Scrapy Guide

Scrapy is a Python framework for building web crawlers. Unlike requests + BeautifulSoup, Scrapy handles everything — sending requests, parsing HTML, following links, and saving data — all in one structured project.

Use Scrapy when your crawler needs to:

• Handle many pages or multiple URLs
• Follow links automatically
• Save data to files or databases in a consistent way

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How Scrapy Works

The Spider defines what to crawl and how to parse it. The Engine handles scheduling and data flow. The Pipeline processes and saves the scraped data.

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Installation

pip install scrapy

Verify the installation:

scrapy version

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Create a Scrapy Project

scrapy startproject my_crawler
cd my_crawler

Project Structure

my_crawler/
├── scrapy.cfg
└── my_crawler/
    ├── __init__.py
    ├── items.py        # Define data structure
    ├── middlewares.py  # Custom request/response hooks
    ├── pipelines.py    # Process and save scraped data
    ├── settings.py     # Project configuration
    └── spiders/        # Put your spiders here
        └── __init__.py

The most important files for beginners are:

• spiders/: Where you write your crawling logic
• items.py: Define the fields you want to collect
• pipelines.py: Process the collected data (e.g., save to CSV, database)
• settings.py: Control crawl speed, headers, pipelines, etc.

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Create Your First Spider

Create a file inside spiders/ — for example, spiders/quotes_spider.py:

import scrapy

class QuotesSpider(scrapy.Spider):
    name = "quotes"  # Unique name for this spider
    start_urls = [
        "https://quotes.toscrape.com/",
    ]

    def parse(self, response):
        for quote in response.css("div.quote"):
            yield {
                "text": quote.css("span.text::text").get(),
                "author": quote.css("small.author::text").get(),
            }

        # Follow the "next" page link
        next_page = response.css("li.next a::attr(href)").get()
        if next_page:
            yield response.follow(next_page, self.parse)

Key parts of a spider:

• name: Used to run the spider from the terminal
• start_urls: The pages Scrapy will crawl first
• parse(): Called for each response — extract data and follow links here

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CSS Selectors in Scrapy

Scrapy uses CSS selectors (similar to BeautifulSoup) to extract data from HTML.

Method	Description
response.css("tag::text").get()	Get text from the first matching element
response.css("tag::text").getall()	Get text from all matching elements
response.css("tag::attr(href)").get()	Get an attribute value
response.css("tag")	Select elements (returns a list)

Example

# HTML: <a href="/page/2" class="next">Next</a>
response.css("a.next::attr(href)").get()   # → "/page/2"
response.css("a.next::text").get()          # → "Next"

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Run the Spider

scrapy crawl quotes

Save Output to a File

# Save as JSON
scrapy crawl quotes -o output.json

# Save as CSV
scrapy crawl quotes -o output.csv

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Use Scrapy Shell to Debug

Before writing your spider, use the Scrapy shell to test your selectors interactively.

scrapy shell "https://quotes.toscrape.com/"

Inside the shell:

# Test selectors
response.css("div.quote span.text::text").getall()
response.css("small.author::text").getall()

This saves a lot of time — you can confirm the selector works before putting it in your spider.

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Custom Headers and Settings

Edit settings.py to configure the crawler:

# Slow down requests to be polite
DOWNLOAD_DELAY = 1  # seconds between requests

# Set a custom User-Agent
USER_AGENT = "Mozilla/5.0 (compatible; MyBot/1.0)"

For pipeline configuration, see Scrapy Pipeline.

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Define Items (Optional but Recommended)

Using items.py makes your data structure explicit and easier to reuse.

# items.py
import scrapy

class QuoteItem(scrapy.Item):
    text = scrapy.Field()
    author = scrapy.Field()

In your spider, yield the item instead of a plain dict:

from my_crawler.items import QuoteItem

def parse(self, response):
    for quote in response.css("div.quote"):
        item = QuoteItem()
        item["text"] = quote.css("span.text::text").get()
        item["author"] = quote.css("small.author::text").get()
        yield item

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Scrapy vs requests + BeautifulSoup

Feature	requests + BS4	Scrapy
Setup	Simple	Requires project structure
Multi-page crawling	Manual loop	Built-in with follow()
Concurrency	No (sequential)	Yes (async by default)
Data pipeline	Manual	Built-in pipeline
Best for	Small, one-off scripts	Large or recurring crawlers

Use requests + BeautifulSoup for quick scripts. Use Scrapy when you need something more structured and scalable.