💖💖作者：计算机编程小咖 💙💙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 深度学习实战项目
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基于大数据的国内各省高校数据分析可视化系统介绍
《基于大数据的国内各省高校数据分析可视化系统》是一套面向计算机专业毕业设计的大数据分析项目，系统采用Hadoop+Spark大数据处理框架作为核心技术架构，利用HDFS分布式文件系统存储海量高校数据，通过Spark SQL进行高效的数据查询与分析处理，结合Pandas和NumPy进行深度数据挖掘与统计计算。系统提供Python+Django和Java+Spring Boot两种后端实现版本，前端采用Vue+ElementUI构建交互界面，集成Echarts图表库实现数据可视化展示，同时运用HTML、CSS、JavaScript、jQuery等技术优化用户体验，数据持久化采用MySQL关系型数据库。功能模块涵盖首页总览、各省高校数据查询、高校属性结构分析、综合实力与特色挖掘、区域与类型交叉分析、空间分布分析、数据大屏可视化以及系统公告等八大核心板块，能够多维度展现全国各省高校的办学层次、学科特色、地域分布等关键信息，通过Spark分布式计算实现大规模数据的快速处理与实时分析，借助Echarts动态图表将复杂的统计结果以柱状图、饼图、地图、折线图等形式直观呈现，为高等教育数据分析提供完整的技术解决方案，整个系统从大数据采集、存储、处理到可视化展示形成完整的技术闭环,充分体现了大数据技术在教育领域数据分析中的实际应用价值。
基于大数据的国内各省高校数据分析可视化系统演示视频
基于大数据的国内各省高校数据分析可视化系统演示图片
基于大数据的国内各省高校数据分析可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, when, desc, row_number, dense_rank
from pyspark.sql.window import Window
import pandas as pd
import numpy as np
def analyze_university_attribute_structure(province=None):
spark = SparkSession.builder.appName("UniversityAttributeAnalysis").master("local[*]").config("spark.sql.shuffle.partitions", "4").getOrCreate()
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/university_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "university_info").option("user", "root").option("password", "123456").load()
if province:
df = df.filter(col("province") == province)
attribute_stats = df.groupBy("university_type").agg(count("*").alias("total_count"),avg("school_level").alias("avg_level"),sum(when(col("is_985") == 1, 1).otherwise(0)).alias("count_985"),sum(when(col("is_211") == 1, 1).otherwise(0)).alias("count_211"),sum(when(col("is_double_first_class") == 1, 1).otherwise(0)).alias("count_double_first"))
attribute_stats = attribute_stats.withColumn("percentage", (col("total_count") / df.count() * 100))
attribute_stats = attribute_stats.withColumn("quality_score", (col("count_985") * 10 + col("count_211") * 5 + col("count_double_first") * 3) / col("total_count"))
attribute_stats = attribute_stats.orderBy(desc("quality_score"))
type_detail_df = df.groupBy("university_type", "province").agg(count("*").alias("province_count"))
window_spec = Window.partitionBy("university_type").orderBy(desc("province_count"))
type_detail_df = type_detail_df.withColumn("rank", row_number().over(window_spec))
top_provinces = type_detail_df.filter(col("rank") <= 5)
result_pandas = attribute_stats.toPandas()
detail_pandas = top_provinces.toPandas()
result_dict = {"attribute_distribution": result_pandas.to_dict(orient="records"),"top_provinces_by_type": detail_pandas.to_dict(orient="records"),"total_universities": int(df.count()),"analysis_province": province if province else "全国"}
spark.stop()
return result_dict
def comprehensive_strength_mining(min_score=0, region=None):
spark = SparkSession.builder.appName("ComprehensiveStrengthMining").master("local[*]").config("spark.sql.shuffle.partitions", "4").getOrCreate()
university_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/university_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "university_info").option("user", "root").option("password", "123456").load()
discipline_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/university_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "discipline_info").option("user", "root").option("password", "123456").load()
joined_df = university_df.join(discipline_df, university_df.university_id == discipline_df.university_id, "left")
if region:
joined_df = joined_df.filter(col("region") == region)
strength_df = joined_df.groupBy("university_info.university_id", "university_name", "province", "university_type").agg(count("discipline_id").alias("discipline_count"),sum(when(col("discipline_level") == "A+", 1).otherwise(0)).alias("a_plus_count"),sum(when(col("discipline_level").isin(["A+", "A", "A-"]), 1).otherwise(0)).alias("a_level_count"),avg("discipline_score").alias("avg_discipline_score"))
strength_df = strength_df.withColumn("strength_score", col("a_plus_count") * 15 + col("a_level_count") * 8 + col("discipline_count") * 2 + col("avg_discipline_score") * 0.5)
strength_df = strength_df.withColumn("is_985_bonus", when(col("is_985") == 1, 20).otherwise(0))
strength_df = strength_df.withColumn("is_211_bonus", when(col("is_211") == 1, 10).otherwise(0))
strength_df = strength_df.withColumn("final_score", col("strength_score") + col("is_985_bonus") + col("is_211_bonus"))
strength_df = strength_df.filter(col("final_score") >= min_score)
window_spec = Window.orderBy(desc("final_score"))
strength_df = strength_df.withColumn("national_rank", dense_rank().over(window_spec))
province_window = Window.partitionBy("province").orderBy(desc("final_score"))
strength_df = strength_df.withColumn("province_rank", dense_rank().over(province_window))
feature_df = strength_df.withColumn("feature_tag", when(col("a_plus_count") >= 5, "学科优势突出").when(col("discipline_count") >= 50, "学科门类齐全").when(col("avg_discipline_score") >= 80, "整体水平优秀").otherwise("特色发展"))
result_pandas = feature_df.orderBy(desc("final_score")).limit(100).toPandas()
top_by_province = feature_df.filter(col("province_rank") <= 3).toPandas()
result_dict = {"top_universities": result_pandas.to_dict(orient="records"),"top_by_province": top_by_province.to_dict(orient="records"),"total_analyzed": int(feature_df.count()),"avg_national_score": float(feature_df.agg(avg("final_score")).collect()[0][0])}
spark.stop()
return result_dict
def region_type_cross_analysis(target_region=None, target_type=None):
spark = SparkSession.builder.appName("RegionTypeCrossAnalysis").master("local[*]").config("spark.sql.shuffle.partitions", "4").getOrCreate()
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/university_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "university_info").option("user", "root").option("password", "123456").load()
cross_stats = df.groupBy("region", "university_type").agg(count("*").alias("university_count"),avg("enrollment_number").alias("avg_enrollment"),sum("total_students").alias("total_students"),avg("faculty_count").alias("avg_faculty"),sum(when(col("is_985") == 1, 1).otherwise(0)).alias("elite_985_count"),sum(when(col("is_211") == 1, 1).otherwise(0)).alias("elite_211_count"))
cross_stats = cross_stats.withColumn("student_faculty_ratio", col("total_students") / col("avg_faculty"))
cross_stats = cross_stats.withColumn("elite_ratio", (col("elite_985_count") + col("elite_211_count")) / col("university_count") * 100)
region_total = df.groupBy("region").agg(count("*").alias("region_total"))
type_total = df.groupBy("university_type").agg(count("*").alias("type_total"))
cross_with_total = cross_stats.join(region_total, "region").join(type_total, "university_type")
cross_with_total = cross_with_total.withColumn("region_proportion", col("university_count") / col("region_total") * 100)
cross_with_total = cross_with_total.withColumn("type_proportion", col("university_count") / col("type_total") * 100)
if target_region:
cross_with_total = cross_with_total.filter(col("region") == target_region)
if target_type:
cross_with_total = cross_with_total.filter(col("university_type") == target_type)
cross_with_total = cross_with_total.orderBy(desc("university_count"))
region_comparison = df.groupBy("region").agg(count("*").alias("total_count"),avg(when(col("is_985") == 1, 1).otherwise(0)).alias("avg_985_rate"),avg(when(col("is_211") == 1, 1).otherwise(0)).alias("avg_211_rate"))
region_window = Window.orderBy(desc("total_count"))
region_comparison = region_comparison.withColumn("region_rank", dense_rank().over(region_window))
result_pandas = cross_with_total.toPandas()
region_comp_pandas = region_comparison.toPandas()
result_dict = {"cross_analysis_data": result_pandas.to_dict(orient="records"),"region_comparison": region_comp_pandas.to_dict(orient="records"),"total_combinations": int(cross_with_total.count()),"analysis_scope": {"region": target_region if target_region else "全部区域","type": target_type if target_type else "全部类型"}}
spark.stop()
return result_dict
基于大数据的国内各省高校数据分析可视化系统文档展示
💖💖作者：计算机编程小咖 💙💙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 深度学习实战项目
