一、个人简介

💖💖作者：计算机编程果茶熊
💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，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。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
计算机毕业设计选题
💕💕文末获取源码联系计算机编程果茶熊

二、系统介绍

大数据框架：Hadoop+Spark（Hive需要定制修改）
开发语言：Java+Python（两个版本都支持）
数据库：MySQL
后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持）
前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery

《零售时尚精品店销售数据分析系统》是一套基于大数据技术构建的零售行业数据分析平台，采用Hadoop+Spark分布式计算框架处理海量销售数据，通过Python语言开发，后端使用Django框架提供稳定的API服务，前端采用Vue+ElementUI构建现代化用户界面，结合Echarts实现丰富的数据可视化效果。系统运用HDFS进行数据存储，Spark SQL执行复杂的数据查询和分析任务，配合Pandas、NumPy等数据科学库进行深度数据挖掘，MySQL数据库管理结构化数据。系统涵盖用户管理、销售数据管理、销售表现分析、产品特征分析、客户满意度分析、退货行为分析等核心功能模块，通过可视化大屏实时展示关键业务指标，为精品店经营决策提供数据支撑，帮助管理者深入了解销售趋势、产品表现、客户行为等关键信息，提升经营效率和盈利能力。

三、视频解说

四、部分功能展示

五、部分代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import pandas as pd
import numpy as np
import json
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("RetailAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def sales_performance_analysis(request):
    sales_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/retail_db").option("dbtable", "sales_records").option("user", "root").option("password", "password").load()
    product_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/retail_db").option("dbtable", "products").option("user", "root").option("password", "password").load()
    joined_df = sales_df.join(product_df, "product_id")
    daily_sales = joined_df.groupBy("sale_date").agg(sum("amount").alias("total_amount"), count("*").alias("transaction_count"))
    product_sales = joined_df.groupBy("product_name", "category").agg(sum("amount").alias("product_revenue"), sum("quantity").alias("total_quantity"))
    category_performance = joined_df.groupBy("category").agg(sum("amount").alias("category_revenue"), avg("amount").alias("avg_transaction"), count("*").alias("sales_count"))
    top_products = product_sales.orderBy(desc("product_revenue")).limit(10)
    growth_analysis = joined_df.withColumn("month", date_format("sale_date", "yyyy-MM")).groupBy("month").agg(sum("amount").alias("monthly_revenue"))
    growth_analysis = growth_analysis.withColumn("prev_month_revenue", lag("monthly_revenue").over(Window.orderBy("month")))
    growth_analysis = growth_analysis.withColumn("growth_rate", round(((col("monthly_revenue") - col("prev_month_revenue")) / col("prev_month_revenue") * 100), 2))
    peak_hours = joined_df.withColumn("hour", hour("sale_time")).groupBy("hour").agg(sum("amount").alias("hourly_revenue"), count("*").alias("hourly_transactions"))
    seasonal_trends = joined_df.withColumn("season", when(month("sale_date").isin([12, 1, 2]), "Winter").when(month("sale_date").isin([3, 4, 5]), "Spring").when(month("sale_date").isin([6, 7, 8]), "Summer").otherwise("Autumn"))
    seasonal_performance = seasonal_trends.groupBy("season").agg(sum("amount").alias("seasonal_revenue"), avg("amount").alias("avg_seasonal_transaction"))
    daily_sales_pd = daily_sales.toPandas()
    product_sales_pd = product_sales.toPandas()
    category_performance_pd = category_performance.toPandas()
    top_products_pd = top_products.toPandas()
    growth_analysis_pd = growth_analysis.toPandas()
    peak_hours_pd = peak_hours.toPandas()
    seasonal_performance_pd = seasonal_performance.toPandas()
    revenue_trend = daily_sales_pd['total_amount'].rolling(window=7).mean().fillna(0)
    performance_score = np.where(revenue_trend > revenue_trend.mean(), "优秀", np.where(revenue_trend > revenue_trend.mean() * 0.8, "良好", "需改进"))
    result = {"daily_sales": daily_sales_pd.to_dict('records'), "product_sales": product_sales_pd.to_dict('records'), "category_performance": category_performance_pd.to_dict('records'), "top_products": top_products_pd.to_dict('records'), "growth_analysis": growth_analysis_pd.to_dict('records'), "peak_hours": peak_hours_pd.to_dict('records'), "seasonal_performance": seasonal_performance_pd.to_dict('records'), "performance_scores": performance_score.tolist()}
    return JsonResponse(result, safe=False)
@csrf_exempt
def customer_satisfaction_analysis(request):
    feedback_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/retail_db").option("dbtable", "customer_feedback").option("user", "root").option("password", "password").load()
    sales_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/retail_db").option("dbtable", "sales_records").option("user", "root").option("password", "password").load()
    customer_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/retail_db").option("dbtable", "customers").option("user", "root").option("password", "password").load()
    satisfaction_scores = feedback_df.groupBy("customer_id").agg(avg("satisfaction_score").alias("avg_satisfaction"), count("*").alias("feedback_count"))
    customer_purchase_behavior = sales_df.groupBy("customer_id").agg(sum("amount").alias("total_spent"), count("*").alias("purchase_frequency"), avg("amount").alias("avg_purchase_amount"))
    comprehensive_analysis = satisfaction_scores.join(customer_purchase_behavior, "customer_id").join(customer_df, "customer_id")
    satisfaction_segments = comprehensive_analysis.withColumn("satisfaction_level", when(col("avg_satisfaction") >= 4.5, "非常满意").when(col("avg_satisfaction") >= 3.5, "满意").when(col("avg_satisfaction") >= 2.5, "一般").otherwise("不满意"))
    loyalty_analysis = comprehensive_analysis.withColumn("loyalty_score", (col("total_spent") / 1000 + col("purchase_frequency") / 10 + col("avg_satisfaction")) / 3)
    satisfaction_by_age = comprehensive_analysis.withColumn("age_group", when(col("age") < 25, "18-24").when(col("age") < 35, "25-34").when(col("age") < 45, "35-44").when(col("age") < 55, "45-54").otherwise("55+"))
    age_satisfaction = satisfaction_by_age.groupBy("age_group").agg(avg("avg_satisfaction").alias("group_satisfaction"), count("*").alias("group_count"))
    gender_satisfaction = comprehensive_analysis.groupBy("gender").agg(avg("avg_satisfaction").alias("gender_satisfaction"), avg("total_spent").alias("avg_spending"))
    feedback_trends = feedback_df.withColumn("feedback_month", date_format("feedback_date", "yyyy-MM")).groupBy("feedback_month").agg(avg("satisfaction_score").alias("monthly_satisfaction"), count("*").alias("feedback_volume"))
    satisfaction_correlation = comprehensive_analysis.select("avg_satisfaction", "total_spent", "purchase_frequency").toPandas()
    correlation_matrix = satisfaction_correlation.corr()
    improvement_areas = feedback_df.groupBy("feedback_category").agg(avg("satisfaction_score").alias("category_satisfaction"), count("*").alias("category_count")).orderBy("category_satisfaction")
    satisfaction_segments_pd = satisfaction_segments.toPandas()
    loyalty_analysis_pd = loyalty_analysis.toPandas()
    age_satisfaction_pd = age_satisfaction.toPandas()
    gender_satisfaction_pd = gender_satisfaction.toPandas()
    feedback_trends_pd = feedback_trends.toPandas()
    improvement_areas_pd = improvement_areas.toPandas()
    satisfaction_prediction = np.where(satisfaction_segments_pd['avg_satisfaction'] > 4.0, "高价值客户", np.where(satisfaction_segments_pd['avg_satisfaction'] > 3.0, "潜力客户", "风险客户"))
    result = {"satisfaction_segments": satisfaction_segments_pd.to_dict('records'), "loyalty_analysis": loyalty_analysis_pd.to_dict('records'), "age_satisfaction": age_satisfaction_pd.to_dict('records'), "gender_satisfaction": gender_satisfaction_pd.to_dict('records'), "feedback_trends": feedback_trends_pd.to_dict('records'), "improvement_areas": improvement_areas_pd.to_dict('records'), "correlation_matrix": correlation_matrix.to_dict(), "customer_predictions": satisfaction_prediction.tolist()}
    return JsonResponse(result, safe=False)
@csrf_exempt
def return_behavior_analysis(request):
    return_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/retail_db").option("dbtable", "return_records").option("user", "root").option("password", "password").load()
    sales_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/retail_db").option("dbtable", "sales_records").option("user", "root").option("password", "password").load()
    product_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/retail_db").option("dbtable", "products").option("user", "root").option("password", "password").load()
    return_with_product = return_df.join(product_df, "product_id").join(sales_df, ["product_id", "customer_id"])
    return_rate_by_product = return_with_product.groupBy("product_name", "category").agg(count("return_id").alias("return_count"), sum("return_amount").alias("total_return_amount"))
    sales_by_product = sales_df.join(product_df, "product_id").groupBy("product_name", "category").agg(count("sale_id").alias("sales_count"), sum("amount").alias("total_sales_amount"))
    return_rate_analysis = return_rate_by_product.join(sales_by_product, ["product_name", "category"])
    return_rate_analysis = return_rate_analysis.withColumn("return_rate", round((col("return_count") / col("sales_count") * 100), 2))
    return_rate_analysis = return_rate_analysis.withColumn("financial_impact", round((col("total_return_amount") / col("total_sales_amount") * 100), 2))
    return_reasons = return_df.groupBy("return_reason").agg(count("*").alias("reason_count"), avg("return_amount").alias("avg_return_amount"))
    seasonal_returns = return_df.withColumn("return_month", date_format("return_date", "yyyy-MM")).groupBy("return_month").agg(count("*").alias("monthly_returns"), sum("return_amount").alias("monthly_return_value"))
    customer_return_behavior = return_df.groupBy("customer_id").agg(count("*").alias("customer_returns"), sum("return_amount").alias("customer_return_value"), avg("datediff(return_date, sale_date)").alias("avg_return_days"))
    problematic_products = return_rate_analysis.filter(col("return_rate") > 15.0).orderBy(desc("return_rate"))
    return_time_analysis = return_df.withColumn("return_days", datediff("return_date", "sale_date")).groupBy("return_days").agg(count("*").alias("frequency"))
    category_return_impact = return_rate_analysis.groupBy("category").agg(avg("return_rate").alias("avg_category_return_rate"), sum("total_return_amount").alias("category_return_loss"))
    return_trend_analysis = seasonal_returns.withColumn("prev_month_returns", lag("monthly_returns").over(Window.orderBy("return_month")))
    return_trend_analysis = return_trend_analysis.withColumn("return_trend", when(col("monthly_returns") > col("prev_month_returns"), "上升").when(col("monthly_returns") < col("prev_month_returns"), "下降").otherwise("稳定"))
    return_rate_analysis_pd = return_rate_analysis.toPandas()
    return_reasons_pd = return_reasons.toPandas()
    seasonal_returns_pd = seasonal_returns.toPandas()
    customer_return_behavior_pd = customer_return_behavior.toPandas()
    problematic_products_pd = problematic_products.toPandas()
    category_return_impact_pd = category_return_impact.toPandas()
    return_trend_analysis_pd = return_trend_analysis.toPandas()
    risk_products = np.where(return_rate_analysis_pd['return_rate'] > 20, "高风险", np.where(return_rate_analysis_pd['return_rate'] > 10, "中风险", "低风险"))
    quality_score = 100 - return_rate_analysis_pd['return_rate']
    result = {"return_rate_analysis": return_rate_analysis_pd.to_dict('records'), "return_reasons": return_reasons_pd.to_dict('records'), "seasonal_returns": seasonal_returns_pd.to_dict('records'), "customer_return_behavior": customer_return_behavior_pd.to_dict('records'), "problematic_products": problematic_products_pd.to_dict('records'), "category_return_impact": category_return_impact_pd.to_dict('records'), "return_trend_analysis": return_trend_analysis_pd.to_dict('records'), "risk_assessment": risk_products.tolist(), "quality_scores": quality_score.tolist()}
    return JsonResponse(result, safe=False)

六、部分文档展示

七、END

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