前言
💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目 计算机毕业设计选题 💜💜
一.开发工具简介
大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL
二.系统内容简介
《手机定价数据可视化分析系统》是一款基于大数据技术构建的智能分析平台，采用Hadoop+Spark分布式计算框架处理海量手机市场数据，通过Python语言开发，后端采用Django框架构建RESTful API服务，前端运用Vue.js结合ElementUI组件库和ECharts可视化引擎打造交互式分析界面。系统整合MySQL数据库存储结构化数据，利用HDFS分布式文件系统管理大规模数据集，通过Spark SQL进行高效数据查询和计算，结合Pandas、NumPy等科学计算库实现数据预处理和统计分析。平台提供可视化大屏展示、手机定价数据管理、摄像头性能分析、功能配置分析、硬件规格分析、性能跑分分析、价格趋势分析、屏幕参数分析等八大核心功能模块，通过多维度数据挖掘和可视化呈现，为手机厂商、经销商、消费者和市场分析师提供科学的决策支持，实现对手机市场定价规律的深度洞察和智能化分析。
三.系统功能演示
四.系统界面展示
五.系统源码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, max, min, count, when, desc, asc
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, FloatType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("PhonePricingAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def camera_analysis_core(request):
phone_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/phone_db").option("dbtable", "phone_data").option("user", "root").option("password", "password").load()
camera_stats = phone_df.groupBy("main_camera_pixels").agg(avg("price").alias("avg_price"), count("*").alias("phone_count"), min("price").alias("min_price"), max("price").alias("max_price"))
camera_performance = camera_stats.withColumn("price_performance_ratio", col("avg_price") / col("main_camera_pixels"))
sorted_camera_data = camera_performance.orderBy(desc("main_camera_pixels"))
camera_trend_analysis = phone_df.groupBy("main_camera_pixels", "brand").agg(avg("price").alias("brand_avg_price"), count("*").alias("brand_count"))
brand_camera_comparison = camera_trend_analysis.groupBy("main_camera_pixels").agg(avg("brand_avg_price").alias("market_avg_price"), count("brand").alias("brand_variety"))
camera_price_correlation = phone_df.select("main_camera_pixels", "price", "front_camera_pixels", "video_recording_quality")
correlation_matrix = camera_price_correlation.stat.corr("main_camera_pixels", "price")
high_end_cameras = phone_df.filter(col("main_camera_pixels") >= 64).select("brand", "model", "main_camera_pixels", "price", "front_camera_pixels")
camera_segments = phone_df.withColumn("camera_segment", when(col("main_camera_pixels") >= 100, "Ultra High").when(col("main_camera_pixels") >= 64, "High End").when(col("main_camera_pixels") >= 32, "Mid Range").otherwise("Entry Level"))
segment_analysis = camera_segments.groupBy("camera_segment").agg(avg("price").alias("segment_avg_price"), count("*").alias("segment_count"), min("price").alias("segment_min_price"), max("price").alias("segment_max_price"))
camera_evolution = phone_df.groupBy("release_year", "main_camera_pixels").agg(avg("price").alias("yearly_avg_price"), count("*").alias("yearly_count")).orderBy("release_year", desc("main_camera_pixels"))
premium_camera_features = phone_df.filter((col("main_camera_pixels") >= 64) & (col("price") >= 3000)).select("brand", "model", "main_camera_pixels", "price", "camera_features", "optical_zoom")
camera_roi_analysis = phone_df.withColumn("camera_price_ratio", col("price") / col("main_camera_pixels")).groupBy("brand").agg(avg("camera_price_ratio").alias("brand_camera_efficiency"))
result_data = {"camera_stats": sorted_camera_data.collect(), "brand_comparison": brand_camera_comparison.collect(), "correlation": correlation_matrix, "segment_analysis": segment_analysis.collect(), "evolution_trend": camera_evolution.collect()}
return JsonResponse(result_data, safe=False)
def performance_analysis_core(request):
phone_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/phone_db").option("dbtable", "phone_data").option("user", "root").option("password", "password").load()
processor_performance = phone_df.groupBy("processor_model", "processor_brand").agg(avg("antutu_score").alias("avg_antutu"), avg("price").alias("avg_price"), count("*").alias("model_count"))
performance_efficiency = processor_performance.withColumn("performance_per_yuan", col("avg_antutu") / col("avg_price"))
ram_storage_analysis = phone_df.groupBy("ram_size", "storage_size").agg(avg("antutu_score").alias("config_avg_score"), avg("price").alias("config_avg_price"), count("*").alias("config_count"))
gaming_performance = phone_df.filter(col("antutu_score") >= 600000).groupBy("processor_brand").agg(avg("gpu_score").alias("avg_gpu_score"), avg("price").alias("gaming_avg_price"), count("*").alias("gaming_phone_count"))
performance_segments = phone_df.withColumn("performance_tier", when(col("antutu_score") >= 800000, "Flagship").when(col("antutu_score") >= 500000, "High Performance").when(col("antutu_score") >= 300000, "Mid Performance").otherwise("Entry Performance"))
tier_analysis = performance_segments.groupBy("performance_tier").agg(avg("price").alias("tier_avg_price"), avg("antutu_score").alias("tier_avg_score"), count("*").alias("tier_count"))
brand_performance_comparison = phone_df.groupBy("brand").agg(avg("antutu_score").alias("brand_avg_score"), avg("cpu_score").alias("brand_avg_cpu"), avg("gpu_score").alias("brand_avg_gpu"), avg("price").alias("brand_avg_price"))
performance_price_correlation = phone_df.select("antutu_score", "price", "cpu_score", "gpu_score", "ram_size")
score_price_corr = performance_price_correlation.stat.corr("antutu_score", "price")
cpu_price_corr = performance_price_correlation.stat.corr("cpu_score", "price")
gpu_price_corr = performance_price_correlation.stat.corr("gpu_score", "price")
flagship_performance = phone_df.filter((col("antutu_score") >= 800000) & (col("price") >= 4000)).select("brand", "model", "antutu_score", "price", "processor_model", "ram_size")
performance_evolution = phone_df.groupBy("release_year").agg(avg("antutu_score").alias("yearly_avg_score"), max("antutu_score").alias("yearly_max_score"), avg("price").alias("yearly_avg_price")).orderBy("release_year")
price_performance_ranking = phone_df.withColumn("score_per_yuan", col("antutu_score") / col("price")).orderBy(desc("score_per_yuan")).limit(20)
overclocking_analysis = phone_df.filter(col("cpu_frequency") >= 3.0).groupBy("processor_model").agg(avg("antutu_score").alias("overclock_avg_score"), avg("price").alias("overclock_avg_price"))
result_data = {"processor_performance": performance_efficiency.collect(), "ram_storage_analysis": ram_storage_analysis.collect(), "gaming_performance": gaming_performance.collect(), "tier_analysis": tier_analysis.collect(), "brand_comparison": brand_performance_comparison.collect(), "correlations": {"score_price": score_price_corr, "cpu_price": cpu_price_corr, "gpu_price": gpu_price_corr}, "evolution": performance_evolution.collect(), "ranking": price_performance_ranking.collect()}
return JsonResponse(result_data, safe=False)
def price_analysis_core(request):
phone_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/phone_db").option("dbtable", "phone_data").option("user", "root").option("password", "password").load()
price_distribution = phone_df.groupBy("brand").agg(avg("price").alias("brand_avg_price"), min("price").alias("brand_min_price"), max("price").alias("brand_max_price"), count("*").alias("brand_model_count"))
price_segments = phone_df.withColumn("price_segment", when(col("price") >= 5000, "Premium").when(col("price") >= 3000, "High End").when(col("price") >= 1500, "Mid Range").when(col("price") >= 800, "Budget").otherwise("Entry Level"))
segment_distribution = price_segments.groupBy("price_segment", "brand").agg(count("*").alias("segment_count"), avg("price").alias("segment_brand_avg"))
market_share_analysis = price_segments.groupBy("price_segment").agg(count("*").alias("total_models"), avg("price").alias("segment_avg_price"))
total_models = phone_df.count()
market_share_with_percentage = market_share_analysis.withColumn("market_share_percentage", (col("total_models") / total_models) * 100)
price_trend_analysis = phone_df.groupBy("release_year", "brand").agg(avg("price").alias("yearly_brand_avg"), count("*").alias("yearly_brand_count")).orderBy("release_year", "brand")
feature_price_impact = phone_df.select("price", "main_camera_pixels", "ram_size", "storage_size", "battery_capacity", "screen_size", "antutu_score")
camera_price_corr = feature_price_impact.stat.corr("main_camera_pixels", "price")
ram_price_corr = feature_price_impact.stat.corr("ram_size", "price")
storage_price_corr = feature_price_impact.stat.corr("storage_size", "price")
battery_price_corr = feature_price_impact.stat.corr("battery_capacity", "price")
screen_price_corr = feature_price_impact.stat.corr("screen_size", "price")
score_price_corr = feature_price_impact.stat.corr("antutu_score", "price")
premium_value_analysis = phone_df.filter(col("price") >= 4000).groupBy("brand").agg(avg("antutu_score").alias("premium_avg_score"), avg("main_camera_pixels").alias("premium_avg_camera"), avg("ram_size").alias("premium_avg_ram"), count("*").alias("premium_model_count"))
budget_value_analysis = phone_df.filter(col("price") <= 1500).groupBy("brand").agg(avg("antutu_score").alias("budget_avg_score"), avg("main_camera_pixels").alias("budget_avg_camera"), avg("ram_size").alias("budget_avg_ram"), count("*").alias("budget_model_count"))
price_performance_matrix = phone_df.withColumn("value_score", (col("antutu_score") * 0.4 + col("main_camera_pixels") * 0.3 + col("ram_size") * 100 * 0.3) / col("price")).orderBy(desc("value_score"))
competitive_pricing = phone_df.groupBy("processor_model", "ram_size").agg(avg("price").alias("config_avg_price"), min("price").alias("config_min_price"), max("price").alias("config_max_price"), count("*").alias("config_count")).filter(col("config_count") >= 3)
price_volatility = phone_df.groupBy("brand", "release_year").agg(avg("price").alias("yearly_avg"), min("price").alias("yearly_min"), max("price").alias("yearly_max")).withColumn("price_range", col("yearly_max") - col("yearly_min"))
result_data = {"price_distribution": price_distribution.collect(), "segment_distribution": segment_distribution.collect(), "market_share": market_share_with_percentage.collect(), "price_trends": price_trend_analysis.collect(), "feature_correlations": {"camera_price": camera_price_corr, "ram_price": ram_price_corr, "storage_price": storage_price_corr, "battery_price": battery_price_corr, "screen_price": screen_price_corr, "score_price": score_price_corr}, "premium_analysis": premium_value_analysis.collect(), "budget_analysis": budget_value_analysis.collect(), "value_ranking": price_performance_matrix.limit(30).collect(), "competitive_pricing": competitive_pricing.collect()}
return JsonResponse(result_data, safe=False)
六.系统文档展示
结束
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