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
《基于大数据的世界五百强企业数据分析与可视化系统》是一个综合运用现代大数据技术栈的企业数据处理与分析平台。该系统采用Hadoop分布式存储架构和Spark大数据处理引擎作为核心技术基础，通过HDFS实现海量企业数据的可靠存储，利用Spark SQL进行高效的数据查询与分析操作。系统支持Python和Java双语言开发模式，后端分别采用Django和Spring Boot框架提供RESTful API服务。 前端基于Vue.js框架配合ElementUI组件库构建用户交互界面，通过ECharts图表库实现丰富的数据可视化效果。系统核心功能模块包括用户个人中心管理、世界五百强企业基础信息展示、企业规模多维度分析、地理分布统计分析、特殊群体企业筛选分析、行业分布趋势分析以及综合数据大屏展示等。整个系统充分发挥大数据技术在处理大规模结构化数据方面的优势，结合Pandas和NumPy科学计算库进行数据预处理与统计分析，为用户提供直观、高效的企业数据分析体验，实现从原始数据到可视化洞察的完整分析流程。
三、基于大数据的世界五百强企业数据分析与可视化系统-视频解说
大数据毕业设计推荐：世界五百强企业数据分析与可视化系统Hadoop+Spark实战开发|毕设|计算机毕设|程序开发|项目实战
四、基于大数据的世界五百强企业数据分析与可视化系统-功能展示
五、基于大数据的世界五百强企业数据分析与可视化系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, desc, asc
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("Fortune500Analysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def enterprise_scale_analysis(request):
"""企业规模分析核心业务处理"""
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/fortune500").option("dbtable", "enterprise_info").option("user", "root").option("password", "password").load()
revenue_ranges = [(0, 10000), (10000, 50000), (50000, 100000), (100000, 200000), (200000, 500000)]
scale_analysis_results = []
for min_revenue, max_revenue in revenue_ranges:
filtered_df = df.filter((col("revenue") >= min_revenue) & (col("revenue") < max_revenue))
enterprise_count = filtered_df.count()
avg_revenue = filtered_df.agg(avg("revenue")).collect()[0][0] if enterprise_count > 0 else 0
avg_employees = filtered_df.agg(avg("employees")).collect()[0][0] if enterprise_count > 0 else 0
total_market_value = filtered_df.agg(sum("market_value")).collect()[0][0] if enterprise_count > 0 else 0
scale_analysis_results.append({
"revenue_range": f"{min_revenue}-{max_revenue}百万美元",
"enterprise_count": enterprise_count,
"avg_revenue": round(avg_revenue, 2) if avg_revenue else 0,
"avg_employees": int(avg_employees) if avg_employees else 0,
"total_market_value": round(total_market_value, 2) if total_market_value else 0
})
employee_ranges = [(0, 10000), (10000, 50000), (50000, 100000), (100000, 500000), (500000, 2000000)]
for min_emp, max_emp in employee_ranges:
emp_filtered_df = df.filter((col("employees") >= min_emp) & (col("employees") < max_emp))
emp_count = emp_filtered_df.count()
emp_avg_revenue = emp_filtered_df.agg(avg("revenue")).collect()[0][0] if emp_count > 0 else 0
scale_analysis_results.append({
"employee_range": f"{min_emp}-{max_emp}人",
"enterprise_count": emp_count,
"avg_revenue": round(emp_avg_revenue, 2) if emp_avg_revenue else 0
})
return {"status": "success", "data": scale_analysis_results}
def geographical_distribution_analysis(request):
"""地理分布分析核心业务处理"""
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/fortune500").option("dbtable", "enterprise_info").option("user", "root").option("password", "password").load()
country_distribution = df.groupBy("country").agg(count("*").alias("enterprise_count"), sum("revenue").alias("total_revenue"), avg("revenue").alias("avg_revenue"), sum("employees").alias("total_employees"), avg("market_value").alias("avg_market_value")).orderBy(desc("enterprise_count"))
country_results = []
for row in country_distribution.collect():
country_results.append({
"country": row["country"],
"enterprise_count": row["enterprise_count"],
"total_revenue": round(row["total_revenue"], 2) if row["total_revenue"] else 0,
"avg_revenue": round(row["avg_revenue"], 2) if row["avg_revenue"] else 0,
"total_employees": row["total_employees"] if row["total_employees"] else 0,
"avg_market_value": round(row["avg_market_value"], 2) if row["avg_market_value"] else 0
})
continent_distribution = df.groupBy("continent").agg(count("*").alias("enterprise_count"), sum("revenue").alias("total_revenue"), avg("employees").alias("avg_employees")).orderBy(desc("total_revenue"))
continent_results = []
for row in continent_distribution.collect():
continent_results.append({
"continent": row["continent"],
"enterprise_count": row["enterprise_count"],
"total_revenue": round(row["total_revenue"], 2) if row["total_revenue"] else 0,
"avg_employees": int(row["avg_employees"]) if row["avg_employees"] else 0
})
city_distribution = df.groupBy("headquarters_city").agg(count("*").alias("enterprise_count"), avg("revenue").alias("avg_revenue")).filter(col("enterprise_count") >= 2).orderBy(desc("enterprise_count"))
city_results = [{"city": row["headquarters_city"], "enterprise_count": row["enterprise_count"], "avg_revenue": round(row["avg_revenue"], 2)} for row in city_distribution.collect()]
return {"status": "success", "data": {"countries": country_results, "continents": continent_results, "cities": city_results}}
def industry_distribution_analysis(request):
"""行业分布分析核心业务处理"""
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/fortune500").option("dbtable", "enterprise_info").option("user", "root").option("password", "password").load()
industry_analysis = df.groupBy("industry").agg(count("*").alias("enterprise_count"), sum("revenue").alias("total_revenue"), avg("revenue").alias("avg_revenue"), sum("employees").alias("total_employees"), avg("employees").alias("avg_employees"), avg("profit_margin").alias("avg_profit_margin")).orderBy(desc("total_revenue"))
industry_results = []
for row in industry_analysis.collect():
industry_results.append({
"industry": row["industry"],
"enterprise_count": row["enterprise_count"],
"total_revenue": round(row["total_revenue"], 2) if row["total_revenue"] else 0,
"avg_revenue": round(row["avg_revenue"], 2) if row["avg_revenue"] else 0,
"total_employees": row["total_employees"] if row["total_employees"] else 0,
"avg_employees": int(row["avg_employees"]) if row["avg_employees"] else 0,
"avg_profit_margin": round(row["avg_profit_margin"], 2) if row["avg_profit_margin"] else 0
})
revenue_industry_ranking = df.groupBy("industry").agg(sum("revenue").alias("industry_revenue")).orderBy(desc("industry_revenue"))
ranking_results = []
for idx, row in enumerate(revenue_industry_ranking.collect(), 1):
ranking_results.append({
"rank": idx,
"industry": row["industry"],
"industry_revenue": round(row["industry_revenue"], 2)
})
profit_analysis = df.filter(col("profit_margin").isNotNull()).groupBy("industry").agg(avg("profit_margin").alias("avg_profit"), count("*").alias("sample_count")).filter(col("sample_count") >= 3).orderBy(desc("avg_profit"))
profit_results = [{"industry": row["industry"], "avg_profit_margin": round(row["avg_profit"], 2), "sample_count": row["sample_count"]} for row in profit_analysis.collect()]
growth_rate_analysis = df.filter(col("growth_rate").isNotNull()).groupBy("industry").agg(avg("growth_rate").alias("avg_growth")).orderBy(desc("avg_growth"))
growth_results = [{"industry": row["industry"], "avg_growth_rate": round(row["avg_growth"], 2)} for row in growth_rate_analysis.collect()]
return {"status": "success", "data": {"industry_overview": industry_results, "revenue_ranking": ranking_results, "profit_analysis": profit_results, "growth_analysis": growth_results}}
六、基于大数据的世界五百强企业数据分析与可视化系统-文档展示
七、END
💕💕文末获取源码联系计算机编程果茶熊
