第一步：分析目标网页

观察该网页为异步还是同步加载，异步加载需去XHR获取数据包

获取数据包，观察有用的信息数据所在的位置

观察是post还是get请求

若是post请求，观察多个数据包的payload是否一致

补充关于payload的知识点：

若请求方法是post，参数用payload传，对应请求写法如下：

非scrapy，在发送请求时，应写为：

Requests.post(url = url, headers = headers, json = data)

#快手短视频的例子 url = 'https://www.kuaishou.com/graphql' headers = { 'content-type': 'application/json', 'Cookie': 'clientid=3; did=web_f694eeea1a4227bf198e33436fbca07e; kpf=PC_WEB; kpn=KUAISHOU_VISION; ktrace-context=1|MS43NjQ1ODM2OTgyODY2OTgyLjUxNjI3NDU1LjE2NDQ3MzQ1Mzk3MjAuMTU5MzA1Ng==|MS43NjQ1ODM2OTgyODY2OTgyLjUzMjEzMzU2LjE2NDQ3MzQ1Mzk3MjAuMTU5MzA1Nw==|0|graphql-server|webservice|false|NA', 'Host': 'www.kuaishou.com', 'Origin': 'https://www.kuaishou.com', 'Referer': 'https://www.kuaishou.com/brilliant', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.82 Safari/537.36' } data = {"operationName":"brilliantTypeDataQuery","variables":{"hotChannelId":"00","page":"brilliant","pcursor":"1"},"query":"fragment feedContent on Feed {\n type\n author {\n id\n name\n headerUrl\n following\n headerUrls {\n url\n __typename\n }\n __typename\n }\n photo {\n id\n duration\n caption\n likeCount\n realLikeCount\n coverUrl\n photoUrl\n coverUrls {\n url\n __typename\n }\n timestamp\n expTag\n animatedCoverUrl\n distance\n videoRatio\n liked\n stereoType\n __typename\n }\n canAddComment\n llsid\n status\n currentPcursor\n __typename\n}\n\nfragment photoResult on PhotoResult {\n result\n llsid\n expTag\n serverExpTag\n pcursor\n feeds {\n ...feedContent\n __typename\n }\n webPageArea\n __typename\n}\n\nquery brilliantTypeDataQuery($pcursor: String, $hotChannelId: String, $page: String, $webPageArea: String) {\n brilliantTypeData(pcursor: $pcursor, hotChannelId: $hotChannelId, page: $page, webPageArea: $webPageArea) {\n ...photoResult\n __typename\n }\n}\n"} # 传参要用json response = requests.post(url=url,headers = headers,json=data)

第二步：创建scrapy爬虫文件

创建爬虫项目scrapy startproject 爬虫项目名

cd 爬虫项目名文件夹

scrapy genspider 爬虫名 爬虫名.com

第三步：在爬虫项目名下的爬虫名.py内，建模

使用scrapy创建爬虫过程（详细分解如何翻页爬取并保存的相关数据）(1)

修改起始访问url和域名

class Mp4Spider(scrapy.Spider): name = 'mp4' allowed_domains = ['kuaishou.com'] # 域名 start_urls = ['https://www.kuaishou.com/graphql'] # 起始url

重构起始请求

def start_requests(self): headers = { "content-type": "application/json", "Cookie": "clientid=3; did=web_f694eeea1a4227bf198e33436fbca07e; ktrace-context=1|MS43NjQ1ODM2OTgyODY2OTgyLjMxMTgyNzM3LjE2NDQ3Mjg5NzE5OTYuMTgyMDg5OTg=|MS43NjQ1ODM2OTgyODY2OTgyLjU5ODgxNzI3LjE2NDQ3Mjg5NzE5OTYuMTgyMDg5OTk=|0|graphql-server|webservice|false|NA; kpf=PC_WEB; kpn=KUAISHOU_VISION", "Host": "www.kuaishou.com", "Origin": "https://www.kuaishou.com", "Referer": "https://www.kuaishou.com/brilliant", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.82 Safari/537.36", } data = {"operationName": "brilliantTypeDataQuery", "variables": {"hotChannelId": "00", "page": "brilliant", "pcursor": "1"}, "query": "fragment feedContent on Feed {\n type\n author {\n id\n name\n headerUrl\n following\n headerUrls {\n url\n __typename\n }\n __typename\n }\n photo {\n id\n duration\n caption\n likeCount\n realLikeCount\n coverUrl\n photoUrl\n coverUrls {\n url\n __typename\n }\n timestamp\n expTag\n animatedCoverUrl\n distance\n videoRatio\n liked\n stereoType\n __typename\n }\n canAddComment\n llsid\n status\n currentPcursor\n __typename\n}\n\nfragment photoResult on PhotoResult {\n result\n llsid\n expTag\n serverExpTag\n pcursor\n feeds {\n ...feedContent\n __typename\n }\n webPageArea\n __typename\n}\n\nquery brilliantTypeDataQuery($pcursor: String, $hotChannelId: String, $page: String, $webPageArea: String) {\n brilliantTypeData(pcursor: $pcursor, hotChannelId: $hotChannelId, page: $page, webPageArea: $webPageArea) {\n ...photoResult\n __typename\n }\n}\n"} # post请求，将payload用data接收 # for循环模拟翻页 for page in range(2): # 构造post请求对象 yield scrapy.Request( url=self.start_urls[0], method='POST', # 修改请求方式为post headers=headers, dont_filter=True, # 不过滤相同的url body=json.dumps(data) # 用body请求体接收data，json.dumps()将字典转为字符串，因为body的数据格式需要为字符串 )

解析请求的数据

def parse(self, response): """ 获取响应的json数据 :param response: 响应对象 :return: """ # 获取响应源码内容(str类型) json_str_data = response.body.decode() # response.body的数据是二进制形式，要将二进制数据转为字符串 # print(json_str_data) # 将字符串转为字典 json_dict_data = json.loads(json_str_data) # print(json_dict_data) # 获取所有数据的大字典 feeds_dict = json_dict_data['data']['brilliantTypeData']['feeds'] for feeds in feeds_dict: item = {} # 构建传入管道的item的字典形式的数据 item['excel'] = 'excel数据' # 用于区分保存至excel的数据和保存为视频的数据 """获取文字数据""" # 作者id author_id = feeds['author']['id'] item['author_id'] = author_id # 作者名字 author_name = feeds['author']['name'] item['author_name'] = author_name # 作品名字 video_name = feeds['photo']['caption'] item['video_name'] = video_name # 作品点赞量 like = feeds['photo']['likeCount'] item['like'] = like yield item """获取视频数据""" # 作品名字 video_name = feeds['photo']['caption'] # 视频二进制数据 video_url = feeds['photo']['photoUrl'] # 构造视频下载地址 yield scrapy.Request( url=video_url, headers={ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.82 Safari/537.36"}, dont_filter=True, callback=self.parse_video_url, # 调用def parse_video_url方法解析获取视频二进制数据 meta={'video_name': video_name} #meta用于方法之间参数的传递，将video_name传入def parse_video_url方法 )

定义解析获取视频二进制数据的方法

def parse_video_url(self,response): item = {} # 构建传入管道的item的字典形式的数据 # 获取视频名称 video_name = response.meta['video_name'] # 利用response.meta方法获取video_name的值 item['video_name'] = video_name # 获取视频二进制数据 video_byte = response.body # response.body用于获取二进制数据 item['video_byte'] = video_byte yield item

第四步：将item数据传入管道，做数据保存

设置单独存储视频的文件夹，避免视频直接储存在scrapy文件下，显得很乱

import os, xlwt, xlrd from xlutils.copy import copy # 要导的包 class Mp4SpiderPipeline: def open_spider(self, spider): self.path = os.getcwd() '/快手视频/' if not os.path.exists(self.path): os.mkdir(self.path)

保存数据至excel模板，只需要修改第3,4,6,11,16,18行

def process_item(self, item, spider): if 'excel' in item: # 通过之前在建模步骤设置的excel特殊键值来判断数据是否保存至excel data = { '快手短视频数据': [item['author_id'],item['author_name'],item['video_name'], item['like']] } # data要以字典形式传入 os_mkdir_path = os.getcwd() '/快手数据/' # 判断这个路径是否存在，不存在就创建 if not os.path.exists(os_mkdir_path): os.mkdir(os_mkdir_path) # 判断excel表格是否存在 工作簿文件名称 os_excel_path = os_mkdir_path '快手数据.xls' if not os.path.exists(os_excel_path): # 不存在，创建工作簿(也就是创建excel表格) workbook = xlwt.Workbook(encoding='utf-8') """工作簿中创建新的sheet表""" # 设置表名 worksheet1 = workbook.add_sheet("快手短视频数据", cell_overwrite_ok=True) """设置sheet表的表头""" sheet1_headers = ('作者id', '作者名字', '作品名字', '作品点赞量') # 将表头写入工作簿 for header_num in range(0, len(sheet1_headers)): # 设置表格长度 worksheet1.col(header_num).width = 2560 * 3 # 写入 行, 列, 内容 worksheet1.write(0, header_num, sheet1_headers[header_num]) # 循环结束，代表表头写入完成，保存工作簿 workbook.save(os_excel_path) # 判断工作簿是否存在 if os.path.exists(os_excel_path): # 打开工作簿 workbook = xlrd.open_workbook(os_excel_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.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[name])): new_worksheet.write(rows_old, num, data[name][num]) new_workbook.save(os_excel_path) print(f"{item['video_name']}excel数据---------下载完成！！！")

数据保存为视频格式

else: title = item['video_name'] data = item['video_byte'] with open(self.path title '.mp4', 'wb') as f: # 一定要加视频的后缀格式'.mp4' f.write(data) print(f'视频：{title}----------下载完成!!!') return item

要想使管道顺利运行，需在settings.py文件夹将以下几行代码激活

使用scrapy创建爬虫过程（详细分解如何翻页爬取并保存的相关数据）(2)

第五步：在__init__.py文件夹运行

运行之前，需在settings.py将以下几行代码注销

使用scrapy创建爬虫过程（详细分解如何翻页爬取并保存的相关数据）(3)

之后在__init__.py里输入代码如下

from scrapy import cmdline cmdline.execute('scrapy crawl mp4 --nolog'.split(' ')) # cmdline.execute('scrapy crawl 爬虫名'.split(' '))，上面的mp4是我设置的爬虫名 # --nolog表示不打印红色的运行日志

使用scrapy创建爬虫过程（详细分解如何翻页爬取并保存的相关数据）(4)

没有运行日志的run界面

,