Requests and Beautiful Soup Example
import requests
from bs4 import BeautifulSoup
# Use Requests to fetch the web page content
url = 'http://example.com/'
response = requests.get(url)
# Use Beautiful Soup to parse the HTML content
soup = BeautifulSoup(response.text, 'html.parser')
# Find and print the 'h1' tag from the parsed content
h1_tag = soup.find('h1')
print("H1 Tag using Requests and Beautiful Soup:", h1_tag.text)
In this example, the HTML is parsed using Beautiful Soup and the content of http://example.com/ is fetched using Requests. After that, we take the text out of the tag and print it.
Selenium Example
For Selenium, we'll launch a webpage and simulate a click on a button that, for example, dynamically loads more material.
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
# Set up Selenium WebDriver
service = Service(ChromeDriverManager().install())
driver = webdriver.Chrome(service=service)
# Open a webpage
driver.get('http://example.com/')
# Example: Find a button by its ID and click it
# button = driver.find_element(By.ID, 'loadMoreButton')
# button.click()
# Close the browser
driver.quit()
First, we set up the Chrome WebDriver in this Selenium example (note that you can need to install the webdriver-manager package). Next, we launch a browser window and navigate to http://example.com/. More dynamic content loading is possible with the help of the commented-out lines, which demonstrate how to locate a button by its ID and mimic a click.
In contrast, adding Selenium enables you to scrape data from dynamic websites that primarily rely on JavaScript. Requests and Beautiful Soup together are a potent combination for static web pages.
Requests and Beautiful Soup Example
pythonimport requests from bs4 import BeautifulSoup # Use Requests to fetch the web page content url = 'http://example.com/' response = requests.get(url) # Use Beautiful Soup to parse the HTML content soup = BeautifulSoup(response.text, 'html.parser') # Find and print the 'h1' tag from the parsed content h1_tag = soup.find('h1') print("H1 Tag using Requests and Beautiful Soup:", h1_tag.text)
In this example, the HTML is parsed using Beautiful Soup and the content of http://example.com/ is fetched using Requests. After that, we take the text out of the tag and print it.
Selenium Example
For Selenium, we'll launch a webpage and simulate a click on a button that, for example, dynamically loads more material.
pythonfrom selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from selenium.webdriver.chrome.service import Service from webdriver_manager.chrome import ChromeDriverManager # Set up Selenium WebDriver service = Service(ChromeDriverManager().install()) driver = webdriver.Chrome(service=service) # Open a webpage driver.get('http://example.com/') # Example: Find a button by its ID and click it # button = driver.find_element(By.ID, 'loadMoreButton') # button.click() # Close the browser driver.quit()
First, we set up the Chrome WebDriver in this Selenium example (note that you can need to install the webdriver-manager package). Next, we launch a browser window and navigate to http://example.com/. More dynamic content loading is possible with the help of the commented-out lines, which demonstrate how to locate a button by its ID and mimic a click.
In contrast, adding Selenium enables you to scrape data from dynamic websites that primarily rely on JavaScript. Requests and Beautiful Soup together are a potent combination for static web pages.
Python is commonly used for developing websites and software, task automation, data analysis, and data visualisation. Since it's relatively easy to learn, Python has been adopted by many non-programmers, such as accountants and scientists, for a variety of everyday tasks, like organising finances.
Learning Curve: Python is generally considered easier to learn for beginners due to its simplicity, while Java is more complex but provides a deeper understanding of how programming works.
The point is that Java is more complicated to learn than Python. It doesn't matter the order. You will have to do some things in Java that you don't in Python. The general programming skills you learn from using either language will transfer to another.
Read on for tips on how to maximize your learning. In general, it takes around two to six months to learn the fundamentals of Python. But you can learn enough to write your first short program in a matter of minutes. Developing mastery of Python's vast array of libraries can take months or years.
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The following is a step-by-step guide for beginners interested in learning Python using Windows.
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Write your first Python programStart by writing a simple Python program, such as a classic "Hello, World!" script. This process will help you understand the syntax and structure of Python code.
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