LinkedIn Leads Scraper by code_metal

LinkedIn Lead Scraper

This tool helps you gather leads from LinkedIn by searching profiles that match your keywords. The input schema allows you to specify search keywords and the number of pages to scrape. The scraper uses the requests library to fetch the HTML of LinkedIn search results and BeautifulSoup to parse profile data.

The scraped data in this template include profile details such as name, title, location, industry, summary, and more. The data is then stored in a dataset for easy access and further analysis. The URL of the web page is passed in via input, which is defined by the input schema. The template uses the HTTPX to get the HTML of the page and the Beautiful Soup to parse the data from it. The data are then stored in a dataset where you can easily access them.

The scraped data in this template are page headings but you can easily edit the code to scrape whatever you want from the page.

Upcoming features

• Likes and Reviews Extraction: Gain insights into user engagement and feedback.
• Post Content Extraction: Capture the full content of LinkedIn posts for deeper analysis.
• Impressions Retrieval: Access data on post impressions to measure reach and visibility.

This scraper is perfect for businesses looking to generate leads and gain insights from LinkedIn profiles efficiently.

FOR FEATURE REQUEST - EMAL ME ON PROVIDED EMAIL ID

Included features

• Apify SDK for Python - a toolkit for building Apify Actors and scrapers in Python
• Input schema - define and easily validate a schema for your Actor's input
• Request queue - queues into which you can put the URLs you want to scrape
• Dataset - store structured data where each object stored has the same attributes
• HTTPX - library for making asynchronous HTTP requests in Python
• Beautiful Soup - library for pulling data out of HTML and XML files

How it works

1. Actor.get_input() gets the input where the page URL is defined
2. httpx.AsyncClient().get(url) fetches the page
3. BeautifulSoup(response.content, 'html.parser') loads the page data and enables parsing the headings
4. This parses the headings from the page and here you can edit the code to parse whatever you need from the page

   for heading in soup.find_all(["h1", "h2", "h3", "h4", "h5", "h6"]):
   

5. Actor.push_data(headings) stores the headings in the dataset

Resources

• BeautifulSoup Scraper
• Python tutorials in Academy
• Web scraping with Beautiful Soup and Requests
• Beautiful Soup vs. Scrapy for web scraping
• Integration with Make, GitHub, Zapier, Google Drive, and other apps
• Video guide on getting scraped data using Apify API
• A short guide on how to build web scrapers using code templates:

Getting started

For complete information see this article. In short, you will:

1. Build the Actor
2. Run the Actor

Pull the Actor for local development

If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI:

1. Install apify-cli

   Using Homebrew

   $brew install apify-cli
   

   Using NPM

   $npm -g install apify-cli
   

2. Pull the Actor by its unique <ActorId>, which is one of the following:

   • unique name of the Actor to pull (e.g. "apify/hello-world")
   • or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb")

   You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID.

   This command will copy the Actor into the current directory on your local machine.

   $apify pull <ActorId>
   

Documentation reference

To learn more about Apify and Actors, take a look at the following resources:

• Apify SDK for JavaScript documentation
• Apify SDK for Python documentation
• Apify Platform documentation
• Join our developer community on Discord