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💡 Top 70 Web Scraping Operations in Python

I. Making HTTP Requests (requests)

• Import the library.
import requests

• Make a GET request to a URL.
response = requests.get('http://example.com')

• Check the response status code (200 is OK).
print(response.status_code)

• Access the raw HTML content (as bytes).
html_bytes = response.content

• Access the HTML content (as a string).
html_text = response.text

• Access response headers.
print(response.headers)

• Send a custom User-Agent header.
headers = {'User-Agent': 'My Cool Scraper 1.0'}
response = requests.get('http://example.com', headers=headers)

• Pass URL parameters in a request.
params = {'q': 'python scraping'}
response = requests.get('https://www.google.com/search', params=params)

• Make a POST request with form data.
payload = {'key1': 'value1', 'key2': 'value2'}
response = requests.post('http://httpbin.org/post', data=payload)

• Handle potential request errors.
try:
    response = requests.get('http://example.com', timeout=5)
    response.raise_for_status() # Raise an exception for bad status codes
except requests.exceptions.RequestException as e:
    print(f"An error occurred: {e}")


II. Parsing HTML with BeautifulSoup (Setup & Navigation)

• Import the library.
from bs4 import BeautifulSoup

• Create a BeautifulSoup object from HTML text.
soup = BeautifulSoup(html_text, 'html.parser')

• Prettify the parsed HTML for readability.
print(soup.prettify())

• Access a tag directly by name (gets the first one).
title_tag = soup.title

• Navigate to a tag's parent.
title_parent = soup.title.parent

• Get an iterable of a tag's children.
for child in soup.head.children:
    print(child.name)

• Get the next sibling tag.
first_p = soup.find('p')
next_p = first_p.find_next_sibling('p')

• Get the previous sibling tag.
second_p = soup.find_all('p')[1]
prev_p = second_p.find_previous_sibling('p')


III. Finding Elements with BeautifulSoup
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• Find the first occurrence of a tag.
first_link = soup.find('a')

• Find all occurrences of a tag.
all_links = soup.find_all('a')

• Find tags by their CSS class.
articles = soup.find_all('div', class_='article-content')

• Find a tag by its ID.
main_content = soup.find(id='main-container')

• Find tags by other attributes.
images = soup.find_all('img', attrs={'data-src': True})

• Find using a list of multiple tags.
headings = soup.find_all(['h1', 'h2', 'h3'])

• Find using a regular expression.
import re
links_with_blog = soup.find_all('a', href=re.compile(r'blog'))

• Find using a custom function.
# Finds tags with a 'class' but no 'id'
tags = soup.find_all(lambda tag: tag.has_attr('class') and not tag.has_attr('id'))

• Limit the number of results.
first_five_links = soup.find_all('a', limit=5)

• Use CSS Selectors to find one element.
footer = soup.select_one('#footer > p')

• Use CSS Selectors to find all matching elements.
article_links = soup.select('div.article a')

• Select direct children using CSS selector.
nav_items = soup.select('ul.nav > li')


IV. Extracting Data with BeautifulSoup

• Get the text content from a tag.
title_text = soup.title.get_text()

• Get stripped text content.
link_text = soup.find('a').get_text(strip=True)

• Get all text from the entire document.
all_text = soup.get_text()

• Get an attribute's value (like a URL).
link_url = soup.find('a')['href']

• Get the tag's name.
tag_name = soup.find('h1').name

• Get all attributes of a tag as a dictionary.
attrs_dict = soup.find('img').attrs


V. Parsing with lxml and XPath

• Import the library.
from lxml import html

• Parse HTML content with lxml.
tree = html.fromstring(response.content)

• Select elements using an XPath expression.
# Selects all <a> tags inside <div> tags with class 'nav'
links = tree.xpath('//div[@class="nav"]/a')

• Select text content directly with XPath.
# Gets the text of all <h1> tags
h1_texts = tree.xpath('//h1/text()')

• Select an attribute value with XPath.
# Gets all href attributes from <a> tags
hrefs = tree.xpath('//a/@href')


VI. Handling Dynamic Content (Selenium)

• Import the webdriver.
from selenium import webdriver

• Initialize a browser driver.
driver = webdriver.Chrome() # Requires chromedriver

• Navigate to a webpage.
driver.get('http://example.com')

• Find an element by its ID.
element = driver.find_element('id', 'my-element-id')

• Find elements by CSS Selector.
elements = driver.find_elements('css selector', 'div.item')

• Find an element by XPath.
button = driver.find_element('xpath', '//button[@type="submit"]')

• Click a button.
button.click()

• Enter text into an input field.
search_box = driver.find_element('name', 'q')
search_box.send_keys('Python Selenium')

• Wait for an element to become visible.
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from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

element = WebDriverWait(driver, 10).until(
    EC.presence_of_element_located((By.ID, "myDynamicElement"))
)

• Get the page source after JavaScript has executed.
dynamic_html = driver.page_source

• Close the browser window.
driver.quit()


VII. Common Tasks & Best Practices

• Handle pagination by finding the "Next" link.
next_page_url = soup.find('a', text='Next')['href']

• Save data to a CSV file.
import csv
with open('data.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerow(['Title', 'Link'])
    # writer.writerow([title, url]) in a loop

• Save data to CSV using pandas.
import pandas as pd
df = pd.DataFrame(data, columns=['Title', 'Link'])
df.to_csv('data.csv', index=False)

• Use a proxy with requests.
proxies = {'http': 'http://10.10.1.10:3128', 'https': 'http://10.10.1.10:1080'}
requests.get('http://example.com', proxies=proxies)

• Pause between requests to be polite.
import time
time.sleep(2) # Pause for 2 seconds

• Handle JSON data from an API.
json_response = requests.get('https://api.example.com/data').json()

• Download a file (like an image).
img_url = 'http://example.com/image.jpg'
img_data = requests.get(img_url).content
with open('image.jpg', 'wb') as handler:
    handler.write(img_data)

• Parse a sitemap.xml to find all URLs.
# Get the sitemap.xml file and parse it like any other XML/HTML to extract <loc> tags.


VIII. Advanced Frameworks (Scrapy)

• Create a Scrapy spider (conceptual command).
scrapy genspider example example.com

• Define a parse method to process the response.
# In your spider class:
def parse(self, response):
    # parsing logic here
    pass

• Extract data using Scrapy's CSS selectors.
titles = response.css('h1::text').getall()

• Extract data using Scrapy's XPath selectors.
links = response.xpath('//a/@href').getall()

• Yield a dictionary of scraped data.
yield {'title': response.css('title::text').get()}

• Follow a link to parse the next page.
next_page = response.css('li.next a::attr(href)').get()
if next_page is not None:
    yield response.follow(next_page, callback=self.parse)

• Run a spider from the command line.
scrapy crawl example -o output.json

• Pass arguments to a spider.
scrapy crawl example -a category=books

• Create a Scrapy Item for structured data.
import scrapy
class ProductItem(scrapy.Item):
    name = scrapy.Field()
    price = scrapy.Field()

• Use an Item Loader to populate Items.
from scrapy.loader import ItemLoader
loader = ItemLoader(item=ProductItem(), response=response)
loader.add_css('name', 'h1.product-name::text')


#Python #WebScraping #BeautifulSoup #Selenium #Requests

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