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How to scrape Pitchbook website data?

  Scraping data from PitchBook requires careful planning because PitchBook is a subscription-based financial data platform with strong security and usage policies. Always review their Terms of Service before attempting any data extraction. Here’s a general approach to scraping PitchBook website data: 1️⃣ Understand the Data You Need Identify what information you want to extract: Company profiles Funding rounds Investor details Financial metrics Deal histories Having a structured data requirement will help you design an efficient scraper. 2️⃣ Inspect the Website Structure Use browser developer tools (Inspect → Network tab) to: Identify API calls (if accessible via your account) Analyze HTML structure Detect dynamic content loading (JavaScript-rendered pages) PitchBook heavily relies on dynamic rendering, so traditional requests-based scraping may not work. 3️⃣ Choose the Right Tools For basic scraping: Python requests BeautifulSoup For dynamic content: Selenium Playwright Since Pi...
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How to scrape Flickr Website?

Flickr is a valuable source of image data, metadata, and user-generated content. Businesses, researchers, and marketers often scrape Flickr to collect image URLs, tags, descriptions, upload dates, user profiles, and engagement metrics like views or favorites. However, scraping Flickr requires a careful approach due to dynamic content and usage restrictions. Step 1: Understand What Data You Need Before scraping, define your goals. Common data points include image titles, tags, photographer names, licenses, comments, and geolocation data. Having a clear scope helps reduce unnecessary requests and keeps scraping efficient. Step 2: Inspect Flickr’s Structure Flickr pages rely heavily on JavaScript. Use browser developer tools to inspect network requests and identify APIs or JSON responses that load image data dynamically. This is often more reliable than scraping raw HTML. Step 3: Choose the Right Scraping Method You can scrape Flickr using: Python tools like Requests, BeautifulSoup, or S...
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How to scrape Mediamarkt Website?

MediaMarkt is one of Europe’s largest electronics retailers, making it a valuable source of product, pricing, and availability data. But scraping MediaMarkt is not straightforward . The site uses modern frontend frameworks, dynamic loading, and anti-bot protections that make DIY scraping fragile and hard to scale. Below is a practical overview of how MediaMarkt scraping works , the challenges you’ll face, and why WebScrapingHQ is the best option if you want reliable results. ⚠️ Start With Legal & Ethical Basics Before scraping MediaMarkt, always: Review robots.txt and the Terms of Service Respect rate limits and crawl delays Avoid login-protected or restricted content Use scraping only for compliant, lawful purposes For commercial or large-scale use, manual scraping is often risky and inefficient . 🧱 Why MediaMarkt Is Difficult to Scrape MediaMarkt actively protects its site using: JavaScript-heavy pages (React / dynamic rendering) API-based pro...
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How to scrape Morningstar website?

 Scraping Morningstar website data helps investors and analysts collect insights on mutual funds, ETFs, stocks, ratings, and financial metrics. Below is a clear, practical guide to doing it the right way. What Data Can You Scrape from Morningstar? Fund & ETF names NAV, expense ratio, returns Morningstar ratings Asset allocation Risk metrics Historical performance data ⚠️ Note: Morningstar has strict access controls. Always scrape publicly available data and review their terms of service. Method 1: Scraping Morningstar Using Python (Basic HTML) Step 1: Install Required Libraries pip install requests beautifulsoup4 pandas Step 2: Send a Request import requests url = "https://www.morningstar.com/funds/xnas/fskax/quote" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) html = response.text Step 3: Parse the HTML from bs4 import BeautifulSoup soup = BeautifulSoup(html, "htm...
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How to Scrape Mercari Website?

 Scraping Mercari website data is useful for tracking product prices, demand trends, seller activity, and resale market insights. Below is a clear, practical guide. What Data Can You Scrape from Mercari? Product title & description Price & currency Item condition Seller name & ratings Category & brand Listing status (available/sold) Images & posting date Method 1: Scraping Mercari Using Python Step 1: Install Required Libraries pip install requests beautifulsoup4 pandas Step 2: Send a Request import requests url = "https://www.mercari.com/search/?keyword=iphone" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) html = response.text Step 3: Parse the HTML from bs4 import BeautifulSoup soup = BeautifulSoup(html, "html.parser") items = soup.find_all("li", class_="item-grid__item") Step 4: Extract Listing Data data = [] for item ...
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How to Scrape Kickstarter Website?

 Scraping Kickstarter website data can help you analyze trending projects, funding patterns, backer engagement, and category performance. Below is a practical, step-by-step guide. What Data Can You Scrape from Kickstarter? Project title & description Creator name Category & location Funding goal & amount raised Number of backers Campaign status (live, successful, failed) Launch & end dates Method 1: Scraping Kickstarter Using Python Step 1: Install Required Libraries pip install requests beautifulsoup4 pandas Step 2: Send a Request import requests url = "https://www.kickstarter.com/discover/advanced" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) html = response.text Step 3: Parse the HTML from bs4 import BeautifulSoup soup = BeautifulSoup(html, "html.parser") projects = soup.find_all("div", class_="js-react-proj-card") Step 4: Ex...
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How to scrape Idealista Website?

 Idealista is one of Europe’s largest real-estate marketplaces, listing millions of properties for sale and rent across Spain, Italy, and Portugal. Scraping Idealista helps real-estate agencies, investors, and data analysts monitor prices, track market trends, and discover high-value properties before competitors. What Data Can You Extract from Idealista? Using web scraping, you can collect: Property title & description Price and price per m² Location (city, neighborhood, postal code) Property type (apartment, villa, office, etc.) Number of bedrooms & bathrooms Size (m²) Agent or owner details Listing date and property images How to Scrape Idealista Using Python Step 1: Install Required Libraries pip install requests beautifulsoup4 pandas Step 2: Send Request to Idealista Search Page import requests from bs4 import BeautifulSoup url = "https://www.idealista.com/en/" headers = {"User-Agent": "Mozilla/5.0"} response ...
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