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本篇內容主要講解“Python怎么實現爬取騰訊招聘網崗位信息”，感興趣的朋友不妨來看看。本文介紹的方法操作簡單快捷，實用性強。下面就讓小編來帶大家學習“Python怎么實現爬取騰訊招聘網崗位信息”吧!
開發環境
Windows 10
python3.6
開發工具
pycharm
庫
numpy、matplotlib、time、xlutils.copy、os、xlwt, xlrd, random
代碼運行展示
1.打開騰訊招聘的網址右擊檢查進行抓包，進入網址的時候發現有異步渲染，我們要的數據為異步加載
2.構造起始地址：
start_url = ‘https://careers.tencent.com/tencentcareer/api/post/Query’
參數在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.發送請求，獲取響應
self.start_url = 'https://careers.tencent.com/tencentcareer/api/post/Query'
# 構造請求參數
params = {
# 捕捉當前時間戳
'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.提取數據，獲取崗位信息大列表，提取相應的數據
# 獲取崗位信息大列表 json_data = response['Data']['Posts'] # 判斷結果是否有數據 if json_data is None: # 沒有數據，設置循環條件為False self.is_running = False # 反之，開始提取數據 else: # 循環遍歷，取出列表中的每一個崗位字典 # 通過key取value值的方法進行采集數據 for data in json_data: # 工作地點 LocationName = data['LocationName'] # 往地址大列表中添加數據 self.addr_list.append(LocationName) # 工作屬性 CategoryName = data['CategoryName'] # 往工作屬性大列表中添加數據 self.category_list.append(CategoryName) # 崗位名稱 RecruitPostName = data['RecruitPostName'] # 崗位職責 Responsibility = data['Responsibility'] # 發布時間 LastUpdateTime = data['LastUpdateTime'] # 崗位地址 PostURL = data['PostURL']
5.數據生成折線圖、餅圖、散點圖、柱狀圖
# 第一張圖：根據崗位地址和崗位屬性二者數量生成折線圖
# 146，147兩行代碼解決圖中中文顯示問題
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# 由于二者數據數量不統一，在此進行切片操作
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中參數的含義分別是橫軸值，縱軸值，線的形狀，顏色，透明度,線的寬度和標簽
plt.plot(y_axis_data, x_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='數量')
# 顯示標簽，如果不加這句，即使在plot中加了label='一些數字'的參數，最終還是不會顯示標簽
plt.legend(loc="upper right")
plt.xlabel('地點數量')
plt.ylabel('工作屬性數量')
plt.savefig('根據崗位地址和崗位屬性二者數量生成折線圖.png')
plt.show()
# 第二張圖：根據崗位地址數量生成餅圖 """工作地址餅圖""" 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'崗位地址和崗位屬性百分比分布') plt.savefig(f'崗位地址和崗位屬性百分比分布-餅圖') plt.show()
# 第三張圖：根據崗位地址和崗位屬性二者數量生成散點圖
# 這兩行代碼解決 plt 中文顯示的問題
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# 輸入崗位地址和崗位屬性數據
production = [i for i in data.keys()]
tem = [i for i in data.values()]
colors = np.random.rand(len(tem)) # 顏色數組
plt.scatter(tem, production, s=200, c=colors) # 畫散點圖，大小為 200
plt.xlabel('數量') # 橫坐標軸標題
plt.ylabel('名稱') # 縱坐標軸標題
plt.savefig(f'崗位地址和崗位屬性散點圖')
plt.show()
# 第四張圖：根據崗位地址和崗位屬性二者數量生成柱狀圖
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 # 柱子的寬度
index = np.arange(len(name_list))
plt.bar(index, num_list, width, color='steelblue', tick_label=name_list, label='崗位數量')
plt.legend(['分解能耗', '真實能耗'], prop=zhfont1, labelspacing=1)
for a, b in zip(index, num_list): # 柱子上的數字顯示
plt.text(a, b, '%.2f' % b, ha='center', va='bottom', fontsize=7)
plt.xticks(rotation=270)
plt.title('崗位數量和崗位屬性數量柱狀圖')
plt.ylabel('次')
plt.legend()
plt.savefig(f'崗位數量和崗位屬性數量柱狀圖-柱狀圖', bbox_inches='tight')
plt.show()
"""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 # 字體庫
import time
session = HTMLSession()
class TXSpider(object):
def __init__(self):
# 起始的請求地址
self.start_url = 'https://careers.tencent.com/tencentcareer/api/post/Query'
# 起始的翻頁頁碼
self.start_page = 1
# 翻頁條件
self.is_running = True
# 準備工作地點大列表
self.addr_list = []
# 準備崗位種類大列表
self.category_list = []
def parse_start_url(self):
"""
解析起始的url地址
:return:
"""
# 條件循環模擬翻頁
while self.is_running:
# 構造請求參數
params = {
# 捕捉當前時間戳
'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()
"""調用解析響應方法"""
self.parse_response_json(response)
"""翻頁遞增"""
self.start_page += 1
"""翻頁終止條件"""
if self.start_page == 20:
self.is_running = False
"""翻頁完成，開始生成分析圖"""
self.crate_img_four_func()
def crate_img_four_func(self):
"""
生成四張圖方法
:return:
"""
# 統計數量
data = {} # 大字典
addr_dict = {} # 工作地址字典
cate_dict = {} # 工作屬性字典
for k_addr, v_cate in zip(self.addr_list, self.category_list):
if k_addr in data:
# 大字典統計工作地址數據
data[k_addr] = data[k_addr] + 1
# 地址字典統計數據
addr_dict[k_addr] = addr_dict[k_addr] + 1
else:
data[k_addr] = 1
addr_dict[k_addr] = 1
if v_cate in data:
# 大字典統計工作屬性數據
data[v_cate] = data[v_cate] + 1
# 工作屬性字典統計數據
cate_dict[v_cate] = data[v_cate] + 1
else:
data[v_cate] = 1
cate_dict[v_cate] = 1
# 第一張圖：根據崗位地址和崗位屬性二者數量生成折線圖
# 146，147兩行代碼解決圖中中文顯示問題
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# 由于二者數據數量不統一，在此進行切片操作
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中參數的含義分別是橫軸值，縱軸值，線的形狀，顏色，透明度,線的寬度和標簽
plt.plot(y_axis_data, x_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='數量')
# 顯示標簽，如果不加這句，即使在plot中加了label='一些數字'的參數，最終還是不會顯示標簽
plt.legend(loc="upper right")
plt.xlabel('地點數量')
plt.ylabel('工作屬性數量')
plt.savefig('根據崗位地址和崗位屬性二者數量生成折線圖.png')
plt.show()
# 第二張圖：根據崗位地址數量生成餅圖
"""工作地址餅圖"""
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'崗位地址和崗位屬性百分比分布')
plt.savefig(f'崗位地址和崗位屬性百分比分布-餅圖')
plt.show()
# 第三張圖：根據崗位地址和崗位屬性二者數量生成散點圖
# 這兩行代碼解決 plt 中文顯示的問題
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# 輸入崗位地址和崗位屬性數據
production = [i for i in data.keys()]
tem = [i for i in data.values()]
colors = np.random.rand(len(tem)) # 顏色數組
plt.scatter(tem, production, s=200, c=colors) # 畫散點圖，大小為 200
plt.xlabel('數量') # 橫坐標軸標題
plt.ylabel('名稱') # 縱坐標軸標題
plt.savefig(f'崗位地址和崗位屬性散點圖')
plt.show()
# 第四張圖：根據崗位地址和崗位屬性二者數量生成柱狀圖
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 # 柱子的寬度
index = np.arange(len(name_list))
plt.bar(index, num_list, width, color='steelblue', tick_label=name_list, label='崗位數量')
plt.legend(['分解能耗', '真實能耗'], prop=zhfont1, labelspacing=1)
for a, b in zip(index, num_list): # 柱子上的數字顯示
plt.text(a, b, '%.2f' % b, ha='center', va='bottom', fontsize=7)
plt.xticks(rotation=270)
plt.title('崗位數量和崗位屬性數量柱狀圖')
plt.ylabel('次')
plt.legend()
plt.savefig(f'崗位數量和崗位屬性數量柱狀圖-柱狀圖', bbox_inches='tight')
plt.show()
def parse_response_json(self, response):
"""
解析響應
:param response:
:return:
"""
# 獲取崗位信息大列表
json_data = response['Data']['Posts']
# 判斷結果是否有數據
if json_data is None:
# 沒有數據，設置循環條件為False
self.is_running = False
# 反之，開始提取數據
else:
# 循環遍歷，取出列表中的每一個崗位字典
# 通過key取value值的方法進行采集數據
for data in json_data:
# 工作地點
LocationName = data['LocationName']
# 往地址大列表中添加數據
self.addr_list.append(LocationName)
# 工作屬性
CategoryName = data['CategoryName']
# 往工作屬性大列表中添加數據
self.category_list.append(CategoryName)
# 崗位名稱
RecruitPostName = data['RecruitPostName']
# 崗位職責
Responsibility = data['Responsibility']
# 發布時間
LastUpdateTime = data['LastUpdateTime']
# 崗位地址
PostURL = data['PostURL']
# 構造保存excel所需要的格式字典
data_dict = {
# 該字典的key值與創建工作簿的sheet表的名稱所關聯
'崗位詳情': [RecruitPostName, LocationName, CategoryName, Responsibility, LastUpdateTime, PostURL]
}
"""調用保存excel表格方法，數據字典作為參數"""
self.save_excel(data_dict)
# 提示輸出
print(f"第{self.start_page}頁--崗位{RecruitPostName}----采集完成----logging！！！")
def save_excel(self, data_dict):
"""
保存excel
:param data_dict: 數據字典
:return:
"""
# 判斷保存到當我文件目錄的路徑是否存在
os_path_1 = os.getcwd() + '/數據/'
if not os.path.exists(os_path_1):
# 不存在，即創建這個目錄，即創建”數據“這個文件夾
os.mkdir(os_path_1)
# 判斷將數據保存到表格的這個表格是否存在，不存在，創建表格，寫入表頭
os_path = os_path_1 + '騰訊招聘數據.xls'
if not os.path.exists(os_path):
# 創建新的workbook（其實就是創建新的excel）
workbook = xlwt.Workbook(encoding='utf-8')
# 創建新的sheet表
worksheet1 = workbook.add_sheet("崗位詳情", cell_overwrite_ok=True)
excel_data_1 = ('崗位名稱', '工作地點', '工作屬性', '崗位職責', '發布時間', '崗位地址')
for i in range(0, len(excel_data_1)):
worksheet1.col(i).width = 2560 * 3
# 行，列， 內容， 樣式
worksheet1.write(0, i, excel_data_1[i])
workbook.save(os_path)
# 判斷工作表是否存在
# 存在，開始往表格中添加數據(寫入數據)
if os.path.exists(os_path):
# 打開工作薄
workbook = xlrd.open_workbook(os_path)
# 獲取工作薄中所有表的個數
sheets = workbook.sheet_names()
for i in range(len(sheets)):
for name in data_dict.keys():
worksheet = workbook.sheet_by_name(sheets[i])
# 獲取工作薄中所有表中的表名與數據名對比
if worksheet.name == name:
# 獲取表中已存在的行數
rows_old = worksheet.nrows
# 將xlrd對象拷貝轉化為xlwt對象
new_workbook = copy(workbook)
# 獲取轉化后的工作薄中的第i張表
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):
"""
啟動運行
:return:
"""
self.parse_start_url()
if __name__ == '__main__':
# 創建該類的對象
t = TXSpider()
# 通過實例方法，進行調用
t.run()到此，相信大家對“Python怎么實現爬取騰訊招聘網崗位信息”有了更深的了解，不妨來實際操作一番吧！這里是億速云網站，更多相關內容可以進入相關頻道進行查詢，關注我們，繼續學習！
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