IP | Country | PORT | ADDED |
---|---|---|---|
70.166.167.38 | us | 57728 | 51 minutes ago |
64.202.184.249 | us | 25118 | 51 minutes ago |
199.116.112.6 | us | 4145 | 51 minutes ago |
182.155.254.159 | tw | 80 | 51 minutes ago |
103.118.46.61 | kh | 8080 | 51 minutes ago |
111.59.117.17 | cn | 9091 | 51 minutes ago |
51.210.111.216 | fr | 11926 | 51 minutes ago |
103.118.47.243 | kh | 8080 | 51 minutes ago |
98.170.57.241 | us | 4145 | 51 minutes ago |
103.118.46.176 | kh | 8080 | 51 minutes ago |
72.195.101.99 | us | 4145 | 51 minutes ago |
103.216.50.223 | kh | 8080 | 51 minutes ago |
67.201.58.190 | us | 4145 | 51 minutes ago |
72.205.0.93 | us | 4145 | 51 minutes ago |
41.230.216.70 | tn | 80 | 51 minutes ago |
103.63.190.72 | kh | 8080 | 51 minutes ago |
139.59.1.14 | in | 3128 | 51 minutes ago |
122.151.54.147 | au | 80 | 51 minutes ago |
128.140.113.110 | de | 8080 | 51 minutes ago |
188.191.165.159 | ru | 8080 | 51 minutes ago |
Our proxies work perfectly with all popular tools for web scraping, automation, and anti-detect browsers. Load your proxies into your favorite software or use them in your scripts in just seconds:
Connection formats you know and trust: IP:port or IP:port@login:password.
Any programming language: Python, JavaScript, PHP, Java, and more.
Top automation and scraping tools: Scrapy, Selenium, Puppeteer, ZennoPoster, BAS, and many others.
Anti-detect browsers: Multilogin, GoLogin, Dolphin, AdsPower, and other popular solutions.
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To parse all pages of a website in Python, you can use web scraping libraries such as requests for fetching HTML content and BeautifulSoup or lxml for parsing and extracting data. Additionally, you might need to manage crawling and handle the structure of the website.
Here's a basic example using requests and BeautifulSoup:
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
def get_all_links(url):
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
# Extract all links on the page
links = [a['href'] for a in soup.find_all('a', href=True)]
return links
def parse_all_pages(base_url):
all_links = get_all_links(base_url)
all_pages_content = []
for link in all_links:
# Form the full URL for each link
full_url = urljoin(base_url, link)
# Ensure the link is within the same domain to avoid external links
if urlparse(full_url).netloc == urlparse(base_url).netloc:
# Get HTML content of the page
page_content = requests.get(full_url).text
all_pages_content.append({'url': full_url, 'content': page_content})
return all_pages_content
# Example usage
base_url = 'https://example.com'
all_pages_data = parse_all_pages(base_url)
# Now you have a list of dictionaries with data for each page
for page_data in all_pages_data:
print(f"URL: {page_data['url']}")
# Process HTML content of each page as needed
# For example, you can use BeautifulSoup for further data extraction
This example fetches all links from the initial page and then iterates through each link, fetching and storing the HTML content of the linked pages. Make sure to handle relative URLs and filter external links based on your requirements.
To scrape currency rates, you can use various financial data sources that provide reliable and up-to-date exchange rate information. However, keep in mind that scraping financial data may be subject to the terms of service of the respective websites, and it's crucial to comply with their policies.
Here are some legitimate alternatives to scraping:
Use a Financial Data API: Many financial data providers offer APIs that provide real-time and historical exchange rate data. Examples include:
These services often require an API key, and they may have free and paid plans with different levels of access.
Central Banks and Financial Authorities: Some central banks and financial authorities publish exchange rate information on their official websites. For example, the European Central Bank (ECB) provides daily updated exchange rates.
Financial News Websites: Financial news websites often display live exchange rates. You can check websites like Bloomberg, Reuters, or CNBC.
Remember to always check the terms of service and licensing agreements of any data provider you choose to use. Using a legitimate API is generally more reliable and ensures that you're accessing accurate and authorized data.
Avoid scraping from websites that explicitly prohibit scraping or do not provide permission for such activities. Unauthorized scraping may violate terms of service and legal agreements.
To connect to a proxy server with a password, provide the proxy address, port, and authentication credentials (username and password) in your browser or application settings. For popular browsers like Google Chrome and Mozilla Firefox, follow these general steps:
Open the browser and go to its settings.
Locate the proxy settings section.
Enter the proxy server address, port, username, and password.
Save the settings.
In CentOS, if there is no graphical interface (from the terminal), proxy configuration is done through the export http_proxy=http://User:Pass@Proxy:Port/ command. Accordingly, User is the user, Pass is the password to identify you, Proxy is the IP address of the proxy, and Port is the port number. If you have DE, the configuration can be done via Network Manager (as in any other Linux distribution).
If you intend to use a proxy to work on the Internet, you should first of all clear your browser history. This way, you will get rid of the risk of being identified by past actions on the site. In case you are engaged in Internet promotion, it is also advisable to use proxy servers for this purpose, allowing you to enter different sites safely. This solution will allow you to avoid blocking promoted accounts.
What else…