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How to configure the shell in scrapy

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by vigneshchennai74 dot icon Updated: Aug 31, 2023

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The Scrapy framework is a useful tool. It is called a scrapy shell that lets you explore and test your web scraping code. It gives an order line interface to run and test your scratching code. The scrappy shell lets you test and analyze the data you are scraping. It makes it ideal for testing and debugging.  

 

There are different types of scrapy shells available in the market to cater to various needs:  

  • These scrapy shells are designed for heavy-duty web scraping tasks in large-scale operations. They have advanced features and capabilities to handle high-volume data extraction and processing.  
  • Residential scrapy shells are more suitable for individual or small-scale web scraping projects. They provide a lightweight environment, ideal for scraping tasks. It helps with moderate data volumes.  
  • It is different from industrial and residential scrapy shells. They are specialized scrapy shells tailored for specific purposes. These shells are optimized for particular websites or industries, offering targeted functionalities.  
  • Developers may create their own customized scrapy shells to meet specific scraping requirements. These custom scrapy shells are tailored to their unique needs. It incorporates specific features and functionalities.  
  • Being an open-source framework, Scrapy has a variety of open-source scrapy shells available. These shells are developed and maintained by the Scrapy community. It offers various features and customization options.  
  • Some companies or vendors offer commercial scrapy shells with extra features. These scrapy shells are available for sale or subscription.  
  • Cloud-based scrapy shells provide a web-based interface to execute web scraping tasks. They offer scalability, convenience, and the ability to run scraping operations.  
  • Containerized scrapy shells leverage containerization technologies like Docker to provide isolation. It has portable environments for running scraping tasks. They offer ease of deployment and replication across different systems.  
  • Hybrid scrapy shells combine features and functionalities from different types of scrapy shells. It provides a versatile and flexible scraping environment. They may incorporate a mix of industrial and residential features.  
  • The scrapy shell market is evolving with the introduction of new shells. These advancements bring improved features and capabilities. It ensures that users can leverage the most advanced tools for web scraping.  

 

Scrapy shells are amazing assets in the field of web scratching. It offers the capacity to make complex selectors and separate information from sites. Private scrapy shells are reasonable for individual or limited-scope projects. Each type provides distinct features and capabilities to meet various scraping requirements. Web scrubbers can improve their information extraction dod do precise and proficient outcomes. It is when it's for modern scratching errands or private undertakings.  

 

Preview of the output that you will get on running this code from your IDE

Code

The Scrapy shell is an interactive environment that allows you to test and debug your Scrapy spiders, execute XPath/CSS selectors, and interactively explore and extract data from websites.

My Scrapy Shell Commands Work but Output is Empty

PythondotLines of Code : 59dotLicense : Strong Copyleft (CC BY-SA 4.0)

import scrapy
from scrapy.crawler import CrawlerProcess


class LivescoresTodayList(scrapy.Spider):
    name = 'todayMatcheslist'
    custom_settings = {'CONCURRENT_REQUESTS': '1'}

    def start_requests(self):
        yield scrapy.Request('https://www.livescores.com/?tz=3')

    def parse(self, response):
        for gununMaclari in response.css('div.dh'):
            yield{
                'Home': gununMaclari.css('span.hh span.ih span.kh::text').get(),
                'Away': gununMaclari.css('span.hh span.jh span.kh::text').get()
            }


process = CrawlerProcess(settings={
    "FEEDS": {
        "todayMatcheslist.json": {"format": "json", "overwrite": True},
    },
})

process.crawl(LivescoresTodayList)
process.start()

import scrapy
from scrapy.crawler import CrawlerRunner
from scrapy.utils.log import configure_logging
from twisted.internet import reactor


class LivescoresTodayList(scrapy.Spider):
    name = 'todayMatcheslist'
    custom_settings = {'CONCURRENT_REQUESTS': '1'}

    def start_requests(self):
        yield scrapy.Request('https://www.livescores.com/?tz=3')

    def parse(self, response):
        for gununMaclari in response.css('div.dh'):
            yield{
                'Home': gununMaclari.css('span.hh span.ih span.kh::text').get(),
                'Away': gununMaclari.css('span.hh span.jh span.kh::text').get()
            }


configure_logging({'LOG_FORMAT': '%(levelname)s: %(message)s'})
runnerTodayList = CrawlerRunner(settings={
    "FEEDS": {
        "todayMatcheslist.json": {"format": "json", "overwrite": True},
    },
})
d = runnerTodayList.crawl(LivescoresTodayList)
d.addBoth(lambda _: reactor.stop())
reactor.run()

Instructions

  1. Download and install VS Code on your desktop.
  2. Open VS Code and create a new file in the editor.
  3. Copy the code snippet that you want to run, using the "Copy" button or by selecting the text and using the copy command (Ctrl+C on Windows/Linux or Cmd+C on Mac).,
  4. Paste the code into your file in VS Code, and save the file with a meaningful name and the appropriate file extension for Python use (.py).file extension.
  5. To run the code, open the file in VS Code and click the "Run" button in the top menu, or use the keyboard shortcut Ctrl+Alt+N (on Windows and Linux) or Cmd+Alt+N (on Mac). The output of your code will appear in the VS Code output console.


I hope you have found this useful. I have added the version information in the following section.


I found this code snippet by searching " My Scrapy Shell Commands Work but Output is Empty " in kandi. you can try any use case.

Environment Test

I tested this solution in the following versions. Be mindful of changes when working with other versions.


  1. The solution is created and tested using Vscode 1.77.2 version
  2. The solution is created in Python 3.7.15 version
  3. The solution is created in Scrapy 2.9.0 version


To configure the shell in Scrapy, run the "scrapy shell" command in the terminal, enabling interactive exploration and testing of selectors. The Scrapy shell allows interactive testing of selectors, facilitating quick and efficient data extraction from websites during web scraping development.

Dependent Library

scrapyby scrapy

Python doticonstar image 47503 doticonVersion:2.9.0doticon
License: Permissive (BSD-3-Clause)

Scrapy, a fast high-level web crawling & scraping framework for Python.

Support
    Quality
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            scrapyby scrapy

            Python doticon star image 47503 doticonVersion:2.9.0doticon License: Permissive (BSD-3-Clause)

            Scrapy, a fast high-level web crawling & scraping framework for Python.
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have the requests library that is required to run this code, you can install them by clicking on the above link.

                      You can search for any dependent library on kandi - like scrapy

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                      FAQ  

                      1. What is the difference between Python and Scrapy code for web scraping?  

                      The difference between Python and Scrapy code lies in their approach and functionality. Python is a general-purpose programming language. That provides a wide range of libraries and tools for various tasks. It offers flexibility and allows for custom code implementation to scrape websites. But Scrapy is a specialized web crawling framework built on top of Python. It provides a high-level abstraction, making it easier to write web scraping code. Handle common scraping tasks such as requesting web pages, parsing HTML/XML.  

                       

                      2. How can large-scale web scraping be accomplished using a Web Crawling Framework?  

                      Large-scale web scraping can be accomplished using a Web Crawling Framework like Scrapy. Scrapy offers features such as distributed crawling, parallelism, and built-in throttling mechanisms. That enables efficient scraping of a large number of web pages. Developers scale web scraping with optimized crawl processes and handle large data. 

                       

                      3. How does XPath simplify extracting data from HTML documents?  

                      XPath simplifies the process of extracting data from HTML documents by providing concise. It allows you to navigate through the HTML structure using path expressions. It is when directories are navigated in a file system. XPath expressions can target specific elements, attributes, or text within HTML documents. It makes it easier to extract the desired data. It handles complex HTML structures, making XPath a popular data extraction choice.  

                       

                      4. What is the purpose of a Python console when working with a scrapy shell?  

                      The Python console within the scrapy shell serves as an interactive environment. where you can execute Python code and interact with the scraped data. It allows you to test and debug code snippets. It explores the structure of the scraped data and performs data manipulation tasks. Python console provides a convenient way to experiment with different data extraction techniques. 

                       

                      5. Is there any advantage to using a regular Python shell instead of a scrapy shell?  

                      The scrapy shell provides a specialized environment tailored for web scraping. Using a regular Python shell can have its advantages. You have access to full functionality. It can leverage more libraries that may not be available within the scrapy shell. It can be useful for more complex data manipulation tasks. It integrates with other systems or experiments with custom code outside the framework.  

                       

                      6. How do you know if your scrapy shell is working properly?  

                      To ensure that your scrapy shell is working, you can perform various checks:  

                      • Test basic functionality by running simple commands like requesting a web page.  
                      • Execute sample scraping code to confirm that we can extract the desired data.  
                      • Verify that the scraped data matches the expected structure and content.  
                      • Test different selectors and XPath expressions to ensure they return the desired elements.

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