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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》是一套专门针对游戏行业销售数据进行深度分析和可视化展示的综合性大数据处理系统，该系统采用当前主流的大数据技术架构，底层基于Hadoop分布式文件系统HDFS进行海量游戏销售数据的存储管理，利用Spark大数据计算引擎结合Spark SQL进行高效的数据处理和分析计算，同时集成Pandas和NumPy等数据科学库增强数据处理能力，后端采用Django框架或Spring Boot框架构建RESTful API服务，前端基于Vue.js框架结合ElementUI组件库和Echarts可视化图表库打造现代化的用户界面，数据持久化采用MySQL关系型数据库确保数据安全可靠。系统核心功能模块包括系统首页展示、用户个人信息管理、密码修改等基础功能，以及大屏可视化展示、市场总览分析、平台策略分析、游戏类型偏好分析、发行商策略分析和销售特征分析等专业数据分析模块，通过对游戏行业多维度销售数据的采集、清洗、处理、分析和可视化，为游戏行业从业者、投资者和研究人员提供全面深入的市场洞察和决策支持，帮助用户准确把握游戏市场发展趋势、了解不同平台和游戏类型的销售表现、分析发行商市场策略效果，从而制定更加科学合理的商业决策。
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 *
from django.http import JsonResponse
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("GameSalesAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def market_overview_analysis(request):
game_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gamedb").option("dbtable", "game_sales").option("user", "root").option("password", "password").load()
total_sales = game_df.agg(sum("sales_amount")).collect()[0][0]
total_games = game_df.count()
avg_price = game_df.agg(avg("game_price")).collect()[0][0]
platform_sales = game_df.groupBy("platform").agg(sum("sales_amount").alias("total_sales"), count("game_id").alias("game_count")).orderBy(desc("total_sales"))
genre_sales = game_df.groupBy("game_genre").agg(sum("sales_amount").alias("genre_sales"), avg("user_rating").alias("avg_rating")).orderBy(desc("genre_sales"))
monthly_trend = game_df.withColumn("month", date_format("sale_date", "yyyy-MM")).groupBy("month").agg(sum("sales_amount").alias("monthly_sales")).orderBy("month")
top_games = game_df.select("game_name", "sales_amount", "game_genre", "platform").orderBy(desc("sales_amount")).limit(10)
platform_data = [{"platform": row["platform"], "sales": float(row["total_sales"]), "count": row["game_count"]} for row in platform_sales.collect()]
genre_data = [{"genre": row["game_genre"], "sales": float(row["genre_sales"]), "rating": float(row["avg_rating"])} for row in genre_sales.collect()]
trend_data = [{"month": row["month"], "sales": float(row["monthly_sales"])} for row in monthly_trend.collect()]
top_games_data = [{"name": row["game_name"], "sales": float(row["sales_amount"]), "genre": row["game_genre"], "platform": row["platform"]} for row in top_games.collect()]
growth_rate = calculate_growth_rate(monthly_trend.collect())
market_share = calculate_market_share(platform_sales.collect(), total_sales)
result = {"total_sales": float(total_sales), "total_games": total_games, "avg_price": float(avg_price), "platform_distribution": platform_data, "genre_analysis": genre_data, "monthly_trend": trend_data, "top_games": top_games_data, "growth_rate": growth_rate, "market_share": market_share}
return JsonResponse(result)
def platform_strategy_analysis(request):
platform = request.GET.get('platform', 'all')
game_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gamedb").option("dbtable", "game_sales").option("user", "root").option("password", "password").load()
if platform != 'all':
filtered_df = game_df.filter(col("platform") == platform)
else:
filtered_df = game_df
platform_performance = filtered_df.groupBy("platform").agg(sum("sales_amount").alias("total_revenue"), avg("sales_amount").alias("avg_game_revenue"), count("game_id").alias("total_games"), avg("user_rating").alias("avg_rating"), max("sales_amount").alias("max_single_sale"))
genre_platform_analysis = filtered_df.groupBy("platform", "game_genre").agg(sum("sales_amount").alias("genre_revenue"), count("game_id").alias("genre_count")).orderBy("platform", desc("genre_revenue"))
price_strategy = filtered_df.groupBy("platform").agg(avg("game_price").alias("avg_price"), min("game_price").alias("min_price"), max("game_price").alias("max_price"), percentile_approx("game_price", 0.5).alias("median_price"))
seasonal_analysis = filtered_df.withColumn("quarter", quarter("sale_date")).groupBy("platform", "quarter").agg(sum("sales_amount").alias("quarterly_sales")).orderBy("platform", "quarter")
user_preference = filtered_df.filter(col("user_rating") >= 4.0).groupBy("platform").agg(count("game_id").alias("high_rated_games"), avg("user_rating").alias("high_rated_avg"))
competitive_analysis = filtered_df.groupBy("platform").agg(sum("sales_amount").alias("platform_sales")).withColumn("market_share", col("platform_sales") / sum("platform_sales").over())
platform_results = [{"platform": row["platform"], "revenue": float(row["total_revenue"]), "avg_revenue": float(row["avg_game_revenue"]), "games": row["total_games"], "rating": float(row["avg_rating"]), "max_sale": float(row["max_single_sale"])} for row in platform_performance.collect()]
genre_results = [{"platform": row["platform"], "genre": row["game_genre"], "revenue": float(row["genre_revenue"]), "count": row["genre_count"]} for row in genre_platform_analysis.collect()]
price_results = [{"platform": row["platform"], "avg_price": float(row["avg_price"]), "min_price": float(row["min_price"]), "max_price": float(row["max_price"]), "median_price": float(row["median_price"])} for row in price_strategy.collect()]
seasonal_results = [{"platform": row["platform"], "quarter": row["quarter"], "sales": float(row["quarterly_sales"])} for row in seasonal_analysis.collect()]
preference_results = [{"platform": row["platform"], "high_rated_count": row["high_rated_games"], "avg_rating": float(row["high_rated_avg"])} for row in user_preference.collect()]
competitive_results = [{"platform": row["platform"], "sales": float(row["platform_sales"]), "market_share": float(row["market_share"])} for row in competitive_analysis.collect()]
strategy_recommendations = generate_platform_recommendations(platform_results, genre_results, price_results)
result = {"platform_performance": platform_results, "genre_analysis": genre_results, "pricing_strategy": price_results, "seasonal_trends": seasonal_results, "user_preferences": preference_results, "competitive_landscape": competitive_results, "recommendations": strategy_recommendations}
return JsonResponse(result)
def sales_feature_analysis(request):
start_date = request.GET.get('start_date')
end_date = request.GET.get('end_date')
game_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gamedb").option("dbtable", "game_sales").option("user", "root").option("password", "password").load()
if start_date and end_date:
filtered_df = game_df.filter((col("sale_date") >= start_date) & (col("sale_date") <= end_date))
else:
filtered_df = game_df
sales_correlation = filtered_df.select("sales_amount", "user_rating", "game_price", "marketing_spend").toPandas().corr()
price_sales_analysis = filtered_df.withColumn("price_range", when(col("game_price") < 20, "Low").when(col("game_price") < 50, "Medium").otherwise("High")).groupBy("price_range").agg(sum("sales_amount").alias("total_sales"), avg("sales_amount").alias("avg_sales"), count("game_id").alias("game_count"))
rating_impact = filtered_df.withColumn("rating_category", when(col("user_rating") < 3.0, "Poor").when(col("user_rating") < 4.0, "Average").otherwise("Excellent")).groupBy("rating_category").agg(sum("sales_amount").alias("category_sales"), avg("sales_amount").alias("avg_category_sales"))
geographic_analysis = filtered_df.groupBy("region").agg(sum("sales_amount").alias("regional_sales"), count("game_id").alias("regional_count"), avg("user_rating").alias("regional_rating")).orderBy(desc("regional_sales"))
time_series_features = filtered_df.withColumn("day_of_week", dayofweek("sale_date")).withColumn("month", month("sale_date")).groupBy("day_of_week", "month").agg(sum("sales_amount").alias("temporal_sales"), count("game_id").alias("temporal_count"))
promotional_impact = filtered_df.filter(col("is_promotion") == True).groupBy("promotion_type").agg(sum("sales_amount").alias("promo_sales"), avg("sales_amount").alias("avg_promo_sales"), count("game_id").alias("promo_count"))
customer_segment = filtered_df.withColumn("customer_type", when(col("purchase_frequency") > 5, "Frequent").when(col("purchase_frequency") > 2, "Regular").otherwise("Casual")).groupBy("customer_type").agg(sum("sales_amount").alias("segment_sales"), avg("sales_amount").alias("avg_segment_sales"))
outlier_detection = filtered_df.select("sales_amount").summary("count", "mean", "stddev", "min", "25%", "50%", "75%", "max")
feature_importance = calculate_feature_importance(filtered_df.toPandas())
seasonal_patterns = filtered_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")).groupBy("season").agg(sum("sales_amount").alias("seasonal_sales"), avg("user_rating").alias("seasonal_rating"))
correlation_data = sales_correlation.to_dict()
price_data = [{"range": row["price_range"], "sales": float(row["total_sales"]), "avg_sales": float(row["avg_sales"]), "count": row["game_count"]} for row in price_sales_analysis.collect()]
rating_data = [{"category": row["rating_category"], "sales": float(row["category_sales"]), "avg_sales": float(row["avg_category_sales"])} for row in rating_impact.collect()]
geo_data = [{"region": row["region"], "sales": float(row["regional_sales"]), "count": row["regional_count"], "rating": float(row["regional_rating"])} for row in geographic_analysis.collect()]
temporal_data = [{"day": row["day_of_week"], "month": row["month"], "sales": float(row["temporal_sales"]), "count": row["temporal_count"]} for row in time_series_features.collect()]
promo_data = [{"type": row["promotion_type"], "sales": float(row["promo_sales"]), "avg_sales": float(row["avg_promo_sales"]), "count": row["promo_count"]} for row in promotional_impact.collect()]
segment_data = [{"type": row["customer_type"], "sales": float(row["segment_sales"]), "avg_sales": float(row["avg_segment_sales"])} for row in customer_segment.collect()]
outlier_data = [{"metric": row["summary"], "value": row["sales_amount"]} for row in outlier_detection.collect()]
seasonal_data = [{"season": row["season"], "sales": float(row["seasonal_sales"]), "rating": float(row["seasonal_rating"])} for row in seasonal_patterns.collect()]
result = {"correlation_analysis": correlation_data, "price_impact": price_data, "rating_impact": rating_data, "geographic_patterns": geo_data, "temporal_patterns": temporal_data, "promotional_effects": promo_data, "customer_segments": segment_data, "outlier_summary": outlier_data, "seasonal_analysis": seasonal_data, "feature_importance": feature_importance}
return JsonResponse(result)
def calculate_growth_rate(monthly_data):
if len(monthly_data) < 2:
return 0.0
recent_sales = monthly_data[-1]["monthly_sales"]
previous_sales = monthly_data[-2]["monthly_sales"]
return ((recent_sales - previous_sales) / previous_sales) * 100 if previous_sales > 0 else 0.0
def calculate_market_share(platform_data, total_sales):
return [{"platform": item["platform"], "share": (item["total_sales"] / total_sales) * 100} for item in platform_data]
def generate_platform_recommendations(performance, genre, pricing):
recommendations = []
for platform_data in performance:
if platform_data["rating"] < 3.5:
recommendations.append(f"{platform_data['platform']}平台需要提升游戏品质")
if platform_data["avg_revenue"] < 1000:
recommendations.append(f"{platform_data['platform']}平台建议优化定价策略")
return recommendations
def calculate_feature_importance(df):
correlation_matrix = df[["sales_amount", "user_rating", "game_price", "marketing_spend"]].corr()
importance_scores = abs(correlation_matrix["sales_amount"]).sort_values(ascending=False)
return {feature: float(score) for feature, score in importance_scores.items() if feature != "sales_amount"}
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：计算机编程小咖
💙💙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！
💜💜
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