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
基于大数据的牛油果数据可视化分析系统是一套专门针对牛油果市场数据进行深度分析和可视化展示的综合性大数据处理平台。该系统采用Hadoop分布式存储架构结合Spark大数据计算引擎，实现对海量牛油果相关数据的高效处理和实时分析。系统后端基于Django框架构建稳定的数据处理服务，前端运用Vue框架搭配ElementUI组件库和Echarts图表库，为用户提供直观友好的数据可视化界面。系统核心功能涵盖牛油果基础数据管理、多维度概览分析、物理性质特征分析、颜色属性分析以及综合特征对比分析等模块，通过Spark SQL进行复杂数据查询和统计运算，结合Pandas和NumPy进行数据清洗和科学计算，最终以丰富的图表形式展现牛油果市场的各项关键指标和发展趋势，为相关从业者和研究人员提供有价值的数据洞察和决策支持。
三、基于大数据的牛油果数据可视化分析系统-视频解说
Python大数据毕设首选：牛油果数据可视化分析系统Django+Vue技术栈详解|系统设计
四、基于大数据的牛油果数据可视化分析系统-功能展示
五、基于大数据的牛油果数据可视化分析系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, max, min, when, desc, asc
from django.http import JsonResponse
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("AvocadoDataAnalysis").getOrCreate()
def avocado_overview_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/avocado_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "avocado_data").option("user", "root").option("password", "password").load()
total_count = df.count()
avg_weight = df.agg(avg("weight").alias("avg_weight")).collect()[0]["avg_weight"]
max_diameter = df.agg(max("diameter").alias("max_diameter")).collect()[0]["max_diameter"]
min_diameter = df.agg(min("diameter").alias("min_diameter")).collect()[0]["min_diameter"]
weight_distribution = df.groupBy("weight_category").agg(count("*").alias("count")).orderBy(desc("count")).collect()
diameter_stats = df.select("diameter").toPandas()
diameter_median = np.median(diameter_stats["diameter"])
diameter_std = np.std(diameter_stats["diameter"])
quality_distribution = df.groupBy("quality_grade").agg(count("*").alias("count"), avg("weight").alias("avg_weight")).collect()
seasonal_analysis = df.groupBy("harvest_season").agg(count("*").alias("total_count"), avg("diameter").alias("avg_diameter"), avg("weight").alias("avg_weight")).orderBy("harvest_season").collect()
region_comparison = df.groupBy("origin_region").agg(count("*").alias("sample_count"), avg("weight").alias("avg_weight"), max("diameter").alias("max_diameter")).orderBy(desc("sample_count")).collect()
overview_data = {"total_samples": total_count, "average_weight": round(avg_weight, 2), "max_diameter": max_diameter, "min_diameter": min_diameter, "diameter_median": round(diameter_median, 2), "diameter_std": round(diameter_std, 2), "weight_distribution": [{"category": row["weight_category"], "count": row["count"]} for row in weight_distribution], "quality_distribution": [{"grade": row["quality_grade"], "count": row["count"], "avg_weight": round(row["avg_weight"], 2)} for row in quality_distribution], "seasonal_analysis": [{"season": row["harvest_season"], "count": row["total_count"], "avg_diameter": round(row["avg_diameter"], 2), "avg_weight": round(row["avg_weight"], 2)} for row in seasonal_analysis], "region_comparison": [{"region": row["origin_region"], "sample_count": row["sample_count"], "avg_weight": round(row["avg_weight"], 2), "max_diameter": row["max_diameter"]} for row in region_comparison]}
return JsonResponse(overview_data)
def avocado_physical_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/avocado_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "avocado_data").option("user", "root").option("password", "password").load()
hardness_stats = df.agg(avg("hardness").alias("avg_hardness"), max("hardness").alias("max_hardness"), min("hardness").alias("min_hardness")).collect()[0]
oil_content_analysis = df.groupBy("oil_content_level").agg(count("*").alias("count"), avg("hardness").alias("avg_hardness"), avg("weight").alias("avg_weight")).collect()
hardness_weight_correlation = df.select("hardness", "weight").toPandas()
correlation_coefficient = np.corrcoef(hardness_weight_correlation["hardness"], hardness_weight_correlation["weight"])[0, 1]
texture_distribution = df.groupBy("texture_type").agg(count("*").alias("count"), avg("hardness").alias("avg_hardness"), avg("oil_content").alias("avg_oil")).orderBy(desc("count")).collect()
diameter_hardness_relation = df.select("diameter", "hardness").rdd.map(lambda row: (int(row["diameter"] // 5) * 5, row["hardness"])).toDF(["diameter_range", "hardness"])
diameter_hardness_stats = diameter_hardness_relation.groupBy("diameter_range").agg(avg("hardness").alias("avg_hardness"), count("*").alias("sample_count")).orderBy("diameter_range").collect()
physical_quality_mapping = df.groupBy("quality_grade").agg(avg("hardness").alias("avg_hardness"), avg("oil_content").alias("avg_oil"), avg("diameter").alias("avg_diameter")).collect()
weight_hardness_categories = df.withColumn("weight_hardness_category", when((col("weight") > 200) & (col("hardness") > 50), "heavy_hard").when((col("weight") > 200) & (col("hardness") <= 50), "heavy_soft").when((col("weight") <= 200) & (col("hardness") > 50), "light_hard").otherwise("light_soft"))
category_distribution = weight_hardness_categories.groupBy("weight_hardness_category").agg(count("*").alias("count")).collect()
oil_hardness_scatter = df.select("oil_content", "hardness", "quality_grade").collect()
physical_data = {"hardness_stats": {"avg": round(hardness_stats["avg_hardness"], 2), "max": hardness_stats["max_hardness"], "min": hardness_stats["min_hardness"]}, "correlation_coefficient": round(correlation_coefficient, 3), "oil_content_analysis": [{"level": row["oil_content_level"], "count": row["count"], "avg_hardness": round(row["avg_hardness"], 2), "avg_weight": round(row["avg_weight"], 2)} for row in oil_content_analysis], "texture_distribution": [{"texture": row["texture_type"], "count": row["count"], "avg_hardness": round(row["avg_hardness"], 2), "avg_oil": round(row["avg_oil"], 2)} for row in texture_distribution], "diameter_hardness_stats": [{"diameter_range": row["diameter_range"], "avg_hardness": round(row["avg_hardness"], 2), "sample_count": row["sample_count"]} for row in diameter_hardness_stats], "physical_quality_mapping": [{"grade": row["quality_grade"], "avg_hardness": round(row["avg_hardness"], 2), "avg_oil": round(row["avg_oil"], 2), "avg_diameter": round(row["avg_diameter"], 2)} for row in physical_quality_mapping], "category_distribution": [{"category": row["weight_hardness_category"], "count": row["count"]} for row in category_distribution], "oil_hardness_scatter": [{"oil_content": row["oil_content"], "hardness": row["hardness"], "quality_grade": row["quality_grade"]} for row in oil_hardness_scatter]}
return JsonResponse(physical_data)
def avocado_color_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/avocado_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "avocado_data").option("user", "root").option("password", "password").load()
color_distribution = df.groupBy("main_color").agg(count("*").alias("count")).orderBy(desc("count")).collect()
color_quality_relation = df.groupBy("main_color", "quality_grade").agg(count("*").alias("count")).collect()
color_ripeness_analysis = df.groupBy("main_color").agg(avg("ripeness_score").alias("avg_ripeness"), count("*").alias("sample_count")).orderBy(desc("avg_ripeness")).collect()
brightness_stats = df.agg(avg("brightness_value").alias("avg_brightness"), max("brightness_value").alias("max_brightness"), min("brightness_value").alias("min_brightness")).collect()[0]
color_brightness_correlation = df.select("brightness_value", "ripeness_score").toPandas()
brightness_ripeness_corr = np.corrcoef(color_brightness_correlation["brightness_value"], color_brightness_correlation["ripeness_score"])[0, 1]
color_weight_analysis = df.groupBy("main_color").agg(avg("weight").alias("avg_weight"), avg("diameter").alias("avg_diameter")).collect()
hue_saturation_distribution = df.select("hue_value", "saturation_value").rdd.map(lambda row: (int(row["hue_value"] // 30) * 30, int(row["saturation_value"] // 20) * 20)).toDF(["hue_range", "saturation_range"])
hue_saturation_stats = hue_saturation_distribution.groupBy("hue_range", "saturation_range").agg(count("*").alias("count")).collect()
color_defect_analysis = df.filter(col("has_defects") == True).groupBy("main_color").agg(count("*").alias("defect_count")).collect()
total_by_color = df.groupBy("main_color").agg(count("*").alias("total_count")).collect()
defect_rates = []
for defect in color_defect_analysis:
for total in total_by_color:
if defect["main_color"] == total["main_color"]:
defect_rate = (defect["defect_count"] / total["total_count"]) * 100
defect_rates.append({"color": defect["main_color"], "defect_rate": round(defect_rate, 2)})
rgb_analysis = df.select("red_value", "green_value", "blue_value", "quality_grade").collect()
color_seasonal_variation = df.groupBy("main_color", "harvest_season").agg(count("*").alias("count"), avg("brightness_value").alias("avg_brightness")).collect()
color_data = {"color_distribution": [{"color": row["main_color"], "count": row["count"]} for row in color_distribution], "color_quality_relation": [{"color": row["main_color"], "quality": row["quality_grade"], "count": row["count"]} for row in color_quality_relation], "color_ripeness_analysis": [{"color": row["main_color"], "avg_ripeness": round(row["avg_ripeness"], 2), "sample_count": row["sample_count"]} for row in color_ripeness_analysis], "brightness_stats": {"avg": round(brightness_stats["avg_brightness"], 2), "max": brightness_stats["max_brightness"], "min": brightness_stats["min_brightness"]}, "brightness_ripeness_correlation": round(brightness_ripeness_corr, 3), "color_weight_analysis": [{"color": row["main_color"], "avg_weight": round(row["avg_weight"], 2), "avg_diameter": round(row["avg_diameter"], 2)} for row in color_weight_analysis], "hue_saturation_stats": [{"hue_range": row["hue_range"], "saturation_range": row["saturation_range"], "count": row["count"]} for row in hue_saturation_stats], "defect_rates": defect_rates, "rgb_analysis": [{"red": row["red_value"], "green": row["green_value"], "blue": row["blue_value"], "quality": row["quality_grade"]} for row in rgb_analysis], "color_seasonal_variation": [{"color": row["main_color"], "season": row["harvest_season"], "count": row["count"], "avg_brightness": round(row["avg_brightness"], 2)} for row in color_seasonal_variation]}
return JsonResponse(color_data)
六、基于大数据的牛油果数据可视化分析系统-文档展示
七、END
- 💛💛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
