✨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《基于大数据的巧克力销售数据分析与可视化》主要围绕巧克力销售数据展开用Hadoop和Spark搭建大数据处理链路把销售明细、国家市场、产品结构、销售人员、时间趋势、消费模式等数据从HDFS读取后借助Spark SQL、Pandas和NumPy做清洗、聚合、统计和分析再把结果存入MySQL由后端接口输出给前端。后端提供PythonDjango和JavaSpring Boot两个版本前端使用Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery完成页面展示。系统功能包括系统首页、数据中心、用户、销售明细、国家市场、产品结构、销售人员、时间趋势、消费模式、个人信息和修改密码。数据中心展示订单量、销售额、国家数、产品数等总览国家市场按国家对比销售表现产品结构看不同巧克力产品的销售占比销售人员查看业绩分布时间趋势观察月度或年度变化消费模式分析购买数量、客单价和消费区间销售明细支持查询和筛选。整个系统重点是用大数据思路完成数据分析与可视化让销售情况更直观。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巧克力销售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frompyspark.sqlimportSparkSession,functionsasF sparkSparkSession.builder.appName(ChocolateSalesAnalysis).master(local[*]).getOrCreate()defdata_center(request):ifrequest.method!GET:return{code:405,msg:请求方式不支持}dfspark.read.option(header,true).option(inferSchema,true).csv(hdfs:///chocolate/sales.csv)df.createOrReplaceTempView(chocolate_sales)total_rowspark.sql(SELECT COUNT(*) AS order_count, SUM(sales) AS total_sales, COUNT(DISTINCT country) AS country_count, COUNT(DISTINCT product) AS product_count FROM chocolate_sales).collect()[0]result{order_count:total_row[order_count],total_sales:float(total_row[total_sales]or0),country_count:total_row[country_count],product_count:total_row[product_count]}month_dfspark.sql(SELECT date_format(order_date,yyyy-MM) AS month, SUM(sales) AS month_sales FROM chocolate_sales GROUP BY date_format(order_date,yyyy-MM) ORDER BY month)result[month_trend][{month:row[month],sales:float(row[month_sales]or0)}forrowinmonth_df.collect()]country_dfspark.sql(SELECT country, SUM(sales) AS country_sales FROM chocolate_sales GROUP BY country ORDER BY country_sales DESC LIMIT 5)result[country_top][{country:row[country],sales:float(row[country_sales]or0)}forrowincountry_df.collect()]product_dfspark.sql(SELECT product, SUM(sales) AS product_sales FROM chocolate_sales GROUP BY product ORDER BY product_sales DESC LIMIT 5)result[product_top][{product:row[product],sales:float(row[product_sales]or0)}forrowinproduct_df.collect()]result[update_time]spark.sql(SELECT current_timestamp() AS now).collect()[0][now]return{code:200,data:result}defcountry_market(request):ifrequest.method!GET:return{code:405,msg:请求方式不支持}dfspark.read.option(header,true).option(inferSchema,true).csv(hdfs:///chocolate/sales.csv)df.createOrReplaceTempView(chocolate_sales)country_dfspark.sql(SELECT country, COUNT(*) AS order_count, SUM(sales) AS total_sales, AVG(sales) AS avg_sales, SUM(quantity) AS total_quantity FROM chocolate_sales GROUP BY country)total_salescountry_df.agg(F.sum(total_sales)).collect()[0][0]or0country_dfcountry_df.withColumn(sale_ratio,F.round(F.col(total_sales)/F.lit(total_sales),4))country_dfcountry_df.orderBy(F.col(total_sales).desc())rowscountry_df.collect()result[{country:row[country],order_count:row[order_count],total_sales:float(row[total_sales]or0),avg_sales:float(row[avg_sales]or0),total_quantity:row[total_quantity],sale_ratio:float(row[sale_ratio]or0)}forrowinrows]top_countryresult[0]ifresultelse{}low_countryresult[-1]ifresultelse{}chart{xAxis:[item[country]foriteminresult],series:[item[total_sales]foriteminresult]}return{code:200,data:result,top:top_country,low:low_country,chart:chart}deftime_trend(request):dfspark.read.option(header,true).option(inferSchema,true).csv(hdfs:///chocolate/sales.csv)df.createOrReplaceTempView(chocolate_sales)month_dfspark.sql(SELECT date_format(order_date,yyyy-MM) AS month, SUM(sales) AS sales, COUNT(*) AS order_count, SUM(quantity) AS quantity FROM chocolate_sales GROUP BY date_format(order_date,yyyy-MM) ORDER BY month)month_rowsmonth_df.collect()trend_data[]last_salesNoneforrowinmonth_rows:current_salesfloat(row[sales]or0)growth0.0iflast_salesandlast_sales!0:growthround((current_sales-last_sales)/last_sales*100,2)trend_data.append({month:row[month],sales:current_sales,order_count:row[order_count],quantity:row[quantity],growth:growth})last_salescurrent_sales max_monthmax(trend_data,keylambdaitem:item[sales])iftrend_dataelse{}min_monthmin(trend_data,keylambdaitem:item[sales])iftrend_dataelse{}avg_salessum(item[sales]foritemintrend_data)/len(trend_data)iftrend_dataelse0chart{xAxis:[item[month]foritemintrend_data],sales:[item[sales]foritemintrend_data],growth:[item[growth]foritemintrend_data]}return{code:200,data:trend_data,max:max_month,min:min_month,avg:avg_sales,chart:chart}五、论文参考计算机毕业设计选题推荐-基于大数据的巧克力销售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巧克力销售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:基于大数据的巧克力销售数据分析与可视化|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