_path, author_data_path, book_data_path)

6. SQL Operations

Implement SQL statements and queries to perform additional operations and use Python to execute your SQL statements. You might create an additional table, insert new records, and perform data querying (with filters, sorting, and joining tables), data aggregation, and record update and deletion.

Include the following SQL files:

  1. create_tables.sql - create your database schema using sql
  2. insert_records.sql - insert at least 10 additional records into each table.
  3. update_records.sql - update 1 or more records in a table.
  4. delete_records.sql - delete 1 or more records from a table.
  5. query_aggregation.sql - use aggregation functions including COUNT, AVG, SUM.
  6. query_filter.sql - use WHERE to filter data based on conditions.
  7. query_sorting.sql - use ORDER BY to sort data.
  8. query_group_by.sql - use GROUP BY clause (and optionally with aggregation)
  9. query_join.sql - use INNER JOIN operation and optionally include LEFT JOIN, RIGHT JOIN, etc.

7. Python and SQL Integration

Use Python to interact with the SQL database and execute SQL commands. Here's a generic example that you can use to customize.

import sqlite3

def execute_sql_from_file(db_filepath, sql_file):
    with sqlite3.connect(db_filepath) as conn:
        with open(sql_file, 'r') as file:
            sql_script = file.read()
        conn.executescript(sql_script)
        print(f"Executed SQL from {sql_file}")

8. Define Main Function for SQL Operations Script

Implement a main() function to execute the project SQL operations logic.

def main():
    ...

    # Create database schema and populate with data
    ... your code here to perform all required operations

    logging.info("All SQL operations completed successfully")

9. Conditional Script Execution

Ensure the main function only executes when the script is run directly, not when imported as a module by using standard boilerplate code.

# Conditionally execute the main() function if this is the script being run
if __name__ == "__main__":
    main()

Module Design

  • Include a docstring at the top of the file describing its purpose.
  • The code should be clear, well-organized, and demonstrate good practices.
  • Include comments and docstrings for clarity.

Evaluation Criteria

  • Functionality: The project should be functional and meet all requirements.
  • Documentation: The project should be well-written and well-documented.
  • Presentation: The project should be presented in a clear and organized manner.
  • Professionalism: The project should be submitted on-time and reflect an original, creative effort.

See rubric for additional information.