✨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、前言本系统《基于大数据的汽车销售数据可视化分析》主要围绕汽车销售数据展开用Hadoop和HDFS完成销售数据的存储与基础管理用Spark和Spark SQL做数据清洗、汇总统计、趋势计算和指标分析后端采用PythonDjango实现接口逻辑也支持JavaSpring Boot版本数据保存在MySQL中前端通过Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery完成页面交互与图表展示。系统首页用来汇总关键信息大屏可视化展示汽车销售整体情况数据管理模块维护用户、汽车销售信息等基础数据数据分析模块包含总销量时间趋势、厂商竞争格局、车型市场竞争、价格区间市场、动力结构与新能源、月度季节性、智能洞察聚类等内容个人信息和修改密码用于维护登录用户资料。整体上这个系统把大数据处理、后端接口和可视化展示连在一起能够对汽车销售数据进行查看、管理和多角度分析适合作为计算机专业大四学生的毕业设计项目。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汽车销售数据可视化分析系统界面展示四、部分代码设计项目实战-代码参考sparkSparkSession.builder.appName(CarSalesBigDataAnalysis).master(local[*]).config(spark.sql.shuffle.partitions,4).getOrCreate()deftotal_sales_trend(request):sales_dfspark.read.option(header,True).option(inferSchema,True).csv(hdfs://localhost:9000/car_sales/car_sales.csv)sales_df.createOrReplaceTempView(car_sales)trend_dfspark.sql(SELECT date_format(sale_date, yyyy-MM) AS month, SUM(sales_volume) AS total_sales, SUM(sales_amount) AS total_amount FROM car_sales WHERE sale_date IS NOT NULL GROUP BY date_format(sale_date, yyyy-MM) ORDER BY month)trend_pdtrend_df.toPandas()trend_pd[total_sales]trend_pd[total_sales].fillna(0).astype(int)trend_pd[total_amount]trend_pd[total_amount].fillna(0).round(2)monthstrend_pd[month].tolist()salestrend_pd[total_sales].tolist()amountstrend_pd[total_amount].tolist()max_salesint(trend_pd[total_sales].max())iflen(trend_pd)0else0min_salesint(trend_pd[total_sales].min())iflen(trend_pd)0else0avg_salesround(float(trend_pd[total_sales].mean()),2)iflen(trend_pd)0else0result{months:months,sales:sales,amounts:amounts,maxSales:max_sales,minSales:min_sales,avgSales:avg_sales}returnJsonResponse({code:200,msg:总销量时间趋势分析完成,data:result})defmanufacturer_competition(request):sales_dfspark.read.option(header,True).option(inferSchema,True).csv(hdfs://localhost:9000/car_sales/car_sales.csv)sales_df.createOrReplaceTempView(car_sales)comp_dfspark.sql(SELECT manufacturer, SUM(sales_volume) AS total_sales, SUM(sales_amount) AS total_amount, COUNT(DISTINCT model) AS model_count FROM car_sales WHERE manufacturer IS NOT NULL GROUP BY manufacturer ORDER BY total_sales DESC)comp_pdcomp_df.toPandas()comp_pd[total_sales]comp_pd[total_sales].fillna(0).astype(int)comp_pd[total_amount]comp_pd[total_amount].fillna(0).round(2)comp_pd[model_count]comp_pd[model_count].fillna(0).astype(int)total_salesint(comp_pd[total_sales].sum())comp_pd[market_share](comp_pd[total_sales]/total_sales*100).round(2)iftotal_sales0else0top_dfcomp_pd.head(10)manufacturerstop_df[manufacturer].tolist()salestop_df[total_sales].tolist()amountstop_df[total_amount].tolist()sharestop_df[market_share].tolist()model_countstop_df[model_count].tolist()result{manufacturers:manufacturers,sales:sales,amounts:amounts,shares:shares,modelCounts:model_counts}returnJsonResponse({code:200,msg:厂商竞争格局分析完成,data:result})defprice_range_market(request):sales_dfspark.read.option(header,True).option(inferSchema,True).csv(hdfs://localhost:9000/car_sales/car_sales.csv)sales_df.createOrReplaceTempView(car_sales)price_dfspark.sql(SELECT CASE WHEN price 100000 THEN 10万以下 WHEN price 100000 AND price 200000 THEN 10-20万 WHEN price 200000 AND price 300000 THEN 20-30万 WHEN price 300000 AND price 500000 THEN 30-50万 ELSE 50万以上 END AS price_range, SUM(sales_volume) AS total_sales, SUM(sales_amount) AS total_amount, COUNT(DISTINCT model) AS model_count FROM car_sales WHERE price IS NOT NULL GROUP BY CASE WHEN price 100000 THEN 10万以下 WHEN price 100000 AND price 200000 THEN 10-20万 WHEN price 200000 AND price 300000 THEN 20-30万 WHEN price 300000 AND price 500000 THEN 30-50万 ELSE 50万以上 END ORDER BY total_sales DESC)price_pdprice_df.toPandas()price_pd[total_sales]price_pd[total_sales].fillna(0).astype(int)price_pd[total_amount]price_pd[total_amount].fillna(0).round(2)price_pd[model_count]price_pd[model_count].fillna(0).astype(int)total_salesint(price_pd[total_sales].sum())price_pd[sales_ratio](price_pd[total_sales]/total_sales*100).round(2)iftotal_sales0else0rangesprice_pd[price_range].tolist()salesprice_pd[total_sales].tolist()amountsprice_pd[total_amount].tolist()ratiosprice_pd[sales_ratio].tolist()model_countsprice_pd[model_count].tolist()top_rangeprice_pd.iloc[0][price_range]iflen(price_pd)0elseresult{ranges:ranges,sales:sales,amounts:amounts,ratios:ratios,modelCounts:model_counts,topRange:top_range}returnJsonResponse({code:200,msg:价格区间市场分析完成,data:result})五、论文参考计算机毕业设计选题推荐-基于大数据的汽车销售数据可视化分析系统-论文参考六、系统视频基于大数据的汽车销售数据可视化分析系统-项目视频项目演示视频结语计算机毕业设计选题推荐:基于大数据的汽车销售数据可视化分析|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