python毕设基于django就业系统源码.zip
资源文件列表(大概)
bishe-master/.idea/dataSources.xml
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bishe-master/.idea/dataSources/
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bishe-master/.idea/dataSources/2473e3dc-b14c-42a4-aa4e-c9461c3d0f9c.xml
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bishe-master/.idea/misc.xml
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bishe-master/.idea/modules.xml
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bishe-master/.idea/testdjango.iml
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bishe-master/.idea/vcs.xml
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bishe-master/data_sum/all - 副本.csv
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bishe-master/data_sum/all.csv
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bishe-master/data_sum/all原始.csv
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bishe-master/data_sum/buchong_sum.csv
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bishe-master/data_sum/hadoop_sum.csv
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bishe-master/data_sum/java_sum.csv
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bishe-master/data_sum/mm.csv
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bishe-master/data_sum/python_sum.csv
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bishe-master/data_sum/python_sum（保留学历不限不新增高中).csv
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bishe-master/data_sum/updata_java_ceshi222.csv
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bishe-master/data_sum/updata_python_ceshi.csv
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bishe-master/data_sum/updata_suanfa_ceshi.csv
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bishe-master/data_sum/updata_web_ceshi.csv
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bishe-master/data_sum/前端_sum(保留学历不限).csv
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bishe-master/data_sum/前端_sum.csv
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bishe-master/data_sum/数据分析_sum.csv
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bishe-master/data_sum/算法_sum.csv
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bishe-master/db.sqlite3
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bishe-master/django_test_django_admin_log.sql
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bishe-master/manage.py
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bishe-master/static/css/
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bishe-master/static/css/comon0.css
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bishe-master/static/css/predict.css
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bishe-master/static/css/zhuce.css
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bishe-master/static/font/
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bishe-master/static/font/DS-DIGIT.TTF
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bishe-master/static/images/
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bishe-master/static/images/J.png
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bishe-master/static/images/bg.jpg
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bishe-master/static/images/head_bg.png
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bishe-master/static/images/jt.png
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bishe-master/static/images/lbx.png
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bishe-master/static/images/line.png
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bishe-master/static/images/loading.gif
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bishe-master/static/images/map.png
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bishe-master/static/images/mid_1.png
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bishe-master/static/images/mid_2.png
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bishe-master/static/images/mid_3.png
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bishe-master/static/images/zhu.png
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bishe-master/static/js/area_echarts.js
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bishe-master/static/js/china.js
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bishe-master/static/js/echarts.min.js
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bishe-master/static/js/index.html
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bishe-master/static/js/index2.html
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bishe-master/static/js/jquery.js
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bishe-master/static/js/js.js
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bishe-master/static/js/pyecharts.min.js
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bishe-master/static/js/vector.js
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bishe-master/templates/ceshi2.html
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bishe-master/templates/index.html
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bishe-master/templates/job_demand_pie_sum.html
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bishe-master/templates/predict_xinzi.html
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bishe-master/templates/source/
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bishe-master/templates/source/no_favorite
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bishe-master/templates/test.html
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bishe-master/templates/xinzi_bar_sum.html
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bishe-master/templates/zhuce.html
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bishe-master/templates/zhuye.html
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bishe-master/testdjango/
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bishe-master/testdjango/__init__.py
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bishe-master/testdjango/settings.py
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bishe-master/testdjango/urls.py
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bishe-master/testdjango/wsgi.py
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bishe-master/user/__init__.py
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bishe-master/user/admin.py
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bishe-master/user/apps.py
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bishe-master/user/migrations/
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bishe-master/user/migrations/0001_initial.py
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bishe-master/user/migrations/0002_comp_job_stu.py
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bishe-master/user/migrations/0003_stu_password.py
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bishe-master/user/migrations/0004_job.py
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bishe-master/user/migrations/0005_auto_20210602_1640.py
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bishe-master/user/migrations/0006_auto_20210604_1547.py
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bishe-master/user/migrations/0007_auto_20210613_0859.py
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bishe-master/user/migrations/__init__.py
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bishe-master/user/models.py
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bishe-master/user/resource.py
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bishe-master/user/tests.py
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bishe-master/user/views.py
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资源内容介绍
python毕设基于django就业系统源码.zip，python毕设基于django就业系统源码.zippython毕设基于django就业系统源码.zippython毕设基于django就业系统源码.zip from django.shortcuts import render,redirect,render_to_responsefrom django.http import HttpResponsefrom django import formsimport pandas as pd# Create your views here.from user.models import *from django.contrib.auth.models import Userfrom django.contrib.auth import authenticate,login,logoutfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_split#核心算法函数def forest(list,df): df['job_salary_range'] = df['job_salary_range'].astype(str).map({'0-10K': 0, '10-20K': 1, '20-30K': 2, '>30K': 3}) y = df['job_salary_range'] x = df.drop(labels=['job_salary_range', 'job_name', 'company_name'], axis=1) # 删除掉无关列 x = pd.get_dummies(x) #独热编码 xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=5) # test_size是x，y测试集占总的20% rfc = RandomForestClassifier(max_depth=None, min_samples_split=2, random_state=0) # 实例化rfc = rfc.fit(xtrain, ytrain) #用训练集数据训练 rfc = rfc.fit(xtrain, ytrain) # result = rfc.score(xtest, ytest) # 导入测试集，rfc的接口score计算的是模型准确率accuracy res = rfc.predict(list) return resdef xinzi_predict(request): if request.method == 'GET': return render(request,'predict_xinzi.html') else: list1 = [] list_sum = [] java1 = request.POST.get('java1') #JAVA要求 spring1 = request.POST.get('spring1') sql1 = request.POST.get('sql1') python1 = request.POST.get('python1') #Python要求 linux1 = request.POST.get('linux1') spider1 = request.POST.get('spider1') html1 = request.POST.get('html1') # web要求 cssjs1 = request.POST.get('cssjs1') vue1 = request.POST.get('vue1') jiqi1 = request.POST.get('jiqi1') # 算法工程师要求 tuxiang1 = request.POST.get('tuxiang1') C1 = request.POST.get('C1') city = request.POST.get('city') demand = request.POST.get('demand') guimo = request.POST.get('guimo') a = request.POST.get('job_name') global df #声明全局变量 if a == 'Java开发工程师': list1.append(java1) list1.append(spring1) list1.append(sql1) df = pd.read_csv('C:\\Users\\独为我唱\\Desktop\\data_sum\\updata_java_ceshi222.csv', encoding='gbk') elif a == 'Python开发工程师': list1.append(python1) list1.append(linux1) list1.append(spider1) df = pd.read_csv('C:\\Users\\独为我唱\\Desktop\\data_sum\\updata_python_ceshi.csv', encoding='gbk') elif a == 'web前端开发师': list1.append(html1) list1.append(cssjs1) list1.append(vue1) df = pd.read_csv('C:\\Users\\独为我唱\\Desktop\\data_sum\\updata_web_ceshi.csv', encoding='gbk') elif a == '算法工程师': list1.append(jiqi1) list1.append(tuxiang1) list1.append(C1) df = pd.read_csv('C:\\Users\\独为我唱\\Desktop\\data_sum\\updata_suanfa_ceshi.csv', encoding='gbk') city = city.split(',') list1.extend(city) demand = demand.split(',') list1.extend(demand) guimo = guimo.split(',') list1.extend(guimo) list_sum.append(list1) # 得到双中括号包起来的列表，并且里面的元素都变成了算法可以直接调用的元素 res = forest(list_sum,df) if res[0] == 0: message = '预测薪资范围是每月5-10K' elif res[0] == 1: message = '预测薪资范围是每月10-20K' elif res[0] == 2: message = '预测薪资范围是每月20-30K' else: message = '预测薪资范围是每月在30k以上' return render(request, 'predict_xinzi.html', {'message': message})def ceshi2(request): return render(request ,'ceshi2.html', { 'name':'all', 'users':['ab','qwe'], 'user_dict':{'k1':'v1', 'k2':'v2'}, 'us':[ {'id':1,'name':'xiaomm','email':'1111@qq.com'}, {'id':2, 'name':'xoapxaopx', 'email':'ssss@163.com'}, ] } )def job_demand(request): return render(request, 'job_demand_pie_sum.html',)def xinzi_bar(request): return render(request,'xinzi_bar_sum.html')def denglu(request): if request.method == "GET": return render(request, 'zhuce.html') else: name = request.POST.get('username') pwd = request.POST.get('password') test = stu.objects.filter(stuid = name, password = pwd) name = test.values('name')[0]['name'] #通过学号和登录密码查询到学生的姓名 if test: return render(request, 'zhuye.html', {'username': name}) else: error_msg = '用户名或密码错误' return render(request,'zhuce.html',{"error_msg":error_msg})def zhuce(request): if request.method == "POST": name = request.POST.get("uname") stuid = request.POST.get("stuid") if stu.objects.filter(stuid = stuid): return render(request, 'zhuce.html',{"message": '该账号已存在，请重新注册！'}) aca = request.POST.get("aca") clas = request.POST.get("class") password = request.POST.get("password") age = request.POST.get("age") stu.objects.create(name = name, stuid = stuid, academy= aca, stuclass=clas, age= age,gender=1, password=password ) return render(request, 'zhuce.html',{"msg":'注册成功'}) else: return render(request, "zhuce.html")#总的岗位数量的饼图和柱状图def pie_bar_test(request): df = pd.read_csv('C:\\Users\\独为我唱\\Desktop\\data_sum\\all.csv', encoding='gbk' , low_memory=False,converters={'work_demand':str}) dd = df.loc[df['job_name'] != '其他职业'] pie_data_index = list(dd['job_name'].value_counts().index) pie_data = list(dd['job_name'].value_counts()) data = [] for i in range(len(pie_data)): dic = {} dic['name'] = pie_data_index[i] dic['value'] = pie_data[i] data.append(dic) return render(request, 'test.html', {"pie_data_index":pie_data_index, "data":data, "pie_data":pie_data, })#辅助函数，用于主展示屏展示工作要求饼图def abi_class(list): newlist = [] for ele in list: newlist += ele.split(',') newlist = [x.strip() for x in newlist] # 这两行是为了使原df的工作要求单个呈现以逗号分割 res = dict() for a in set(newlist): res[a] = newlist.count(a) ll = sorted(res.items(), key=lambda item: item[1], reverse=True) # 按从大到小排序每种技能的出现次数 ll = ll[0:6] # 取出list前6个值 return lldef test_pic(request): df = pd.read_csv('C:\\Users\\独为我唱\\Desktop\\data_sum\\all.csv', encoding='gbk' , low_memory=False,converters={'work_demand':str}) # 取出每个城市及其岗位数 job = list(df['company_locale'].value_counts().index) job1 = list(df['company_locale'].value_counts()) #修改成元素为字典的list，以便地图绘制 data2 = [] for i in range(len(job)): dic = {} dic['name'] = job[i] dic['value'] = job1[i] data2.append(dic) # 取出java技能各占比 a = df['work_demand'].str.split() list1 = [] x = a.copy() for i in range(len(x)): if 'Java' in df['job_name'][i]: list