How to create a web scraping bot?

Table of Contents :

What if you could send a little robot to browse the web for you? That's exactly what a bot does. web scraping : automatic data collection that interest you.

A web scraping bot is an automated program that crawls websites to extract specific data. ©Christina for Alucare.fr

Requirements for creating a web scraping bot

To begin with, it is important to choose the right programming language for create a web scraping bot.

Python : This is the most popular language for web scraping. It is easy to use and offers numerous libraries.
Node.js It is ideal for managing asynchronous tasks and therefore very efficient for the scraping dynamic sites.
Other languages For certain projects, you can also opt for the web scraping with PHP.

Once you have chosen your language, you need to select the right libraries and frameworks to simplify your scraping tasks. Here are some of the most effective:

➡ For Python:

Requests : allows you to send HTTP requests.
BeautifulSoup for parsing and extracting data from HTML.
Scrapy a complete framework for more complex scraping projects.

➡ For Node.js :

Axios Where Fetch to send HTTP requests.
Cheerio similar to BeautifulSoup, very efficient for browsing and manipulating the DOM.
puppeteer Where Playwright essential for scraping dynamic sites that use a lot of JavaScript.

Tutorial for creating a web scraping bot

Create a web scraping bot may seem complex. But don't worry! By following these steps, you'll have a working script in no time.

⚠ Make sure you have Python installed, along with the necessary libraries.

Step 1: Analyze the target site

Before coding, you need to know where the data is located. To do this :

1. Open the site in your browser.
2. Right-click, then select “Inspect” on the item you are interested in.
3. Identify the HTML tags, classes or IDs that contain the data to be extracted (Example : .product, .title, .price).
4. Test CSS selectors tags in the console (Example: if product titles are in <h2 class="title">use this selector in your code).

Step 2: Send an HTTP request

Your bot will behave like a browser: it sends an HTTP request to the site's server, and the server returns the HTML code.

# pip install requests
import requests

url = "https://exemple.com/produits"
headers = {"User-Agent": "Mozilla/5.0"}

resp = requests.get(url, headers=headers, timeout=15)
resp.raise_for_status() # error if code != 200

html = resp.text
print(html[:500]) # preview

Step 3: Parsing HTML content

Now that you've retrieved the page, you need to transform it into a manipulatable object.

It is the role of BeautifulSoup.

# pip install beautifulsoup4
from bs4 import BeautifulSoup

soup = BeautifulSoup(html, "html.parser")

products = soup.select(".product")
print(f "Products found : {len(products)}")

for p in produits[:3]:
    title = p.select_one("h2.title").get_text(strip=True)
    price = p.select_one(".price").get_text(strip=True)
    link = p.select_one("a")["href"]
    print({"title": title, "price": price, "link": link})

Step 4: Extract data

This is the most interesting step: finding specific information such as titles, prices, and links.

from urllib.parse import urljoin

base_url = "https://exemple.com"
data = []

for p in soup.select(".product"):
    title = p.select_one("h2.title").get_text(strip=True)
    prix_txt = p.select_one(".price").get_text(strip=True)
    lien_rel = p.select_one("a")["href"]
    lien_abs = urljoin(base_url, lien_rel)

    # normalization price
    price = float(price_txt.replace("€","").replace(",",".").strip())

    data.append({"title": title, "price": price, "url": link_abs})

print(data[:5])

Step 5: Back up data

To avoid losing your results, you can save them in the CSV Where JSON.

import csv, json, pathlib

pathlib.Path("export").mkdir(exist_ok=True)

# CSV
with open("export/produits.csv", "w", newline="", encoding="utf-8") as f:
    fields = ["title", "price", "url"]
    writer = csv.DictWriter(f, fieldnames=champs, delimiter=";")
    writer.writeheader()
    writer.writerows(data)

# JSON
with open("export/products.json", "w", encoding="utf-8") as f:
    json.dump(data, f, ensure_ascii=False, indent=2)

print("Export complete!")

How to circumvent web scraping protection measures?

It's important to know that sites use a number of mechanisms to protect their data. Understanding these protections is essential to scrapping efficiently and responsibly.

robots.txt

📌The robots.txt file indicates which pages a bot can or cannot visit.

✅ Always check this file before scraping a website. Complying with it will help you avoid unauthorized actions and legal issues.

Captchas

📌 They are used to verify that the user is human.

✅ To bypass them, use automation libraries to simulate a real browser or third-party services specializing in solving captchas.

You are asked to type the word displayed. — Captcha: you are asked to type the word displayed. ©Christina for Alucare.fr

Blocking by IP address

📌 Some websites detect a large number of requests coming from the same IP address and block access.

✅ It is therefore recommended to use proxies or a VPN to change your IP address regularly.

User-agent blocks

📌 Sites can refuse requests from bots identified by suspicious User-Agent.

✅ The trick is to define a realistic User-Agent in your HTTP requests to simulate a conventional browser.

JavaScript websites

📌 Some pages load their content via JavaScript, preventing simple HTTP requests from retrieving the data.

✅ To get around them, you can use tools like Selenium, Playwright or Puppeteer.

FAQs

What's the difference between a web scraping bot and a web crawler?

Web scraping	Web crawler
Focuses on specific data titles, prices, product links, etc. The bot reads the HTML, identifies the relevant elements and extracts them for further use (analysis, storage, export, etc.).	It is a program that automatically browses web pages by following links in order to discover content. Its main purpose is to crawl the web to map and index information, but not necessarily to extract specific data.