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
《近8年软科中国大学排名数据可视化分析系统》是一个基于大数据技术的高等教育分析平台，采用Hadoop+Spark分布式计算架构处理海量教育数据，通过Django后端框架和Vue前端技术栈构建完整的Web应用系统。系统核心功能涵盖用户管理、软科中国大学排名数据管理、高校个体发展轨迹分析、排名总体趋势分析、区域分布特征分析以及高校类型层次分类分析等模块。通过集成ElementUI组件库和Echarts可视化引擎，系统能够将复杂的排名数据转化为直观的图表展示，支持多维度数据筛选和动态交互分析。系统运用Spark SQL进行大规模数据查询优化，结合Pandas和NumPy进行数据处理和统计分析，实现了从数据采集、存储、计算到可视化展示的完整数据处理链路，为教育管理部门、高校决策者以及教育研究人员提供了一个功能完善的数据分析工具平台。
三.系统功能演示
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, desc, asc, count, avg, sum, max, min, when, rank, dense_rank
from pyspark.sql.window import Window
from django.http import JsonResponse
from django.views.decorators.http import require_http_methods
import pandas as pd
import numpy as np
from datetime import datetime
import json
spark = SparkSession.builder.appName("UniversityRankingAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@require_http_methods(["POST"])
def university_development_analysis(request):
data = json.loads(request.body)
university_id = data.get('university_id')
start_year = data.get('start_year', 2016)
end_year = data.get('end_year', 2023)
ranking_df = spark.sql(f"SELECT year, university_name, overall_rank, total_score, teaching_score, research_score, citation_score, industry_score, international_score FROM university_rankings WHERE university_id = {university_id} AND year BETWEEN {start_year} AND {end_year}")
ranking_pandas = ranking_df.toPandas()
trend_analysis = {}
if len(ranking_pandas) > 1:
rank_trend = np.polyfit(ranking_pandas['year'], ranking_pandas['overall_rank'], 1)
trend_analysis['rank_slope'] = float(rank_trend[0])
trend_analysis['rank_direction'] = 'improving' if rank_trend[0] < 0 else 'declining'
score_columns = ['total_score', 'teaching_score', 'research_score', 'citation_score', 'industry_score', 'international_score']
for col_name in score_columns:
if col_name in ranking_pandas.columns:
col_data = ranking_pandas[col_name].dropna()
if len(col_data) > 0:
trend_analysis[f'{col_name}_avg'] = float(col_data.mean())
trend_analysis[f'{col_name}_std'] = float(col_data.std())
trend_analysis[f'{col_name}_max'] = float(col_data.max())
trend_analysis[f'{col_name}_min'] = float(col_data.min())
year_over_year_changes = []
for i in range(1, len(ranking_pandas)):
prev_row = ranking_pandas.iloc[i-1]
curr_row = ranking_pandas.iloc[i]
change_data = {
'year': int(curr_row['year']),
'rank_change': int(prev_row['overall_rank'] - curr_row['overall_rank']),
'score_change': float(curr_row['total_score'] - prev_row['total_score']) if pd.notna(curr_row['total_score']) and pd.notna(prev_row['total_score']) else 0
}
year_over_year_changes.append(change_data)
performance_indicators = {}
performance_indicators['stability_index'] = float(1 / (1 + ranking_pandas['overall_rank'].std())) if ranking_pandas['overall_rank'].std() > 0 else 1.0
performance_indicators['improvement_count'] = len([x for x in year_over_year_changes if x['rank_change'] > 0])
performance_indicators['decline_count'] = len([x for x in year_over_year_changes if x['rank_change'] < 0])
performance_indicators['best_year'] = int(ranking_pandas.loc[ranking_pandas['overall_rank'].idxmin(), 'year']) if not ranking_pandas.empty else None
performance_indicators['worst_year'] = int(ranking_pandas.loc[ranking_pandas['overall_rank'].idxmax(), 'year']) if not ranking_pandas.empty else None
return JsonResponse({
'status': 'success',
'data': {
'ranking_data': ranking_pandas.to_dict('records'),
'trend_analysis': trend_analysis,
'year_over_year_changes': year_over_year_changes,
'performance_indicators': performance_indicators
}
})
@require_http_methods(["GET"])
def overall_ranking_analysis(request):
year = request.GET.get('year', 2023)
region = request.GET.get('region', 'all')
university_type = request.GET.get('type', 'all')
base_query = f"SELECT * FROM university_rankings WHERE year = {year}"
if region != 'all':
base_query += f" AND region = '{region}'"
if university_type != 'all':
base_query += f" AND university_type = '{university_type}'"
ranking_df = spark.sql(base_query)
total_universities = ranking_df.count()
rank_distribution = ranking_df.groupBy(
when(col("overall_rank") <= 50, "Top 50")
.when(col("overall_rank") <= 100, "51-100")
.when(col("overall_rank") <= 200, "101-200")
.when(col("overall_rank") <= 300, "201-300")
.otherwise("300+").alias("rank_tier")
).count().collect()
tier_distribution = {row['rank_tier']: row['count'] for row in rank_distribution}
region_analysis = ranking_df.groupBy("region").agg(
count("*").alias("university_count"),
avg("overall_rank").alias("avg_rank"),
min("overall_rank").alias("best_rank"),
max("overall_rank").alias("worst_rank"),
avg("total_score").alias("avg_score")
).collect()
region_stats = []
for row in region_analysis:
region_stats.append({
'region': row['region'],
'university_count': row['university_count'],
'average_rank': round(float(row['avg_rank']), 2),
'best_rank': row['best_rank'],
'worst_rank': row['worst_rank'],
'average_score': round(float(row['avg_score']), 2) if row['avg_score'] else 0
})
type_analysis = ranking_df.groupBy("university_type").agg(
count("*").alias("university_count"),
avg("overall_rank").alias("avg_rank"),
avg("total_score").alias("avg_score")
).collect()
type_stats = []
for row in type_analysis:
type_stats.append({
'type': row['university_type'],
'university_count': row['university_count'],
'average_rank': round(float(row['avg_rank']), 2),
'average_score': round(float(row['avg_score']), 2) if row['avg_score'] else 0
})
score_analysis = ranking_df.select([avg(col).alias(f"avg_{col}") for col in ["teaching_score", "research_score", "citation_score", "industry_score", "international_score"]]).collect()[0]
score_breakdown = {
'teaching': round(float(score_analysis['avg_teaching_score']), 2) if score_analysis['avg_teaching_score'] else 0,
'research': round(float(score_analysis['avg_research_score']), 2) if score_analysis['avg_research_score'] else 0,
'citation': round(float(score_analysis['avg_citation_score']), 2) if score_analysis['avg_citation_score'] else 0,
'industry': round(float(score_analysis['avg_industry_score']), 2) if score_analysis['avg_industry_score'] else 0,
'international': round(float(score_analysis['avg_international_score']), 2) if score_analysis['avg_international_score'] else 0
}
return JsonResponse({
'status': 'success',
'data': {
'total_universities': total_universities,
'tier_distribution': tier_distribution,
'region_statistics': region_stats,
'type_statistics': type_stats,
'score_breakdown': score_breakdown
}
})
@require_http_methods(["GET"])
def regional_distribution_analysis(request):
start_year = request.GET.get('start_year', 2016)
end_year = request.GET.get('end_year', 2023)
analysis_type = request.GET.get('analysis_type', 'count')
regional_data = spark.sql(f"SELECT year, region, university_type, overall_rank, total_score FROM university_rankings WHERE year BETWEEN {start_year} AND {end_year}")
yearly_regional_counts = regional_data.groupBy("year", "region").count().orderBy("year", "region").collect()
regional_evolution = {}
for row in yearly_regional_counts:
year = row['year']
region = row['region']
count = row['count']
if year not in regional_evolution:
regional_evolution[year] = {}
regional_evolution[year][region] = count
regional_performance = regional_data.groupBy("region").agg(
count("*").alias("total_universities"),
avg("overall_rank").alias("avg_rank"),
min("overall_rank").alias("best_rank"),
max("overall_rank").alias("worst_rank"),
avg("total_score").alias("avg_score"),
sum(when(col("overall_rank") <= 100, 1).otherwise(0)).alias("top100_count"),
sum(when(col("overall_rank") <= 50, 1).otherwise(0)).alias("top50_count")
).collect()
performance_metrics = []
for row in regional_performance:
performance_metrics.append({
'region': row['region'],
'total_universities': row['total_universities'],
'average_rank': round(float(row['avg_rank']), 2),
'best_rank': row['best_rank'],
'worst_rank': row['worst_rank'],
'average_score': round(float(row['avg_score']), 2) if row['avg_score'] else 0,
'top100_count': row['top100_count'],
'top50_count': row['top50_count'],
'top100_ratio': round(float(row['top100_count']) / float(row['total_universities']) * 100, 2),
'excellence_index': round((float(row['top50_count']) * 2 + float(row['top100_count'])) / float(row['total_universities']), 2)
})
regional_trends = {}
for region_data in regional_data.select("region").distinct().collect():
region = region_data['region']
region_yearly = regional_data.filter(col("region") == region).groupBy("year").agg(
count("*").alias("count"),
avg("overall_rank").alias("avg_rank"),
avg("total_score").alias("avg_score")
).orderBy("year").collect()
yearly_data = []
for year_row in region_yearly:
yearly_data.append({
'year': year_row['year'],
'count': year_row['count'],
'avg_rank': round(float(year_row['avg_rank']), 2),
'avg_score': round(float(year_row['avg_score']), 2) if year_row['avg_score'] else 0
})
if len(yearly_data) > 1:
rank_trend = np.polyfit([x['year'] for x in yearly_data], [x['avg_rank'] for x in yearly_data], 1)
regional_trends[region] = {
'yearly_data': yearly_data,
'rank_trend_slope': float(rank_trend[0]),
'trend_direction': 'improving' if rank_trend[0] < 0 else 'declining'
}
geographic_concentration = {}
total_top100 = regional_data.filter(col("overall_rank") <= 100).count()
for region_data in regional_data.select("region").distinct().collect():
region = region_data['region']
region_top100 = regional_data.filter((col("region") == region) & (col("overall_rank") <= 100)).count()
geographic_concentration[region] = {
'top100_count': region_top100,
'concentration_ratio': round(float(region_top100) / float(total_top100) * 100, 2) if total_top100 > 0 else 0
}
return JsonResponse({
'status': 'success',
'data': {
'regional_evolution': regional_evolution,
'performance_metrics': performance_metrics,
'regional_trends': regional_trends,
'geographic_concentration': geographic_concentration
}
})
六.系统文档展示
结束
💕💕文末获取源码联系 计算机程序员小杨
