Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
import timeit
from statistics import mean
import requests
from autoscraper import AutoScraper
from bs4 import BeautifulSoup
from lxml import etree, html
from mechanicalsoup import StatefulBrowser
from parsel import Selector
from pyquery import PyQuery as pq
from selectolax.parser import HTMLParser
from scrapling import Adaptor
large_html = '<html><body>' + '<div class="item">' * 5000 + '</div>' * 5000 + '</body></html>'
def benchmark(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
benchmark_name = func.__name__.replace('test_', '').replace('_', ' ')
print(f"-> {benchmark_name}", end=" ", flush=True)
# Warm-up phase
timeit.repeat(lambda: func(*args, **kwargs), number=2, repeat=2, globals=globals())
# Measure time (1 run, repeat 100 times, take average)
times = timeit.repeat(
lambda: func(*args, **kwargs), number=1, repeat=100, globals=globals(), timer=time.process_time
)
min_time = round(mean(times) * 1000, 2) # Convert to milliseconds
print(f"average execution time: {min_time} ms")
return min_time
return wrapper
@benchmark
def test_lxml():
return [
e.text
for e in etree.fromstring(
large_html,
# Scrapling and Parsel use the same parser inside so this is just to make it fair
parser=html.HTMLParser(recover=True, huge_tree=True)
).cssselect('.item')]
@benchmark
def test_bs4_lxml():
return [e.text for e in BeautifulSoup(large_html, 'lxml').select('.item')]
@benchmark
def test_bs4_html5lib():
return [e.text for e in BeautifulSoup(large_html, 'html5lib').select('.item')]
@benchmark
def test_pyquery():
return [e.text() for e in pq(large_html)('.item').items()]
@benchmark
def test_scrapling():
# No need to do `.extract()` like parsel to extract text
# Also, this is faster than `[t.text for t in Adaptor(large_html, auto_match=False).css('.item')]`
# for obvious reasons, of course.
return Adaptor(large_html, auto_match=False).css('.item::text')
@benchmark
def test_parsel():
return Selector(text=large_html).css('.item::text').extract()
@benchmark
def test_mechanicalsoup():
browser = StatefulBrowser()
browser.open_fake_page(large_html)
return [e.text for e in browser.page.select('.item')]
@benchmark
def test_selectolax():
return [node.text() for node in HTMLParser(large_html).css('.item')]
def display(results):
# Sort and display results
sorted_results = sorted(results.items(), key=lambda x: x[1]) # Sort by time
scrapling_time = results['Scrapling']
print("\nRanked Results (fastest to slowest):")
print(f" i. {'Library tested':<18} | {'avg. time (ms)':<15} | vs Scrapling")
print('-' * 50)
for i, (test_name, test_time) in enumerate(sorted_results, 1):
compare = round(test_time / scrapling_time, 3)
print(f" {i}. {test_name:<18} | {str(test_time):<15} | {compare}")
@benchmark
def test_scrapling_text(request_html):