💖💖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
基于大数据的我国婚姻状况数据分析系统介绍
我国婚姻状况数据分析系统是一款基于Hadoop+Spark大数据技术栈构建的专业数据分析平台，采用Python+Django后端框架和Vue+ElementUI前端技术实现。系统集成了HDFS分布式存储、Spark SQL数据处理、Pandas数据分析和NumPy科学计算等核心技术，为婚姻状况数据提供全方位的统计分析服务。系统包含用户管理、婚姻状况信息管理、婚姻状况总体分析、婚姻年龄特征分析、婚姻性别差异分析、婚姻模式变迁分析、婚姻数据挖掘分析、系统公告管理等八大核心功能模块。通过Echarts可视化组件，系统能够生成直观的图表展示分析结果，支持多维度数据探索和深度挖掘。整个系统架构采用前后端分离设计，数据存储在MySQL数据库中，通过Spark引擎进行大数据处理，实现了从数据采集、处理、分析到可视化展示的完整数据分析流程，为研究我国婚姻状况变化趋势提供了强有力的技术支撑。
基于大数据的我国婚姻状况数据分析系统演示视频
基于大数据的我国婚姻状况数据分析系统演示图片
基于大数据的我国婚姻状况数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import count, avg, max, min, sum, when, col, year, month, desc
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("MarriageDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def marriage_overall_analysis(request):
marriage_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/marriage_db").option("dbtable", "marriage_status").option("user", "root").option("password", "123456").option("driver", "com.mysql.cj.jdbc.Driver").load()
marriage_df.createOrReplaceTempView("marriage_data")
total_marriages = spark.sql("SELECT COUNT(*) as total FROM marriage_data WHERE status = '已婚'").collect()[0]['total']
total_divorces = spark.sql("SELECT COUNT(*) as total FROM marriage_data WHERE status = '离异'").collect()[0]['total']
total_single = spark.sql("SELECT COUNT(*) as total FROM marriage_data WHERE status = '未婚'").collect()[0]['total']
total_remarried = spark.sql("SELECT COUNT(*) as total FROM marriage_data WHERE status = '再婚'").collect()[0]['total']
marriage_rate = round((total_marriages / (total_marriages + total_single + total_divorces + total_remarried)) * 100, 2)
divorce_rate = round((total_divorces / (total_marriages + total_divorces + total_remarried)) * 100, 2)
regional_analysis = spark.sql("SELECT province, COUNT(*) as count, AVG(marriage_age) as avg_age FROM marriage_data WHERE status = '已婚' GROUP BY province ORDER BY count DESC LIMIT 10").collect()
yearly_trend = spark.sql("SELECT YEAR(marriage_date) as year, COUNT(*) as marriages FROM marriage_data WHERE status = '已婚' AND YEAR(marriage_date) >= 2015 GROUP BY YEAR(marriage_date) ORDER BY year").collect()
trend_data = [{'year': row['year'], 'marriages': row['marriages']} for row in yearly_trend]
regional_data = [{'province': row['province'], 'count': row['count'], 'avg_age': round(row['avg_age'], 1)} for row in regional_analysis]
status_distribution = {'married': total_marriages, 'divorced': total_divorces, 'single': total_single, 'remarried': total_remarried}
analysis_result = {'status_distribution': status_distribution, 'marriage_rate': marriage_rate, 'divorce_rate': divorce_rate, 'regional_data': regional_data, 'trend_data': trend_data}
return JsonResponse(analysis_result, safe=False)
def marriage_age_analysis(request):
marriage_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/marriage_db").option("dbtable", "marriage_status").option("user", "root").option("password", "123456").option("driver", "com.mysql.cj.jdbc.Driver").load()
marriage_df.createOrReplaceTempView("marriage_age_data")
age_groups = spark.sql("SELECT CASE WHEN marriage_age < 25 THEN '25岁以下' WHEN marriage_age BETWEEN 25 AND 30 THEN '25-30岁' WHEN marriage_age BETWEEN 31 AND 35 THEN '31-35岁' WHEN marriage_age BETWEEN 36 AND 40 THEN '36-40岁' ELSE '40岁以上' END as age_group, COUNT(*) as count FROM marriage_age_data WHERE status = '已婚' GROUP BY age_group ORDER BY count DESC").collect()
avg_marriage_age = spark.sql("SELECT AVG(marriage_age) as avg_age FROM marriage_age_data WHERE status = '已婚'").collect()[0]['avg_age']
max_marriage_age = spark.sql("SELECT MAX(marriage_age) as max_age FROM marriage_age_data WHERE status = '已婚'").collect()[0]['max_age']
min_marriage_age = spark.sql("SELECT MIN(marriage_age) as min_age FROM marriage_age_data WHERE status = '已婚'").collect()[0]['min_age']
age_by_education = spark.sql("SELECT education_level, AVG(marriage_age) as avg_age, COUNT(*) as count FROM marriage_age_data WHERE status = '已婚' GROUP BY education_level ORDER BY avg_age DESC").collect()
age_trend_by_year = spark.sql("SELECT YEAR(marriage_date) as year, AVG(marriage_age) as avg_age FROM marriage_age_data WHERE status = '已婚' AND YEAR(marriage_date) >= 2015 GROUP BY YEAR(marriage_date) ORDER BY year").collect()
urban_rural_age = spark.sql("SELECT residence_type, AVG(marriage_age) as avg_age, COUNT(*) as count FROM marriage_age_data WHERE status = '已婚' GROUP BY residence_type").collect()
age_variance = spark.sql("SELECT VARIANCE(marriage_age) as variance FROM marriage_age_data WHERE status = '已婚'").collect()[0]['variance']
age_statistics = {'avg_age': round(avg_marriage_age, 2), 'max_age': max_marriage_age, 'min_age': min_marriage_age, 'variance': round(age_variance, 2)}
age_group_data = [{'age_group': row['age_group'], 'count': row['count']} for row in age_groups]
education_data = [{'education': row['education_level'], 'avg_age': round(row['avg_age'], 2), 'count': row['count']} for row in age_by_education]
yearly_trend = [{'year': row['year'], 'avg_age': round(row['avg_age'], 2)} for row in age_trend_by_year]
residence_data = [{'type': row['residence_type'], 'avg_age': round(row['avg_age'], 2), 'count': row['count']} for row in urban_rural_age]
result = {'age_statistics': age_statistics, 'age_groups': age_group_data, 'education_analysis': education_data, 'yearly_trend': yearly_trend, 'residence_analysis': residence_data}
return JsonResponse(result, safe=False)
def marriage_data_mining(request):
marriage_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/marriage_db").option("dbtable", "marriage_status").option("user", "root").option("password", "123456").option("driver", "com.mysql.cj.jdbc.Driver").load()
marriage_df.createOrReplaceTempView("mining_data")
correlation_analysis = spark.sql("SELECT education_level, income_level, AVG(marriage_age) as avg_age, COUNT(*) as count FROM mining_data WHERE status = '已婚' GROUP BY education_level, income_level ORDER BY avg_age DESC").collect()
divorce_factors = spark.sql("SELECT education_level, income_level, residence_type, COUNT(*) as divorce_count FROM mining_data WHERE status = '离异' GROUP BY education_level, income_level, residence_type HAVING COUNT(*) > 5 ORDER BY divorce_count DESC").collect()
remarriage_patterns = spark.sql("SELECT age_group, CASE WHEN marriage_age < 30 THEN '30岁以下' WHEN marriage_age BETWEEN 30 AND 40 THEN '30-40岁' ELSE '40岁以上' END as age_group, COUNT(*) as count FROM mining_data WHERE status = '再婚' GROUP BY age_group ORDER BY count DESC").collect()
regional_marriage_patterns = spark.sql("SELECT province, education_level, AVG(marriage_age) as avg_age, COUNT(*) as marriages FROM mining_data WHERE status = '已婚' GROUP BY province, education_level HAVING COUNT(*) > 10 ORDER BY province, avg_age").collect()
income_marriage_correlation = spark.sql("SELECT income_level, AVG(marriage_age) as avg_age, STDDEV(marriage_age) as age_stddev, COUNT(*) as count FROM mining_data WHERE status = '已婚' GROUP BY income_level ORDER BY avg_age").collect()
marriage_stability_score = spark.sql("SELECT province, (COUNT(CASE WHEN status = '已婚' THEN 1 END) * 1.0 / COUNT(*)) * 100 as stability_score FROM mining_data GROUP BY province HAVING COUNT(*) > 50 ORDER BY stability_score DESC").collect()
education_income_matrix = spark.sql("SELECT education_level, income_level, COUNT(*) as population, AVG(CASE WHEN status = '已婚' THEN 1 ELSE 0 END) as marriage_ratio FROM mining_data GROUP BY education_level, income_level HAVING COUNT(*) > 5 ORDER BY education_level, income_level").collect()
seasonal_marriage_trend = spark.sql("SELECT MONTH(marriage_date) as month, COUNT(*) as marriages FROM mining_data WHERE status = '已婚' GROUP BY MONTH(marriage_date) ORDER BY marriages DESC").collect()
cluster_analysis_data = []
for row in correlation_analysis:
cluster_score = (row['avg_age'] - 25) * 0.3 + row['count'] * 0.0001
cluster_analysis_data.append({'education': row['education_level'], 'income': row['income_level'], 'avg_age': round(row['avg_age'], 2), 'count': row['count'], 'cluster_score': round(cluster_score, 3)})
mining_results = {'correlation_patterns': cluster_analysis_data, 'divorce_factors': [{'education': r['education_level'], 'income': r['income_level'], 'residence': r['residence_type'], 'count': r['divorce_count']} for r in divorce_factors], 'remarriage_patterns': [{'age_group': r['age_group'], 'count': r['count']} for r in remarriage_patterns], 'regional_patterns': [{'province': r['province'], 'education': r['education_level'], 'avg_age': round(r['avg_age'], 2), 'marriages': r['marriages']} for r in regional_marriage_patterns], 'income_correlation': [{'income_level': r['income_level'], 'avg_age': round(r['avg_age'], 2), 'age_stddev': round(r['age_stddev'], 2), 'count': r['count']} for r in income_marriage_correlation], 'stability_scores': [{'province': r['province'], 'stability_score': round(r['stability_score'], 2)} for r in marriage_stability_score], 'seasonal_trends': [{'month': r['month'], 'marriages': r['marriages']} for r in seasonal_marriage_trend]}
return JsonResponse(mining_results, safe=False)
基于大数据的我国婚姻状况数据分析系统文档展示
💖💖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
