Python实现爬取腾讯招聘网岗位信息(岗位属性)

介绍

开发环境

Windows 10

python3.6

开发工具

pycharm

库

numpy、matplotlib、time、xlutils.copy、os、xlwt, xlrd, random

效果展示

代码运行展示

实现思路

1.打开腾讯招聘的网址右击检查进行抓包，进入网址的时候发现有异步渲染，我们要的数据为异步加载

2.构造起始地址：

start_url = ‘https://careers.tencent.com/tencentcareer/api/post/Query’

参数在headers的最下面

timestamp: 1625641250509

countryId:

cityId:

bgIds:

productId:

categoryId:

parentCategoryId:

attrId:

keyword:

pageIndex: 1

pageSize: 10

language: zh-cn

area: cn

3.发送请求，获取响应

self.start_url = 'https://careers.tencent.com/tencentcareer/api/post/Query'
 # 构造请求参数
params = {
 # 捕捉当前时间戳
 'timestamp': str(int(time.time() * 1000)),
 'countryId': '',
 'cityId': '',
 'bgIds': '',
 'productId': '',
 'categoryId': '',
 'parentCategoryId': '',
 'attrId': '',
 'keyword': '',
 'pageIndex': str(self.start_page),
 'pageSize': '10',
 'language': 'zh-cn',
 'area': 'cn'
}
headers = {
 'user-agent': random.choice(USER_AGENT_LIST)
}
response = session.get(url=self.start_url, headers=headers, params=params).json()

4.提取数据，获取岗位信息大列表，提取相应的数据

# 获取岗位信息大列表
  json_data = response['Data']['Posts']
  # 判断结果是否有数据
  if json_data is None:
# 没有数据，设置循环条件为False
self.is_running = False
  # 反之，开始提取数据
  else:
# 循环遍历，取出列表中的每一个岗位字典
# 通过key取value值的方法进行采集数据
for data in json_data:
 # 工作地点
 LocationName = data['LocationName']
 # 往地址大列表中添加数据
 self.addr_list.append(LocationName)
 # 工作属性
 CategoryName = data['CategoryName']
 # 往工作属性大列表中添加数据
 self.category_list.append(CategoryName)
 # 岗位名称
 RecruitPostName = data['RecruitPostName']
 # 岗位职责
 Responsibility = data['Responsibility']
 # 发布时间
 LastUpdateTime = data['LastUpdateTime']
 # 岗位地址
 PostURL = data['PostURL']

5.数据生成折线图、饼图、散点图、柱状图

# 第一张图：根据岗位地址和岗位属性二者数量生成折线图
  # 146，147两行代码解决图中中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
  plt.rcParams['axes.unicode_minus'] = False
  # 由于二者数据数量不统一，在此进行切片操作
  x_axis_data = [i for i in addr_dict.values()][:5]
  y_axis_data = [i for i in cate_dict.values()][:5]
  # print(x_axis_data, y_axis_data)
  # plot中参数的含义分别是横轴值，纵轴值，线的形状，颜色，透明度,线的宽度和标签
  plt.plot(y_axis_data, x_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='数量') 
  # 显示标签，如果不加这句，即使在plot中加了label='一些数字'的参数，最终还是不会显示标签
  plt.legend(loc="upper right")
  plt.xlabel('地点数量')
  plt.ylabel('工作属性数量')
  plt.savefig('根据岗位地址和岗位属性二者数量生成折线图.png')
  plt.show()

# 第二张图：根据岗位地址数量生成饼图
  """工作地址饼图"""
  addr_dict_key = [k for k in addr_dict.keys()]
  addr_dict_value = [v for v in addr_dict.values()]
  plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
  plt.rcParams['axes.unicode_minus'] = False
  plt.pie(addr_dict_value, labels=addr_dict_key, autopct='%1.1f%%')
  plt.title(f'岗位地址和岗位属性百分比分布')
  plt.savefig(f'岗位地址和岗位属性百分比分布-饼图')
  plt.show()

# 第三张图：根据岗位地址和岗位属性二者数量生成散点图
  # 这两行代码解决 plt 中文显示的问题
  plt.rcParams['font.sans-serif'] = ['SimHei']
  plt.rcParams['axes.unicode_minus'] = False
  # 输入岗位地址和岗位属性数据
  production = [i for i in data.keys()]
  tem = [i for i in data.values()]
  colors = np.random.rand(len(tem))  # 颜色数组
  plt.scatter(tem, production, s=200, c=colors)  # 画散点图，大小为 200
  plt.xlabel('数量')  # 横坐标轴标题
  plt.ylabel('名称')  # 纵坐标轴标题
  plt.savefig(f'岗位地址和岗位属性散点图')
  plt.show()

# 第四张图：根据岗位地址和岗位属性二者数量生成柱状图
  import matplotlib;matplotlib.use('TkAgg')
  plt.rcParams['font.sans-serif'] = ['SimHei']
  plt.rcParams['axes.unicode_minus'] = False
  zhfont1 = matplotlib.font_manager.FontProperties(fname='C:\Windows\Fonts\simsun.ttc')
  name_list = [name for name in data.keys()]
  num_list = [value for value in data.values()]
  width = 0.5  # 柱子的宽度
  index = np.arange(len(name_list))
  plt.bar(index, num_list, width, color='steelblue', tick_label=name_list, label='岗位数量')
  plt.legend(['分解能耗', '真实能耗'], prop=zhfont1, labelspacing=1)
  for a, b in zip(index, num_list):  # 柱子上的数字显示
plt.text(a, b, '%.2f' % b, ha='center', va='bottom', fontsize=7)
  plt.xticks(rotation=270)
  plt.title('岗位数量和岗位属性数量柱状图')
  plt.ylabel('次')
  plt.legend()
  plt.savefig(f'岗位数量和岗位属性数量柱状图-柱状图', bbox_inches='tight')
  plt.show()

源码展示

"""ua大列表"""
USER_AGENT_LIST = [
'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3451.0 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:57.0) Gecko/20100101 Firefox/57.0',
'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.71 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.2999.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.70 Safari/537.36',
'Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4; en-US; rv:1.9.2.2) Gecko/20100316 Firefox/3.6.2',
'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.155 Safari/537.36 OPR/31.0.1889.174',
'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.1.4322; MS-RTC LM 8; InfoPath.2; Tablet PC 2.0)',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36 OPR/55.0.2994.61',
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.814.0 Safari/535.1',
'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; ja-jp) AppleWebKit/418.9.1 (KHTML, like Gecko) Safari/419.3',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36',
'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; Trident/6.0; Touch; MASMJS)',
'Mozilla/5.0 (X11; Linux i686) AppleWebKit/535.21 (KHTML, like Gecko) Chrome/19.0.1041.0 Safari/535.21',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3451.0 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:57.0) Gecko/20100101 Firefox/57.0',
'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.71 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.2999.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.70 Safari/537.36',
'Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4; en-US; rv:1.9.2.2) Gecko/20100316 Firefox/3.6.2',
'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.155 Safari/537.36 OPR/31.0.1889.174',
'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.1.4322; MS-RTC LM 8; InfoPath.2; Tablet PC 2.0)',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36 OPR/55.0.2994.61',
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.814.0 Safari/535.1',
'Mozilla/5.0 (Macintosh; U; PPC Mac OS X; ja-jp) AppleWebKit/418.9.1 (KHTML, like Gecko) Safari/419.3',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36',
'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; Trident/6.0; Touch; MASMJS)',
'Mozilla/5.0 (X11; Linux i686) AppleWebKit/535.21 (KHTML, like Gecko) Chrome/19.0.1041.0 Safari/535.21',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4093.3 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko; compatible; Swurl) Chrome/77.0.3865.120 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4086.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:75.0) Gecko/20100101 Firefox/75.0',
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) coc_coc_browser/91.0.146 Chrome/85.0.4183.146 Safari/537.36',
'Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US) AppleWebKit/537.36 (KHTML, like Gecko) Safari/537.36 VivoBrowser/8.4.72.0 Chrome/62.0.3202.84',
'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36 Edg/87.0.664.60',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.16; rv:83.0) Gecko/20100101 Firefox/83.0',
'Mozilla/5.0 (X11; CrOS x86_64 13505.63.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:68.0) Gecko/20100101 Firefox/68.0',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Safari/537.36 OPR/72.0.3815.400',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36',
]
from requests_html import HTMLSession
import os, xlwt, xlrd, random
from xlutils.copy import copy
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.font_manager import FontProperties  # 字体库
import time
session = HTMLSession()

class TXSpider(object):
 def __init__(self):
  # 起始的请求地址
  self.start_url = 'https://careers.tencent.com/tencentcareer/api/post/Query'
  # 起始的翻页页码
  self.start_page = 1
  # 翻页条件
  self.is_running = True
  # 准备工作地点大列表
  self.addr_list = []
  # 准备岗位种类大列表
  self.category_list = []
 def parse_start_url(self):
  """
  解析起始的url地址
  :return:
  """
  # 条件循环模拟翻页
  while self.is_running:
# 构造请求参数
params = {
 # 捕捉当前时间戳
 'timestamp': str(int(time.time() * 1000)),
 'countryId': '',
 'cityId': '',
 'bgIds': '',
 'productId': '',
 'categoryId': '',
 'parentCategoryId': '',
 'attrId': '',
 'keyword': '',
 'pageIndex': str(self.start_page),
 'pageSize': '10',
 'language': 'zh-cn',
 'area': 'cn'
}
headers = {
 'user-agent': random.choice(USER_AGENT_LIST)
}
response = session.get(url=self.start_url, headers=headers, params=params).json()
"""调用解析响应方法"""
self.parse_response_json(response)
"""翻页递增"""
self.start_page += 1
"""翻页终止条件"""
if self.start_page == 20:
 self.is_running = False
  """翻页完成，开始生成分析图"""
  self.crate_img_four_func()
 def crate_img_four_func(self):
  """
  生成四张图方法
  :return:
  """
  # 统计数量
  data = {}# 大字典
  addr_dict = {} # 工作地址字典
  cate_dict = {} # 工作属性字典
  for k_addr, v_cate in zip(self.addr_list, self.category_list):
if k_addr in data:
 # 大字典统计工作地址数据
 data[k_addr] = data[k_addr] + 1
 # 地址字典统计数据
 addr_dict[k_addr] = addr_dict[k_addr] + 1
else:
 data[k_addr] = 1
 addr_dict[k_addr] = 1
if v_cate in data:
 # 大字典统计工作属性数据
 data[v_cate] = data[v_cate] + 1
 # 工作属性字典统计数据
 cate_dict[v_cate] = data[v_cate] + 1
else:
 data[v_cate] = 1
 cate_dict[v_cate] = 1
  # 第一张图：根据岗位地址和岗位属性二者数量生成折线图
  # 146，147两行代码解决图中中文显示问题
  plt.rcParams['font.sans-serif'] = ['SimHei']
  plt.rcParams['axes.unicode_minus'] = False
  # 由于二者数据数量不统一，在此进行切片操作
  x_axis_data = [i for i in addr_dict.values()][:5]
  y_axis_data = [i for i in cate_dict.values()][:5]
  # print(x_axis_data, y_axis_data)
  # plot中参数的含义分别是横轴值，纵轴值，线的形状，颜色，透明度,线的宽度和标签
  plt.plot(y_axis_data, x_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='数量')
  # 显示标签，如果不加这句，即使在plot中加了label='一些数字'的参数，最终还是不会显示标签
  plt.legend(loc="upper right")
  plt.xlabel('地点数量')
  plt.ylabel('工作属性数量')
  plt.savefig('根据岗位地址和岗位属性二者数量生成折线图.png')
  plt.show()
  # 第二张图：根据岗位地址数量生成饼图
  """工作地址饼图"""
  addr_dict_key = [k for k in addr_dict.keys()]
  addr_dict_value = [v for v in addr_dict.values()]
  plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
  plt.rcParams['axes.unicode_minus'] = False
  plt.pie(addr_dict_value, labels=addr_dict_key, autopct='%1.1f%%')
  plt.title(f'岗位地址和岗位属性百分比分布')
  plt.savefig(f'岗位地址和岗位属性百分比分布-饼图')
  plt.show()
  # 第三张图：根据岗位地址和岗位属性二者数量生成散点图
  # 这两行代码解决 plt 中文显示的问题
  plt.rcParams['font.sans-serif'] = ['SimHei']
  plt.rcParams['axes.unicode_minus'] = False
  # 输入岗位地址和岗位属性数据
  production = [i for i in data.keys()]
  tem = [i for i in data.values()]
  colors = np.random.rand(len(tem))  # 颜色数组
  plt.scatter(tem, production, s=200, c=colors)  # 画散点图，大小为 200
  plt.xlabel('数量')  # 横坐标轴标题
  plt.ylabel('名称')  # 纵坐标轴标题
  plt.savefig(f'岗位地址和岗位属性散点图')
  plt.show()
  # 第四张图：根据岗位地址和岗位属性二者数量生成柱状图
  import matplotlib;matplotlib.use('TkAgg')
  plt.rcParams['font.sans-serif'] = ['SimHei']
  plt.rcParams['axes.unicode_minus'] = False
  zhfont1 = matplotlib.font_manager.FontProperties(fname='C:\Windows\Fonts\simsun.ttc')
  name_list = [name for name in data.keys()]
  num_list = [value for value in data.values()]
  width = 0.5  # 柱子的宽度
  index = np.arange(len(name_list))
  plt.bar(index, num_list, width, color='steelblue', tick_label=name_list, label='岗位数量')
  plt.legend(['分解能耗', '真实能耗'], prop=zhfont1, labelspacing=1)
  for a, b in zip(index, num_list):  # 柱子上的数字显示
plt.text(a, b, '%.2f' % b, ha='center', va='bottom', fontsize=7)
  plt.xticks(rotation=270)
  plt.title('岗位数量和岗位属性数量柱状图')
  plt.ylabel('次')
  plt.legend()
  plt.savefig(f'岗位数量和岗位属性数量柱状图-柱状图', bbox_inches='tight')
  plt.show()
 def parse_response_json(self, response):
  """
  解析响应
  :param response:
  :return:
  """
  # 获取岗位信息大列表
  json_data = response['Data']['Posts']
  # 判断结果是否有数据
  if json_data is None:
# 没有数据，设置循环条件为False
self.is_running = False
  # 反之，开始提取数据
  else:
# 循环遍历，取出列表中的每一个岗位字典
# 通过key取value值的方法进行采集数据
for data in json_data:
 # 工作地点
 LocationName = data['LocationName']
 # 往地址大列表中添加数据
 self.addr_list.append(LocationName)
 # 工作属性
 CategoryName = data['CategoryName']
 # 往工作属性大列表中添加数据
 self.category_list.append(CategoryName)
 # 岗位名称
 RecruitPostName = data['RecruitPostName']
 # 岗位职责
 Responsibility = data['Responsibility']
 # 发布时间
 LastUpdateTime = data['LastUpdateTime']
 # 岗位地址
 PostURL = data['PostURL']
 # 构造保存excel所需要的格式字典
 data_dict = {
  # 该字典的key值与创建工作簿的sheet表的名称所关联
  '岗位详情': [RecruitPostName, LocationName, CategoryName, Responsibility, LastUpdateTime, PostURL]
 }
 """调用保存excel表格方法，数据字典作为参数"""
 self.save_excel(data_dict)
 # 提示输出
 print(f"第{self.start_page}页--岗位{RecruitPostName}----采集完成----logging！！！")
 def save_excel(self, data_dict):
  """
  保存excel
  :param data_dict: 数据字典
  :return:
  """
  # 判断保存到当我文件目录的路径是否存在
  os_path_1 = os.getcwd() + '/数据/'
  if not os.path.exists(os_path_1):
# 不存在，即创建这个目录，即创建”数据“这个文件夹
os.mkdir(os_path_1)
  # 判断将数据保存到表格的这个表格是否存在，不存在，创建表格，写入表头
  os_path = os_path_1 + '腾讯招聘数据.xls'
  if not os.path.exists(os_path):
# 创建新的workbook（其实就是创建新的excel）
workbook = xlwt.Workbook(encoding='utf-8')
# 创建新的sheet表
worksheet1 = workbook.add_sheet("岗位详情", cell_overwrite_ok=True)
excel_data_1 = ('岗位名称', '工作地点', '工作属性', '岗位职责', '发布时间', '岗位地址')
for i in range(0, len(excel_data_1)):
 worksheet1.col(i).width = 2560 * 3
 #行，列，  内容，样式
 worksheet1.write(0, i, excel_data_1[i])
workbook.save(os_path)
  # 判断工作表是否存在
  # 存在，开始往表格中添加数据(写入数据)
  if os.path.exists(os_path):
# 打开工作薄
workbook = xlrd.open_workbook(os_path)
# 获取工作薄中所有表的个数
sheets = workbook.sheet_names()
for i in range(len(sheets)):
 for name in data_dict.keys():
  worksheet = workbook.sheet_by_name(sheets[i])
  # 获取工作薄中所有表中的表名与数据名对比
  if worksheet.name == name:# 获取表中已存在的行数rows_old = worksheet.nrows# 将xlrd对象拷贝转化为xlwt对象new_workbook = copy(workbook)# 获取转化后的工作薄中的第i张表new_worksheet = new_workbook.get_sheet(i)for num in range(0, len(data_dict[name])):
new_worksheet.write(rows_old, num, data_dict[name][num])new_workbook.save(os_path)
 def run(self):
  """
  启动运行
  :return:
  """
  self.parse_start_url()

if __name__ == '__main__':
 # 创建该类的对象
 t = TXSpider()
 # 通过实例方法，进行调用
 t.run()

以上就是Python实现爬取腾讯招聘网岗位信息的详细内容，更多关于Python爬取招聘网岗位信息的资料请关注本站其它相关文章！

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