毕业设计：基于python招聘就业数据采集分析可视化系统+Flask框架（源码+文档）✅
毕业设计：2023-2024年计算机专业毕业设计选题汇总（建议收藏）
毕业设计：2023-2024年最新最全计算机专业毕设选题推荐汇总
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1、项目介绍
技术栈：
Python语言、Flask框架、MySQL数据库、requests爬虫、前程无忧全国招聘信息爬虫
Flask前程无忧数据采集分析可视化系统是一个基于Flask框架开发的数据处理工具。它可以帮助用户采集、分析和可视化前程无忧网站上的就业数据。
该系统具有以下特点：
-
数据采集：系统通过爬虫技术，从前程无忧网站上获取就业数据。用户可以根据自己的需求，选择不同的搜索条件和筛选规则，获取特定的就业信息。
-
数据分析：系统提供了多种数据分析功能，帮助用户深入了解就业市场的趋势和变化。用户可以通过系统提供的统计图表和数据报告，分析不同行业、地区和职位的就业情况，从而做出更明智的职业决策。
2、项目界面
（1）岗位行业分析
（2）岗位应聘要求分析
（3）互联网岗位分析
（4）各地区平均薪资分析
（5）招聘数据展示
3、项目说明
Flask前程无忧数据采集分析可视化系统是一个基于Flask框架开发的数据处理工具。它可以帮助用户采集、分析和可视化前程无忧网站上的就业数据。
该系统具有以下特点：
-
数据采集：系统通过爬虫技术，从前程无忧网站上获取就业数据。用户可以根据自己的需求，选择不同的搜索条件和筛选规则，获取特定的就业信息。
-
数据分析：系统提供了多种数据分析功能，帮助用户深入了解就业市场的趋势和变化。用户可以通过系统提供的统计图表和数据报告，分析不同行业、地区和职位的就业情况，从而做出更明智的职业决策。
-
可视化展示：系统通过可视化技术，将采集到的数据以图表和图形的形式展示出来。这样用户可以更直观地了解就业市场的状况，并发现潜在的就业机会。同时，系统还支持用户自定义展示方式，满足不同用户的需求。
-
用户友好性：系统注重用户体验，提供了简洁直观的界面和操作流程。用户可以快速上手，轻松完成数据采集、分析和可视化的工作。
总之，Flask前程无忧数据采集分析可视化系统是一个功能强大、易于使用的工具，帮助用户更好地了解就业市场，做出职业规划和决策。无论是求职者还是招聘方，都可以从中获得有价值的信息和洞察。
4、核心代码
#!/usr/bin/python
# coding=utf-8
import sqlite3
import pandas as pd
from flask import Flask, render_template, jsonify, request
import numpy as np
import json
import jieba
app = Flask(__name__)
app.config.from_object('config')
@app.route('/job_hangye_analysis')
def job_hangye_analysis():
"""行业分析"""
conn = sqlite3.connect(DATABASE)
cursor = conn.cursor()
check_sql = "SELECT hangye, salary FROM job"
cursor.execute(check_sql)
jobs = cursor.fetchall()
# 行业的个数
hangye_counts = {}
hangye_salary = {}
for job in jobs:
hangye, salary = job
if hangye not in hangye_counts:
hangye_counts[hangye] = 0
hangye_counts[hangye] += 1
if not salary.endswith('/月'):
continue
if salary.endswith('千/月'):
scale = 1000
elif salary.endswith('万/月'):
scale = 10000
else:
continue
salary = salary[:-3]
# 计算平均薪资
salary = sum(map(float, salary.split('-'))) / 2 * scale
if hangye not in hangye_salary:
hangye_salary[hangye] = []
hangye_salary[hangye].append(salary)
hangye_counts = list(zip(list(hangye_counts.keys()), list(hangye_counts.values())))
hangye_counts = sorted(hangye_counts, key=lambda k: k[1], reverse=True)
# 过滤掉一些在招岗位很少的行业
hangye_counts = [v for v in hangye_counts if v[1] > 10]
hangye1 = [v[0] for v in hangye_counts][:40]
counts = [v[1] for v in hangye_counts][:40]
# 计算行业的平均薪资
for hangye in hangye_salary:
hangye_salary[hangye] = np.mean(hangye_salary[hangye])
hangye_salary = list(zip(list(hangye_salary.keys()), list(hangye_salary.values())))
hangye_salary = sorted(hangye_salary, key=lambda k: k[1], reverse=False)
hangye2 = [v[0] for v in hangye_salary][:40]
salary = [v[1] for v in hangye_salary][:40]
return jsonify({'行业': hangye1, '岗位数': counts, '行业2': hangye2, '平均薪资': salary})
@app.route('/dili_fengqu_analysis/' )
def dili_fengqu_analysis(fengqu):
conn = sqlite3.connect(DATABASE)
cursor = conn.cursor()
check_sql = "SELECT hangye, location, salary FROM job"
cursor.execute(check_sql)
jobs = cursor.fetchall()
# 行业的个数
hangye_counts = {}
hangye_salary = {}
for job in jobs:
hangye, location, salary = job
if location + '市' not in city_fenqu_maps:
continue
if city_fenqu_maps[location + '市'] != fengqu:
continue
if hangye not in hangye_counts:
hangye_counts[hangye] = 0
hangye_counts[hangye] += 1
if not salary.endswith('/月'):
continue
if salary.endswith('千/月'):
scale = 1000
elif salary.endswith('万/月'):
scale = 10000
else:
continue
salary = salary[:-3]
# 计算平均薪资
salary = sum(map(float, salary.split('-'))) / 2 * scale
if hangye not in hangye_salary:
hangye_salary[hangye] = []
hangye_salary[hangye].append(salary)
hangye_counts = list(zip(list(hangye_counts.keys()), list(hangye_counts.values())))
hangye_counts = sorted(hangye_counts, key=lambda k: k[1], reverse=True)
# 过滤掉一些在招岗位很少的行业
hangye1 = [v[0] for v in hangye_counts][:20]
counts = [v[1] for v in hangye_counts][:20]
# 计算行业的平均薪资
for hangye in hangye_salary:
hangye_salary[hangye] = np.mean(hangye_salary[hangye])
hangye_salary = list(zip(list(hangye_salary.keys()), list(hangye_salary.values())))
hangye_salary = sorted(hangye_salary, key=lambda k: k[1], reverse=False)
hangye2 = [v[0] for v in hangye_salary][:20]
salary = [v[1] for v in hangye_salary][:20]
high_salary_hangyes = ' > '.join(hangye2[::-1][:3])
return jsonify({'行业': hangye1, '岗位数': counts, '行业2': hangye2, '平均薪资': salary,
'高薪行业推荐': high_salary_hangyes})
@app.route('/fengqu_salary_analysis')
def fengqu_salary_analysis():
conn = sqlite3.connect(DATABASE)
cursor = conn.cursor()
check_sql = "SELECT hangye, location, salary FROM job"
cursor.execute(check_sql)
jobs = cursor.fetchall()
fengqu_high_salary = {'华东': [], '华北': [], '华中': [], '华南': [], '西南': [], '西北': [], '东北': []}
fengqu_low_salary = {'华东': [], '华北': [], '华中': [], '华南': [], '西南': [], '西北': [], '东北': []}
for job in jobs:
hangye, location, salary = job
if location + '市' not in city_fenqu_maps:
continue
if not salary.endswith('/月'):
continue
if salary.endswith('千/月'):
scale = 1000
elif salary.endswith('万/月'):
scale = 10000
else:
continue
fengqu = city_fenqu_maps[location + '市']
salary = salary[:-3]
low_salary, high_salary = map(float, salary.split('-'))
fengqu_high_salary[fengqu].append(high_salary * scale)
fengqu_low_salary[fengqu].append(low_salary * scale)
fengqu = ['华东', '华北', '华中', '华南', '西南', '西北', '东北']
high_salary = [np.mean(fengqu_high_salary[fq]) for fq in fengqu]
low_salary = [np.mean(fengqu_low_salary[fq]) for fq in fengqu]
return jsonify({'fengqu': fengqu, 'high_salary': high_salary, 'low_salary': low_salary})
@app.route('/query_yingpin_yaoqiu/' )
def query_yingpin_yaoqiu(search):
conn = sqlite3.connect(DATABASE)
cursor = conn.cursor()
check_sql = "SELECT jingyan, xueli, salary, job_name FROM job"
cursor.execute(check_sql)
jobs = cursor.fetchall()
jingyan_salary = {}
xueli_salary = {}
for job in jobs:
jingyan, xueli, salary, job_name = job
if search != '无':
if search.lower() not in job_name.lower():
continue
try:
jingyan = int(jingyan)
jingyan = '{}年经验'.format(str(jingyan))
except:
pass
if jingyan not in jingyan_salary:
jingyan_salary[jingyan] = []
if xueli not in xueli_salary:
xueli_salary[xueli] = []
if not salary.endswith('/月'):
continue
if salary.endswith('千/月'):
scale = 1000
elif salary.endswith('万/月'):
scale = 10000
else:
continue
salary = salary[:-3]
# 计算平均薪资
salary = sum(map(float, salary.split('-'))) / 2 * scale
jingyan_salary[jingyan].append(salary)
xueli_salary[xueli].append(salary)
jingyan_job_counts = {}
for jingyan in jingyan_salary:
jingyan_job_counts[jingyan] = len(jingyan_salary[jingyan])
jingyan_salary[jingyan] = np.mean(jingyan_salary[jingyan])
jingyan_salary = list(zip(list(jingyan_salary.keys()), list(jingyan_salary.values())))
jingyan_salary = sorted(jingyan_salary, key=lambda k: k[1], reverse=True)
jingyan = [v[0] for v in jingyan_salary]
jingyan_salary = [v[1] for v in jingyan_salary]
jingyan_job_counts = [jingyan_job_counts[jy] for jy in jingyan]
xueli_job_counts = {}
for xueli in xueli_salary:
xueli_job_counts[xueli] = len(xueli_salary[xueli])
xueli_salary[xueli] = np.mean(xueli_salary[xueli] + [0])
xueli_salary = list(zip(list(xueli_salary.keys()), list(xueli_salary.values())))
xueli_salary = sorted(xueli_salary, key=lambda k: k[1], reverse=True)
xueli = [v[0] for v in xueli_salary if '人' not in v[0]]
xueli_salary = [v[1] for v in xueli_salary if '人' not in v[0]]
xueli_job_counts = [xueli_job_counts[xl] for xl in xueli]
results = {'经验': jingyan, '经验平均薪资': jingyan_salary, '经验岗位数': jingyan_job_counts,
'学历': xueli, '学历平均薪资': xueli_salary, '学历岗位数': xueli_job_counts}
return jsonify(results)
@app.route('/query_city_salary_region')
def query_city_salary_region():
conn = sqlite3.connect(DATABASE)
cursor = conn.cursor()
check_sql = "SELECT location, salary FROM job"
cursor.execute(check_sql)
jobs = cursor.fetchall()
city_salary = {}
city_region = {}
for job in jobs:
location, salary = job
if not salary.endswith('/月'):
continue
if salary.endswith('千/月'):
scale = 1000
elif salary.endswith('万/月'):
scale = 10000
else:
continue
salary = salary[:-3]
# 计算平均薪资
salary = sum(map(float, salary.split('-'))) / 2 * scale
city = location + '市'
if city not in city_region_dict:
continue
if city not in city_salary:
city_salary[city] = []
city_salary[city].append(salary)
loc = city_region_dict[city]
city_region[city] = [loc['longitude'], loc['latitude']]
city_mean_salary = []
for city in city_salary:
city_mean_salary.append({'name': city, 'value': np.mean(city_salary[city])})
results = {'city_mean_salary': city_mean_salary, 'city_region': city_region}
print(results)
return jsonify(results)
@app.route('/get_all_hangye')
def get_all_hangye():
"""获取所有行业"""
conn = sqlite3.connect(DATABASE)
cursor = conn.cursor()
sql = 'select distinct hangye from job'
cursor.execute(sql)
hangyes = cursor.fetchall()
hangyes = [h[0] for h in hangyes]
print(hangyes)
return jsonify(hangyes)
@app.route('/hangye_fengqu_salary')
def hangye_fengqu_salary():
conn = sqlite3.connect(DATABASE)
cursor = conn.cursor()
hangye = request.args.get('hangye')
print(hangye)
check_sql = "SELECT hangye, location, salary FROM job where hangye=='{}'".format(hangye)
cursor.execute(check_sql)
jobs = cursor.fetchall()
fengqu_salary = {}
for job in jobs:
hangye, location, salary = job
if location + '市' not in city_fenqu_maps:
continue
fengqu = city_fenqu_maps[location + '市']
if not salary.endswith('/月'):
continue
if salary.endswith('千/月'):
scale = 1000
elif salary.endswith('万/月'):
scale = 10000
else:
continue
salary = salary[:-3]
# 计算平均薪资
salary = sum(map(float, salary.split('-'))) / 2 * scale
if fengqu not in fengqu_salary:
fengqu_salary[fengqu] = []
fengqu_salary[fengqu].append(salary)
# 计算平均薪资
for fengqu in fengqu_salary:
fengqu_salary[fengqu] = np.mean(fengqu_salary[fengqu])
fengqu_salary = list(zip(list(fengqu_salary.keys()), list(fengqu_salary.values())))
fengqu_salary = sorted(fengqu_salary, key=lambda k: k[1], reverse=False)
fengqu = [v[0] for v in fengqu_salary]
salary = [v[1] for v in fengqu_salary]
max_salary = max(salary)
return jsonify({'分区': fengqu, '平均薪资': salary, '最高薪资': max_salary})
if __name__ == "__main__":
app.run(host='127.0.0.1')
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