如何使用Python抓取无限滚动的网页

如何用Python抓取无限滚动的网页

您好，Crawlee 开发人员，欢迎回到 Crawlee 博客上的另一个教程。本教程将教您如何使用 Crawlee for Python 抓取无限滚动的网站。

就上下文而言，无限滚动页面是经典分页的现代替代方案。当用户滚动到网页底部而不是选择下一页时，页面会自动加载更多数据，用户可以滚动更多。

作为一个球鞋大佬，我会以耐克鞋无限滚动网站为例，我们会从中抓取数千双球鞋。

Crawlee for Python 具有一些令人惊叹的初始功能，例如 HTTP 和无头浏览器爬行的统一接口、自动重试等等。

先决条件和引导项目

让我们通过使用以下命令安装 Crawlee for Python 来开始本教程：

pipx run crawlee create nike-crawler

在继续之前，如果您喜欢阅读此博客，如果您在 GitHub 上给 Crawlee for Python 一颗星，我们将非常高兴！

apify / 爬虫-python

Crawlee——一个用于 Python 的网络抓取和浏览器自动化库，用于构建可靠的爬虫。提取 AI、LLM、RAG 或 GPT 的数据。从网站下载 HTML、PDF、JPG、PNG 和其他文件。适用于 BeautifulSoup、Playwright 和原始 HTTP。有头模式和无头模式。通过代理轮换。

网页抓取和浏览器自动化库

Crawlee 涵盖了端到端的爬行和抓取，并且帮助您构建可靠的抓取工具。快。

？ Crawlee for Python 向早期采用者开放！

即使使用默认配置，您的爬虫也会看起来几乎像人类一样，并且在现代机器人保护的雷达下飞行。 Crawlee 为您提供了在网络上抓取链接、抓取数据并将其持久存储为机器可读格式的工具，而无需担心技术细节。得益于丰富的配置选项，如果默认设置无法满足您的项目需求，您几乎可以调整 Crawlee 的任何方面。

？ 在 Crawlee 项目网站上查看完整文档、指南和示例 ?

我们还有 Crawlee 的 TypeScript 实现，您可以在您的项目中探索和利用它。请访问我们的 GitHub 存储库，了解 GitHub 上 Crawlee for JS/TS 的更多信息。

安装

我们…

在 GitHub 上查看

We will scrape using headless browsers. Select PlaywrightCrawler in the terminal when Crawlee for Python asks for it.

After installation, Crawlee for Python will create boilerplate code for you. Redirect into the project folder and then run this command for all the dependencies installation:

poetry install

How to scrape infinite scrolling webpages

1. Handling accept cookie dialog

2. Adding request of all shoes links

3. Extract data from product details

4. Accept Cookies context manager

5. Handling infinite scroll on the listing page

6. Exporting data to CSV format

Handling accept cookie dialog

After all the necessary installations, we'll start looking into the files and configuring them accordingly.

When you look into the folder, you'll see many files, but for now, let’s focus on main.py and routes.py.

In __main__.py, let's change the target location to the Nike website. Then, just to see how scraping will happen, we'll add headless = False to the PlaywrightCrawler parameters. Let's also increase the maximum requests per crawl option to 100 to see the power of parallel scraping in Crawlee for Python.

The final code will look like this:

import asyncio

from crawlee.playwright_crawler import PlaywrightCrawler

from .routes import router


async def main() -> None:

    crawler = PlaywrightCrawler(
        headless=False,
        request_handler=router,
        max_requests_per_crawl=100,
    )

    await crawler.run(
        [
            'https://nike.com/,
        ]
    )


if __name__ == '__main__':
    asyncio.run(main())

Now coming to routes.py, let’s remove:

await context.enqueue_links()

As we don’t want to scrape the whole website.

Now, if you run the crawler using the command:

poetry run python -m nike-crawler

As the cookie dialog is blocking us from crawling more than one page's worth of shoes, let’s get it out of our way.

We can handle the cookie dialog by going to Chrome dev tools and looking at the test_id of the "accept cookies" button, which is dialog-accept-button.

Now, let’s remove the context.push_data call that was left there from the project template and add the code to accept the dialog in routes.py. The updated code will look like this:

from crawlee.router import Router
from crawlee.playwright_crawler import PlaywrightCrawlingContext

router = Router[PlaywrightCrawlingContext]()

@router.default_handler
async def default_handler(context: PlaywrightCrawlingContext) -> None:

    # Wait for the popup to be visible to ensure it has loaded on the page.
    await context.page.get_by_test_id('dialog-accept-button').click()

Adding request of all shoes links

Now, if you hover over the top bar and see all the sections, i.e., man, woman, and kids, you'll notice the “All shoes” section. As we want to scrape all the sneakers, this section interests us. Let’s use get_by_test_id with the filter of has_text=’All shoes’ and add all the links with the text “All shoes” to the request handler. Let’s add this code to the existing routes.py file:

    shoe_listing_links = (
        await context.page.get_by_test_id('link').filter(has_text='All shoes').all()
    )
    await context.add_requests(
        [
            Request.from_url(url, label='listing')
            for link in shoe_listing_links
            if (url := await link.get_attribute('href'))
        ]
    )

@router.handler('listing')
async def listing_handler(context: PlaywrightCrawlingContext) -> None:
    """Handler for shoe listings."""

Extract data from product details

Now that we have all the links to the pages with the title “All Shoes,” the next step is to scrape all the products on each page and the information provided on them.

We'll extract each shoe's URL, title, price, and description. Again, let's go to dev tools and extract each parameter's relevant test_id. After scraping each of the parameters, we'll use the context.push_data function to add it to the local storage. Now let's add the following code to the listing_handler and update it in the routes.py file:

@router.handler('listing')
async def listing_handler(context: PlaywrightCrawlingContext) -> None:
    """Handler for shoe listings."""        

    await context.enqueue_links(selector='a.product-card__link-overlay', label='detail')


@router.handler('detail')
async def detail_handler(context: PlaywrightCrawlingContext) -> None:
    """Handler for shoe details."""

    title = await context.page.get_by_test_id(
        'product_title',
    ).text_content()

    price = await context.page.get_by_test_id(
        'currentPrice-container',
    ).first.text_content()

    description = await context.page.get_by_test_id(
        'product-description',
    ).text_content()

    await context.push_data(
        {
            'url': context.request.loaded_url,
            'title': title,
            'price': price,
            'description': description,
        }
    )

Accept Cookies context manager

Since we're dealing with multiple browser pages with multiple links and we want to do infinite scrolling, we may encounter an accept cookie dialog on each page. This will prevent loading more shoes via infinite scroll.

We'll need to check for cookies on every page, as each one may be opened with a fresh session (no stored cookies) and we'll get the accept cookie dialog even though we already accepted it in another browser window. However, if we don't get the dialog, we want the request handler to work as usual.

To solve this problem, we'll try to deal with the dialog in a parallel task that will run in the background. A context manager is a nice abstraction that will allow us to reuse this logic in all the router handlers. So, let's build a context manager:

from playwright.async_api import TimeoutError as PlaywrightTimeoutError

@asynccontextmanager
async def accept_cookies(page: Page):
    task = asyncio.create_task(page.get_by_test_id('dialog-accept-button').click())
    try:
        yield
    finally:
        if not task.done():
            task.cancel()

        with suppress(asyncio.CancelledError, PlaywrightTimeoutError):
            await task

This context manager will make sure we're accepting the cookie dialog if it exists before scrolling and scraping the page. Let’s implement it in the routes.py file, and the updated code is here

Handling infinite scroll on the listing page

Now for the last and most interesting part of the tutorial! How to handle the infinite scroll of each shoe listing page and make sure our crawler is scrolling and scraping the data constantly.

To handle infinite scrolling in Crawlee for Python, we just need to make sure the page is loaded, which is done by waiting for the network_idle load state, and then use the infinite_scroll helper function which will keep scrolling to the bottom of the page as long as that makes additional items appear.

Let’s add two lines of code to the listing handler:

@router.handler('listing')
async def listing_handler(context: PlaywrightCrawlingContext) -> None:
    # Handler for shoe listings

    async with accept_cookies(context.page):
        await context.page.wait_for_load_state('networkidle')
        await context.infinite_scroll()
        await context.enqueue_links(
            selector='a.product-card__link-overlay', label='detail'
        )

Exporting data to CSV format

As we want to store all the shoe data into a CSV file, we can just add a call to the export_data helper into the __main__.py file just after the crawler run:

await crawler.export_data('shoes.csv')

Working crawler and its code

Now, we have a crawler ready that can scrape all the shoes from the Nike website while handling infinite scrolling and many other problems, like the cookies dialog.

You can find the complete working crawler code here on the GitHub repository.

If you have any doubts regarding this tutorial or using Crawlee for Python, feel free to join our discord community and ask fellow developers or the Crawlee team.

Crawlee & Apify

This is the official developer community of Apify and Crawlee. | 8365 members

discord.com

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This tutorial is taken from the webinar held on August 5th where Jan Buchar, Senior Python Engineer at Apify, gave a live demo about this use case. Watch the whole webinar here.

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