【数据分析】基于大数据的上海租房数据分析与可视化系统 | 大数据毕设实战项目 数据可视化分析大屏 hadoop SPark
💖💖作者：计算机毕业设计江挽
💙💙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！
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目录
- 基于大数据的上海租房数据分析与可视化系统系统介绍
- 基于大数据的上海租房数据分析与可视化系统系统演示视频
- 基于大数据的上海租房数据分析与可视化系统系统演示图片
- 基于大数据的上海租房数据分析与可视化系统系统代码展示
- 基于大数据的上海租房数据分析与可视化系统系统文档展示
基于大数据的上海租房数据分析与可视化系统系统介绍
基于大数据的上海租房数据分析与可视化系统是一套面向租房市场数据挖掘与智能分析的综合性平台。系统采用Hadoop分布式存储架构，通过HDFS管理海量租房数据，利用Spark计算引擎进行高效的数据清洗、转换与分析处理。后端基于Django框架构建RESTful API接口，前端采用Vue+ElementUI+Echarts技术栈实现交互式数据可视化。系统核心功能涵盖数据大屏展示、区域房源分析、房源特征分析、市场洞察分析、地铁交通分析等九大模块，通过Spark SQL对租房数据进行多维度统计分析，结合Pandas和NumPy进行数据预处理与特征提取。平台不仅提供租房信息查询与展示功能,还集成了交流论坛和通知公告模块,方便用户获取市场动态。整个系统从数据采集、存储、计算到可视化呈现形成完整的大数据处理链路,为租房市场提供数据驱动的决策支持,同时也为大数据技术在实际场景中的应用提供了完整的实践案例。
基于大数据的上海租房数据分析与可视化系统系统演示视频
演示视频
基于大数据的上海租房数据分析与可视化系统系统演示图片
基于大数据的上海租房数据分析与可视化系统系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, round, when, desc, asc
from django.http import JsonResponse
from django.views import View
import json
spark = SparkSession.builder.appName("ShanghaiRentAnalysis").config("spark.sql.warehouse.dir", "/user/hive/warehouse").config("spark.executor.memory", "2g").config("spark.driver.memory", "1g").getOrCreate()
class RegionAnalysisView(View):
def get(self, request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/rent_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "rent_info").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("rent_data")
region_stats = spark.sql("SELECT district, COUNT(*) as house_count, ROUND(AVG(price), 2) as avg_price, ROUND(AVG(area), 2) as avg_area, MIN(price) as min_price, MAX(price) as max_price FROM rent_data WHERE district IS NOT NULL AND price > 0 GROUP BY district ORDER BY avg_price DESC")
region_list = region_stats.collect()
result_data = []
for row in region_list:
price_level = "高价区" if row['avg_price'] > 8000 else "中价区" if row['avg_price'] > 5000 else "低价区"
density_level = "密集" if row['house_count'] > 500 else "适中" if row['house_count'] > 200 else "稀疏"
result_data.append({"district": row['district'], "house_count": row['house_count'], "avg_price": float(row['avg_price']), "avg_area": float(row['avg_area']), "min_price": row['min_price'], "max_price": row['max_price'], "price_level": price_level, "density_level": density_level, "price_range": row['max_price'] - row['min_price']})
total_houses = sum([item['house_count'] for item in result_data])
for item in result_data:
item['proportion'] = round(item['house_count'] / total_houses * 100, 2)
return JsonResponse({"code": 200, "message": "区域分析成功", "data": result_data, "total_districts": len(result_data), "total_houses": total_houses})
class HouseFeatureAnalysisView(View):
def get(self, request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/rent_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "rent_info").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("house_data")
room_stats = spark.sql("SELECT room_type, COUNT(*) as count, ROUND(AVG(price), 2) as avg_price, ROUND(AVG(area), 2) as avg_area FROM house_data WHERE room_type IS NOT NULL GROUP BY room_type ORDER BY count DESC")
orientation_stats = spark.sql("SELECT orientation, COUNT(*) as count, ROUND(AVG(price), 2) as avg_price FROM house_data WHERE orientation IS NOT NULL GROUP BY orientation ORDER BY count DESC")
floor_stats = spark.sql("SELECT floor_level, COUNT(*) as count, ROUND(AVG(price), 2) as avg_price FROM house_data WHERE floor_level IS NOT NULL GROUP BY floor_level")
decoration_stats = spark.sql("SELECT decoration, COUNT(*) as count, ROUND(AVG(price), 2) as avg_price FROM house_data WHERE decoration IS NOT NULL GROUP BY decoration ORDER BY avg_price DESC")
room_data = [{"room_type": row['room_type'], "count": row['count'], "avg_price": float(row['avg_price']), "avg_area": float(row['avg_area']), "unit_price": round(row['avg_price'] / row['avg_area'], 2) if row['avg_area'] > 0 else 0} for row in room_stats.collect()]
orientation_data = [{"orientation": row['orientation'], "count": row['count'], "avg_price": float(row['avg_price'])} for row in orientation_stats.collect()]
floor_data = [{"floor_level": row['floor_level'], "count": row['count'], "avg_price": float(row['avg_price'])} for row in floor_stats.collect()]
decoration_data = [{"decoration": row['decoration'], "count": row['count'], "avg_price": float(row['avg_price'])} for row in decoration_stats.collect()]
price_area_relation = spark.sql("SELECT CASE WHEN area < 30 THEN '30㎡以下' WHEN area < 50 THEN '30-50㎡' WHEN area < 70 THEN '50-70㎡' WHEN area < 90 THEN '70-90㎡' ELSE '90㎡以上' END as area_range, COUNT(*) as count, ROUND(AVG(price), 2) as avg_price FROM house_data WHERE area > 0 GROUP BY area_range ORDER BY avg_price")
area_price_data = [{"area_range": row['area_range'], "count": row['count'], "avg_price": float(row['avg_price'])} for row in price_area_relation.collect()]
return JsonResponse({"code": 200, "message": "房源特征分析成功", "data": {"room_analysis": room_data, "orientation_analysis": orientation_data, "floor_analysis": floor_data, "decoration_analysis": decoration_data, "area_price_relation": area_price_data}})
class SubwayTrafficAnalysisView(View):
def get(self, request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/rent_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "rent_info").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("traffic_data")
subway_impact = spark.sql("SELECT subway_distance, COUNT(*) as house_count, ROUND(AVG(price), 2) as avg_price, ROUND(AVG(area), 2) as avg_area FROM traffic_data WHERE subway_distance IS NOT NULL GROUP BY subway_distance ORDER BY subway_distance")
subway_line_stats = spark.sql("SELECT subway_line, COUNT(*) as house_count, ROUND(AVG(price), 2) as avg_price FROM traffic_data WHERE subway_line IS NOT NULL AND subway_line != '' GROUP BY subway_line ORDER BY house_count DESC LIMIT 15")
distance_range_stats = spark.sql("SELECT CASE WHEN subway_distance < 500 THEN '500米内' WHEN subway_distance < 1000 THEN '500-1000米' WHEN subway_distance < 1500 THEN '1000-1500米' ELSE '1500米以上' END as distance_range, COUNT(*) as house_count, ROUND(AVG(price), 2) as avg_price FROM traffic_data WHERE subway_distance IS NOT NULL GROUP BY distance_range ORDER BY avg_price DESC")
subway_data = [{"distance": row['subway_distance'], "house_count": row['house_count'], "avg_price": float(row['avg_price']), "avg_area": float(row['avg_area'])} for row in subway_impact.collect()]
line_data = [{"subway_line": row['subway_line'], "house_count": row['house_count'], "avg_price": float(row['avg_price'])} for row in subway_line_stats.collect()]
range_data = [{"distance_range": row['distance_range'], "house_count": row['house_count'], "avg_price": float(row['avg_price'])} for row in distance_range_stats.collect()]
if len(range_data) >= 2:
price_diff = range_data[0]['avg_price'] - range_data[-1]['avg_price']
diff_percentage = round(price_diff / range_data[-1]['avg_price'] * 100, 2) if range_data[-1]['avg_price'] > 0 else 0
else:
price_diff = 0
diff_percentage = 0
total_near_subway = sum([item['house_count'] for item in range_data[:2]]) if len(range_data) >= 2 else 0
total_houses = sum([item['house_count'] for item in range_data])
near_subway_ratio = round(total_near_subway / total_houses * 100, 2) if total_houses > 0 else 0
return JsonResponse({"code": 200, "message": "地铁交通分析成功", "data": {"subway_distance_analysis": subway_data, "subway_line_analysis": line_data, "distance_range_analysis": range_data, "price_difference": price_diff, "diff_percentage": diff_percentage, "near_subway_ratio": near_subway_ratio}})
基于大数据的上海租房数据分析与可视化系统系统文档展示
💖💖作者：计算机毕业设计江挽
💙💙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
