一、个人简介
- 💖💖作者：计算机编程果茶熊
- 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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
- 计算机毕业设计选题
- 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
大数据框架：Hadoop+Spark（Hive需要定制修改） 开发语言：Java+Python（两个版本都支持） 数据库：MySQL 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持） 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery 基于大数据的我国婚姻状况数据分析与可视化系统是一个集数据处理、分析挖掘与可视化展示于一体的Python大数据毕业设计项目。该系统采用Hadoop+Spark大数据框架作为底层数据处理引擎，结合Django后端框架构建稳定的Web服务架构，前端运用Vue+ElementUI+Echarts技术栈实现丰富的交互体验和数据可视化效果。系统核心功能涵盖婚姻状况信息的全方位管理与深度分析，包括婚姻状况总体趋势分析、不同年龄段婚姻特征挖掘、性别差异对比分析、婚姻模式历史变迁追踪以及基于机器学习的婚姻数据深度挖掘等模块。通过Spark SQL进行大规模数据查询处理，利用Pandas和NumPy进行数据清洗和统计计算，最终通过Echarts图表组件将分析结果以直观的柱状图、折线图、饼图等形式进行可视化展示，为用户提供全面的婚姻状况数据洞察服务。
三、基于大数据的我国婚姻状况数据分析与可视化系统-视频解说
Python大数据毕设：基于Django的婚姻状况数据挖掘分析系统详解|计算机毕业设计
四、基于大数据的我国婚姻状况数据分析与可视化系统-功能展示
五、基于大数据的我国婚姻状况数据分析与可视化系统-代码展示
from pyspark.sql import SparkSession
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import pandas as pd
import numpy as np
from collections import Counter
spark = SparkSession.builder.appName("MarriageDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def marriage_status_overall_analysis(request):
if request.method == 'POST':
try:
marriage_df = spark.sql("SELECT marriage_status, age, gender, education, region, marriage_year FROM marriage_data WHERE marriage_status IS NOT NULL")
status_counts = marriage_df.groupBy("marriage_status").count().collect()
status_distribution = {row.marriage_status: row.count for row in status_counts}
total_records = sum(status_distribution.values())
status_percentages = {status: round((count/total_records)*100, 2) for status, count in status_distribution.items()}
yearly_trends = marriage_df.groupBy("marriage_year", "marriage_status").count().orderBy("marriage_year").collect()
trend_data = {}
for row in yearly_trends:
year = row.marriage_year
status = row.marriage_status
count = row.count
if year not in trend_data:
trend_data[year] = {}
trend_data[year][status] = count
region_analysis = marriage_df.groupBy("region", "marriage_status").count().collect()
region_data = {}
for row in region_analysis:
region = row.region
status = row.marriage_status
count = row.count
if region not in region_data:
region_data[region] = {}
region_data[region][status] = count
education_marriage = marriage_df.groupBy("education", "marriage_status").count().collect()
education_data = {}
for row in education_marriage:
edu = row.education
status = row.marriage_status
count = row.count
if edu not in education_data:
education_data[edu] = {}
education_data[edu][status] = count
result = {
'status_distribution': status_distribution,
'status_percentages': status_percentages,
'yearly_trends': trend_data,
'region_analysis': region_data,
'education_analysis': education_data,
'total_records': total_records
}
return JsonResponse({'code': 200, 'data': result, 'message': '婚姻状况总体分析完成'})
except Exception as e:
return JsonResponse({'code': 500, 'message': f'分析过程中发生错误: {str(e)}'})
@csrf_exempt
def marriage_age_characteristic_analysis(request):
if request.method == 'POST':
try:
age_marriage_df = spark.sql("SELECT age, marriage_status, gender, marriage_year, region FROM marriage_data WHERE age IS NOT NULL AND age > 0")
age_groups = age_marriage_df.withColumn("age_group",
spark.sql("CASE WHEN age < 25 THEN '18-24岁' WHEN age < 30 THEN '25-29岁' WHEN age < 35 THEN '30-34岁' WHEN age < 40 THEN '35-39岁' WHEN age < 45 THEN '40-44岁' ELSE '45岁以上' END").alias("age_group"))
age_group_stats = age_groups.groupBy("age_group", "marriage_status").count().collect()
age_distribution = {}
for row in age_group_stats:
age_group = row.age_group
status = row.marriage_status
count = row.count
if age_group not in age_distribution:
age_distribution[age_group] = {}
age_distribution[age_group][status] = count
marriage_age_stats = age_marriage_df.filter(age_marriage_df.marriage_status == "已婚").select("age").collect()
married_ages = [row.age for row in marriage_age_stats]
if married_ages:
avg_marriage_age = round(np.mean(married_ages), 2)
median_marriage_age = round(np.median(married_ages), 2)
marriage_age_std = round(np.std(married_ages), 2)
else:
avg_marriage_age = median_marriage_age = marriage_age_std = 0
gender_age_analysis = age_marriage_df.filter(age_marriage_df.marriage_status == "已婚").groupBy("gender").avg("age").collect()
gender_avg_age = {row.gender: round(row['avg(age)'], 2) for row in gender_age_analysis}
yearly_age_trends = age_marriage_df.filter(age_marriage_df.marriage_status == "已婚").groupBy("marriage_year").avg("age").orderBy("marriage_year").collect()
age_trend_data = {row.marriage_year: round(row['avg(age)'], 2) for row in yearly_age_trends}
regional_age_diff = age_marriage_df.filter(age_marriage_df.marriage_status == "已婚").groupBy("region").avg("age").collect()
regional_age_data = {row.region: round(row['avg(age)'], 2) for row in regional_age_diff}
result = {
'age_group_distribution': age_distribution,
'average_marriage_age': avg_marriage_age,
'median_marriage_age': median_marriage_age,
'marriage_age_std': marriage_age_std,
'gender_average_age': gender_avg_age,
'yearly_age_trends': age_trend_data,
'regional_age_differences': regional_age_data
}
return JsonResponse({'code': 200, 'data': result, 'message': '婚姻年龄特征分析完成'})
except Exception as e:
return JsonResponse({'code': 500, 'message': f'年龄分析过程中发生错误: {str(e)}'})
@csrf_exempt
def marriage_data_mining_analysis(request):
if request.method == 'POST':
try:
mining_df = spark.sql("SELECT age, gender, education, income, region, marriage_status, marriage_year FROM marriage_data WHERE age IS NOT NULL AND income IS NOT NULL")
correlation_data = mining_df.toPandas()
numeric_cols = ['age', 'income', 'marriage_year']
correlation_matrix = correlation_data[numeric_cols].corr().round(3).to_dict()
education_income_pattern = mining_df.groupBy("education").avg("income").collect()
edu_income_mapping = {row.education: round(row['avg(income)'], 2) for row in education_income_pattern}
sorted_edu_income = dict(sorted(edu_income_mapping.items(), key=lambda x: x[1], reverse=True))
high_income_marriage = mining_df.filter(mining_df.income > mining_df.select(spark_functions.avg("income")).collect()[0][0])
high_income_status = high_income_marriage.groupBy("marriage_status").count().collect()
high_income_distribution = {row.marriage_status: row.count for row in high_income_status}
age_education_clusters = mining_df.groupBy("age", "education").count().filter("count > 10").collect()
cluster_patterns = []
for row in age_education_clusters:
pattern = {
'age': row.age,
'education': row.education,
'frequency': row.count
}
cluster_patterns.append(pattern)
cluster_patterns.sort(key=lambda x: x['frequency'], reverse=True)
regional_marriage_patterns = mining_df.groupBy("region", "marriage_status").count().collect()
region_marriage_matrix = {}
for row in regional_marriage_patterns:
region = row.region
status = row.marriage_status
count = row.count
if region not in region_marriage_matrix:
region_marriage_matrix[region] = {}
region_marriage_matrix[region][status] = count
marriage_probability = {}
for region, status_dict in region_marriage_matrix.items():
total_in_region = sum(status_dict.values())
married_count = status_dict.get('已婚', 0)
probability = round((married_count / total_in_region) * 100, 2) if total_in_region > 0 else 0
marriage_probability[region] = probability
income_marriage_segments = mining_df.withColumn("income_segment",
spark.sql("CASE WHEN income < 5000 THEN '低收入' WHEN income < 10000 THEN '中等收入' WHEN income < 20000 THEN '高收入' ELSE '超高收入' END").alias("income_segment"))
segment_marriage_rate = income_marriage_segments.groupBy("income_segment", "marriage_status").count().collect()
income_segment_data = {}
for row in segment_marriage_rate:
segment = row.income_segment
status = row.marriage_status
count = row.count
if segment not in income_segment_data:
income_segment_data[segment] = {}
income_segment_data[segment][status] = count
result = {
'correlation_matrix': correlation_matrix,
'education_income_patterns': sorted_edu_income,
'high_income_marriage_distribution': high_income_distribution,
'age_education_clusters': cluster_patterns[:20],
'regional_marriage_probability': marriage_probability,
'income_segment_marriage_rate': income_segment_data
}
return JsonResponse({'code': 200, 'data': result, 'message': '婚姻数据挖掘分析完成'})
except Exception as e:
return JsonResponse({'code': 500, 'message': f'数据挖掘分析过程中发生错误: {str(e)}'})
六、基于大数据的我国婚姻状况数据分析与可视化系统-文档展示
七、END
- 💛💛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
- 计算机毕业设计选题
- 💕💕文末获取源码联系计算机编程果茶熊
