Frontend Development 20 min read

Python Seat Reservation System with PySide2 GUI and Command-Line Tools

This guide demonstrates how to build a Python-based seat reservation system using PySide2 for the graphical interface, JSON configuration files for seat data, and command‑line scripts for booking, checking in, and leaving, including full code examples and usage instructions.

Python Programming Learning Circle
Python Programming Learning Circle
Python Programming Learning Circle
Python Seat Reservation System with PySide2 GUI and Command-Line Tools

Overview of a seat reservation system implemented in Python, featuring a PySide2 graphical interface that displays tables and chairs, and allows users to select seats.

Required Python packages: PySide2, datetime, json, argparse.

Seat layout is defined in seatInf.json , specifying table positions, sizes, types, chair positions, and styles.

Application data for orders and occupied seats are stored in orderList.json and occupied.json .

Command‑line utilities:

Python slotCard_in.py -i="student_id" – check‑in a booked seat.

Python slotCard_out.py -i="student_id" – mark temporary leave.

Python cmd_order.py -i="student_id" -n="name" -s="seat_id" – create a reservation.

Python cmd_leave.py -i="student_id" -t="temp" or -t="free" – request temporary leave or release the seat.

Sample outputs show available seats, booking success messages, error handling for occupied or invalid seats, and instructions for leaving.

The main GUI code ( orderSystem.py ) sets up window size, fonts, style sheets, loads JSON data, draws the seat map, handles seat selection callbacks, and processes booking actions.

A secondary display module ( orderDataDisplay.py ) shows booking confirmation with student information, time, and seat ID, and provides buttons for temporary leave or final exit.

GUIcommand-lineseat-reservationpyside2
Python Programming Learning Circle
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Python Programming Learning Circle

A global community of Chinese Python developers offering technical articles, columns, original video tutorials, and problem sets. Topics include web full‑stack development, web scraping, data analysis, natural language processing, image processing, machine learning, automated testing, DevOps automation, and big data.

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