7 posts tagged with "community"

Python developers know the drill: you need reliable company data, and Crunchbase has it. This guide shows you how to build an effective Crunchbase scraper in Python that gets you the data you need.

Crunchbase tracks details that matter: locations, business focus, founders, and investment histories. Manual extraction from such a large dataset isn't practical -automation is essential for transforming this information into an analyzable format.

By the end of this blog, we'll explore three different ways to extract data from Crunchbase using Crawlee for Python. We'll fully implement two of them and discuss the specifics and challenges of the third. This will help us better understand how important it is to properly choose the right data source.

Key steps we'll cover:

1. Project setup
2. Choosing the data source
3. Implementing sitemap-based crawler
4. Analysis of search-based approach and its limitations
5. Implementing the official API crawler
6. Conclusion and repository access

Millions of people use Google Maps daily, leaving behind a goldmine of data just waiting to be analyzed. In this guide, I'll show you how to build a reliable scraper using Crawlee and Python to extract locations, ratings, and reviews from Google Maps, all while handling its dynamic content challenges.

What data will we extract from Google Maps?​

We’ll collect information about hotels in a specific city. You can also customize your search to meet your requirements. For example, you might search for "hotels near me", "5-star hotels in Bombay", or other similar queries.

We’ll extract important data, including the hotel name, rating, review count, price, a link to the hotel page on Google Maps, and all available amenities. Here’s an example of what the extracted data will look like:

{
    "name": "Vividus Hotels, Bangalore",
    "rating": "4.3",
    "reviews": "633",
    "price": "₹3,667",
    "amenities": [
        "Pool available",
        "Free breakfast available",
        "Free Wi-Fi available",
        "Free parking available"
    ],
    "link": "https://www.google.com/maps/place/Vividus+Hotels+,+Bangalore/..."
}

Scraping Google Search delivers essential SERP analysis, SEO optimization, and data collection capabilities. Modern scraping tools make this process faster and more reliable.

In this guide, we'll create a Google Search scraper using Crawlee for Python that can handle result ranking and pagination.

We'll create a scraper that:

• Extracts titles, URLs, and descriptions from search results
• Handles multiple search queries
• Tracks ranking positions
• Processes multiple result pages
• Saves data in a structured format

Typically, tutorials focus on the technical aspects, on what you can replicate: "Start here, follow this path, and you'll end up here." This is great for learning a particular technology, but it's sometimes difficult to understand why the author decided to do things a certain way or what guides their development process.

In this blog, I'll discuss the general rules and principles that guide me when I work on web scraping projects and allow me to achieve great results.

So, let's explore the mindset of a web scraping developer.

Dynamic websites that use JavaScript for content rendering and backend interaction often create challenges for web scraping. The traditional approach to solving this problem is browser emulation, but it's not very efficient in terms of resource consumption.

In this article, we'll explore an alternative method based on in-depth site analysis and the use of an HTTP client. We'll go through the entire process from analyzing a dynamic website to implementing an efficient web crawler using the Crawlee for Python framework.

Introduction​

Greetings! I'm Max, a Python developer from Ukraine, a developer with expertise in web scraping, data analysis, and processing.

My journey in web scraping started in 2016 when I was solving lead generation challenges for a small company. Initially, I used off-the-shelf solutions such as Import.io and Kimono Labs. However, I quickly encountered limitations such as blocking, inaccurate data extraction, and performance issues. This led me to learn Python. Those were the glory days when requests and lxml/beautifulsoup were enough to extract data from most websites. And if you knew how to work with threads, you were already a respected expert :)

As a freelancer, I've built small solutions and large, complex data mining systems for products over the years.

Today, I want to discuss the realities of web scraping with Python in 2024. We'll look at the mistakes I sometimes see and the problems you'll encounter and offer solutions to some of them.

Let's get started.

Just take requests and beautifulsoup and start making a lot of money...

No, this is not that kind of article.

In this blog, we'll guide you through the process of using Vite and Crawlee to build a website that recommends Netflix shows based on their categories and genres. To do that, we will first scrape the shows and categories from Netflix using Crawlee, and then visualize the scraped data in a React app built with Vite. By the end of this guide, you'll have a functional web show recommender that can provide Netflix show suggestions.