datas = {'first': 'false','pn': x,'kd': 'python',}

#!/usr/bin/env python3.4
# encoding: utf-8
"""
Created on 19-5-05
@title: ''
@author: Xusl
"""
import json
import requests
import xlwt
import time# 获取存储职位信息的json对象，遍历获得公司名、福利待遇、工作地点、学历要求、工作类型、发布时间、职位名称、薪资、工作年限
def get_json(url, datas):my_headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.119 Safari/537.36","Referer": "https://www.lagou.com/jobs/list_Python?city=%E5%85%A8%E5%9B%BD&cl=false&fromSearch=true&labelWords=&suginput=","Content-Type": "application/x-www-form-urlencoded;charset = UTF-8"}time.sleep(5)ses = requests.session()    # 获取sessionses.headers.update(my_headers)  # 更新ses.get("https://www.lagou.com/jobs/list_python?city=%E5%85%A8%E5%9B%BD&cl=false&fromSearch=true&labelWords=&suginput=")content = ses.post(url=url, data=datas)result = content.json()info = result['content']['positionResult']['result']info_list = []for job in info:information = []information.append(job['positionId'])  # 岗位对应IDinformation.append(job['city'])  # 岗位对应城市information.append(job['companyFullName'])  # 公司全名information.append(job['companyLabelList'])  # 福利待遇information.append(job['district'])  # 工作地点information.append(job['education'])  # 学历要求information.append(job['firstType'])  # 工作类型information.append(job['formatCreateTime'])  # 发布时间information.append(job['positionName'])  # 职位名称information.append(job['salary'])  # 薪资information.append(job['workYear'])  # 工作年限info_list.append(information)# 将列表对象进行json格式的编码转换,其中indent参数设置缩进值为2# print(json.dumps(info_list, ensure_ascii=False, indent=2))# print(info_list)return info_listdef main():page = int(input('请输入你要抓取的页码总数：'))# kd = input('请输入你要抓取的职位关键字：')# city = input('请输入你要抓取的城市：')info_result = []title = ['岗位id', '城市', '公司全名', '福利待遇', '工作地点', '学历要求', '工作类型', '发布时间', '职位名称', '薪资', '工作年限']info_result.append(title)for x in range(1, page+1):url = 'https://www.lagou.com/jobs/positionAjax.json?needAddtionalResult=false'datas = {'first': 'false','pn': x,'kd': 'python',}try:info = get_json(url, datas)info_result = info_result + infoprint("第%s页正常采集" % x)except Exception as msg:print("第%s页出现问题" % x)# 创建workbook,即excelworkbook = xlwt.Workbook(encoding='utf-8')# 创建表,第二参数用于确认同一个cell单元是否可以重设值worksheet = workbook.add_sheet('lagouzp', cell_overwrite_ok=True)for i, row in enumerate(info_result):# print(row)for j, col in enumerate(row):# print(col)worksheet.write(i, j, col)workbook.save('lagouzp.xls')if __name__ == '__main__':main()

pip install echarts-countries-pypkg
pip install echarts-china-provinces-pypkg
pip install echarts-china-cities-pypkg

from pyecharts import Barcity_nms_top10 = ['北京', '上海', '深圳', '成都', '杭州', '广州', '武汉', '南京', '苏州', '郑州', '天津', '西安', '东莞', '珠海', '合肥', '厦门', '宁波','南宁', '重庆', '佛山', '大连', '哈尔滨', '长沙', '福州', '中山']
city_nums_top10 = [149, 95, 77, 22, 17, 17, 16, 13, 7, 5, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]bar = Bar("Python岗位", "各城市数量")
bar.add("数量", city_nms, city_nums, is_more_utils=True)
# bar.print_echarts_options() # 该行只为了打印配置项，方便调试时使用
bar.render('Python岗位各城市数量.html')  # 生成本地 HTML 文件

from pyecharts import Geocity_datas = [('北京', 149), ('上海', 95), ('深圳', 77), ('成都', 22), ('杭州', 17), ('广州', 17), ('武汉', 16), ('南京', 13), ('苏州', 7),('郑州', 5), ('天津', 4), ('西安', 4), ('东莞', 3), ('珠海', 2), ('合肥', 2), ('厦门', 2), ('宁波', 1), ('南宁', 1), ('重庆', 1),('佛山', 1), ('大连', 1), ('哈尔滨', 1), ('长沙', 1), ('福州', 1), ('中山', 1)]
geo = Geo("Python岗位城市分布地图", "数据来源拉勾", title_color="#fff",title_pos="center", width=1200,height=600, background_color='#404a59')
attr, value = geo.cast(city_datas)
geo.add("", attr, value, visual_range=[0, 200], visual_text_color="#fff",symbol_size=15, is_visualmap=True)
geo.render("Python岗位城市分布地图_scatter.html")geo = Geo("Python岗位城市分布地图", "数据来源拉勾", title_color="#fff",title_pos="center", width=1200,height=600, background_color='#404a59')attr, value = geo.cast(city_datas)geo.add("", attr, value, type="heatmap", visual_range=[0, 10], visual_text_color="#fff",symbol_size=15, is_visualmap=True)geo.render("Python岗位城市分布地图_heatmap.html")

from pyecharts import Piecity_nms_top10 = ['北京', '上海', '深圳', '成都', '广州', '杭州', '武汉', '南京', '苏州', '郑州']
city_nums_top10 = [149, 95, 77, 22, 17, 17, 16, 13, 7, 5]
pie = Pie()
pie.add("", city_nms_top10, city_nums_top10, is_label_show=True)
# pie.show_config()
pie.render('Python岗位各城市分布饼图.html')