Adventures in Machine Learning

Scraping with Scrapy and MongoDB: Ethical and Efficient Data Extraction

Scraping data from the web is a common practice in the modern digital era. With the abundance of data available online, it’s easy to be tempted to scrape everything that we can get our hands on.

However, it’s important to understand that not all web data is ours to scrape and use as we please. This article will cover the use of Scrapy and PyMongo to scrape data in a freelance gig, while also highlighting the importance of ethical scraping practices.

Installing PyMongo

PyMongo is a Python library used to interact with MongoDB, a popular NoSQL database. We can install PyMongo using pip, a package manager for Python, and creating a requirements.txt file to track the library’s dependencies.

To install PyMongo, open your terminal and type the following command:

“`

$ pip install pymongo

“`

To create a requirements.txt file, navigate to the root directory of your project and run the following command:

“`

$ pip freeze > requirements.txt

“`

This will create a file named requirements.txt in your project’s root directory, containing the names and versions of all the packages currently installed in your project. This file can be used to install all the required dependencies of your project on another machine.

Starting a new Scrapy Project

Scrapy is a Python framework used for web scraping. It provides a high-level API to extract data from websites efficiently.

Here’s how to start a new Scrapy project:

1. Open your terminal and create a new directory for your project by typing the following command:

“`

$ mkdir project-name

$ cd project-name

“`

2.

In the root directory of your project, create a new Scrapy project by typing the following command:

“`

$ scrapy startproject project-name

“`

This will create a new directory named project-name containing the boilerplate code for your Scrapy project. 3.

In the root directory of your project, create a new spider by typing the following command:

“`

$ scrapy genspider spider-name domain-name

“`

This will create a new spider file named spider-name.py in the spiders directory of your project. The domain-name argument specifies the domain that you want to scrape.

4. In the spider file, modify the start_urls variable to specify the URLs that you want to scrape.

5. Define the items that you want to scrape by creating a new Python file named items.py in the root directory of your project and defining a class for each item that you want to scrape.

Each class should have attributes that correspond to the data that you want to scrape. 6.

Create a new Python file named pipelines.py in the root directory of your project and define a class for each pipeline that you want to use. Each pipeline class should define methods that manipulate the scraped items as they pass through the pipeline.

7. Modify the settings.py file in the root directory of your project to enable the pipelines that you want to use.

Ethical Scraping Practices

Scraping data from the web can be a powerful tool, but we must be aware of the ethical implications of web scraping. Here are some ethical scraping practices:

1.

Respect the website’s terms of service: Before scraping any website, read and understand the website’s terms of service. Some websites explicitly prohibit web scraping, while others have limits on the number of requests per second or hour.

2. Don’t scrape personal data: Avoid scraping personal data such as names, addresses, and phone numbers.

This type of data is often protected by privacy laws and can cause legal problems if used improperly. 3.

Don’t overload the website: Be mindful of the website’s server capacity. If you send too many requests too quickly, you can overload the website’s server and cause it to crash.

This can lead to legal repercussions and damage your reputation. 4.

Don’t misrepresent yourself: Be transparent about who you are and why you are scraping the website. If you misrepresent yourself or your intentions, you can damage your reputation and cause legal problems.

Conclusion

In this article, we have discussed the use of Scrapy and PyMongo to scrape data in a freelance gig, while also highlighting the importance of ethical scraping practices.

Installing PyMongo involves using pip and creating a requirements.txt file.

Starting a new Scrapy project involves creating a new directory, generating a new spider, modifying the start_urls, defining items, pipelines, and modifying the settings. Lastly, we discussed the importance of ethical scraping practices, including respecting the website’s terms of service, avoiding scraping personal data, being mindful of the website’s server capacity, and being transparent about who you are and why you are scraping the website.

By following ethical scraping practices, we can use web scraping as a powerful tool ethically and responsibly.

3) Specify Data

In Scrapy, the first step in scraping data is to specify which data we want to scrape. This is done by defining the items that we want to extract in the items.py file.

Let’s take a look at how we can do this in more detail. In the root directory of your Scrapy project, create a new file named items.py.

In this file, define a class for each item that you want to extract. The name of the class can be anything you want, but it should be descriptive of the data that you want to extract.

Here’s an example of how to define an item for a Stack Overflow post:

“`

import scrapy

class StackItem(scrapy.Item):

title = scrapy.Field()

url = scrapy.Field()

“`

In this example, we have defined a class named StackItem that inherits from scrapy.Item. We have also defined two attributes for the class: title and url.

These attributes correspond to the data that we want to extract from Stack Overflow posts.

4) Create the Spider

Now that we have specified the data that we want to extract, we need to create a spider to scrape the data from the website. In Scrapy, a spider is a Python class that defines how to follow links and what data to extract from each page.

Let’s take a look at how we can create a spider to extract data from Stack Overflow.

Defining the class and attributes

In the root directory of your project, create a new file named stack_spider.py. In this file, define a new class named StackSpider that inherits from scrapy.Spider:

“`

import scrapy

class StackSpider(scrapy.Spider):

name = “stackoverflow”

allowed_domains = [“stackoverflow.com”]

start_urls = [“https://stackoverflow.com/questions”]

“`

In this example, we have defined a class named StackSpider that inherits from scrapy.Spider. We have also defined three attributes for the class:

– name: The name of the spider.

This is used to identify the spider when running Scrapy. – allowed_domains: A list of domains that the spider is allowed to scrape.

Any URLs that are not under one of these domains will not be followed. – start_urls: A list of URLs to start scraping from.

In this example, we have specified that the spider should only scrape pages on the stackoverflow.com domain and that it should start by scraping the questions page on the Stack Overflow website.

Using XPath Selectors

Now that we have defined the class and attributes for our spider, we need to define how to follow links and extract data from each page. In Scrapy, this is done using XPath selectors.

XPath is a query language used to navigate XML and HTML documents. In Scrapy, we use XPath selectors to identify specific elements on a page that we want to extract data from.

Let’s take a look at how to use XPath selectors to extract data from Stack Overflow. To use XPath selectors, we need to first inspect the HTML of the page that we want to scrape.

We can do this using the JavaScript Console or the Inspect Element feature of our web browser. Once we have identified the element that we want to extract data from, we can use the XPath selector to select that element in our spider.

Let’s take a look at how to use XPath selectors to extract the title and URL of each question on the Stack Overflow questions page. “`

import scrapy

class StackSpider(scrapy.Spider):

name = “stackoverflow”

allowed_domains = [“stackoverflow.com”]

start_urls = [“https://stackoverflow.com/questions”]

def parse(self, response):

for question in response.xpath(‘//div[@class=”question-summary”]’):

item = StackItem()

item[‘title’] = question.xpath(‘div[2]/h3/a/text()’).get()

item[‘url’] = question.xpath(‘div[2]/h3/a/@href’).get()

yield item

“`

In this example, we have defined a new method named parse in our StackSpider class. This method is called once for each page that the spider scrapes.

In this method, we use the XPath selector to select each question on the page and extract its title and URL. The XPath selector ‘//div[@class=”question-summary”]’ selects all div elements with a class of “question-summary” on the page.

For each question, we create a new StackItem object and set its title and URL attributes using the XPath selectors ‘div[2]/h3/a/text()’ and ‘div[2]/h3/a/@href’, respectively. Finally, we yield the item, which tells Scrapy to save the item and continue following links on the page.

Conclusion

In this expansion, we covered how to specify the data that we want to extract in the items.py file and how to create a spider to scrape data using Scrapy. We also discussed how to use XPath selectors to extract data from specific elements on a page.

By using these techniques, we can scrape data from the web ethically and efficiently.

5) Extract the Data

With our Spider and data specification in place, the next step is to extract the data from the web page. This is done in the parse method of our Spider by using the response object to extract the data.

“`

def parse(self, response):

for post in response.css(‘div.post’):

item = StackItem()

item[‘title’] = post.css(‘.post-title a::text’).get()

item[‘link’] = post.css(‘.post-title a::attr(href)’).get()

yield item

“`

In this example, we iterate over each post on the page using the CSS selector `div.post`. We then extract the title of the post using `.css(‘.post-title a::text’).get()` CSS selector, and the link using `.css(‘.post-title a::attr(href)’).get()` CSS selector.

Finally, we yield an instance of the `StackItem` class which contains the title and link.

6) Store the Data in MongoDB

Once we have extracted our data, the next step is to store it. Storing the data in a database allows us to query, manipulate, and analyze it.

MongoDB is a popular NoSQL document-oriented database that is well-suited for storing scraped data in JSON-like documents.

Creating the Database

The first step in storing the data in MongoDB is to create a database for it. In the root directory of your project, open the `settings.py` file and add the following line to the `ITEM_PIPELINES` list:

“`

‘project_name.pipelines.MongoDBPipeline’: 300,

“`

This sets up a pipeline that processes the extracted data and sends it to MongoDB.

Connecting to the Database

To connect to the MongoDB database, we need to create a `MongoDBPipeline` class and define the connection settings. In the `pipelines.py` file, add the following code:

“`

import pymongo

class MongoDBPipeline(object):

def __init__(self, mongo_uri, mongo_db):

self.mongo_uri = mongo_uri

self.mongo_db = mongo_db

@classmethod

def from_crawler(cls, crawler):

return cls(

mongo_uri=crawler.settings.get(‘MONGO_URI’),

mongo_db=crawler.settings.get(‘MONGO_DATABASE’, ‘items’)

)

def open_spider(self, spider):

self.client = pymongo.MongoClient(self.mongo_uri)

self.db = self.client[self.mongo_db]

def close_spider(self, spider):

self.client.close()

“`

In this code, we define a `MongoDBPipeline` class that creates a connection to the MongoDB database. The `from_crawler` method retrieves the connection settings from the Scrapy `settings.py` file.

The `open_spider` method initializes the MongoDB connection, and the `close_spider` method closes the connection when the spider is finished.

Processing the Data and Saving it to the Database

To process the data and save it to the MongoDB database, we define a `process_item` method in the `MongoDBPipeline` class. “`

def process_item(self, item, spider):

valid = True

for data in item:

if not data:

valid = False

raise DropItem(“Missing {0}!”.format(data))

if valid:

self.db[self.collection_name].insert(dict(item))

log.msg(“Question added to MongoDB database!”,

level=log.DEBUG, spider=spider)

return item

“`

In this code, we loop through each item in the `StackItem` object and check that the data is present.

If any data is missing, we raise an exception and drop the item. The item is then inserted into the database collection and a log message is generated to verify that the data has been saved.

We can further customize the MongoDB collection by specifying its name, indexing rules, and other settings in the `settings.py` file.

“`

MONGODB_SERVER = ‘localhost’

MONGODB_PORT = 27017

MONGODB_DB = ‘stackoverflow’

MONGODB_COLLECTION = ‘questions’

ITEM_PIPELINES = {

‘project_name.pipelines.MongoDBPipeline’: 300,

}

MONGO_URI = ‘mongodb://{0}:{1}/’.format(MONGODB_SERVER, MONGODB_PORT)

“`

With these settings configured, our Scrapy spider will now extract and save Stack Overflow posts to our MongoDB database.

Conclusion

In this expansion, we have covered the final steps of storing our extracted data in MongoDB. We first set up a pipeline in `settings.py` file to direct our data into MongoDB via the `MongoDbPipeline` class in `pipelines.py`.

We then connected to our MongoDB database, specifying the database name and collection name in the Scrapy settings. Lastly, we processed and saved the data to the database using the `process_item` method, which ensures that all necessary data is present and logs each item that is added to the database.

With the data now stored in MongoDB, we have successfully extracted, transformed, and loaded (ETL) our web data in an ethical and compelling way.

7) Pipeline Management

After creating a pipeline to connect Scrapy and MongoDB, it’s important to manage it effectively to ensure the accuracy and reliability of our data. Let’s take a closer look at how we can connect Scrapy and MongoDB through a pipeline, and how to effectively manage that pipeline.

Connecting Scrapy and MongoDB through a Pipeline

To connect Scrapy and MongoDB through a pipeline, we need to create a new class in the `pipelines.py` file that inherits from `scrapy.ItemPipeline`. Inside the new class, we define the methods `open_spider`, `process_item`, and `close_spider` to manage the connection to MongoDB.

Here’s an example of how this could look:

“`

from scrapy.exceptions import DropItem

from pymongo import MongoClient

class MongoDBPipeline(object):

def __init__(self, mongo_uri, mongo_db, mongo_collection):

self.mongo_uri = mongo_uri

self.mongo_db = mongo_db

self.mongo_collection = mongo_collection

@classmethod

def from_crawler(cls, crawler):

return cls(

mongo_uri=crawler.settings.get(‘MONGODB_URI’),

mongo_db=crawler.settings.get(‘MONGODB_DB’, ‘scrapy_db’),

mongo_collection=crawler.settings.get(‘MONGODB_COLLECTION’, ‘scrapy_collection’)

)

def open_spider(self, spider):

self.client = MongoClient(self.mongo_uri)

self.db = self.client[self.mongo_db]

self.collection = self.db[self.mongo_collection]

def process_item(self