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Creating and Using Pipelines

Introduction​

Pipelines in Scrapy are a powerful tool for performing a series of operations on the items, or data, that your spiders scrape. They're perfect for cleaning, validating, and storing your scraped data.

Basic Structure of a Pipeline​

Here's a basic structure of a Scrapy pipeline:

class MyPipeline:
    def process_item(self, item, spider):
        # operation on the item
        return item

The process_item method takes two parameters: item and spider. This method is called for every item pipeline component.

Creating a Pipeline​

Let's create a basic pipeline that takes a scraped item and prints it out.

class PrintItemPipeline:
    def process_item(self, item, spider):
        print(item)
        return item

To use this pipeline, add it to your settings.py file:

ITEM_PIPELINES = {'myproject.pipelines.PrintItemPipeline': 1}

The number 1 is the order in which the pipelines are processed. Pipelines with lower numbers are processed first.

Using Pipelines for Data Cleaning​

Pipelines can be used to clean the data you've scraped. Let's say we want to remove any leading or trailing whitespaces from all data. We could create a pipeline like this:

class CleanTextPipeline:
    def process_item(self, item, spider):
        for field in item:
            item[field] = item[field].strip()  # remove leading/trailing spaces
        return item

Using Pipelines for Data Validation​

Pipelines can also validate your data. If an item does not pass the validation, it will be dropped and not processed by any further pipeline stages. For example, if we only want items that have a certain field, we could do this:

class ValidateItemPipeline:
    def process_item(self, item, spider):
        if 'my_field' not in item:
            raise DropItem("Missing my_field in %s" % item)
        return item

Remember to import the DropItem exception at the top of your file:

from scrapy.exceptions import DropItem

Using Pipelines for Storing Data​

Finally, pipelines can be used to store your data. Here's an example of a pipeline that stores all items in a JSON file:

import json

class JsonWriterPipeline:
    def open_spider(self, spider):
        self.file = open('items.jl', 'w')

    def close_spider(self, spider):
        self.file.close()

    def process_item(self, item, spider):
        line = json.dumps(dict(item)) + "\n"
        self.file.write(line)
        return item

The open_spider and close_spider methods are special methods that get called when the spider opens and closes. We use them to open and close our file.

Conclusion​

Scrapy pipelines are a powerful tool for processing your scraped data. They allow you to clean, validate, and even store your data in a way that fits your needs. By chaining multiple pipelines together, you can create a complex data processing mechanism that fits your exact needs.