Beginner's Guide to Web Data Crawling: Python vs. NextRows

In today's data-driven world, the ability to collect web data efficiently is a must-have skill for engineers, marketers, and analysts alike. Whether you're tracking competitor prices, gathering market insights, or building your own dataset, web crawling is the first step.

Two Approaches to Web Data Extraction

In this guide, we'll walk through two ways to crawl web data:

Option 1: Writing Python Code

Using libraries like requests and BeautifulSoup for full control

Option 2: Using NextRows

A no-code tool that makes data crawling as easy as copy-paste

Method 1: Crawling with Python

If you're comfortable writing code, Python gives you full control over how you collect and structure your data. Below is a step-by-step example you can follow as a beginner.

Step 1: Install Required Libraries

pip install requests beautifulsoup4 pandas

What each library does:

• requests – Sends HTTP requests to websites
• beautifulsoup4 – Parses HTML so you can extract the data you want
• pandas – Helps store and manipulate data in a structured table

Step 2: Fetch the Webpage

import requests
 
url = "https://example.com"
response = requests.get(url)
 
print(response.status_code)  # 200 means success
print(response.text[:500])   # show a snippet of the HTML

Step 3: Parse HTML with BeautifulSoup

from bs4 import BeautifulSoup
 
soup = BeautifulSoup(response.text, "html.parser")
 
# Example: grab all the links
links = soup.find_all("a")
 
for link in links[:10]:
    print(link.get("href"))

Step 4: Extract Specific Data

Let's say we want to extract product information:

products = soup.find_all("div", class_="product-item")
 
data = []
for product in products:
    title = product.find("h2").get_text(strip=True)
    price = product.find("span", class_="price").get_text(strip=True)
    data.append({"title": title, "price": price})

Step 5: Save Data to CSV

import pandas as pd
 
df = pd.DataFrame(data)
df.to_csv("products.csv", index=False)
 
print("Data saved to products.csv")

✅ Success! You now have a dataset ready for analysis.

Common Challenges with DIY Crawlers

• ❌ Technical barrier – Requires programming knowledge
• ❌ Fragile code – Pages often change HTML structure, breaking your scraper
• ❌ Complex scenarios – Handling pagination, JavaScript-heavy sites, or CAPTCHAs gets tricky
• ❌ Rate limiting – Risk of getting blocked without proper throttling
• ❌ Maintenance burden – Constant updates needed as websites evolve

Method 2: Crawling with NextRows

If you want to skip the technical details and get straight to usable data, NextRows makes the process much easier.

How It Works

Instead of writing 50+ lines of code, you simply:

1. Copy & paste the webpage link into NextRows
2. Click to select the data you want (titles, prices, descriptions)
3. Export instantly to a clean table (CSV, Excel, or Google Sheets)

What NextRows Handles Automatically

• ✅ Pagination – Crawls through all pages seamlessly
• ✅ JavaScript sites – Works with modern dynamic websites
• ✅ Scheduling – Auto-updates your data daily/weekly
• ✅ Data cleaning – Removes duplicates and formats data
• ✅ Error handling – Retries failed requests automatically

💡 Pro Tip: For non-engineers—or engineers who just want results faster—NextRows can save hours of development time.

Python vs. NextRows: Which Should You Choose?

Quick Comparison

Final Verdict

Choose Python When:

• You want to learn web scraping fundamentals
• You need highly customized logic
• You enjoy coding and problem-solving
• You have more time than budget

Choose NextRows When:

• You need data quickly (minutes, not hours)
• You want reliable, scheduled updates
• Your team includes non-technical members
• You prefer focusing on insights over infrastructure

💡 Ready to start? Whether you choose to code your own solution or use NextRows, the important thing is to start collecting the data you need to make better decisions.