Installation

The Apify SDK for Python is available on PyPI as the apify package. For default installation, using Pip, run the following:

pip install apify

For users interested in integrating Apify with Scrapy, we provide a package extra called scrapy. To install Apify with the scrapy extra, use the following command:

pip install apify[scrapy]

Examples

Below are few examples demonstrating how to use the Apify SDK with some web scraping-related libraries.

Guides

To see how you can use the Apify SDK with other popular libraries used for web scraping, check out our guides for using BeautifulSoup with HTTPX, Parsel with Impit, Playwright, Selenium, Crawlee, or Scrapy.

Usage concepts

To learn more about the features of the Apify SDK and how to use them, check out the Usage Concepts section in the sidebar, particularly the guides for the Actor lifecycle, working with storages, handling Actor events or how to use proxies.

Apify SDK with HTTPX and BeautifulSoup

This example illustrates how to integrate the Apify SDK with HTTPX and BeautifulSoup to scrape data from web pages.

from bs4 import BeautifulSoup
from httpx import AsyncClient

from apify import Actor


async def main() -> None:
    async with Actor:
        # Retrieve the Actor input, and use default values if not provided.
        actor_input = await Actor.get_input() or {}
        start_urls = actor_input.get('start_urls', [{'url': 'https://apify.com'}])

        # Open the default request queue for handling URLs to be processed.
        request_queue = await Actor.open_request_queue()

        # Enqueue the start URLs.
        for start_url in start_urls:
            url = start_url.get('url')
            await request_queue.add_request(url)

        # Process the URLs from the request queue.
        while request := await request_queue.fetch_next_request():
            Actor.log.info(f'Scraping {request.url} ...')

            # Fetch the HTTP response from the specified URL using HTTPX.
            async with AsyncClient() as client:
                response = await client.get(request.url)

            # Parse the HTML content using Beautiful Soup.
            soup = BeautifulSoup(response.content, 'html.parser')

            # Extract the desired data.
            data = {
                'url': request.url,
                'title': soup.title.string,
                'h1s': [h1.text for h1 in soup.find_all('h1')],
                'h2s': [h2.text for h2 in soup.find_all('h2')],
                'h3s': [h3.text for h3 in soup.find_all('h3')],
            }

            # Store the extracted data to the default dataset.
            await Actor.push_data(data)

Apify SDK with PlaywrightCrawler from Crawlee

This example demonstrates how to use the Apify SDK alongside PlaywrightCrawler from Crawlee to perform web scraping.

from crawlee.crawlers import PlaywrightCrawler, PlaywrightCrawlingContext

from apify import Actor


async def main() -> None:
    async with Actor:
        # Retrieve the Actor input, and use default values if not provided.
        actor_input = await Actor.get_input() or {}
        start_urls = [url.get('url') for url in actor_input.get('start_urls', [{'url': 'https://apify.com'}])]

        # Exit if no start URLs are provided.
        if not start_urls:
            Actor.log.info('No start URLs specified in Actor input, exiting...')
            await Actor.exit()

        # Create a crawler.
        crawler = PlaywrightCrawler(
            # Limit the crawl to max requests. Remove or increase it for crawling all links.
            max_requests_per_crawl=50,
            headless=True,
        )

        # Define a request handler, which will be called for every request.
        @crawler.router.default_handler
        async def request_handler(context: PlaywrightCrawlingContext) -> None:
            url = context.request.url
            Actor.log.info(f'Scraping {url}...')

            # Extract the desired data.
            data = {
                'url': context.request.url,
                'title': await context.page.title(),
                'h1s': [await h1.text_content() for h1 in await context.page.locator('h1').all()],
                'h2s': [await h2.text_content() for h2 in await context.page.locator('h2').all()],
                'h3s': [await h3.text_content() for h3 in await context.page.locator('h3').all()],
            }

            # Store the extracted data to the default dataset.
            await context.push_data(data)

            # Enqueue additional links found on the current page.
            await context.enqueue_links()

        # Run the crawler with the starting URLs.
        await crawler.run(start_urls)