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Each row represents a sales entry recorded by a sales representative during a customer visit.  \n\n### Columns:\n- `ENTRY_ID` → Unique entry/transaction identifier  \n- `SALES_REP_ID` → Unique ID for each sales representative  \n- `SALES_REP` → Name of the sales representative  \n- `CUSTOMER_ID` → Unique ID for each customer  \n- `CUSTOMER_CODE` → Business/system-specific customer code  \n- `CUSTOMER_NAME` → Full name of the customer  \n- `SKU_NAME` → Product (Stock Keeping Unit) sold  \n- `UNIT_SOLD` → Quantity of SKU sold  \n- `TOTAL_VALUE_SOLD` → Total value of SKU sold  \n- `CHECKIN_TIME` → Start time of customer visit  \n- `CHECKOUT_TIME` → End time of customer visit  \n\n---\n\n## 📝 Assignment Tasks\n\n### 1. Top 10 SKUs Analysis\n- Top 10 most-selling SKUs  \n  - By **Quantity Sold**  \n  - By **Value Sold**  \n- Top 10 least-selling SKUs  \n  - By **Quantity Sold**  \n  - By **Value Sold**  \n\n### 2. Customer Analysis\n- Top 10 Customers by **Total Value Purchased**  \n\n### 3. Sales Representative Performance\n- Top 10 Sales Representatives  \n  - By **Value Sold**  \n  - By **Time Spent**  \n- For Top 10 Sales Reps (by Value):  \n  - **Day-wise Average Value Sold**  \n  - **Day-wise Average Time Spent**  \n\n### 4. Reports for Top 3 Sales Representatives\n- **Day-wise Transactions Sheets** with:  \n  - SKU Sold  \n  - Price per SKU  \n  - Quantity Sold  \n  - Value Sold  \n- **Summary Report Sheet** with:  \n  - Date  \n  - Total Quantity Sold  \n  - Total Value Sold  \n  - Unique SKUs Sold  \n  - Unique Customers Served  \n  - Number of Visits  \n  - Conversion %  \n  - Total Time Spent  \n\n---\n\n## 🐳 Running with Docker\n\nThis project is containerized using **Docker** with `ubuntu:latest` and **Python3**.  \nDependencies are managed using **uv**.  \n\n### 🔧 Build the Docker Image\n```bash\ndocker build -t sales-analysis .\n```\n\n### 🚀 Run the Container\n```bash\ndocker run -it -v /path/to/workspace/assignment:/workspace sales-analysis\n```\n\nHere:  \n- `-v` maps your **local workspace** to `/workspace` inside the container.  \n- All reports will be available in the `/workspace/output` directory.  \n\n---\n\n## ▶️ Usage\n\nOnce inside the container, run the following command to generate all reports:\n\n```bash\nuv run src/report.py\nur run src/generate_excle.py\n```\n\nThis will process the dataset from `resource/`, perform the analysis, and save the results in the `output/` folder.  \n\n---\n\n## 📑 Output\n\nThe `output/` directory contains:  \n\n1. **CSV Reports**  \n   - **Top 10 SKUs Analysis**  \n     - Top 10 most-selling SKUs  \n       - By Quantity Sold  \n       - By Value Sold  \n     - Top 10 least-selling SKUs  \n       - By Quantity Sold  \n       - By Value Sold  \n   - **Customer Analysis**  \n     - Top 10 Customers by Total Value Purchased  \n   - **Sales Representative Performance**  \n     - Top 10 Sales Representatives  \n       - By Value Sold  \n       - By Time Spent  \n     - For Top 10 Sales Reps (by Value):  \n       - Day-wise Average Value Sold  \n       - Day-wise Average Time Spent  \n\n2. **Excel Reports** (for Top 3 Sales Representatives)  \n   - **Day-wise transactions**  \n   - **Summary report**  \n\n---\n\n## 👨‍💻 Tech Stack\n- **Python 3** (Data Processing)  \n- **Pandas / SQL / Spark** (Data Analysis)  \n- **OpenPyXL** (Excel Report Generation)  \n- **Docker** (Environment Management)  \n- **uv** (Python dependency manager)  \n\n---\n\n## 📬 Submitted By\n**Devashish Roy - 2024PGCSIS08**  \nMaster of Technology, Information Systems Security Engineering  \nNIT Jamshedpur  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Froydevashish%2Falgo8.ai-data-manipulation-assignment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Froydevashish%2Falgo8.ai-data-manipulation-assignment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Froydevashish%2Falgo8.ai-data-manipulation-assignment/lists"}