hanenia / datafun-05-sql

Overview integrates Python and SQL, focusing on database interactions using SQLite, introduction to logging, varoius SQL opertions.

Project Start

1. Create GitHub repository ( README.md)

2. Clone the repostory to your machine.

3. Creat and avtivate a Python virtal eniroment

4. Add a .gitignore file

5. Creat and avtivate a Python virtal eniroment and freeze into your requirements.txt. ..venv\Scripts\Activate.ps1 py -m pip install pandas pyarrow py -m pip freeze > requirements.txt

6. push to GitHub. git add . git commit -m "initial commit" git push origin main

7. Create docstring vcr_manager.py

8. Import Dependencies import sqlite3 import pandas as pd import pathlib

9. Logging: Implement logging to enhance debugging and maintain a record of program execution.

10. Desgin and Database Creation 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}"

11. SQL Operations def main(): db_filepath = 'your_database.db'

    Create database schema and populate with data

    execute_sql_from_file(db_filepath, 'insert_records.sql') execute_sql_from_file(db_filepath, 'update_records.sql') execute_sql_from_file(db_filepath, 'delete_records.sql') execute_sql_from_file(db_filepath, 'query_aggregation.sql') execute_sql_from_file(db_filepath, 'query_filter.sql') execute_sql_from_file(db_filepath, 'query_sorting.sql') execute_sql_from_file(db_filepath, 'query_group_by.sql') execute_sql_from_file(db_filepath, 'query_join.sql')

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