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刷赞网底价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,    # 不过滤相同的名文刷赞网底价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

很赞哦!（8）