创建scrapy爬虫文件创建爬虫项目名文件夹项目

第一步：剖析目标网页

观察该网页为异步还是创建创建同步加载，异步加载需去XHR获取数据包

获取数据包，爬虫爬虫观察有用的文件抖音辅助器刷赞辅助器信息数据所在的位置

观察是post还是get恳求

若是post恳求快手怎么保存别人视频，观察多个数据包的项目项目payload是否一致

补充关于payload的知识点：

若恳求方式是post，参数用payload传，名文对应恳求写法如下：

非scrapy，创建创建在发送恳求时，爬虫爬虫应写为：

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

#快手短视频的文件例子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jsonresponse = requests.post(url=url,headers = headers,json=data)

第二步：创建scrapy爬虫文件

创建爬虫项目scrapystartproject爬虫项目名

cd爬虫项目名文件夹

scrapygenspider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

更改起始访问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

构建起始恳求

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，项目项目将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,    # 不过滤相同的名文抖音辅助器刷赞辅助器url                 body=json.dumps(data)    # 用body请求体接收data，json.dumps()将字典转为字符串，创建创建因为body的爬虫爬虫数据格式需要为字符串            )

解析恳求的数据

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)    # 获取所有数据的文件大字典    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补码数据的项目项目方式

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数据传入管线，做数据保存

设置单独储存视频的名文文件夹快手怎么保存别人视频，防止视频直接储存在scrapy文件下，变得很乱

import os, xlwt, xlrdfrom 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，只须要更改第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

要想使管线顺利运行，需在settings.py文件夹将以下几行代码激活

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

然后在__init__.py里输入代码如下

from scrapy import cmdlinecmdline.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

没有运行日志的run界面