💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
基于大数据的游戏行业销售数据可视化分析系统介绍
基于大数据的游戏行业销售数据可视化分析系统是一套集数据采集、处理、分析与可视化展示于一体的综合性平台，专门针对游戏行业的销售数据进行深度挖掘和智能分析。系统采用Hadoop分布式存储架构作为底层数据仓库，利用Spark强大的内存计算能力对海量游戏销售数据进行实时处理和分析，通过Django框架构建稳定的后端服务接口，前端采用Vue+ElementUI+Echarts技术栈打造直观友好的交互界面和丰富的数据可视化图表。平台核心功能涵盖市场总览分析、平台策略分析、类型偏好分析、发行商策略分析以及销售特征分析等多个维度，能够帮助用户全面了解游戏市场的发展趋势、不同平台的表现差异、玩家对游戏类型的偏好变化、各大发行商的市场策略效果以及销售数据背后的深层规律，为游戏行业从业者提供科学的数据支撑和决策参考，同时也为学习大数据技术的同学提供了一个完整的实践案例。
基于大数据的游戏行业销售数据可视化分析系统演示视频
基于大数据的游戏行业销售数据可视化分析系统演示图片
基于大数据的游戏行业销售数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum as spark_sum, avg, count, desc, asc
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, FloatType
def market_overview_analysis(request):
spark = SparkSession.builder.appName("GameMarketOverview").config("spark.sql.adaptive.enabled", "true").getOrCreate()
game_sales_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/game_data/sales_data.csv")
game_sales_df = game_sales_df.withColumn("sales_amount", col("sales_amount").cast(FloatType()))
game_sales_df = game_sales_df.withColumn("player_count", col("player_count").cast(IntegerType()))
total_sales = game_sales_df.agg(spark_sum("sales_amount").alias("total_sales")).collect()[0]["total_sales"]
total_games = game_sales_df.select("game_id").distinct().count()
avg_sales_per_game = game_sales_df.groupBy("game_id").agg(spark_sum("sales_amount").alias("game_total")).agg(avg("game_total").alias("avg_sales")).collect()[0]["avg_sales"]
monthly_sales = game_sales_df.groupBy("sale_month").agg(spark_sum("sales_amount").alias("monthly_total")).orderBy("sale_month")
top_selling_games = game_sales_df.groupBy("game_name").agg(spark_sum("sales_amount").alias("total_sales")).orderBy(desc("total_sales")).limit(10)
platform_distribution = game_sales_df.groupBy("platform").agg(spark_sum("sales_amount").alias("platform_sales"), count("*").alias("game_count")).orderBy(desc("platform_sales"))
monthly_sales_list = monthly_sales.collect()
top_games_list = top_selling_games.collect()
platform_dist_list = platform_distribution.collect()
market_growth_rate = calculate_growth_rate(monthly_sales_list)
market_concentration = calculate_market_concentration(top_games_list, total_sales)
result_data = {"total_sales": total_sales, "total_games": total_games, "avg_sales_per_game": avg_sales_per_game, "monthly_trends": monthly_sales_list, "top_games": top_games_list, "platform_distribution": platform_dist_list, "growth_rate": market_growth_rate, "market_concentration": market_concentration}
spark.stop()
return JsonResponse(result_data)
def game_type_preference_analysis(request):
spark = SparkSession.builder.appName("GameTypePreference").config("spark.sql.adaptive.enabled", "true").getOrCreate()
game_info_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/game_data/game_info.csv")
sales_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/game_data/sales_data.csv")
sales_df = sales_df.withColumn("sales_amount", col("sales_amount").cast(FloatType()))
sales_df = sales_df.withColumn("player_count", col("player_count").cast(IntegerType()))
merged_df = sales_df.join(game_info_df, "game_id", "inner")
type_sales_stats = merged_df.groupBy("game_type").agg(spark_sum("sales_amount").alias("total_sales"), avg("sales_amount").alias("avg_sales"), count("*").alias("sales_records"), spark_sum("player_count").alias("total_players"))
type_monthly_trend = merged_df.groupBy("game_type", "sale_month").agg(spark_sum("sales_amount").alias("monthly_sales")).orderBy("game_type", "sale_month")
type_platform_cross = merged_df.groupBy("game_type", "platform").agg(spark_sum("sales_amount").alias("cross_sales"), count("*").alias("cross_count"))
top_games_by_type = merged_df.groupBy("game_type", "game_name").agg(spark_sum("sales_amount").alias("game_sales")).withColumn("rank", row_number().over(Window.partitionBy("game_type").orderBy(desc("game_sales")))).filter(col("rank") <= 5)
user_preference_score = merged_df.groupBy("game_type").agg((spark_sum("player_count") / spark_sum("sales_amount") * 1000).alias("preference_score"))
type_stats_list = type_sales_stats.collect()
monthly_trend_list = type_monthly_trend.collect()
platform_cross_list = type_platform_cross.collect()
top_games_list = top_games_by_type.collect()
preference_scores = user_preference_score.collect()
type_popularity_ranking = sorted(type_stats_list, key=lambda x: x["total_sales"], reverse=True)
seasonal_patterns = analyze_seasonal_patterns(monthly_trend_list)
cross_platform_performance = analyze_cross_platform_performance(platform_cross_list)
result_data = {"type_statistics": type_stats_list, "monthly_trends": monthly_trend_list, "platform_cross_analysis": platform_cross_list, "top_games_by_type": top_games_list, "user_preference_scores": preference_scores, "popularity_ranking": type_popularity_ranking, "seasonal_patterns": seasonal_patterns, "cross_platform_performance": cross_platform_performance}
spark.stop()
return JsonResponse(result_data)
def sales_feature_analysis(request):
spark = SparkSession.builder.appName("SalesFeatureAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
sales_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/game_data/sales_data.csv")
game_info_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/game_data/game_info.csv")
sales_df = sales_df.withColumn("sales_amount", col("sales_amount").cast(FloatType()))
sales_df = sales_df.withColumn("player_count", col("player_count").cast(IntegerType()))
sales_df = sales_df.withColumn("unit_price", col("unit_price").cast(FloatType()))
full_data_df = sales_df.join(game_info_df, "game_id", "inner")
price_range_analysis = full_data_df.withColumn("price_range", when(col("unit_price") < 20, "低价(0-20)").when(col("unit_price") < 50, "中价(20-50)").otherwise("高价(50+)")).groupBy("price_range").agg(spark_sum("sales_amount").alias("range_sales"), avg("sales_amount").alias("avg_sales"), count("*").alias("sales_count"))
temporal_patterns = full_data_df.groupBy("sale_month", "sale_day_of_week").agg(spark_sum("sales_amount").alias("daily_sales"), count("*").alias("transaction_count")).orderBy("sale_month", "sale_day_of_week")
regional_performance = full_data_df.groupBy("region").agg(spark_sum("sales_amount").alias("regional_sales"), spark_sum("player_count").alias("regional_players"), avg("unit_price").alias("avg_regional_price"))
correlation_analysis = full_data_df.select("unit_price", "sales_amount", "player_count", "user_rating")
sales_velocity = full_data_df.groupBy("game_id", "game_name").agg((spark_sum("sales_amount") / count("*")).alias("sales_velocity"), max("sales_amount").alias("peak_sales"), min("sales_amount").alias("min_sales"))
customer_behavior = full_data_df.groupBy("customer_segment").agg(spark_sum("sales_amount").alias("segment_sales"), avg("unit_price").alias("avg_spending"), count("*").alias("purchase_frequency"))
seasonal_fluctuation = full_data_df.groupBy("quarter", "game_type").agg(spark_sum("sales_amount").alias("quarterly_sales"), avg("player_count").alias("avg_players"))
price_elasticity = calculate_price_elasticity(full_data_df)
market_saturation = calculate_market_saturation_index(full_data_df)
price_range_list = price_range_analysis.collect()
temporal_list = temporal_patterns.collect()
regional_list = regional_performance.collect()
velocity_list = sales_velocity.collect()
behavior_list = customer_behavior.collect()
seasonal_list = seasonal_fluctuation.collect()
result_data = {"price_range_analysis": price_range_list, "temporal_patterns": temporal_list, "regional_performance": regional_list, "sales_velocity": velocity_list, "customer_behavior": behavior_list, "seasonal_fluctuation": seasonal_list, "price_elasticity": price_elasticity, "market_saturation": market_saturation, "correlation_matrix": correlation_analysis.toPandas().corr().to_dict()}
spark.stop()
return JsonResponse(result_data)
基于大数据的游戏行业销售数据可视化分析系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
