Mangadex Proxy

Scraping without libraries in Python typically involves making HTTP requests, parsing HTML (or other markup languages), and extracting data using basic string manipulation or regular expressions. However, it's important to note that using established libraries like requests for making HTTP requests and BeautifulSoup or lxml for parsing HTML is generally recommended due to their ease of use, reliability, and built-in features.

Here's a simple example of scraping without libraries, where we use Python's built-in urllib for making an HTTP request and then perform basic string manipulation to extract data. In this example, we'll scrape the title of a website:

import urllib.request

def scrape_website(url):
    try:
        # Make an HTTP request
        response = urllib.request.urlopen(url)
        
        # Read the HTML content
        html_content = response.read().decode('utf-8')

        # Extract the title using string manipulation
        title_start = html_content.find('', title_start)
        title = html_content[title_start:title_end].strip()

        return title
    except Exception as e:
        print(f"Error: {e}")
        return None

# Replace 'https://example.com' with the URL you want to scrape
url_to_scrape = 'https://example.com'
scraped_title = scrape_website(url_to_scrape)

if scraped_title:
    print(f"Scraped title: {scraped_title}")
else:
    print("Scraping failed.")

Keep in mind that scraping without libraries can quickly become complex as you need to handle various aspects such as handling redirects, managing cookies, dealing with different encodings, and more. Libraries like requests and BeautifulSoup abstract away many of these complexities and provide a more robust solution.

Using established libraries is generally recommended for web scraping due to the potential pitfalls and challenges involved in handling various edge cases on the web. Always ensure that your scraping activities comply with the website's terms of service and legal requirements.

Parsing huge XML files can be challenging due to their size. Here are some tips for efficient XML parsing:

1. Use Streaming Parsers:

   • Streaming parsers, like SAX (Simple API for XML) or StAX (Streaming API for XML), process XML data sequentially. They don't load the entire XML document into memory, making them memory-efficient for large files.

2. XPath for Selective Parsing:

   • XPath is a query language for XML that allows you to navigate through elements and attributes. Use XPath to selectively parse only the parts of the XML document that you need, rather than parsing the entire file.

3. Incremental Parsing:

   • Break the XML document into smaller chunks and parse them incrementally. This can be useful when dealing with extremely large files. Process one chunk at a time instead of loading the entire file into memory.

4. Memory Management:

   • If using a DOM (Document Object Model) parser, be mindful of memory usage. Large XML files can consume a significant amount of memory when loaded into a DOM. Consider using a streaming approach instead.

5. Parallel Processing:

   • If the XML document is partitioned into multiple sections, you can leverage parallel processing to parse different sections concurrently. Be cautious with synchronization to avoid conflicts.

6. Compression:

   • If the XML files are too large, consider compressing them before parsing. This can reduce the file size and speed up the parsing process. Remember to decompress the file before parsing.

7. Optimize Code and Libraries:

   • Ensure that you are using an efficient XML parsing library. Some libraries are better optimized for large files. Additionally, optimize your code for performance by minimizing unnecessary operations.

8. Use Memory-Mapped Files:

   • Memory-mapped files allow parts of a file to be mapped directly into memory. This can be beneficial for random access to different parts of a large XML file.

9. Consider External Tools:

   • In some cases, using external tools specifically designed for processing large XML files might be a good option. These tools are often optimized for performance and memory usage.

Remember that the optimal approach may vary depending on the specific requirements of your application and the characteristics of the XML files you are dealing with.

To configure a Socks5 proxy for Chrome in Selenium using Python, you can use the --proxy-server command-line option with the Socks5 proxy address. Here's an example using the webdriver.Chrome class in Python:

from selenium import webdriver
from selenium.webdriver.chrome.service import Service as ChromeService

# Socks5 proxy configuration
socks5_proxy = "socks5://127.0.0.1:1080"  # Replace with your actual Socks5 proxy address

# Configure Chrome options with proxy settings
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument(f'--proxy-server={socks5_proxy}')

# Create a Chrome WebDriver instance with the configured options
chrome_service = ChromeService(executable_path="path/to/chromedriver")  # Replace with the actual path
driver = webdriver.Chrome(service=chrome_service, options=chrome_options)

# Example: Navigate to a website using the configured proxy
driver.get("https://www.example.com")

# Perform other actions with the WebDriver as needed

# Close the browser window
driver.quit()

- Replace "socks5://127.0.0.1:1080" with the actual Socks5 proxy address you want to use.
- Download the ChromeDriver executable from the official ChromeDriver download page and provide the path to the executable in the executable_path parameter of ChromeService.
- Update the driver.get() method to navigate to the website you want.

Make sure to have the selenium library installed (pip install selenium) and ensure that the ChromeDriver version is compatible with the Chrome browser installed on your system.

Open the browser settings and go to the "Advanced" section. Click on "System" and then, in the window that opens, click on "Open proxy settings for computer". A window will appear in front of you, showing all the current settings. Another way to find out the http proxy is to download and install the SocialKit Proxy Checker utility on your computer.

Under such parsing we mean the collection of keywords from services such as Yandex Wordstat. These data will later be required for SEO-promotion of the site. The resulting word combinations are then integrated into the content of the resource, which improves its position in SERPs on a particular topic.