By Junaind Ahmed
Pandas is an open-source Python library built on top of NumPy, designed for data manipulation and analysis. It’s especially powerful when working with tabular data like spreadsheets or SQL tables.
Inventory Management System Using Pandas
Overview: This project demonstrates how a retail store can manage its inventory using Python and Pandas. It’s a command-line interface (CLI) tool that stores data in Excel files and uses Pandas for all the heavy lifting.
🧰 Key Features:
- Add, update, and delete products from inventory
- Search functionality to find items quickly
- Generate inventory reports using Pandas DataFrames
- Excel integration via
openpyxlfor persistent storage
🧪 Real-World Use Case:
Imagine a small retail shop that tracks stock manually. This system automates that process:
- When new stock arrives, the shopkeeper adds it via the CLI.
- Pandas updates the Excel file and recalculates totals.
- At the end of the day, the shopkeeper runs a report to see what sold and what needs restocking.
Real-World Uses:
- Retail & Inventory Management: Track product sales, manage stock levels, and forecast demand using DataFrames.
- Finance: Analyze stock prices, calculate moving averages, and model portfolio performance.
- Healthcare: Clean and merge patient records, handle missing data, and visualize treatment outcomes.
- Marketing & Sales: Segment customers, analyze campaign performance, and generate reports.
Key Features:
- Data Cleaning & Merging: Combine messy datasets from multiple sources.
- Handling Missing Data: Fill, drop, or interpolate missing values.
- GroupBy Operations: Aggregate data by categories (e.g., sales by region).
- Visualization Integration: Works seamlessly with Matplotlib and Seaborn.