目录
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其它相关文章！
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