💖💖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
基于大数据的计算机岗位招聘数据可视化分析系统介绍
基于Spark技术的计算机岗位招聘可视化分析系统是一套集大数据处理、数据分析与可视化展示于一体的综合性毕业设计项目。该系统充分运用Hadoop分布式存储和Spark大数据计算引擎的技术优势，对海量计算机岗位招聘信息进行深度挖掘和智能分析。系统采用Python作为主要开发语言，后端基于Django框架构建RESTful API接口，前端运用Vue.js结合ElementUI组件库和Echarts可视化图表库，为用户提供直观友好的数据展示界面。通过Spark SQL进行复杂查询分析，结合Pandas和NumPy进行数据清洗与统计计算，系统能够实现对计算机行业招聘趋势、薪资分布、技能需求、地域分析等多维度数据的实时处理与动态展示。整个系统包含系统首页、个人信息管理、系统管理和数据可视化四大核心模块，支持多种图表类型展示分析结果，为计算机专业学生了解行业动态和企业需求提供了有效的数据支撑工具。
基于大数据的计算机岗位招聘数据可视化分析系统演示视频
基于大数据的计算机岗位招聘数据可视化分析系统演示图片
基于大数据的计算机岗位招聘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, desc, asc, when, regexp_extract
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType
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("ComputerJobAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def analyze_salary_distribution(request):
job_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/job_analysis").option("dbtable", "job_info").option("user", "root").option("password", "123456").load()
salary_df = job_df.select("position_name", "salary_min", "salary_max", "city", "experience", "education").filter(col("salary_min").isNotNull() & col("salary_max").isNotNull())
salary_df = salary_df.withColumn("avg_salary", (col("salary_min") + col("salary_max")) / 2)
city_salary = salary_df.groupBy("city").agg(avg("avg_salary").alias("avg_salary"), count("*").alias("job_count")).orderBy(desc("avg_salary"))
experience_salary = salary_df.groupBy("experience").agg(avg("avg_salary").alias("avg_salary"), count("*").alias("job_count")).orderBy(desc("avg_salary"))
education_salary = salary_df.groupBy("education").agg(avg("avg_salary").alias("avg_salary"), count("*").alias("job_count")).orderBy(desc("avg_salary"))
salary_ranges = salary_df.withColumn("salary_range", when(col("avg_salary") < 8000, "8K以下").when(col("avg_salary") < 15000, "8K-15K").when(col("avg_salary") < 25000, "15K-25K").when(col("avg_salary") < 40000, "25K-40K").otherwise("40K以上"))
range_distribution = salary_ranges.groupBy("salary_range").agg(count("*").alias("job_count")).orderBy(asc("salary_range"))
top_positions = salary_df.groupBy("position_name").agg(avg("avg_salary").alias("avg_salary"), count("*").alias("job_count")).filter(col("job_count") >= 10).orderBy(desc("avg_salary")).limit(20)
city_result = city_salary.toPandas().to_dict('records')
experience_result = experience_salary.toPandas().to_dict('records')
education_result = education_salary.toPandas().to_dict('records')
range_result = range_distribution.toPandas().to_dict('records')
position_result = top_positions.toPandas().to_dict('records')
result_data = {"city_salary": city_result, "experience_salary": experience_result, "education_salary": education_result, "salary_distribution": range_result, "top_positions": position_result}
return JsonResponse({"status": "success", "data": result_data})
@csrf_exempt
def analyze_skill_demand(request):
job_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/job_analysis").option("dbtable", "job_info").option("user", "root").option("password", "123456").load()
skill_keywords = ["Java", "Python", "JavaScript", "C++", "Go", "React", "Vue", "Spring", "Django", "MySQL", "Redis", "Docker", "Kubernetes", "Linux", "Git", "HTML", "CSS", "Node.js", "PHP", "C#"]
skill_df = job_df.select("id", "position_name", "job_description", "city", "salary_min", "salary_max", "company_name")
skill_counts = {}
for skill in skill_keywords:
skill_pattern = f"(?i)\\b{skill}\\b"
skill_matched = skill_df.filter(col("job_description").rlike(skill_pattern))
skill_count = skill_matched.count()
if skill_count > 0:
avg_salary_df = skill_matched.filter(col("salary_min").isNotNull() & col("salary_max").isNotNull())
avg_salary_df = avg_salary_df.withColumn("avg_salary", (col("salary_min") + col("salary_max")) / 2)
avg_salary = avg_salary_df.agg(avg("avg_salary")).collect()[0][0] if avg_salary_df.count() > 0 else 0
skill_counts[skill] = {"count": skill_count, "avg_salary": round(avg_salary, 2) if avg_salary else 0}
sorted_skills = sorted(skill_counts.items(), key=lambda x: x[1]["count"], reverse=True)
skill_city_analysis = {}
for skill, _ in sorted_skills[:10]:
skill_pattern = f"(?i)\\b{skill}\\b"
skill_city_df = skill_df.filter(col("job_description").rlike(skill_pattern))
city_distribution = skill_city_df.groupBy("city").agg(count("*").alias("job_count")).orderBy(desc("job_count")).limit(10)
skill_city_analysis[skill] = city_distribution.toPandas().to_dict('records')
skill_combination_df = skill_df.select("job_description")
combination_analysis = []
for i, (skill1, _) in enumerate(sorted_skills[:5]):
for skill2, _ in sorted_skills[i+1:6]:
pattern1 = f"(?i)\\b{skill1}\\b"
pattern2 = f"(?i)\\b{skill2}\\b"
combo_count = skill_combination_df.filter(col("job_description").rlike(pattern1) & col("job_description").rlike(pattern2)).count()
if combo_count > 10:
combination_analysis.append({"skill_combo": f"{skill1}+{skill2}", "count": combo_count})
combination_analysis = sorted(combination_analysis, key=lambda x: x["count"], reverse=True)[:10]
result_data = {"skill_ranking": [{"skill": skill, "count": data["count"], "avg_salary": data["avg_salary"]} for skill, data in sorted_skills], "skill_city_distribution": skill_city_analysis, "skill_combinations": combination_analysis}
return JsonResponse({"status": "success", "data": result_data})
@csrf_exempt
def analyze_market_trends(request):
job_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/job_analysis").option("dbtable", "job_info").option("user", "root").option("password", "123456").load()
monthly_df = job_df.withColumn("publish_month", regexp_extract(col("publish_date"), r"(\d{4}-\d{2})", 1)).filter(col("publish_month") != "")
monthly_trends = monthly_df.groupBy("publish_month").agg(count("*").alias("job_count")).orderBy(asc("publish_month"))
city_trends = job_df.groupBy("city").agg(count("*").alias("job_count")).orderBy(desc("job_count")).limit(15)
company_size_df = job_df.filter(col("company_size").isNotNull() & (col("company_size") != ""))
size_distribution = company_size_df.groupBy("company_size").agg(count("*").alias("job_count")).orderBy(desc("job_count"))
industry_trends = job_df.filter(col("industry").isNotNull() & (col("industry") != "")).groupBy("industry").agg(count("*").alias("job_count")).orderBy(desc("job_count")).limit(10)
experience_trends = job_df.filter(col("experience").isNotNull() & (col("experience") != "")).groupBy("experience").agg(count("*").alias("job_count"), avg("salary_min").alias("avg_min_salary"), avg("salary_max").alias("avg_max_salary")).orderBy(desc("job_count"))
education_trends = job_df.filter(col("education").isNotNull() & (col("education") != "")).groupBy("education").agg(count("*").alias("job_count"), avg("salary_min").alias("avg_min_salary"), avg("salary_max").alias("avg_max_salary")).orderBy(desc("job_count"))
position_category_df = job_df.withColumn("category", when(col("position_name").rlike("(?i).*(前端|front).*"), "前端开发").when(col("position_name").rlike("(?i).*(后端|后台|back).*"), "后端开发").when(col("position_name").rlike("(?i).*(全栈|full).*"), "全栈开发").when(col("position_name").rlike("(?i).*(算法|AI|机器学习|深度学习).*"), "算法/AI").when(col("position_name").rlike("(?i).*(测试|test).*"), "软件测试").when(col("position_name").rlike("(?i).*(运维|devops|系统管理).*"), "运维/系统").when(col("position_name").rlike("(?i).*(产品|product).*"), "产品经理").when(col("position_name").rlike("(?i).*(数据|data).*"), "数据分析").otherwise("其他"))
category_trends = position_category_df.groupBy("category").agg(count("*").alias("job_count"), avg("salary_min").alias("avg_min_salary"), avg("salary_max").alias("avg_max_salary")).orderBy(desc("job_count"))
remote_work_df = job_df.withColumn("is_remote", when(col("job_description").rlike("(?i).*(远程|remote|在家|居家).*"), "支持远程").otherwise("不支持远程"))
remote_trends = remote_work_df.groupBy("is_remote").agg(count("*").alias("job_count")).orderBy(desc("job_count"))
monthly_result = monthly_trends.toPandas().to_dict('records')
city_result = city_trends.toPandas().to_dict('records')
size_result = size_distribution.toPandas().to_dict('records')
industry_result = industry_trends.toPandas().to_dict('records')
experience_result = experience_trends.toPandas().to_dict('records')
education_result = education_trends.toPandas().to_dict('records')
category_result = category_trends.toPandas().to_dict('records')
remote_result = remote_trends.toPandas().to_dict('records')
result_data = {"monthly_trends": monthly_result, "city_distribution": city_result, "company_size_distribution": size_result, "industry_trends": industry_result, "experience_trends": experience_result, "education_trends": education_result, "position_category_trends": category_result, "remote_work_trends": remote_result}
return JsonResponse({"status": "success", "data": result_data})
基于大数据的计算机岗位招聘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
