Task 7 — Sales Data Analysis (SQLite + Python + Pandas)

This project demonstrates how to:

1. Work with a small sales dataset.
2. Store it in a SQLite database.
3. Use SQL to aggregate revenue and quantities.
4. Read the results into Python/pandas.
5. Create a bar chart visualization.

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📂 Files in this folder

File	Description
sales_data.db	SQLite database containing the table sales.
sales.csv	CSV copy of the same data for reference.
sql_db_using_python.py	Python script that connects to the DB, runs queries, prints a summary.
sales_chart.png	Bar chart output: revenue by product.
README.md	This file — instructions & notes.

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📊 Dataset description

The table sales contains 12 sample rows:

Column	Type	Description
sale_id	INTEGER	Unique ID for each sale
date	TEXT	Date of sale (YYYY-MM-DD)
product	TEXT	Product name
quantity	INTEGER	Quantity of units sold
price	REAL	Unit price in USD

Example rows:

sale_id	date	product	quantity	price
1	2025-09-01	Widget A	3	19.99
4	2025-09-02	Widget A	5	19.99
9	2025-09-05	Gizmo D	10	9.99
…	…	…	…	…

Revenue is calculated as quantity * price.

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⚙️ Requirements

• Python 3.x
• pandas
• matplotlib
• sqlite3 (built-in with Python)

Install the Python packages if needed:

pip install pandas matplotlib