Pros of awesome-spider

• Comprehensive collection of spider resources and projects
• Well-organized with categories for different types of spiders
• Includes a wide range of languages and frameworks

Cons of awesome-spider

• Lacks detailed explanations or tutorials for each project
• May not be as beginner-friendly as python-spider
• Some listed projects might be outdated or no longer maintained

Code Comparison

python-spider example:

import requests
from bs4 import BeautifulSoup

url = 'https://example.com'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

awesome-spider doesn't provide direct code examples, as it's a curated list of resources. However, it links to various projects with their own implementations.

Summary

python-spider is more focused on providing practical Python-based web scraping examples and tutorials, making it ideal for beginners and those specifically interested in Python. awesome-spider, on the other hand, offers a broader overview of web scraping resources across multiple languages and frameworks, making it valuable for developers seeking diverse approaches to web scraping.

Pros of examples-of-web-crawlers

• More diverse examples covering various platforms (WeChat, Bilibili, Douban, etc.)
• Includes practical applications like sending emails and generating word clouds
• Better documentation with detailed explanations for each crawler

Cons of examples-of-web-crawlers

• Less frequently updated compared to python-spider
• Fewer stars and forks, indicating potentially less community engagement
• Some examples may be outdated due to changes in target websites

Code Comparison

examples-of-web-crawlers (WeChat friends analysis):

itchat.auto_login(hotReload=True)
friends = itchat.get_friends(update=True)[0:]
male = female = other = 0
for i in friends[1:]:
    sex = i["Sex"]
    if sex == 1:
        male += 1
    elif sex == 2:
        female += 1
    else:
        other += 1

python-spider (Bilibili video downloader):

def download(self, url, name):
    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36'}
    r = requests.get(url=url, headers=headers, stream=True)
    with open(name, 'wb') as f:
        for chunk in r.iter_content(chunk_size=1024):
            if chunk:
                f.write(chunk)

Both repositories offer valuable resources for learning web scraping in Python, with examples-of-web-crawlers providing a wider range of practical applications and python-spider offering more frequent updates and a larger community.

Pros of proxy_pool

• Focuses specifically on proxy management, providing a robust solution for maintaining and utilizing proxy pools
• Offers a RESTful API for easy integration with other projects
• Includes automatic proxy validation and scoring system

Cons of proxy_pool

• More limited in scope compared to python-spider's diverse collection of web scraping examples
• May require additional setup and maintenance for the proxy pool infrastructure
• Less suitable for beginners looking for general web scraping tutorials

Code Comparison

proxy_pool example (proxy retrieval):

import requests

def get_proxy():
    return requests.get("http://127.0.0.1:5010/get/").json()

proxy = get_proxy().get("proxy")

python-spider example (basic web scraping):

import requests
from bs4 import BeautifulSoup

url = "https://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

proxy_pool is specialized for proxy management, while python-spider provides a broader range of web scraping examples and techniques. The choice between them depends on the specific needs of your project, with proxy_pool being more suitable for projects requiring extensive proxy usage and python-spider offering a wider variety of web scraping tutorials and examples.

Pros of awesome-python-login-model

• Focuses specifically on login models, providing a more specialized resource
• Includes a wider variety of login examples for different websites and platforms
• Offers more detailed explanations and comments in the code samples

Cons of awesome-python-login-model

• Less comprehensive in terms of overall web scraping techniques
• Fewer examples of data extraction and processing after login
• May not be as frequently updated as python-spider

Code Comparison

python-spider:

def get_page(url):
    response = requests.get(url, headers=headers)
    return response.text

def parse_page(html):
    soup = BeautifulSoup(html, 'html.parser')
    # Extract data from soup

awesome-python-login-model:

def login(username, password):
    session = requests.Session()
    login_data = {'username': username, 'password': password}
    response = session.post(login_url, data=login_data, headers=headers)
    return session if response.status_code == 200 else None

def get_protected_content(session, url):
    response = session.get(url)
    # Process protected content

The code comparison shows that python-spider focuses more on general web scraping techniques, while awesome-python-login-model emphasizes the login process and accessing protected content.

Pros of PSpider

• More comprehensive and structured framework for building scalable spiders
• Better documentation and code organization
• Includes built-in support for distributed crawling

Cons of PSpider

• Steeper learning curve due to its more complex architecture
• Less frequently updated compared to python-spider
• Fewer example scripts for specific use cases

Code Comparison

PSpider example:

from pspider import spider, parser

@spider.route()
def start_page(spider):
    yield "http://example.com"

@parser.route()
def parse_page(parser):
    title = parser.css("h1::text").extract_first()
    yield {"title": title}

python-spider example:

import requests
from bs4 import BeautifulSoup

url = "http://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
title = soup.find("h1").text
print({"title": title})

PSpider offers a more structured approach with decorators and built-in parsing methods, while python-spider provides simpler, more straightforward scripts using popular libraries like requests and BeautifulSoup.

Pros of pyspider

• More comprehensive framework with a web-based UI for managing crawlers
• Built-in support for distributed crawling and task queue management
• Extensive documentation and active community support

Cons of pyspider

• Steeper learning curve due to its more complex architecture
• May be overkill for simple scraping tasks
• Less frequently updated compared to python-spider

Code Comparison

pyspider:

from pyspider.libs.base_handler import *

class Handler(BaseHandler):
    @every(minutes=24 * 60)
    def on_start(self):
        self.crawl('http://example.com/', callback=self.index_page)

    @config(age=10 * 24 * 60 * 60)
    def index_page(self, response):
        for each in response.doc('a[href^="http"]').items():
            self.crawl(each.attr.href, callback=self.detail_page)

python-spider:

import requests
from bs4 import BeautifulSoup

def crawl(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    links = soup.find_all('a', href=True)
    for link in links:
        print(link['href'])

The pyspider example showcases its framework-based approach with decorators and callbacks, while python-spider demonstrates a simpler, script-based method using popular libraries like requests and BeautifulSoup.