Pros of awesome-crawler

• More focused on crawling and distributed systems
• Includes resources for anti-anti-crawling techniques
• Provides a section on crawler deployment

Cons of awesome-crawler

• Less comprehensive overall compared to awesome-web-scraping
• Fewer language-specific resources
• Limited information on data extraction tools

Code Comparison

While both repositories primarily consist of curated lists rather than code, they may include code snippets in their documentation. Here's a hypothetical comparison of how they might present a simple Python scraping example:

awesome-web-scraping:

import requests
from bs4 import BeautifulSoup

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

awesome-crawler:

import scrapy

class ExampleSpider(scrapy.Spider):
    name = 'example'
    start_urls = ['https://example.com']

    def parse(self, response):
        # Extract data here
        pass

Both repositories serve as valuable resources for web scraping and crawling, with awesome-web-scraping offering a broader range of tools and languages, while awesome-crawler provides more depth in specific areas like distributed crawling and anti-detection techniques.

Pros of awesome-spider

• Focuses specifically on spider/crawler tools and libraries
• Includes more Chinese-language resources and tools
• Organized by programming language for easier navigation

Cons of awesome-spider

• Less comprehensive overall compared to awesome-web-scraping
• Fewer resources for data extraction and processing
• Limited information on legal and ethical considerations

Code Comparison

awesome-web-scraping example (Python):

import requests
from bs4 import BeautifulSoup

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

awesome-spider example (Python):

import scrapy

class ExampleSpider(scrapy.Spider):
    name = 'example'
    start_urls = ['https://example.com']

    def parse(self, response):
        # Extract data here
        pass

Both repositories provide valuable resources for web scraping and crawling. awesome-web-scraping offers a more comprehensive list of tools, libraries, and resources across various programming languages and aspects of web scraping. It also includes sections on data processing, web services, and legal considerations.

awesome-spider, on the other hand, is more focused on spider/crawler tools and libraries, with a stronger emphasis on Chinese-language resources. It organizes its content by programming language, making it easier for developers to find relevant tools for their preferred language.

While awesome-web-scraping provides a broader overview of the web scraping ecosystem, awesome-spider might be more suitable for developers specifically looking for crawler tools or those working with Chinese websites.

Pros of awesome-streaming

• Focuses specifically on streaming technologies and frameworks
• Includes a wider range of streaming-related topics (e.g., stream processing, CEP, messaging systems)
• More comprehensive coverage of big data streaming tools and platforms

Cons of awesome-streaming

• Less emphasis on web-specific tools and libraries
• Fewer language-specific resources for general-purpose programming languages
• May not be as useful for developers primarily interested in web scraping

Code comparison

awesome-web-scraping example (Python):

import requests
from bs4 import BeautifulSoup

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

awesome-streaming example (Apache Flink):

DataStream<String> textStream = env.socketTextStream("localhost", 9999);
DataStream<Tuple2<String, Integer>> counts = textStream
    .flatMap(new Tokenizer())
    .keyBy(0)
    .sum(1);

Summary

awesome-web-scraping is better suited for developers focused on extracting data from websites, offering a variety of tools and libraries across multiple programming languages. awesome-streaming, on the other hand, is more appropriate for those working with real-time data processing, stream analytics, and big data technologies. While there is some overlap in terms of data acquisition, the repositories cater to different aspects of data handling and processing.

Pros of python-guide

• Broader scope covering Python best practices, not just web scraping
• More comprehensive guide for Python developers at all levels
• Includes sections on writing, deploying, and testing Python code

Cons of python-guide

• Less focused on web scraping specifically
• May not cover as many specialized web scraping tools and libraries
• Updates less frequently than awesome-web-scraping

Code comparison

python-guide example (configuration):

import configparser

config = configparser.ConfigParser()
config['DEFAULT'] = {'ServerAliveInterval': '45',
                     'Compression': 'yes',
                     'CompressionLevel': '9'}

awesome-web-scraping example (web scraping):

import requests
from bs4 import BeautifulSoup

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

Summary

python-guide is a comprehensive resource for Python developers, covering a wide range of topics beyond web scraping. It's suitable for beginners and experienced developers alike. awesome-web-scraping, on the other hand, is more focused and specialized, providing a curated list of web scraping tools and libraries. Choose python-guide for general Python knowledge and best practices, or awesome-web-scraping for specific web scraping resources and techniques.

Pros of awesome-machine-learning

• Broader scope, covering various aspects of machine learning
• More comprehensive, with a larger number of resources and tools
• Includes resources for multiple programming languages

Cons of awesome-machine-learning

• Less focused, which may be overwhelming for beginners
• May include outdated resources due to its broad coverage
• Requires more frequent updates to maintain relevance

Code comparison

While both repositories are curated lists and don't contain actual code, they differ in their organization. Here's a sample of how they structure their content:

awesome-machine-learning:

## Python

#### Computer Vision

* [SimpleCV](http://simplecv.org/) - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.

awesome-web-scraping:

## Python

### Network

* [urllib](https://docs.python.org/3/library/urllib.html) - network library (stdlib).
* [requests](https://github.com/psf/requests) - network library.

Both repositories organize content by programming language and then by specific categories within each language. However, awesome-machine-learning tends to have more detailed descriptions and a wider range of categories, while awesome-web-scraping is more concise and focused on specific web scraping tools and libraries.

Pros of awesome-python

• Broader scope, covering a wide range of Python topics and libraries
• Larger community and more frequent updates
• More comprehensive, with a greater number of resources and tools listed

Cons of awesome-python

• Less focused on web scraping specifically
• May be overwhelming for users looking for targeted web scraping resources
• Requires more time to navigate and find relevant web scraping tools

Code comparison

While both repositories are curated lists and don't contain actual code, they differ in how they organize and present information. Here's a comparison of their README structures:

awesome-python:

## Contents
- [Admin Panels](#admin-panels)
- [Algorithms and Design Patterns](#algorithms-and-design-patterns)
- [ASGI Servers](#asgi-servers)
...

awesome-web-scraping:

## Programming Languages

### Python
* Network
    * [urllib](https://docs.python.org/3/library/urllib.html) - network library (stdlib)
    * [requests](https://github.com/psf/requests) - network library
...

awesome-python uses a flat structure with main categories, while awesome-web-scraping organizes content by programming language and then by subcategories.