bluemoss enables you to easily scrape websites.
Project description
bluemoss
a simple way to scrape the web
Getting Started
pip install bluemoss
Full Documentation
Read the docs
What is bluemoss?
Webscraping is officially easy!
Built on top of lxml,
bluemoss lets you scrape any website by defining a single Node object.
Below is an example of the most basic Node object one could define.
from bluemoss import Node, scrape
node = Node('a') # scrapes the text contained in the first a-tag
scrape(node, YOUR_HTML) # happy scraping
Bluemoss lets you craft a single `Node` object that does it all:
**1) scraping**, **2) transforming** and **3) structuring** website data seamlessly, into the format you need.
Since **bluemoss** builds on top of lxml, it uses XPath 1.0 to locate html tags. If you are new to XPath, no problem — ChatGPT has got your back to help kick off those initial queries.
What is XPath?
ChatGPT says: "XPath, which stands for XML Path Language, is a query language
that is used for selecting nodes from an XML document. It can also be used with HTML as it is an application of XML."
Pro Tip: bluemoss uses XPath to locate tags in HTML documents.
Let's get started - Examples
This section will show you how bluemoss helps you scrape any website.
For all examples that follow, we are going to scrape the html document below.
<html>
<head>
<title>Portfolio</title>
</head>
<body>
<li>
<div>
<a href="/portfolio?company=apple">
Apple
</a>
<div class="location_us">
<p>Cupertino</p>
</div>
</div>
</li>
<li>
<div>
<a href="/portfolio?company=google">
Google
</a>
<div class="location_us">
<p>Mountain View</p>
</div>
</div>
</li>
<li>
<div>
<a href="/portfolio?company=tesla">
Tesla
</a>
<div class="location_us">
<p>Austin</p>
</div>
</div>
</li>
<li>
<div>
<a href="/portfolio?company=deepmind">
DeepMind
</a>
<div class="location_uk">
<p>London</p>
</div>
</div>
</li>
</body>
</html>
Introduction
Goal - Scrape the text within the first a-tag: "Apple"
The Node object defined below tells the scrape function to find the first a-tag and extract the text it contains.
from bluemoss import Node, scrape
node = Node('a')
scrape(node, HTML) == 'Apple'
Pro Tip: Good Node objects have a short xpath argument.
Filter
Filter a single tag
Goal - Scrape the second company headquarters which are located in the US
node = Node('div[contains(@class, "location_us")]', filter=1)
scrape(node, HTML) == 'Mountain View'
Pro Tip: Learning XPath is easy and fast with ChatGPT. Bluemoss supports XPath 1.0.
This example introduces the filter argument which determines which of those tag(s) that match the xpath locator will be scraped. The default value for the filter arg is 0 (index 0), which will scrape the very first (index 0) tag that matches the given xpath. Our goal for this example was to extract the text of the second tag (index 1) that matches our xpath, therefor we set filter = 1.
Filter all tags
Goal - Scrape ALL company headquarters which are located in the US
node = Node('div[contains(@class, "location_us")]', filter=None)
scrape(node, HTML) == ['Cupertino', 'Mountain View', 'Austin']
Setting filter=None will filter for all tags matched against the given xpath.
Filter multiple tags, part 1
Goal - Scrape the first and third company names.
Node('a', filter=[0, 2])
scrape(node, HTML) == ['Apple', 'Google']
In this example we set the filter arg a list of ints. Those int values refer to the first and third index (0 and 2).
Filter multiple tags, part 2
Goal - Scrape all company names, but exclude the first one.
The expected scrape result is
['Google', 'Tesla', 'DeepMind']
# 'Apple' not included, since it is located in the first a-tag (index 0)
Let's show 5 different ways of achieving this goal:
example 1
from bluemoss import Node, Range
Node('a', filter=Range(1))
# Range(1) filters the matched tags from index 1 onwards
example 2
Node('li//a', filter=Range(1))
# the xpaths 'a' and 'li//a' match the same tags in our html doc
example 3
Node('li/div/a', filter=Range(1))
example 4
Node('a', filter=[1, 2, 3])
example 5
Node('a', filter=Range(1, 4))
# The Range class accepts a second int argument (the stop index).
# Range(x, y) filters for the matched tags at indices x, ..., y-1
# e.g. Range(2, 6) filters for indices 2, 3, 4, 5.
Filter multiple tags, part 3
Goal - Scrape all company names from index 1 onwards in reverse order, and do it in 3 different ways.
The expected scrape result is
['DeepMind', 'Tesla', 'Google']
Node('a', filter=Range(1, reverse=True)) # set reverse to True
Node('a', filter=Range(1, 4, reverse=True)) # set reverse to True
Node('a', filter=Range(1), transform=lambda res: res[::-1]) # use transform arg
The first two examples simply set the reverse arg of the Range object to True. The last example uses a different approach. It introduces the transform arg of the Node class.
The transform function is the function being executed once
- the tags were matched with the given xpath
- the matched tags were further filtered using the Node.filter arg
Pro Tip: The transform function defines the last step in scraping.
Building Dictionaries
example 1
Goal - Scrape the last two company names and store the result in a dict under the key 'companies'
Node('a', filter=[-2, -1], key='companies')
scrape(node, HTML) == {'companies': ['Tesla', 'DeepMind']}
In the example above we provide the indexes -2 and -1 to the filter-list as those indices represent the last two elements in a Python list.
example 2
Goal - Scrape the first company name and store the result in a dict under the key 'companies'
Node('a', key='companies')
scrape(node, HTML) == {'companies': 'Tesla'}
Extract & Transform
Goal - Scrape the first company id in 3 different ways.
What is the company id? Every a-tag in our html doc defines an href property.
The first a-tag declares the href /portfolio?company=apple, and we regard the company-id to be the value of the company
key extracted from the href-query-string. Therefor, in our html doc, the first company-id is apple, which is the value we expect to extract.
Let us first define a helper function that will receive the href-value as an argument and return the company id:
def get_company_id(href: str) -> str:
return href.split('=')[-1]
example 1
from src.bluemoss import Node, Ex
Node('a', extract=Ex.HREF, transform=get_company_id)
# Declare the tag-property to be extracted by using the 'extract' arg.
# Introducing the 'Ex' Enum which provides handy types of extraction.
example 2
Node('a', extract='href', transform=get_company_id)
# The 'extract' arg also accept string values.
example 3
Node('a/@href', transform=get_company_id)
# We can also just use xpath to extract the href property.
Advanced Scraping
Lists
Goal - Scrape the name and headquarters of every company.
The expected result:
[
['Apple', 'Cupertino'],
['Google', 'Mountain View'],
['Tesla', 'Austin'],
['DeepMind', 'London']
]
Solution
Node(
'li', # match 'li' tags
filter=None, # scrape all the matched 'li' tags
nodes=[
Node('a'), # within the 'li' tag, match the first 'a' tag and extract the text
Node('p') # within the 'li' tag, match the first 'p' tag and extract the text
]
)
Pro Tip: The **nodes** arg let's you scrape multiple different tags within the same parent tag.
Dicts
Goal - Scrape the name and headquarters of every company, where each item in the result list is a dict.
The expected result:
[
{'name': 'Apple', 'location': 'Cupertino'},
{'name': 'Google', 'location': 'Mountain View'},
{'name': 'Tesla', 'location': 'Austin'},
{'name': 'DeepMind', 'location': 'London'}
]
Solution
Node(
'li',
filter=None,
nodes=[
Node('a', key='name'),
Node('p', key='location')
]
)
Pro Tip: All Nodes in a nodes list either define the 'key' arg or none of them do.
Dataclasses
Goal - In this last example, we want to scrape the name and location of every company, as well as the total amount of companies located in the US and UK. We also want to store the scraped data not in a dict or list as we did in the previous examples, but instead want to store the data in dataclass instances.
Info - The code snippet below shows that we assume a dataclass called Companies in which we will store the entire scrape-result. The expected result also assumes, that the Companies instance exposed two properties dict and json
# expected result
companies: Companies = scrape(node, HTML)
companies.dict == {
'companies': [
{'name': 'Apple', 'location': 'Cupertino'},
{'name': 'Google', 'location': 'Mountain View'},
{'name': 'Tesla', 'location': 'Austin'},
{'name': 'DeepMind', 'location': 'London'},
],
'amount_uk_companies': 1,
'amount_us_companies': 3,
'amount_companies': 4
}
companies.json == """{
"companies": [
{
"name": "Apple",
"location": "Cupertino"
},
{
"name": "Google",
"location": "Mountain View"
},
{
"name": "Tesla",
"location": "Austin"
},
{
"name": "DeepMind",
"location": "London"
}
],
"amount_uk_companies": 1,
"amount_us_companies": 3,
"amount_companies": 4
}"""
# solution
from dataclasses import dataclass
from bluemoss import Node, Jsonify
@dataclass
class Company(Jsonify):
name: str
location: str
@dataclass
class Companies(Jsonify):
companies: list[Company]
amount_uk_companies: int
amount_us_companies: int
@property
def amount_companies(self) -> int:
return self.amount_us_companies + self.amount_uk_companies
@property
def dict(self):
return super().dict | {'amount_companies': self.amount_companies}
Node(
target=Companies,
nodes=[
Node(
"count(//div[@class='location_uk'])",
key='amount_uk_companies',
transform=lambda count: int(count) if count else None
),
Node(
"count(//div[@class='location_us'])",
key='amount_us_companies',
transform=lambda count: int(count) if count else None
),
Node(
'li',
filter=None,
key='companies',
target=Company,
nodes=[
Node('a', key='name'),
Node('p', key='location')
]
)
]
)
The solution in the code snippet introduces two new things:
- The Node.target parameter allows us to specify what class or dataclass to use in order to store the data scraped in the Node.nodes list. If a Node instance sets a target, then all instances in Node.nodes must set their key parameter and every key must map to one of the init parameters of Node.target.
- The Jsonify class exposes the two properties dict and json. It therefor makes it easy transform the scraped data stored in dataclass instances into a Python dict or json string.
Pro Tip: The Jsonify class will exclude any parameters starting with an underscore "_" from appearing in the return of the .dict and .json properties. This enables us to hide certain parameters from appearing in those returns.
Why use Dataclasses?
- type safety - Dataclass instances as used in this example enforce typed parameters.
- properties - Sometimes we want our data transformations to take place inside the dataclass, e.g. through properties. Properties provide a simple way to derive data from the instance parameters of a class instance. By moving the data transformation step from the Node.transform parameter to a dataclass property, we make the transformation explicitly available to the dataclass.
- post_init - The post_init method that is available in Python dataclasses is yet another nice step to manipulate the instance parameters and therefore move the data transformation step partially or as a whole from the Node.transform parameter to the post_init method of the dataclass.
Supported Platforms
- Linux
- MacOS
- Windows
Supported Python Versions
- 3.9
- 3.10
- 3.11
- 3.12
License
- Apache 2.0
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