前言
💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 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 计算机毕业设计选题 💜💜
一.开发工具简介
大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL
二.系统内容简介
基于大数据的人口普查收入数据分析与可视化系统是一个综合性的数据处理与分析平台，采用Hadoop+Spark大数据处理架构，实现对人口普查收入数据的深度挖掘和多维度分析。系统通过Spark SQL进行大规模数据处理，结合Pandas和NumPy进行统计分析，利用ECharts和Vue框架构建交互式可视化界面。系统涵盖用户管理、收入数据管理、人口结构特征分析、工作特征收入分析、教育回报差异分析、婚姻家庭角色分析、用户资本收益分析等七大功能模块，并提供可视化大屏展示。后端支持Django和Spring Boot双框架实现，前端采用Vue+ElementUI构建现代化用户界面，数据存储基于MySQL数据库，整体架构能够处理海量人口普查数据，为收入分配研究和社会经济分析提供有力的技术支撑和数据洞察。
三.系统功能演示
大数据毕业设计推荐：基于Hadoop+Spark的人口普查收入数据分析与可视化系统
四.系统界面展示
五.系统源码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, sum, count, desc, asc
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json
spark = SparkSession.builder.appName("PopulationIncomeAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def population_structure_analysis(request):
"""人口结构特征分析核心处理函数"""
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/population_db").option("dbtable", "income_data").option("user", "root").option("password", "password").load()
age_groups = df.withColumn("age_group",
when(col("age") < 25, "18-24")
.when(col("age") < 35, "25-34")
.when(col("age") < 45, "35-44")
.when(col("age") < 55, "45-54")
.otherwise("55+"))
age_income_stats = age_groups.groupBy("age_group").agg(
avg("income").alias("avg_income"),
count("*").alias("population_count"),
sum("income").alias("total_income")).orderBy("age_group")
gender_analysis = df.groupBy("gender").agg(
avg("income").alias("avg_income"),
count("*").alias("count")).collect()
region_stats = df.groupBy("region").agg(
avg("income").alias("avg_income"),
sum("income").alias("total_income")).orderBy(desc("avg_income")).collect()
age_data = [{"age_group": row.age_group, "avg_income": float(row.avg_income), "population": row.population_count} for row in age_income_stats.collect()]
gender_data = [{"gender": row.gender, "avg_income": float(row.avg_income), "count": row.count} for row in gender_analysis]
region_data = [{"region": row.region, "avg_income": float(row.avg_income), "total_income": float(row.total_income)} for row in region_stats]
income_distribution = df.select("income").rdd.map(lambda x: x[0]).collect()
percentiles = np.percentile(income_distribution, [25, 50, 75, 90, 95])
distribution_stats = {"p25": float(percentiles[0]), "median": float(percentiles[1]), "p75": float(percentiles[2]), "p90": float(percentiles[3]), "p95": float(percentiles[4])}
result = {"age_analysis": age_data, "gender_analysis": gender_data, "region_analysis": region_data, "distribution_stats": distribution_stats}
return JsonResponse(result)
def work_income_analysis(request):
"""工作特征收入分析核心处理函数"""
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/population_db").option("dbtable", "income_data").option("user", "root").option("password", "password").load()
industry_analysis = df.groupBy("industry").agg(
avg("income").alias("avg_income"),
count("*").alias("worker_count"),
sum("income").alias("total_income")).orderBy(desc("avg_income"))
occupation_stats = df.groupBy("occupation").agg(
avg("income").alias("avg_income"),
count("*").alias("count")).orderBy(desc("avg_income")).limit(20)
work_experience_groups = df.withColumn("experience_group",
when(col("work_years") < 3, "0-2年")
.when(col("work_years") < 6, "3-5年")
.when(col("work_years") < 11, "6-10年")
.otherwise("10年以上"))
experience_income = work_experience_groups.groupBy("experience_group").agg(
avg("income").alias("avg_income"),
count("*").alias("count")).collect()
employment_type_analysis = df.groupBy("employment_type").agg(
avg("income").alias("avg_income"),
count("*").alias("count")).collect()
company_size_stats = df.groupBy("company_size").agg(
avg("income").alias("avg_income"),
count("*").alias("count")).orderBy(desc("avg_income")).collect()
work_hour_correlation = df.select("work_hours", "income").toPandas()
correlation_coeff = work_hour_correlation.corr().iloc[0, 1]
industry_data = [{"industry": row.industry, "avg_income": float(row.avg_income), "worker_count": row.worker_count, "total_income": float(row.total_income)} for row in industry_analysis.collect()]
occupation_data = [{"occupation": row.occupation, "avg_income": float(row.avg_income), "count": row.count} for row in occupation_stats.collect()]
experience_data = [{"experience": row.experience_group, "avg_income": float(row.avg_income), "count": row.count} for row in experience_income]
result = {"industry_analysis": industry_data, "occupation_analysis": occupation_data, "experience_analysis": experience_data, "work_hour_correlation": float(correlation_coeff)}
return JsonResponse(result)
def education_return_analysis(request):
"""教育回报差异分析核心处理函数"""
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/population_db").option("dbtable", "income_data").option("user", "root").option("password", "password").load()
education_income_stats = df.groupBy("education_level").agg(
avg("income").alias("avg_income"),
count("*").alias("count"),
sum("income").alias("total_income")).orderBy(desc("avg_income"))
education_age_analysis = df.groupBy("education_level", "age_group").agg(
avg("income").alias("avg_income")).collect()
major_income_ranking = df.filter(col("education_level").isin(["本科", "硕士", "博士"])).groupBy("major").agg(
avg("income").alias("avg_income"),
count("*").alias("graduate_count")).filter(col("graduate_count") >= 10).orderBy(desc("avg_income")).limit(30)
education_gender_gap = df.groupBy("education_level", "gender").agg(
avg("income").alias("avg_income")).collect()
gender_gap_data = {}
for row in education_gender_gap:
edu_level = row.education_level
if edu_level not in gender_gap_data:
gender_gap_data[edu_level] = {}
gender_gap_data[edu_level][row.gender] = float(row.avg_income)
education_roi_calculation = df.select("education_level", "education_years", "income").toPandas()
education_roi = education_roi_calculation.groupby("education_level").agg({"income": "mean", "education_years": "mean"}).reset_index()
education_roi["roi_per_year"] = (education_roi["income"] - education_roi_calculation[education_roi_calculation["education_level"] == "高中"]["income"].mean()) / education_roi["education_years"]
school_ranking_analysis = df.groupBy("school_rank").agg(
avg("income").alias("avg_income"),
count("*").alias("count")).orderBy("school_rank").collect()
education_data = [{"education": row.education_level, "avg_income": float(row.avg_income), "count": row.count, "total_income": float(row.total_income)} for row in education_income_stats.collect()]
major_data = [{"major": row.major, "avg_income": float(row.avg_income), "graduate_count": row.graduate_count} for row in major_income_ranking.collect()]
school_data = [{"school_rank": row.school_rank, "avg_income": float(row.avg_income), "count": row.count} for row in school_ranking_analysis]
result = {"education_stats": education_data, "major_ranking": major_data, "gender_gap": gender_gap_data, "school_ranking": school_data}
return JsonResponse(result)
六.系统文档展示
结束
💛💛想说的话：感谢大家的关注与支持！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 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 计算机毕业设计选题 💜💜
