爬虫入门实战2:动态数据提取
在爬虫入门实战1中,我们学习了如何从静态网页中提取数据。这一节,我们将探索动态网页的数据提取,这是爬虫技术中更为高级也更为常见的一个环节。
什么是动态网页
动态网页与静态网页不同,其内容是可以根据用户操作、请求参数或者是服务器端的数据变化而变化的。动态网页的内容通常是通过客户端的JavaScrip发起异步请求,由服务端动态返回的数据(json、html)
动态网页工作原理
动态网页的数据加载通常有两种方式:
- 客户端渲染:当用户访问一个网页时,服务器只发送HTML框架和JavaScript代码。浏览器执行JavaScript代码,这些代码会向服务器发送请求,获取数据,然后动态地更新页面内容。
- 服务器端渲染:服务器根据请求的不同,动态生成HTML内容发送给客户端。客户端接收到完整的HTML页面,直接渲染显示。
爬取动态网页需要的技术
- 理解AJAX和API请求:动态网页往往通过AJAX请求获取数据,了解这一点对于数据提取至关重要。
- 使用浏览器开发者工具:通过分析网络请求,找出数据加载的具体过程。
- 使用适合动态网页的请求库,如requests、httpx、aiohttp等。
- 使用适合动态网页的驱动库,如Selenium、Puppeteer、Playwirght等,模拟浏览器行为获取数据。
- 学习JavaScript基础,有助于理解网页是如何动态加载数据的。
实战示例1:爬取雅虎财经网站的数字加密货币数据
任务需求描述
目标站点URL:https://finance.yahoo.com/crypto 爬取目标站点所有数字货币的数据,请使用API请求,不要用模拟浏览器驱动的方式
数据保存示例
技术可行性分析
这是一个典型的异步加载方式,当点击翻页时,浏览器整个页面并没有完整刷新,而是加密货币这个表格动态更新(局部页面变化), 那么这一种就是上面说的动态数据提取的应用示例,接下来一步步的去研究这种页面我们该如何去分析与抓取。
如何找到动态数据请求的API
先开F12,选中XHR
, 打开上面的目标页面 https://finance.yahoo.com/crypto 找到表格,点击下面的Next
翻页,随便找一个表格中的数据,在搜索栏目搜索结果
点击搜出来的结果,找到响应信息与表格中的数据对比,我们就能找到这个表格发起的API请求了。
请求信息
响应信息
API Payload参数:
{ "offset": 25, "size": 25, "sortType": "DESC", "sortField": "intradaymarketcap", "quoteType": "CRYPTOCURRENCY", "query": { "operator": "and", "operands": [ { "operator": "eq", "operands": [ "currency", "USD" ] }, { "operator": "eq", "operands": [ "exchange", "CCC" ] } ] }, "userId": "", "userIdType": "guid" }
API JSON 响应数据:
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也可以在chrome浏览器复制该请求的curl命令:
curl 'https://query1.finance.yahoo.com/v1/finance/screener?crumb=UllRf10isbP&lang=en-US®ion=US&formatted=true&corsDomain=finance.yahoo.com' \ -H 'accept: */*' \ -H 'accept-language: zh-CN,zh;q=0.9' \ -H 'content-type: application/json' \ -H 'cookie: GUC=AQEBCAFmDYVmOUIdcARM&s=AQAAANxlE2ny&g=Zgw0yA; A1=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI; A3=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI; axids=gam=y-lf5u4KlE2uJWDQYbXyUTkKMC2GVH7OUj~A&dv360=eS1XSElPM3l4RTJ1SHVVV3hNZVBDeG9aTDlDYXdaQ1dPNX5B&ydsp=y-_wiZU4RE2uIAxUbGalyjvJCoR6Le9iVT~A&tbla=y-gt2Wyc1E2uI4nvAYanhnPTMrhn4c3edZ~A; tbla_id=fde33964-c427-4b9c-b849-90a304938e21-tuctb84a272; A1S=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI; cmp=t=1712472060&j=0&u=1YNN; gpp=DBABBg~BVoIgACA.QA; gpp_sid=8' \ -H 'origin: https://finance.yahoo.com' \ -H 'referer: https://finance.yahoo.com/crypto?count=25&offset=0' \ -H 'sec-ch-ua: "Google Chrome";v="123", "Not:A-Brand";v="8", "Chromium";v="123"' \ -H 'sec-ch-ua-mobile: ?0' \ -H 'sec-ch-ua-platform: "macOS"' \ -H 'sec-fetch-dest: empty' \ -H 'sec-fetch-mode: cors' \ -H 'sec-fetch-site: same-site' \ -H 'user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36' \ --data-raw '{"offset":0,"size":25,"sortType":"DESC","sortField":"intradaymarketcap","quoteType":"CRYPTOCURRENCY","query":{"operator":"and","operands":[{"operator":"eq","operands":["currency","USD"]},{"operator":"eq","operands":["exchange","CCC"]}]},"userId":"","userIdType":"guid"}'
可以在谷歌搜索curl to python request工具,可以把上面的curl命令直接转成python代码,方便调试
转成的python request 代码请求示例
import requests cookies = { 'GUC': 'AQEBCAFmDYVmOUIdcARM&s=AQAAANxlE2ny&g=Zgw0yA', 'A1': 'd=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI', 'A3': 'd=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI', 'axids': 'gam=y-lf5u4KlE2uJWDQYbXyUTkKMC2GVH7OUj~A&dv360=eS1XSElPM3l4RTJ1SHVVV3hNZVBDeG9aTDlDYXdaQ1dPNX5B&ydsp=y-_wiZU4RE2uIAxUbGalyjvJCoR6Le9iVT~A&tbla=y-gt2Wyc1E2uI4nvAYanhnPTMrhn4c3edZ~A', 'tbla_id': 'fde33964-c427-4b9c-b849-90a304938e21-tuctb84a272', 'A1S': 'd=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI', 'cmp': 't=1712472060&j=0&u=1YNN', 'gpp': 'DBABBg~BVoIgACA.QA', 'gpp_sid': '8', } headers = { 'accept': '*/*', 'accept-language': 'zh-CN,zh;q=0.9', 'content-type': 'application/json', # 'cookie': 'GUC=AQEBCAFmDYVmOUIdcARM&s=AQAAANxlE2ny&g=Zgw0yA; A1=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI; A3=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI; axids=gam=y-lf5u4KlE2uJWDQYbXyUTkKMC2GVH7OUj~A&dv360=eS1XSElPM3l4RTJ1SHVVV3hNZVBDeG9aTDlDYXdaQ1dPNX5B&ydsp=y-_wiZU4RE2uIAxUbGalyjvJCoR6Le9iVT~A&tbla=y-gt2Wyc1E2uI4nvAYanhnPTMrhn4c3edZ~A; tbla_id=fde33964-c427-4b9c-b849-90a304938e21-tuctb84a272; A1S=d=AQABBBB0fGQCEKnzzPnIHq8Lm4HEj-GCp50FEgEBCAGFDWY5Zliia3sB_eMBAAcIEHR8ZOGCp50&S=AQAAAgF-nCWw8AxSZ-gyIaeg4aI; cmp=t=1712472060&j=0&u=1YNN; gpp=DBABBg~BVoIgACA.QA; gpp_sid=8', 'origin': 'https://finance.yahoo.com', 'referer': 'https://finance.yahoo.com/crypto?count=25&offset=0', 'sec-ch-ua': '"Google Chrome";v="123", "Not:A-Brand";v="8", "Chromium";v="123"', 'sec-ch-ua-mobile': '?0', 'sec-ch-ua-platform': '"macOS"', 'sec-fetch-dest': 'empty', 'sec-fetch-mode': 'cors', 'sec-fetch-site': 'same-site', 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36', } params = { 'crumb': 'UllRf10isbP', 'lang': 'en-US', 'region': 'US', 'formatted': 'true', 'corsDomain': 'finance.yahoo.com', } json_data = { 'offset': 0, 'size': 25, 'sortType': 'DESC', 'sortField': 'intradaymarketcap', 'quoteType': 'CRYPTOCURRENCY', 'query': { 'operator': 'and', 'operands': [ { 'operator': 'eq', 'operands': [ 'currency', 'USD', ], }, { 'operator': 'eq', 'operands': [ 'exchange', 'CCC', ], }, ], }, 'userId': '', 'userIdType': 'guid', } response = requests.post( 'https://query1.finance.yahoo.com/v1/finance/screener', params=params, cookies=cookies, headers=headers, json=json_data, ) # Note: json_data will not be serialized by requests # exactly as it was in the original request. #data = '{"offset":0,"size":25,"sortType":"DESC","sortField":"intradaymarketcap","quoteType":"CRYPTOCURRENCY","query":{"operator":"and","operands":[{"operator":"eq","operands":["currency","USD"]},{"operator":"eq","operands":["exchange","CCC"]}]},"userId":"","userIdType":"guid"}' #response = requests.post( # 'https://query1.finance.yahoo.com/v1/finance/screener', # params=params, # cookies=cookies, # headers=headers, # data=data, #)
请求参数和响应数据分析
请求参数
{"offset":0,"size":25,"sortType":"DESC","sortField":"intradaymarketcap","quoteType":"CRYPTOCURRENCY","query":{"operator":"and","operands":[{"operator":"eq","operands":["currency","USD"]},{"operator":"eq","operands":["exchange","CCC"]}]},"userId":"","userIdType":"guid"}
主要看offset
、size
这两个参数,这在ajax
动态分页的请求中是最常见的,一般情况下 offset
表示数据的分页的起始位置,size
表示分页的每一次数据(步长)。
我们在页面中点击下一页,看看这两个参数有什么变化。从下面的截图中可以得知,如果我们想要下一页的数据,offset + 25, size不变
变化情况 offset size
第一次请求 0 - 25
第二次请求 25 - 25
第三次请求 50 - 25
...
相当于新的起始位置参数offset 等于 上一次的offset + size.
响应数据 我们要的是每个币种的数据,从下面的响应json中能看见币中数据存在了quotes中
那现在基本就完事了,分页参数有了,响应数据有了
如何提取我们要的目标数据
这一块原本我是想给大家推荐一个jsonpath
的提取工具,但是发现会增加大家的学习成本,就先不讲了,感兴趣的朋友可以去了解一下。
这个提取jsonpath
提取json数据很方便。你可以理解把它为xpath一样的工具,xpath主要是解析xml/html, jsonpath主要是提取json。
好了,言归正传,我们还是用python的内置类型来提取把。
响应数据是json格式,如何转变成python里面的数据格式呢,大多数人都知道用json.loads()方法,但是这个一步到底是在干什么?
json.loads()方法的核心作用是将JSON格式的字符串“翻译”为Python中的数据结构,使得程序可以方便地访问和操作这些数据。这个过程包括了解析、类型转换和构建数据结构等步骤,是数据处理和Web开发中不可或缺的一环。
这一过程叫做json数据的反序列化
, 什么是序列化什么是反序列化,大家可以在问下GPT助手,或者谷歌搜索一下。
简易流程图
代码实现
依赖库安装
# 同步代码安装依赖 pip install requests # 异步代码安装依赖 pip install httpx pip install aiofiles
requests版本
源代码在 源代码/爬虫入门/09_爬虫入门实战2_动态数据提取/002_源码实现_同步版本.py
# -*- coding: utf-8 -*- # @Author : relakkes@gmail.com # @Name : 程序员阿江-Relakkes # @Time : 2024/4/7 17:07 # @Desc : https://finance.yahoo.com/crypto页面的加密货币表格数据 # @Desc : 下面的代码请挂全局的科学上网工具再跑 import csv import random import time from typing import List, Dict, Any import requests from common import SymbolContent, make_req_params_and_headers HOST = "https://query1.finance.yahoo.com" SYMBOL_QUERY_API_URI = "/v1/finance/screener" PAGE_SIZE = 100 # 可选配置(25, 50, 100) def parse_symbol_content(quote_item: Dict) -> SymbolContent: """ 数据提取 :param quote_item: :return: """ symbol_content = SymbolContent() symbol_content.symbol = quote_item["symbol"] symbol_content.name = quote_item["shortName"] symbol_content.price = quote_item["regularMarketPrice"]["fmt"] symbol_content.change_price = quote_item["regularMarketChange"]["fmt"] symbol_content.change_percent = quote_item["regularMarketChangePercent"]["fmt"] symbol_content.market_price = quote_item["marketCap"]["fmt"] return symbol_content def fetch_currency_data_list(max_total_count: int) -> List[SymbolContent]: """ :param max_total_count: :return: """ symbol_data_list: List[SymbolContent] = [] page_start = 0 while page_start <= max_total_count: response_dict: Dict = send_request(page_start=page_start, page_size=PAGE_SIZE) for quote in response_dict["finance"]["result"][0]["quotes"]: parsed_content: SymbolContent = parse_symbol_content(quote) print(parsed_content) symbol_data_list.append(parsed_content) page_start += PAGE_SIZE time.sleep(random.Random().random()) return symbol_data_list def send_request(page_start: int, page_size: int) -> Dict[str, Any]: """ 公共的发送请求的函数 :param page_start: 分页起始位置 :param page_size: 每一页的长度 :return: """ print(f"[send_request] page_start:{page_start}") req_url = HOST + SYMBOL_QUERY_API_URI common_params, headers, common_payload_data = make_req_params_and_headers() # 修改分页变动参数 common_payload_data["offset"] = page_start common_payload_data["size"] = page_size response = requests.post(url=req_url, params=common_params, json=common_payload_data, headers=headers) if response.status_code != 200: raise Exception("发起请求是发生异常,请求发生错误,原因:", response.text) try: response_dict: Dict = response.json() return response_dict except Exception as e: raise e def get_max_total_count() -> int: """ 获取所有币种总数量 :return: """ print("开始获取最大的币种数量") try: response_dict: Dict = send_request(page_start=0, page_size=PAGE_SIZE) total_num: int = response_dict["finance"]["result"][0]["total"] print(f"获取到 {total_num} 种币种") return total_num except Exception as e: print("错误信息:", e) return 0 def save_data_to_csv(save_file_name: str, currency_data_list: List[SymbolContent]) -> None: """ 保存数据存储到CSV文件中 :param save_file_name: 保存的文件名 :param currency_data_list: :return: """ with open(save_file_name, mode='w', newline='', encoding='utf-8') as file: writer = csv.writer(file) # 写入标题行 writer.writerow(SymbolContent.get_fields()) # 遍历数据列表,并将每个币种的名称写入CSV for symbol in currency_data_list: writer.writerow([symbol.symbol, symbol.name, symbol.price, symbol.change_price, symbol.change_percent, symbol.market_price]) def run_crawler(save_file_name: str) -> None: """ 爬虫主流程 :param save_file_name: :return: """ # step1 获取最大数据总量 max_total: int = get_max_total_count() # step2 遍历每一夜数据并解析存储到数据容器中 data_list: List[SymbolContent] = fetch_currency_data_list(max_total) # step3 将数据容器中的数据保存csv save_data_to_csv(save_file_name, data_list) if __name__ == '__main__': timestamp = int(time.time()) save_csv_file_name = f"symbol_data_{timestamp}.csv" run_crawler(save_csv_file_name)
httpx + aiofiles版本
源代码在 源代码/爬虫入门/09_爬虫入门实战2_动态数据提取/003_源码实现_异步.py
# -*- coding: utf-8 -*- # @Author : relakkes@gmail.com # @Name : 程序员阿江-Relakkes # @Time : 2024/4/7 17:08 # @Desc : https://finance.yahoo.com/crypto页面的加密货币表格数据 # @Desc : 下面的代码请挂全局的科学上网工具再跑 import csv import random import asyncio import time from typing import List, Dict, Any import aiofiles import httpx from common import SymbolContent, make_req_params_and_headers HOST = "https://query1.finance.yahoo.com" SYMBOL_QUERY_API_URI = "/v1/finance/screener" PAGE_SIZE = 100 # 可选配置(25, 50, 100) def parse_symbol_content(quote_item: Dict) -> SymbolContent: """ 数据提取 :param quote_item: :return: """ symbol_content = SymbolContent() symbol_content.symbol = quote_item["symbol"] symbol_content.name = quote_item["shortName"] symbol_content.price = quote_item["regularMarketPrice"]["fmt"] symbol_content.change_price = quote_item["regularMarketChange"]["fmt"] symbol_content.change_percent = quote_item["regularMarketChangePercent"]["fmt"] symbol_content.market_price = quote_item["marketCap"]["fmt"] return symbol_content async def fetch_currency_data_list(max_total_count: int) -> List[SymbolContent]: """ :param max_total_count: :return: """ symbol_data_list: List[SymbolContent] = [] page_start = 0 while page_start <= max_total_count: response_dict: Dict = await send_request(page_start=page_start, page_size=PAGE_SIZE) for quote in response_dict["finance"]["result"][0]["quotes"]: parsed_content: SymbolContent = parse_symbol_content(quote) print(parsed_content) symbol_data_list.append(parsed_content) page_start += PAGE_SIZE await asyncio.sleep(random.random()) return symbol_data_list async def send_request(page_start: int, page_size: int) -> Dict[str, Any]: """ 公共的发送请求的函数 :param page_start: 分页起始位置 :param page_size: 每一页的长度 :return: """ print(f"[send_request] page_start:{page_start}") req_url = HOST + SYMBOL_QUERY_API_URI common_params, headers, common_payload_data = make_req_params_and_headers() # 修改分页变动参数 common_payload_data["offset"] = page_start common_payload_data["size"] = page_size async with httpx.AsyncClient() as client: response = await client.post(url=req_url, params=common_params, json=common_payload_data, headers=headers, timeout=30) if response.status_code != 200: raise Exception("发起请求是发生异常,请求发生错误,原因:", response.text) try: response_dict: Dict = response.json() return response_dict except Exception as e: raise e async def get_max_total_count() -> int: """ 获取所有币种总数量 :return: """ print("开始获取最大的币种数量") try: response_dict: Dict = await send_request(page_start=0, page_size=PAGE_SIZE) total_num: int = response_dict["finance"]["result"][0]["total"] print(f"获取到 {total_num} 种币种") return total_num except Exception as e: print("错误信息:", e) return 0 async def save_data_to_csv(save_file_name: str, currency_data_list: List[SymbolContent]) -> None: """ 保存数据存储到CSV文件中 :param save_file_name: 保存的文件名 :param currency_data_list: :return: """ async with aiofiles.open(save_file_name, mode='w', newline='', encoding='utf-8') as file: writer = csv.writer(file) # 写入标题行 await writer.writerow(SymbolContent.get_fields()) # 遍历数据列表,并将每个币种的名称写入CSV for symbol in currency_data_list: await writer.writerow([symbol.symbol, symbol.name, symbol.price, symbol.change_price, symbol.change_percent, symbol.market_price]) async def run_crawler(save_file_name: str) -> None: """ 爬虫主流程 :param save_file_name: :return: """ # step1 获取最大数据总量 max_total: int = await get_max_total_count() # step2 遍历每一夜数据并解析存储到数据容器中 data_list: List[SymbolContent] = await fetch_currency_data_list(max_total) # step3 将数据容器中的数据保存csv await save_data_to_csv(save_file_name, data_list) if __name__ == '__main__': timestamp = int(time.time()) save_csv_file_name = f"symbol_data_{timestamp}.csv" asyncio.run(run_crawler(save_csv_file_name))