Python으로 무한 스크롤 웹페이지를 긁는 방법

Python으로 무한 스크롤 웹페이지를 긁는 방법

안녕하세요, Crawlee 개발자 여러분. Crawlee 블로그의 또 다른 튜토리얼에 다시 오신 것을 환영합니다. 이 튜토리얼에서는 Python용 Crawlee를 사용하여 무한 스크롤 웹사이트를 스크랩하는 방법을 알려드립니다.

문맥상 무한 스크롤 페이지는 기존 페이지 매김에 대한 현대적인 대안입니다. 사용자가 다음 페이지를 선택하는 대신 웹페이지 하단으로 스크롤하면 페이지가 자동으로 더 많은 데이터를 로드하고 사용자는 더 많이 스크롤할 수 있습니다.

나는 큰 운동화광으로서 나이키 신발 무한 스크롤 웹사이트를 예로 들어 여기에서 수천 개의 운동화를 긁어낼 것입니다.

Python용 Crawlee에는 HTTP 및 헤드리스 브라우저 크롤링을 위한 통합 인터페이스, 자동 재시도 등 몇 가지 놀라운 초기 기능이 있습니다.

프로젝트 전제조건 및 부트스트래핑

다음 명령을 사용하여 Python용 Crawlee를 설치하여 튜토리얼을 시작하겠습니다.

pipx run crawlee create nike-crawler

계속 진행하기 전에 이 블로그를 읽고 싶으시면 GitHub에서 Crawlee for Python에 별점을 주시면 정말 기쁠 것입니다!

애피파이 / 크롤리 파이썬

Crawlee - 안정적인 크롤러를 구축하기 위한 Python용 웹 스크래핑 및 브라우저 자동화 라이브러리입니다. AI, LLM, RAG 또는 GPT에 대한 데이터를 추출합니다. 웹사이트에서 HTML, PDF, JPG, PNG 및 기타 파일을 다운로드하세요. BeautifulSoup, Playwright 및 원시 HTTP와 함께 작동합니다. 헤드풀 모드와 헤드리스 모드 모두. 프록시 회전 포함.

웹 스크래핑 및 브라우저 자동화 라이브러리

Crawlee는 크롤링과 스크래핑을 처음부터 끝까지 다루고 신뢰할 수 있는 스크래퍼를 구축하도록 도와줍니다. 빠릅니다.

? Python용 Crawlee는 얼리 어답터에게 열려 있습니다!

크롤러는 기본 구성을 사용해도 거의 인간과 비슷하게 보이며 최신 봇 보호의 레이더를 피해 날아갑니다. Crawlee는 기술적 세부 사항에 대해 걱정할 필요 없이 웹에서 링크를 크롤링하고, 데이터를 스크랩하고, 기계가 읽을 수 있는 형식으로 지속적으로 저장할 수 있는 도구를 제공합니다. 그리고 풍부한 구성 옵션 덕분에 기본 설정으로 충분하지 않은 경우 Crawlee의 거의 모든 측면을 프로젝트 요구 사항에 맞게 조정할 수 있습니다.

? Crawlee 프로젝트 웹사이트에서 전체 문서, 가이드 및 예제 보기 ?

또한 프로젝트에 탐색하고 활용할 수 있는 Crawlee의 TypeScript 구현도 있습니다. GitHub의 JS/TS용 Crawlee에 대한 자세한 내용을 보려면 GitHub 저장소를 방문하세요.

설치

우리는…

GitHub에서 보기

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.

위 내용은 Python으로 무한 스크롤 웹페이지를 긁는 방법의 상세 내용입니다. 자세한 내용은 PHP 중국어 웹사이트의 기타 관련 기사를 참조하세요!