A library for converting any text (xml, html, plain text, stdout, etc) to python datatypes
Project description
Scrape-schema
This library is designed to write structured, readable, reusable parsers for html, raw text and is inspired by dataclasses
!!! Scrape-schema is currently in Pre-Alpha. Please expect breaking changes.
Motivation
Simplifying parsers support, where it is difficult to use or the complete absence of the API interfaces and decrease lines of code
Also structuring, data serialization and use as an intermediate layer for third-party serialization libraries: json, dataclasses, pydantic, etc
Features
- Built top on Parsel
- re, css, xpath, jmespath, chompjs features
- Fluent interface simulate original parsel.Selector API for easy to use.
- Does not depend on the http client implementation, use any!
- Python 3.8+ support
- Reusability, code consistency
- Dataclass-like structure
- Partial support auto type-casting from annotations (str, int, float, bool, list, dict, Optional)
- Detailed logging process to make it easier to write a parser
Install
pip install scrape-schema
Example
The fields interface is similar to the original parsel
# Example from parsel documentation
>>> from parsel import Selector
>>> text = """
<html>
<body>
<h1>Hello, Parsel!</h1>
<ul>
<li><a href="http://example.com">Link 1</a></li>
<li><a href="http://scrapy.org">Link 2</a></li>
</ul>
<script type="application/json">{"a": ["b", "c"]}</script>
</body>
</html>"""
>>> selector = Selector(text=text)
>>> selector.css('h1::text').get()
'Hello, Parsel!'
>>> selector.xpath('//h1/text()').re(r'\w+')
['Hello', 'Parsel']
>>> for li in selector.css('ul > li'):
... print(li.xpath('.//@href').get())
http://example.com
http://scrapy.org
>>> selector.css('script::text').jmespath("a").get()
'b'
>>> selector.css('script::text').jmespath("a").getall()
['b', 'c']
from scrape_schema import BaseSchema, Parsel, Sc
class Schema(BaseSchema):
h1: Sc[str, Parsel().css('h1::text').get()]
words: Sc[list[str], Parsel().xpath('//h1/text()').re(r'\w+')]
urls: Sc[list[str], Parsel().css('ul > li').xpath('.//@href').getall()]
sample_jmespath_1: Sc[str, Parsel().css('script::text').jmespath("a").get()]
sample_jmespath_2: Sc[list[str], Parsel().css('script::text').jmespath("a").getall()]
text = """
<html>
<body>
<h1>Hello, Parsel!</h1>
<ul>
<li><a href="http://example.com">Link 1</a></li>
<li><a href="http://scrapy.org">Link 2</a></li>
</ul>
<script type="application/json">{"a": ["b", "c"]}</script>
</body>
</html>"""
print(Schema(text).dict())
# {'h1': 'Hello, Parsel!',
# 'words': ['Hello', 'Parsel'],
# 'urls': ['http://example.com', 'http://scrapy.org'],
# 'sample_jmespath_1': 'b',
# 'sample_jmespath_2': ['b', 'c']}
Code comparison
html
parsel:
from parsel import Selector
import pprint
import requests
def original_parsel(resp: str):
sel = Selector(resp)
__RATINGS = {"One": 1, "Two": 2, "Three": 3, "Four": 4, "Five": 5}
data: dict[str, list[dict]] = {"books": []}
for book_sel in sel.xpath(".//section/div/ol[@class='row']/li"):
if url := book_sel.xpath('//div[@class="image_container"]/a/@href').get():
url = f"https://books.toscrape.com/catalogue/{url}"
if image := book_sel.xpath('//div[@class="image_container"]/a/img/@src').get():
image = f"https://books.toscrape.com{image[2:]}"
if price := book_sel.xpath('//div[@class="product_price"]/p[@class="price_color"]/text()').get():
price = float(price[2:])
else:
price = .0
name = book_sel.xpath("//h3/a/@title").get()
available = book_sel.xpath('//div[@class="product_price"]/p[@class="instock availability"]/i').attrib.get('class')
available = ('icon-ok' in available)
rating = book_sel.xpath('//p[contains(@class, "star-rating")]').attrib.get('class')
rating = __RATINGS.get(rating.split()[-1], 0)
data['books'].append(dict(url=url, image=image, price=price, name=name, available=available, rating=rating))
return data
if __name__ == '__main__':
response = requests.get("https://books.toscrape.com/catalogue/page-2.html").text
pprint.pprint(original_parsel(response), compact=True)
scrape_schema:
from typing import List
import pprint
import requests
from scrape_schema import BaseSchema, Sc, Nested, sc_param, Parsel
class Book(BaseSchema):
__RATINGS = {"One": 1, "Two": 2, "Three": 3, "Four": 4, "Five": 5}
url: Sc[str, (Parsel()
.xpath('//div[@class="image_container"]/a/@href')
.get()
.concat_l("https://books.toscrape.com/catalogue/"))]
image: Sc[str, (Parsel()
.xpath('//div[@class="image_container"]/a/img/@src')
.get()[2:]
.concat_l("https://books.toscrape.com"))]
price: Sc[float, (Parsel(default=.0)
.xpath('//div[@class="product_price"]/p[@class="price_color"]/text()')
.get()[2:])]
name: Sc[str, Parsel().xpath("//h3/a/@title").get()]
available: Sc[bool, (Parsel()
.xpath('//div[@class="product_price"]/p[@class="instock availability"]/i')
.attrib['class']
.fn(lambda s: s == 'icon-ok') # check available tag
)]
_rating: Sc[str, Parsel().xpath('//p[contains(@class, "star-rating")]').attrib.get(key='class')]
@sc_param
def rating(self) -> int:
return self.__RATINGS.get(self._rating.split()[-1], 0)
class MainPage(BaseSchema):
books: Sc[List[Book], Nested(Parsel().xpath(".//section/div/ol[@class='row']/li").getall())]
if __name__ == '__main__':
response = requests.get("https://books.toscrape.com/catalogue/page-2.html").text
pprint.pprint(MainPage(response).dict(), compact=True)
raw text
original re:
import re
import pprint
TEXT = """
banana potato BANANA POTATO
-foo:10
-bar:20
lorem upsum dolor
192.168.0.1
"""
def parse_text(text: str) -> dict:
if match := re.search(r"(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})", text):
ipv4 = match[1]
else:
ipv4 = None
if matches := re.findall(r"(\d+)", text):
max_digit = max(int(i) for i in matches)
else:
max_digit = None
failed_value = bool(re.search(r"(ora)", text))
if matches := re.findall(r"(\d+)", text):
digits = [int(i) for i in matches]
digits_float = [float(f'{i}.5') for i in matches]
else:
digits = None
digits_float = None
words_lower = matches if (matches := re.findall(r"([a-z]+)", text)) else None
words_upper = matches if (matches := re.findall(r"([A-Z]+)", text)) else None
return dict(ipv4=ipv4, max_digit=max_digit, failed_value=failed_value,
digits=digits, digits_float=digits_float,
words_lower=words_lower, words_upper=words_upper)
if __name__ == '__main__':
pprint.pprint(parse_text(TEXT), width=48, compact=True)
# {'digits': [10, 20, 192, 168, 0, 1],
# 'digits_float': [10.5, 20.5, 192.5, 168.5, 0.5,
# 1.5],
# 'failed_value': False,
# 'ip_v4': '192.168.0.1',
# 'max_digit': 192,
# 'words_lower': ['banana', 'potato', 'foo',
# 'bar', 'lorem', 'upsum',
# 'dolor'],
# 'words_upper': ['BANANA', 'POTATO']}
scrape_schema:
from typing import List # if you usage python3.8. If python3.9 - use build-in list
import pprint
from scrape_schema import Parsel, BaseSchema, Sc, sc_param
# Note: `Sc` is shortcut typing.Annotated
TEXT = """
banana potato BANANA POTATO
-foo:10
-bar:20
lorem upsum dolor
192.168.0.1
"""
class MySchema(BaseSchema):
ipv4: Sc[str, Parsel(raw=True).re_search(r"(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})")[1]]
failed_value: Sc[bool, Parsel(default=False, raw=True).re_search(r"(ora)")[1]]
digits: Sc[List[int], Parsel(raw=True).re_findall(r"(\d+)")]
digits_float: Sc[List[float], Parsel(raw=True).re_findall(r"(\d+)").fn(lambda lst: [f"{s}.5" for s in lst])]
words_lower: Sc[List[str], Parsel(raw=True).re_findall("([a-z]+)")]
words_upper: Sc[List[str], Parsel(raw=True).re_findall(r"([A-Z]+)")]
@sc_param
def sum(self):
return sum(self.digits)
@sc_param
def max_digit(self):
return max(self.digits)
@sc_param
def all_words(self):
return self.words_lower + self.words_upper
if __name__ == '__main__':
pprint.pprint(MySchema(TEXT).dict(), compact=True)
# {'all_words': ['banana', 'potato', 'foo', 'bar', 'lorem', 'upsum', 'dolor',
# 'BANANA', 'POTATO'],
# 'digits': [10, 20, 192, 168, 0, 1],
# 'digits_float': [10.5, 20.5, 192.5, 168.5, 0.5, 1.5],
# 'failed_value': False,
# 'ipv4': '192.168.0.1',
# 'max_digit': 192,
# 'sum': 391,
# 'words_lower': ['banana', 'potato', 'foo', 'bar', 'lorem', 'upsum', 'dolor'],
# 'words_upper': ['BANANA', 'POTATO']}
logging
In this project, logging to the DEBUG level is enabled by default.
To set up logger, you can get it by the name "scrape_schema"
import logging
logger = logging.getLogger("scrape_schema")
logger.setLevel(logging.INFO)
...
See more examples and documentation for get more information/examples
This project is licensed under the terms of the MIT license.
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