先说一下我的两个思路。

使用传统爬虫
模仿浏览器

使用传统爬虫

经过对 btc.com 的分析，有一部分数据是 ajax 产生的。

如何辨别数据是哪里产生

红框中的数据是我想获取的

然后，使用 chrome 的开发者模式，查找到主页的排版是这样的：

看图，说明其并不是一个静态文件返回的，而是，动态加载的。

这时候考虑是否有端口可以获取，查找了相关的文件，发现并没有，所以，猜测是 ajax 得到的。

但是，现在这部分数据，还不着急，先分析主体数据。

当我，爬着爬着，突然就爬不上去了。

返回的页面数据如下:

<html>
<script>
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</html>

经过大规模的测试，发现这部分代码是这样的

有一个加密 cookie，来防止反爬
cookie 有一个 X 值是我们获取不到的，所以，不能伪造 cookie
不用 cookie 也可以使用爬虫，但是，只能爬取几页就被封了，封的时常差不多是一个小时左右
被封的电脑即便是换了 IP 也不能继续爬，初步猜测是和机器码有关，但是，没有验证

所以，使用传统方法爬这个路子暂时行不通。

其 cookie 的样子是这样的

ps: 我尝试过用 python 执行 js 代码，但是，最后的密钥我还是破解不了，失败… …

模拟浏览器

所以，经过查询资料，我打算使用模拟浏览器的方法。

因为我的电脑是 MacBook，所以，我的方案最终如下

MacBook + python3 + chrome + chrome驱动 + selenium

其中 selenium 是 python 的一个模块

安装 selenium

可以使用

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple selenium

安装 chrome 驱动

你应该事先先安装好 chrome 浏览器。

然后在其设置中，找到你是什么版本的 chrome。

比如，我的是

版本 81.0.4044.138（正式版本） （64 位）

也就是 81。

在上面的官网上找到属于自己版本的驱动，然后根据自己的系统下载下来。

下载下来后，我们需要把这个内核驱动放在 /usr/bin 中。

关于如何放置，你可以参考我下面的文章。

macbook | 更改系统级目录的权限

配置完成后，就可以在命令行下执行chromedriver命令了。

试试在终端输入指令：

chromedriver

出现

Starting ChromeDriver 81.0.4044.69 (6813546031a4bc83f717a2ef7cd4ac6ec1199132-refs/branch-heads/4044@{#776}) on port 9515
Only local connections are allowed.
Please protect ports used by ChromeDriver and related test frameworks to prevent access by malicious code.

这个只是测试，在实际写代码的时候，不需要开启这个。

ps: 2021-8-18

我的 mac 系统升级到 11 之后，就不能再把 chromedriver 移到 /usr/bin 了。

会报:

Operation not permitted

所以，我将其放在 /usr/local/bin 中。

但是，如果你直接使用 chromedriver 会出现

由于无法验证开发人员，因此无法打开“ chromedriver”。无法启动 Chrome`浏览器

这个时候，你可以进入到 chromedriver 的存放目录，执行

xattr -d com.apple.quarantine chromedriver

就可以了。

在python程序中测试

执行以下代码，看看是否会打开google

from selenium import webdriver

browser = webdriver.Chrome()
browser.get('http://www.google.com/')

ps:不需要在终端事先输入 chromedriver

如果，一切无误的话，应该可以成功开启。

爬取网页

参考资料

python爬虫:使用Selenium模拟浏览器行为

有一件令人惊喜的是，由于模拟了浏览器，其实也不是模拟浏览器。

Selenium 本身是自动化运维工具，其本来就是在浏览器打开网页，然后模拟人的操作，当然，网页的源代码也会相应的传到代码中。

但是，这么做对我来说有两个好处

浏览器可以自动加载 ajax 数据，然后传给我
cookie 也是浏览器自动加的，我也不需要解密了

总之，网页上呈现的数据我都可以抓到。

下面的工作就是如何解析了。

这里，我用的是 Beautifulsoup。

解析网页

需要注意的是，访问 btc.com 需要走科学上网路线。

其实解析就非常简单了，我下面举一个简单的例子，你们就会了。

from selenium import webdriver

browser = webdriver.Chrome()
response = browser.page_source
html = BeautifulSoup(response, "html.parser", from_encoding="utf-8")
...

解析网页

...

browser.close()

需要注意的是，这段程序会自动打开 chrome，然后进行操作。

ps: 这一博文几乎没怎么用 selenium ,只是借助了它的返回页面的操作。

这个库真正的用处在于模拟人的操作，包括验证码滑动等。

当然，由于部署在服务器端，不需要开启窗口环境，所以，需要自己设置一些限制参数，比如我的是

options = webdriver.ChromeOptions()
options.add_argument("--no-sandbox")
options.add_argument('--headless')
options.add_argument('--disable-gpu')
options.add_argument('blink-settings=imagesEnabled=false')
browser = webdriver.Chrome(chrome_options=options)

更新 2020-6-8

将项目放在新的服务器上，出现了新的问题。

selenium 的得到的网页数据拿不到 ajax 的数据了。

最后的解决办法，我进行了人的模拟滑动操作。

options = webdriver.ChromeOptions()
options.add_argument("--no-sandbox")
options.add_argument('--headless')
options.add_argument('--disable-gpu')
options.add_argument('blink-settings=imagesEnabled=false')
browser = webdriver.Chrome(chrome_options=options)

net_info = dict()
browser.get(root_url)
time.sleep(2)
browser.execute_script("window.scrollTo(0,document.body.scrollHeight)")
time.sleep(2)
response = browser.page_source
pool_tables = BeautifulSoup(response, "html.parser", from_encoding="utf-8").find_all('div', class_="panel-body")

...
...