How to Build Web Scraper with Python

In this article we are going to learn How to Build Web Scraper with Python, Web scraping is a technique that allows you to extract different data from websites. Web Scarping can be powerful tool for data analysis, research and even market intelligence. in this article we will go over how to build web scraper using Python.

Before we start our web scrapping , it is important to note that web scraping can be sensitive issue, especially when dealing with personal data or copyrighted material. it is important to ensure that you are not violating any terms of service or laws while performing web scraping. always check website’s robots.txt file to see if web scraping is allowed, and be mindful of rate limits to avoid overloading servers.

OK so for building web scraper in Python, we need to install some required libraries.

Requests: to send HTTP requests and retrieve website content
BeautifulSoup: to parse HTML content and extract relevant data
pandas: to store the extracted data in structured format

pip install requests
pip install beautifulsoup4
pip install pandas

pip install requests

pip install beautifulsoup4

pip install pandas

After installing these libraries no we need to start building our Python Web Scraper, first step is to send an HTTP request to the website we want to scrape. we are going to use Requests library to do this.

import requests

url = 'https://www.example.com'
response = requests.get(url)

import requests

url = 'https://www.example.com'

response = requests.get(url)

In the above code we are sending GET request to https://www.example.com and storing response in the response variable.

after that we can check the status code of the response to ensure that we have successfully retrieved the website content.

if response.status_code == 200:
    print('Request successful')
else:
    print('Request failed')

if response.status_code == 200:

print('Request successful')

else:

print('Request failed')

So now we have the content of the website, it is time to parse the extracted data, for this we are using BeautifulSoup library.

from bs4 import BeautifulSoup

soup = BeautifulSoup(response.content, 'html.parser')

from bs4 import BeautifulSoup

soup = BeautifulSoup(response.content, 'html.parser')

In the above code we are creating BeautifulSoup object and passing in the website content and the parser we want to use, in this case the HTML parser.

With the website content parsed, we can now extract relevant data. Let’s say we want to extract titles of all the articles on the website.

titles = []
for article in soup.find_all('article'):
    title = article.h2.text.strip()
    titles.append(title)

titles = []

for article in soup.find_all('article'):

title = article.h2.text.strip()

titles.append(title)

In this example, we’re finding all the article elements on the page and extracting the text of h2 element within each article. after that we storing these titles in list.

and finally we can store the extracted data in structured format for further analysis. we will be using pandas library to create DataFrame.

import pandas as pd

df = pd.DataFrame({'Title': titles})
print(df)

import pandas as pd

df = pd.DataFrame({'Title': titles})

print(df)

This is the complete code for How to Build Web Scraper with Python

import requests
from bs4 import BeautifulSoup
import pandas as pd

url = 'https://www.example.com'
response = requests.get(url)

if response.status_code == 200:
    print('Request successful')
else:
    print('Request failed')

soup = BeautifulSoup(response.content, 'html.parser')

titles = []
for article in soup.find_all('article'):
    title = article.h2.text.strip()
    titles.append(title)

df = pd.DataFrame({'Title': titles})
print(df)