90%计算机专业认可：历届奥运会数据可视化系统Vue+ElementUI完整实现

💖💖作者：计算机毕业设计小途
💙💙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！
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基于Python的历届奥运会数据可视化分析系统介绍

基于Python的历届奥运会数据可视化分析系统是一套采用现代化技术栈开发的综合性数据分析平台，该系统支持Java和Python双语言开发环境，后端分别采用Spring Boot和Django框架实现，前端基于Vue框架结合ElementUI组件库构建用户界面，数据存储采用MySQL数据库，整体采用B/S架构模式部署。系统核心功能围绕历届奥运会数据的收集、整理、分析和可视化展示展开，提供了系统首页作为数据总览入口，通过数据看板模块实现奥运会关键指标的图表化展示，历届奥运会模块详细记录了各届奥运会的基本信息和统计数据，运动员数据模块则深入分析了参赛选手的成绩分布、国别统计等多维度信息。系统还配备了完善的内容管理功能，包括轮播图管理用于首页展示内容的动态更新，公告资讯分类和公告资讯模块支持系统公告和奥运相关资讯的发布管理，用户管理和个人中心模块提供了用户权限控制和个性化设置功能，系统管理模块则确保了平台的稳定运行和数据安全。整个系统通过数据可视化技术将复杂的奥运会历史数据转化为直观的图表和分析报告，为用户提供了一个全面了解奥运会发展历程和数据趋势的专业平台，适合作为计算机专业学生的毕业设计项目，展现了数据分析、Web开发和可视化技术的综合应用能力。

基于Python的历届奥运会数据可视化分析系统演示视频

基于Python的历届奥运会数据可视化分析系统演示图片

基于Python的历届奥运会数据可视化分析系统代码展示

spark = SparkSession.builder().appName("OlympicsDataAnalysis").master("local[*]").getOrCreate()
# 数据看板核心处理函数
total_olympics = Olympics.objects.count()
total_athletes = Athlete.objects.count()
total_countries = Athlete.objects.values('country').distinct().count()
recent_olympics = Olympics.objects.filter(year__gte=2000).order_by('-year')[:5]
medal_stats = {}
for olympics in recent_olympics:
   gold_count = Athlete.objects.filter(olympics=olympics, medal_type='Gold').count()
   silver_count = Athlete.objects.filter(olympics=olympics, medal_type='Silver').count()
   bronze_count = Athlete.objects.filter(olympics=olympics, medal_type='Bronze').count()
   medal_stats[olympics.year] = {'gold': gold_count, 'silver': silver_count, 'bronze': bronze_count}
country_performance = Athlete.objects.values('country').annotate(medal_count=Count('id')).order_by('-medal_count')[:10]
sport_distribution = Athlete.objects.values('sport').annotate(athlete_count=Count('id')).order_by('-athlete_count')[:15]
gender_stats = Athlete.objects.values('gender').annotate(count=Count('id'))
age_distribution = Athlete.objects.values('age').annotate(count=Count('id')).order_by('age')
dashboard_data = {
   'total_olympics': total_olympics,
   'total_athletes': total_athletes,
   'total_countries': total_countries,
   'medal_stats': medal_stats,
   'country_performance': list(country_performance),
   'sport_distribution': list(sport_distribution),
   'gender_stats': list(gender_stats),
   'age_distribution': list(age_distribution)
}
spark.read().format("jdbc").option("url", "jdbc:mysql://localhost:3306/olympics").option("dbtable", "olympics_data").option("user", "root").option("password", "password").load().show()
spark.stop()
# 历届奥运会数据管理核心处理函数
olympics_queryset = Olympics.objects.all()
if year_filter:
   olympics_queryset = olympics_queryset.filter(year=year_filter)
if season_filter:
   olympics_queryset = olympics_queryset.filter(season=season_filter)
if host_city_filter:
   olympics_queryset = olympics_queryset.filter(host_city__icontains=host_city_filter)
olympics_list = olympics_queryset.order_by('-year')
for olympics in olympics_list:
   participant_count = Athlete.objects.filter(olympics=olympics).count()
   country_count = Athlete.objects.filter(olympics=olympics).values('country').distinct().count()
   sport_count = Athlete.objects.filter(olympics=olympics).values('sport').distinct().count()
   medal_winners = Athlete.objects.filter(olympics=olympics).exclude(medal_type='').count()
   olympics.participant_count = participant_count
   olympics.country_count = country_count
   olympics.sport_count = sport_count
   olympics.medal_winners = medal_winners
   if olympics.year >= 1992:
       olympics.modern_era = True
   else:
       olympics.modern_era = False
olympics_data = []
for olympics in olympics_list:
   olympics_info = {
       'id': olympics.id,
       'year': olympics.year,
       'season': olympics.season,
       'host_city': olympics.host_city,
       'host_country': olympics.host_country,
       'participant_count': olympics.participant_count,
       'country_count': olympics.country_count,
       'sport_count': olympics.sport_count,
       'medal_winners': olympics.medal_winners,
       'modern_era': olympics.modern_era
   }
   olympics_data.append(olympics_info)
spark.read().format("jdbc").option("url", "jdbc:mysql://localhost:3306/olympics").option("dbtable", "olympics_games").option("user", "root").option("password", "password").load().show()
spark.stop()
# 运动员数据分析核心处理函数
athletes_queryset = Athlete.objects.all()
if name_filter:
   athletes_queryset = athletes_queryset.filter(name__icontains=name_filter)
if country_filter:
   athletes_queryset = athletes_queryset.filter(country=country_filter)
if sport_filter:
   athletes_queryset = athletes_queryset.filter(sport=sport_filter)
if medal_filter:
   athletes_queryset = athletes_queryset.filter(medal_type=medal_filter)
if gender_filter:
   athletes_queryset = athletes_queryset.filter(gender=gender_filter)
athletes_list = athletes_queryset.order_by('name')
for athlete in athletes_list:
   olympics_participated = Olympics.objects.filter(athlete=athlete).count()
   total_medals = Athlete.objects.filter(name=athlete.name, country=athlete.country).exclude(medal_type='').count()
   gold_medals = Athlete.objects.filter(name=athlete.name, country=athlete.country, medal_type='Gold').count()
   silver_medals = Athlete.objects.filter(name=athlete.name, country=athlete.country, medal_type='Silver').count()
   bronze_medals = Athlete.objects.filter(name=athlete.name, country=athlete.country, medal_type='Bronze').count()
   athlete.olympics_participated = olympics_participated
   athlete.total_medals = total_medals
   athlete.gold_medals = gold_medals
   athlete.silver_medals = silver_medals
   athlete.bronze_medals = bronze_medals
   if athlete.age:
       if athlete.age < 20:
           athlete.age_group = 'Youth'
       elif athlete.age < 30:
           athlete.age_group = 'Adult'
       else:
           athlete.age_group = 'Veteran'
   else:
       athlete.age_group = 'Unknown'
athletes_data = []
for athlete in athletes_list:
   athlete_info = {
       'id': athlete.id,
       'name': athlete.name,
       'country': athlete.country,
       'sport': athlete.sport,
       'event': athlete.event,
       'medal_type': athlete.medal_type,
       'gender': athlete.gender,
       'age': athlete.age,
       'age_group': athlete.age_group,
       'olympics_participated': athlete.olympics_participated,
       'total_medals': athlete.total_medals,
       'gold_medals': athlete.gold_medals,
       'silver_medals': athlete.silver_medals,
       'bronze_medals': athlete.bronze_medals
   }
   athletes_data.append(athlete_info)
spark.read().format("jdbc").option("url", "jdbc:mysql://localhost:3306/olympics").option("dbtable", "athletes_data").option("user", "root").option("password", "password").load().show()
spark.stop()

基于Python的历届奥运会数据可视化分析系统文档展示

💖💖作者：计算机毕业设计小途
💙💙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
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