计算机毕设选题大数据方向：基于Django+Spark的印度健康数据可视化分析系统详解

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Django+Spark的印度健康数据可视化分析系统》是一个专门针对印度地区居民健康与生活方式数据进行深度分析的大数据应用系统。该系统采用先进的Hadoop+Spark大数据处理框架作为数据处理引擎，结合Django后端框架和Vue+ElementUI+Echarts前端技术栈，构建了一个功能完善的数据可视化分析平台。系统整合了印度地区不同年龄段、城乡区域的居民健康状况、生活方式偏好、压力水平等多维度数据，通过Spark SQL进行高效的数据查询和统计分析，利用Pandas和NumPy进行数据预处理和科学计算。平台提供年龄演变趋势分析、居民基础画像构建、健康生活方式评估、压力风险行为识别、城乡差异对比等核心分析功能，帮助用户深入理解印度地区居民的健康状况分布规律和生活方式特征。系统采用MySQL数据库存储结构化数据，通过HDFS分布式文件系统管理海量原始数据，实现了从数据采集、存储、处理到可视化展示的完整数据分析流程，为健康数据研究提供了可靠的技术支撑平台。

三、基于Django+Spark的印度健康数据可视化分析系统-视频解说

计算机毕设选题大数据方向：基于Django+Spark的印度健康数据可视化分析系统详解

四、基于Django+Spark的印度健康数据可视化分析系统-功能展示

五、基于Django+Spark的印度健康数据可视化分析系统-代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, when, desc, asc
from pyspark.sql.types import IntegerType, StringType, FloatType
from django.http import JsonResponse
from django.views import View
import pandas as pd
import numpy as np
import json

spark = SparkSession.builder.appName("IndiaHealthAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

def analyze_age_evolution_trends(request):
    health_df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/india_health_data/*.csv")
    health_df = health_df.filter(col("age").isNotNull() & col("health_status").isNotNull() & col("lifestyle_score").isNotNull())
    age_groups = health_df.withColumn("age_group", when(col("age") < 20, "0-19").when(col("age") < 30, "20-29").when(col("age") < 40, "30-39").when(col("age") < 50, "40-49").when(col("age") < 60, "50-59").otherwise("60+"))
    trend_stats = age_groups.groupBy("age_group", "gender").agg(count("*").alias("total_count"), avg("lifestyle_score").alias("avg_lifestyle"), avg("stress_level").alias("avg_stress"), sum(when(col("health_status") == "Good", 1).otherwise(0)).alias("good_health_count"))
    trend_stats = trend_stats.withColumn("health_ratio", (col("good_health_count") / col("total_count") * 100).cast(FloatType()))
    evolution_data = trend_stats.orderBy("age_group", "gender").collect()
    result_data = []
    for row in evolution_data:
        age_detail = {"age_group": row["age_group"], "gender": row["gender"], "total_population": row["total_count"], "average_lifestyle_score": round(row["avg_lifestyle"], 2), "average_stress_level": round(row["avg_stress"], 2), "good_health_percentage": round(row["health_ratio"], 2)}
        result_data.append(age_detail)
    spark.stop()
    return JsonResponse({"status": "success", "data": result_data, "message": "年龄演变趋势分析完成"})

def analyze_resident_basic_profile(request):
    profile_df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/india_health_data/*.csv")
    profile_df = profile_df.filter(col("region").isNotNull() & col("education_level").isNotNull() & col("income_level").isNotNull())
    education_stats = profile_df.groupBy("education_level").agg(count("*").alias("education_count"), avg("health_score").alias("avg_health_score"))
    income_stats = profile_df.groupBy("income_level").agg(count("*").alias("income_count"), avg("lifestyle_satisfaction").alias("avg_satisfaction"))
    regional_stats = profile_df.groupBy("region", "urban_rural").agg(count("*").alias("regional_count"), avg("bmi_index").alias("avg_bmi"), sum(when(col("exercise_frequency") >= 3, 1).otherwise(0)).alias("active_residents"))
    regional_stats = regional_stats.withColumn("active_ratio", (col("active_residents") / col("regional_count") * 100).cast(FloatType()))
    gender_age_stats = profile_df.groupBy("gender").agg(count("*").alias("gender_count"), avg("age").alias("avg_age"), avg("health_awareness").alias("avg_awareness"))
    occupation_stats = profile_df.groupBy("occupation_type").agg(count("*").alias("occupation_count"), avg("work_stress").alias("avg_work_stress"))
    education_data = [{"education": row["education_level"], "count": row["education_count"], "health_score": round(row["avg_health_score"], 2)} for row in education_stats.collect()]
    income_data = [{"income": row["income_level"], "count": row["income_count"], "satisfaction": round(row["avg_satisfaction"], 2)} for row in income_stats.collect()]
    regional_data = [{"region": row["region"], "area_type": row["urban_rural"], "count": row["regional_count"], "bmi": round(row["avg_bmi"], 2), "active_rate": round(row["active_ratio"], 2)} for row in regional_stats.collect()]
    gender_data = [{"gender": row["gender"], "count": row["gender_count"], "avg_age": round(row["avg_age"], 1), "awareness": round(row["avg_awareness"], 2)} for row in gender_age_stats.collect()]
    occupation_data = [{"occupation": row["occupation_type"], "count": row["occupation_count"], "stress": round(row["avg_work_stress"], 2)} for row in occupation_stats.collect()]
    spark.stop()
    return JsonResponse({"status": "success", "education_profile": education_data, "income_profile": income_data, "regional_profile": regional_data, "gender_profile": gender_data, "occupation_profile": occupation_data})

def analyze_stress_risk_behavior(request):
    stress_df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/india_health_data/*.csv")
    stress_df = stress_df.filter(col("stress_level").isNotNull() & col("coping_behavior").isNotNull() & col("risk_factors").isNotNull())
    high_stress_threshold = stress_df.approxQuantile("stress_level", [0.7], 0.1)[0]
    stress_categories = stress_df.withColumn("stress_category", when(col("stress_level") >= high_stress_threshold, "High").when(col("stress_level") >= (high_stress_threshold * 0.6), "Medium").otherwise("Low"))
    stress_behavior_stats = stress_categories.groupBy("stress_category", "coping_behavior").agg(count("*").alias("behavior_count"), avg("mental_health_score").alias("avg_mental_score"))
    risk_factor_analysis = stress_categories.groupBy("risk_factors").agg(count("*").alias("risk_count"), avg("stress_level").alias("avg_stress_by_risk"), sum(when(col("stress_category") == "High", 1).otherwise(0)).alias("high_stress_cases"))
    risk_factor_analysis = risk_factor_analysis.withColumn("high_stress_ratio", (col("high_stress_cases") / col("risk_count") * 100).cast(FloatType()))
    age_stress_correlation = stress_categories.groupBy("age_group", "stress_category").agg(count("*").alias("age_stress_count"))
    gender_stress_patterns = stress_categories.groupBy("gender", "stress_category").agg(count("*").alias("gender_stress_count"), avg("sleep_quality").alias("avg_sleep_quality"))
    occupation_stress_impact = stress_categories.groupBy("occupation_type").agg(count("*").alias("total_workers"), sum(when(col("stress_category") == "High", 1).otherwise(0)).alias("high_stress_workers"), avg("job_satisfaction").alias("avg_job_satisfaction"))
    occupation_stress_impact = occupation_stress_impact.withColumn("stress_prevalence", (col("high_stress_workers") / col("total_workers") * 100).cast(FloatType()))
    behavior_data = [{"stress_level": row["stress_category"], "behavior": row["coping_behavior"], "count": row["behavior_count"], "mental_score": round(row["avg_mental_score"], 2)} for row in stress_behavior_stats.collect()]
    risk_data = [{"risk_factor": row["risk_factors"], "count": row["risk_count"], "avg_stress": round(row["avg_stress_by_risk"], 2), "high_stress_rate": round(row["high_stress_ratio"], 2)} for row in risk_factor_analysis.collect()]
    age_stress_data = [{"age_group": row["age_group"], "stress_category": row["stress_category"], "count": row["age_stress_count"]} for row in age_stress_correlation.collect()]
    gender_stress_data = [{"gender": row["gender"], "stress_category": row["stress_category"], "count": row["gender_stress_count"], "sleep_quality": round(row["avg_sleep_quality"], 2)} for row in gender_stress_patterns.collect()]
    occupation_stress_data = [{"occupation": row["occupation_type"], "total": row["total_workers"], "high_stress": row["high_stress_workers"], "stress_rate": round(row["stress_prevalence"], 2), "satisfaction": round(row["avg_job_satisfaction"], 2)} for row in occupation_stress_impact.collect()]
    spark.stop()
    return JsonResponse({"status": "success", "behavior_analysis": behavior_data, "risk_factors": risk_data, "age_correlation": age_stress_data, "gender_patterns": gender_stress_data, "occupation_impact": occupation_stress_data})

六、基于Django+Spark的印度健康数据可视化分析系统-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