✨作者主页IT毕设梦工厂✨个人简介曾从事计算机专业培训教学擅长Java、Python、PHP、.NET、Node.js、GO、微信小程序、安卓Android等项目实战。接项目定制开发、代码讲解、答辩教学、文档编写、降重等。☑文末获取源码☑精彩专栏推荐⬇⬇⬇Java项目Python项目安卓项目微信小程序项目文章目录一、前言二、开发环境三、系统界面展示四、部分代码设计五、论文参考六、系统视频结语一、前言《基于大数据的广西医疗机构及药店清单数据分析与可视化》这套系统围绕广西医疗机构和药店清单数据展开用Hadoop和Spark作为大数据处理底座通过HDFS读取原始清单再用Spark SQL、Pandas、NumPy完成清洗、分组、聚合和指标计算。系统把城市分布、机构类型、等级结构、药店服务、医保覆盖和资源画像等分析结果落到MySQL由Django或Spring Boot提供数据接口前端用Vue、ElementUI、Echarts等展示系统首页、大屏可视化、药店信息、医疗机构信息、个人信息和修改密码等页面。使用者可以在大屏上查看广西各城市医疗机构与药店的数量差异按机构类型和等级观察结构特征结合医保覆盖和药店服务情况理解区域资源画像让原本分散在表格里的清单数据变成可比较、可筛选、可直观阅读的图表。系统重点不在复杂业务而在把大数据处理链路、数据分析和可视化展示串成一套完整流程适合作为计算机专业大四毕业设计的实践项目。二、开发环境大数据框架HadoopSpark本次没用Hive支持定制开发语言PythonJava两个版本都支持后端框架DjangoSpring Boot(SpringSpringMVCMybatis)两个版本都支持前端VueElementUIEchartsHTMLCSSJavaScriptjQuery详细技术点Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy数据库MySQL三、系统界面展示基于大数据的广西医疗机构及药店清单数据分析与可视化系统界面展示四、部分代码设计项目实战-代码参考sparkSparkSession.builder.appName(GuangxiMedicalPharmacyBigData).master(local[*]).config(spark.sql.shuffle.partitions,8).getOrCreate()defcity_distribution_analysis():medical_dfspark.read.option(header,true).csv(hdfs:///guangxi/medical_institutions.csv)pharmacy_dfspark.read.option(header,true).csv(hdfs:///guangxi/pharmacies.csv)medical_df.createOrReplaceTempView(medical_institutions)pharmacy_df.createOrReplaceTempView(pharmacies)city_medicalspark.sql(SELECT city, COUNT(*) AS medical_count FROM medical_institutions GROUP BY city)city_pharmacyspark.sql(SELECT city, COUNT(*) AS pharmacy_count FROM pharmacies GROUP BY city)city_medical.createOrReplaceTempView(city_medical)city_pharmacy.createOrReplaceTempView(city_pharmacy)city_resultspark.sql(SELECT m.city, m.medical_count, p.pharmacy_count, m.medical_count p.pharmacy_count AS total_resource, ROUND(p.pharmacy_count / m.medical_count, 2) AS pharmacy_medical_ratio FROM city_medical m JOIN city_pharmacy p ON m.city p.city ORDER BY total_resource DESC)pandas_citycity_result.toPandas()pandas_city[resource_rank]pandas_city[total_resource].rank(ascendingFalse,methodmin)pandas_city[density_index]numpy.round(pandas_city[total_resource]/pandas_city[total_resource].max(),4)pandas_city[city_level]pandas_city[density_index].apply(lambdax:高ifx0.7else(中ifx0.4else低))pandas_citypandas_city.sort_values(total_resource,ascendingFalse)mysql_urljdbc:mysql://localhost:3306/guangxi_medical?useSSLfalsecharacterEncodingutf8mysql_props{user:root,password:123456,driver:com.mysql.cj.jdbc.Driver}spark.createDataFrame(pandas_city).write.mode(overwrite).jdbc(mysql_url,city_distribution_result,propertiesmysql_props)returnpandas_city.to_dict(records)definsurance_coverage_analysis():medical_dfspark.read.option(header,true).csv(hdfs:///guangxi/medical_institutions.csv)pharmacy_dfspark.read.option(header,true).csv(hdfs:///guangxi/pharmacies.csv)medical_df.createOrReplaceTempView(medical_institutions)pharmacy_df.createOrReplaceTempView(pharmacies)medical_insurancespark.sql(SELECT city, type, COUNT(*) AS total_count, SUM(CASE WHEN insurance_status 是 THEN 1 ELSE 0 END) AS insurance_count FROM medical_institutions GROUP BY city, type)pharmacy_insurancespark.sql(SELECT city, COUNT(*) AS total_count, SUM(CASE WHEN insurance_status 是 THEN 1 ELSE 0 END) AS insurance_count FROM pharmacies GROUP BY city)medical_insurance.createOrReplaceTempView(medical_insurance)pharmacy_insurance.createOrReplaceTempView(pharmacy_insurance)medical_ratespark.sql(SELECT city, type, total_count, insurance_count, ROUND(insurance_count / total_count, 4) AS insurance_rate FROM medical_insurance)pharmacy_ratespark.sql(SELECT city, 药店 AS type, total_count, insurance_count, ROUND(insurance_count / total_count, 4) AS insurance_rate FROM pharmacy_insurance)coverage_resultmedical_rate.unionByName(pharmacy_rate)coverage_result.createOrReplaceTempView(coverage_result)city_summaryspark.sql(SELECT city, SUM(total_count) AS total_count, SUM(insurance_count) AS insurance_count, ROUND(SUM(insurance_count) / SUM(total_count), 4) AS city_insurance_rate FROM coverage_result GROUP BY city ORDER BY city_insurance_rate DESC)pandas_coveragecity_summary.toPandas()pandas_coverage[coverage_level]numpy.where(pandas_coverage[city_insurance_rate]0.8,高覆盖,numpy.where(pandas_coverage[city_insurance_rate]0.6,中覆盖,低覆盖))pandas_coverage[gap_to_high]numpy.round(0.8-pandas_coverage[city_insurance_rate],4)mysql_urljdbc:mysql://localhost:3306/guangxi_medical?useSSLfalsecharacterEncodingutf8mysql_props{user:root,password:123456,driver:com.mysql.cj.jdbc.Driver}spark.createDataFrame(pandas_coverage).write.mode(overwrite).jdbc(mysql_url,insurance_coverage_result,propertiesmysql_props)returnpandas_coverage.to_dict(records)defresource_profile_analysis():medical_dfspark.read.option(header,true).csv(hdfs:///guangxi/medical_institutions.csv)pharmacy_dfspark.read.option(header,true).csv(hdfs:///guangxi/pharmacies.csv)medical_df.createOrReplaceTempView(medical_institutions)pharmacy_df.createOrReplaceTempView(pharmacies)medical_profilespark.sql(SELECT city, type, level, COUNT(*) AS medical_count, SUM(CASE WHEN insurance_status 是 THEN 1 ELSE 0 END) AS medical_insurance_count FROM medical_institutions GROUP BY city, type, level)pharmacy_profilespark.sql(SELECT city, COUNT(*) AS pharmacy_count, SUM(CASE WHEN insurance_status 是 THEN 1 ELSE 0 END) AS pharmacy_insurance_count, SUM(service_item_count) AS service_item_total FROM pharmacies GROUP BY city)medical_profile.createOrReplaceTempView(medical_profile)pharmacy_profile.createOrReplaceTempView(pharmacy_profile)profile_joinspark.sql(SELECT m.city, m.type, m.level, m.medical_count, m.medical_insurance_count, p.pharmacy_count, p.pharmacy_insurance_count, p.service_item_total, (m.medical_count p.pharmacy_count) AS total_resource FROM medical_profile m JOIN pharmacy_profile p ON m.city p.city)profile_join.createOrReplaceTempView(profile_join)profile_resultspark.sql(SELECT city, type, level, SUM(medical_count) AS medical_count, SUM(pharmacy_count) AS pharmacy_count, SUM(total_resource) AS total_resource, ROUND(SUM(medical_insurance_count pharmacy_insurance_count) / SUM(total_resource), 4) AS insurance_ratio, ROUND(SUM(service_item_total) / SUM(pharmacy_count), 2) AS avg_service_item FROM profile_join GROUP BY city, type, level)pandas_profileprofile_result.toPandas()pandas_profile[resource_score]numpy.round(pandas_profile[total_resource]*0.5pandas_profile[insurance_ratio]*100*0.3pandas_profile[avg_service_item]*0.2,2)pandas_profile[profile_tag]pandas_profile.apply(lambdarow:资源充足型ifrow[resource_score]80else(服务均衡型ifrow[resource_score]50else基础薄弱型),axis1)pandas_profilepandas_profile.sort_values(resource_score,ascendingFalse)mysql_urljdbc:mysql://localhost:3306/guangxi_medical?useSSLfalsecharacterEncodingutf8mysql_props{user:root,password:123456,driver:com.mysql.cj.jdbc.Driver}spark.createDataFrame(pandas_profile).write.mode(overwrite).jdbc(mysql_url,resource_profile_result,propertiesmysql_props)returnpandas_profile.to_dict(records)五、论文参考计算机毕业设计选题推荐-基于大数据的广西医疗机构及药店清单数据分析与可视化系统-论文参考六、系统视频基于大数据的广西医疗机构及药店清单数据分析与可视化系统-项目视频项目演示视频结语计算机毕业设计选题推荐:基于大数据的广西医疗机构及药店清单数据分析与可视化|毕业设计选题|计算机毕设|选题推荐|毕设指导|项目定制|源码|高质量项目大家可以帮忙点赞、收藏、关注、评论啦源码获取⬇⬇⬇精彩专栏推荐⬇⬇⬇Java项目Python项目安卓项目微信小程序项目