💖💖作者：计算机毕业设计小途 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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基于Python的购物管理系统介绍
基于Python的购物管理系统是一套采用现代化技术栈构建的综合性电商管理平台，该系统支持Java+SpringBoot和Python+Django双重技术架构，为不同技术背景的开发者提供灵活选择。系统前端采用Vue.js结合ElementUI组件库构建，提供美观且用户友好的交互界面，后端基于SpringBoot或Django框架开发，确保系统的稳定性和可扩展性，数据存储采用MySQL数据库，保障数据的安全性和完整性。该系统功能模块丰富完善，涵盖系统首页展示、用户管理、商品分类管理、商品信息维护、举报记录处理、充值记录查询、在线留言互动、交流论坛讨论、论坛分类管理、关于我们介绍、公告信息发布、系统简介说明、公告信息分类、轮播图管理等基础功能模块，同时还包含完整的订单管理体系，支持已支付订单、已完成订单、已退款订单、已发货订单的全流程跟踪管理，用户端提供个人中心、密码修改、安全退出等个性化服务功能。系统采用B/S架构设计，支持通过浏览器进行访问和操作，开发工具可选择IDEA（Java版本）或PyCharm（Python版本），整个系统设计注重用户体验和管理效率，既适合作为小型电商平台的管理系统，也非常适合作为计算机专业学生的毕业设计项目，通过该系统的开发和实现，可以全面掌握前后端分离开发、数据库设计、用户权限管理、订单流程处理等核心技术要点。
基于Python的购物管理系统演示视频
基于Python的购物管理系统演示图片
基于Python的购物管理系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
from datetime import datetime
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from django.utils.decorators import method_decorator
from django.views import View
import json
import hashlib
spark = SparkSession.builder.appName("ShoppingManagementSystem").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
class ProductManagementView(View):
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super().dispatch(request, *args, **kwargs)
def post(self, request):
try:
data = json.loads(request.body)
product_name = data.get('product_name')
category_id = data.get('category_id')
price = float(data.get('price'))
stock_quantity = int(data.get('stock_quantity'))
description = data.get('description', '')
product_data = spark.createDataFrame([(product_name, category_id, price, stock_quantity, description, datetime.now())], ["name", "category_id", "price", "stock", "description", "created_time"])
product_data.write.mode("append").option("driver", "com.mysql.cj.jdbc.Driver").option("url", "jdbc:mysql://localhost:3306/shopping_db").option("dbtable", "products").option("user", "root").option("password", "password").save()
stock_analysis = product_data.groupBy("category_id").agg(sum("stock").alias("total_stock"), avg("price").alias("avg_price"), count("*").alias("product_count"))
stock_analysis.show()
if stock_quantity < 10:
low_stock_alert = {"product_name": product_name, "current_stock": stock_quantity, "alert_level": "low", "suggested_restock": 50}
return JsonResponse({"status": "success", "message": "商品添加成功", "alert": low_stock_alert})
return JsonResponse({"status": "success", "message": "商品添加成功", "product_id": hashlib.md5(product_name.encode()).hexdigest()[:8]})
except Exception as e:
return JsonResponse({"status": "error", "message": str(e)})
class OrderProcessingView(View):
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super().dispatch(request, *args, **kwargs)
def post(self, request):
try:
data = json.loads(request.body)
user_id = data.get('user_id')
product_list = data.get('products')
total_amount = 0
order_items = []
for item in product_list:
product_id = item['product_id']
quantity = item['quantity']
unit_price = float(item['price'])
subtotal = quantity * unit_price
total_amount += subtotal
order_items.append((user_id, product_id, quantity, unit_price, subtotal, datetime.now()))
order_df = spark.createDataFrame(order_items, ["user_id", "product_id", "quantity", "unit_price", "subtotal", "order_time"])
order_summary = order_df.groupBy("user_id").agg(sum("subtotal").alias("total_amount"), count("product_id").alias("total_items"), avg("unit_price").alias("avg_price"))
user_order_history = spark.read.option("driver", "com.mysql.cj.jdbc.Driver").option("url", "jdbc:mysql://localhost:3306/shopping_db").option("dbtable", "orders").option("user", "root").option("password", "password").load()
user_stats = user_order_history.filter(col("user_id") == user_id).agg(sum("total_amount").alias("lifetime_spending"), count("order_id").alias("order_count"))
discount_rate = 0
lifetime_spending = user_stats.collect()[0]["lifetime_spending"] if user_stats.count() > 0 else 0
if lifetime_spending and lifetime_spending > 1000:
discount_rate = 0.05
elif lifetime_spending and lifetime_spending > 500:
discount_rate = 0.03
final_amount = total_amount * (1 - discount_rate)
order_id = f"ORD{datetime.now().strftime('%Y%m%d%H%M%S')}{user_id[-4:]}"
order_df.write.mode("append").option("driver", "com.mysql.cj.jdbc.Driver").option("url", "jdbc:mysql://localhost:3306/shopping_db").option("dbtable", "order_details").option("user", "root").option("password", "password").save()
return JsonResponse({"status": "success", "order_id": order_id, "total_amount": final_amount, "discount_applied": discount_rate * 100, "message": "订单创建成功"})
except Exception as e:
return JsonResponse({"status": "error", "message": str(e)})
class UserBehaviorAnalysisView(View):
@method_decorator(csrf_exempt)
def dispatch(self, request, *args, **kwargs):
return super().dispatch(request, *args, **kwargs)
def get(self, request):
try:
user_id = request.GET.get('user_id')
orders_df = spark.read.option("driver", "com.mysql.cj.jdbc.Driver").option("url", "jdbc:mysql://localhost:3306/shopping_db").option("dbtable", "orders").option("user", "root").option("password", "password").load()
products_df = spark.read.option("driver", "com.mysql.cj.jdbc.Driver").option("url", "jdbc:mysql://localhost:3306/shopping_db").option("dbtable", "products").option("user", "root").option("password", "password").load()
user_orders = orders_df.filter(col("user_id") == user_id)
monthly_spending = user_orders.withColumn("month", date_format("order_time", "yyyy-MM")).groupBy("month").agg(sum("total_amount").alias("monthly_total"), count("order_id").alias("order_count"))
category_preference = user_orders.join(products_df, "product_id").groupBy("category_id").agg(sum("quantity").alias("total_quantity"), sum("subtotal").alias("category_spending"), count("order_id").alias("purchase_frequency"))
top_categories = category_preference.orderBy(desc("category_spending")).limit(5)
purchase_patterns = user_orders.withColumn("hour", hour("order_time")).withColumn("day_of_week", dayofweek("order_time")).groupBy("hour", "day_of_week").count().orderBy(desc("count"))
avg_order_value = user_orders.agg(avg("total_amount").alias("avg_order_value")).collect()[0]["avg_order_value"]
total_orders = user_orders.count()
last_order_date = user_orders.agg(max("order_time").alias("last_order")).collect()[0]["last_order"]
days_since_last_order = (datetime.now() - last_order_date).days if last_order_date else None
user_segment = "inactive" if days_since_last_order and days_since_last_order > 30 else "active" if total_orders > 10 else "new"
recommendations = products_df.join(category_preference.select("category_id"), "category_id").orderBy(desc("rating"), desc("sales_count")).limit(10)
analysis_result = {"user_segment": user_segment, "avg_order_value": round(avg_order_value, 2) if avg_order_value else 0, "total_orders": total_orders, "days_since_last_order": days_since_last_order, "top_categories": [row.asDict() for row in top_categories.collect()], "recommended_products": [row.asDict() for row in recommendations.collect()]}
return JsonResponse({"status": "success", "data": analysis_result, "message": "用户行为分析完成"})
except Exception as e:
return JsonResponse({"status": "error", "message": str(e)})
基于Python的购物管理系统文档展示
💖💖作者：计算机毕业设计小途 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
