Kurier Analysis

Lightweight Streamlit dashboard for analyzing courier booking and payment data from Google Sheets and Excel workbooks.

Why It Matters

This project turns a notebook-based logistics analysis workflow into a simple reusable app for month-wise operational reporting. It is designed for quick day-to-day use: load a workbook, pick a month, choose a report, and drill down into the numbers.

Demo

• Quick preview GIF is shown at the top of this README
• Full demo video: Watch the project demo

What It Does

• Accepts a Google Sheet URL or Excel upload
• Detects valid month-year sheets automatically
• Shows the spreadsheet title after analysis
• Saves the latest working source URLs for faster reuse
• Supports drill-down from date summaries to sender-level details

Reports

• Date-wise packet count
• Packets booked per sender
• Packets booked per mode
• Payment received per month
  • Cash amount
  • UPI amount
  • Credit amount
  • Credit count
  • Transaction count
  • Sender-wise breakdown for a selected date

Example Input Workbook

Use this sample file to understand the expected workbook structure and test the app quickly:

Download sample workbook

Included sheets:

• Instructions
• JAN 2026
• FEB 2026
• MARCH 2026

Core columns used by the app:

Column	Example	Used For
DATE	01-03-2026	Date-wise analysis and sorting
AWB NO.	MAR001	Packet counting
SENDER NAME	TRACKON	Sender-wise summaries
MODE	AIR / SURFACE	Mode-wise packet analysis
CREDIT OR CASH	CASH / UPI / CREDIT	Payment split logic
AMOUNT	140 / monthly	Payment totals and credit count

Tech Stack

• Python
• Streamlit
• Pandas
• OpenPyXL

Run Locally

pip install -r requirements.txt
streamlit run app.py

Project Files

• app.py - Streamlit application
• data_analysis_kurierwala.ipynb - original notebook used as the logic base
• examples/kurier_sample_input.xlsx - sample multi-sheet input workbook
• requirements.txt - project dependencies