我要抓取“类别”页面的类别标题下的所有子类别和页面：“类别：计算机科学”。相同的链接如下：http://en.wikipedia.org/wiki/Category:Computer_science。
我从以下链接中指定的以下堆栈溢出答案中获得了有关上述问题的想法。 Pythonic beautifulSoup4 : How to get remaining titles from the next page link of a wikipedia category 和 How to scrape Subcategories and pages in categories of a Category wikipedia page using Python
但是，答案不能完全解决问题。它仅抓取Pages in category "Computer science"。但是，我想提取所有子类别名称及其关联的页面。我希望流程应该以BFS方式报告结果，深度为10。是否有任何方法可以做到这一点？
我从 this linked post 找到了以下代码：
from pprint import pprint
from urllib.parse import urljoin
from bs4 import BeautifulSoup
import requests
base_url = 'https://en.wikipedia.org/wiki/Category:Computer science'
def get_next_link(soup):
return soup.find("a", text="next page")
def extract_links(soup):
return [a['title'] for a in soup.select("#mw-pages li a")]
with requests.Session() as session:
content = session.get(base_url).content
soup = BeautifulSoup(content, 'lxml')
links = extract_links(soup)
next_link = get_next_link(soup)
while next_link is not None: # while there is a Next Page link
url = urljoin(base_url, next_link['href'])
content = session.get(url).content
soup = BeautifulSoup(content, 'lxml')
links += extract_links(soup)
next_link = get_next_link(soup)
pprint(links)
答案 0 :(得分：1)
要抓取子类别，您将必须使用selenium与下拉菜单进行交互。对第二个链接类别的简单遍历将产生页面，但是，要找到所有子类别，需要递归以正确地对数据进行分组。下面的代码利用breadth-first search的简单变体来确定何时停止循环在while循环的每次迭代中生成的下拉切换对象：
from selenium import webdriver
import time
from bs4 import BeautifulSoup as soup
def block_data(_d):
return {_d.find('h3').text:[[i.a.attrs.get('title'), i.a.attrs.get('href')] for i in _d.find('ul').find_all('li')]}
def get_pages(source:str) -> dict:
return [block_data(i) for i in soup(source, 'html.parser').find('div', {'id':'mw-pages'}).find_all('div', {'class':'mw-category-group'})]
d = webdriver.Chrome('/path/to/chromedriver')
d.get('https://en.wikipedia.org/wiki/Category:Computer_science')
all_pages = get_pages(d.page_source)
_seen_categories = []
def get_categories(source):
return [[i['href'], i.text] for i in soup(source, 'html.parser').find_all('a', {'class':'CategoryTreeLabel'})]
def total_depth(c):
return sum(1 if len(b) ==1 and not b[0] else sum([total_depth(i) for i in b]) for a, b in c.items())
def group_categories(source) -> dict:
return {i.find('div', {'class':'CategoryTreeItem'}).a.text:(lambda x:None if not x else [group_categories(c) for c in x])(i.find_all('div', {'class':'CategoryTreeChildren'})) for i in source.find_all('div', {'class':'CategoryTreeSection'})}
while True:
full_dict = group_categories(soup(d.page_source, 'html.parser'))
flag = False
for i in d.find_elements_by_class_name('CategoryTreeToggle'):
try:
if i.get_attribute('data-ct-title') not in _seen_categories:
i.click()
flag = True
time.sleep(1)
except:
pass
else:
_seen_categories.append(i.get_attribute('data-ct-title'))
if not flag:
break
输出：
all_pages：
[{'\xa0': [['Computer science', '/wiki/Computer_science'], ['Glossary of computer science', '/wiki/Glossary_of_computer_science'], ['Outline of computer science', '/wiki/Outline_of_computer_science']]},
{'B': [['Patrick Baudisch', '/wiki/Patrick_Baudisch'], ['Boolean', '/wiki/Boolean'], ['Business software', '/wiki/Business_software']]},
{'C': [['Nigel A. L. Clarke', '/wiki/Nigel_A._L._Clarke'], ['CLEVER score', '/wiki/CLEVER_score'], ['Computational human modeling', '/wiki/Computational_human_modeling'], ['Computational social choice', '/wiki/Computational_social_choice'], ['Computer engineering', '/wiki/Computer_engineering'], ['Critical code studies', '/wiki/Critical_code_studies']]},
{'I': [['Information and computer science', '/wiki/Information_and_computer_science'], ['Instance selection', '/wiki/Instance_selection'], ['Internet Research (journal)', '/wiki/Internet_Research_(journal)']]},
{'J': [['Jaro–Winkler distance', '/wiki/Jaro%E2%80%93Winkler_distance'], ['User:JUehV/sandbox', '/wiki/User:JUehV/sandbox']]},
{'K': [['Krauss matching wildcards algorithm', '/wiki/Krauss_matching_wildcards_algorithm']]},
{'L': [['Lempel-Ziv complexity', '/wiki/Lempel-Ziv_complexity'], ['Literal (computer programming)', '/wiki/Literal_(computer_programming)']]},
{'M': [['Machine learning in bioinformatics', '/wiki/Machine_learning_in_bioinformatics'], ['Matching wildcards', '/wiki/Matching_wildcards'], ['Sidney Michaelson', '/wiki/Sidney_Michaelson']]},
{'N': [['Nuclear computation', '/wiki/Nuclear_computation']]}, {'O': [['OpenCV', '/wiki/OpenCV']]},
{'P': [['Philosophy of computer science', '/wiki/Philosophy_of_computer_science'], ['Prefetching', '/wiki/Prefetching'], ['Programmer', '/wiki/Programmer']]},
{'Q': [['Quaject', '/wiki/Quaject'], ['Quantum image processing', '/wiki/Quantum_image_processing']]},
{'R': [['Reduction Operator', '/wiki/Reduction_Operator']]}, {'S': [['Social cloud computing', '/wiki/Social_cloud_computing'], ['Software', '/wiki/Software'], ['Computer science in sport', '/wiki/Computer_science_in_sport'], ['Supnick matrix', '/wiki/Supnick_matrix'], ['Symbolic execution', '/wiki/Symbolic_execution']]},
{'T': [['Technology transfer in computer science', '/wiki/Technology_transfer_in_computer_science'], ['Trace Cache', '/wiki/Trace_Cache'], ['Transition (computer science)', '/wiki/Transition_(computer_science)']]},
{'V': [['Viola–Jones object detection framework', '/wiki/Viola%E2%80%93Jones_object_detection_framework'], ['Virtual environment', '/wiki/Virtual_environment'], ['Visual computing', '/wiki/Visual_computing']]},
{'W': [['Wiener connector', '/wiki/Wiener_connector']]},
{'Z': [['Wojciech Zaremba', '/wiki/Wojciech_Zaremba']]},
{'Ρ': [['Portal:Computer science', '/wiki/Portal:Computer_science']]}]
full_dict很大，由于大小原因，我无法完全将其张贴在这里，但是，下面是一个遍历结构并选择所有元素的深度为10的函数的实现：
def trim_data(d, depth, count):
return {a:None if count == depth else [trim_data(i, depth, count+1) for i in b] for a, b in d.items()}
final_subcategories = trim_data(full_dict, 10, 0)
编辑：从树中删除叶子的脚本：
def remove_empty_children(d):
return {a:None if len(b) == 1 and not b[0] else
[remove_empty_children(i) for i in b if i] for a, b in d.items()}
运行以上命令时：
c = {'Areas of computer science': [{'Algorithms and data structures': [{'Abstract data types': [{'Priority queues': [{'Heaps (data structures)': [{}]}, {}], 'Heaps (data structures)': [{}]}]}]}]}
d = remove_empty_children(c)
输出：
{'Areas of computer science': [{'Algorithms and data structures': [{'Abstract data types': [{'Priority queues': [{'Heaps (data structures)': None}], 'Heaps (data structures)': None}]}]}]}
编辑2：展平整个结构：
def flatten_groups(d):
for a, b in d.items():
yield a
if b is not None:
for i in map(flatten_groups, b):
yield from i
print(list(flatten_groups(remove_empty_children(c))))
输出：
['Areas of computer science', 'Algorithms and data structures', 'Abstract data types', 'Priority queues', 'Heaps (data structures)', 'Heaps (data structures)']
编辑3：
要访问每个子类别的所有页面到一定级别，可以使用原始的get_pages函数和group_categories方法的稍有不同的版本
def _group_categories(source) -> dict:
return {i.find('div', {'class':'CategoryTreeItem'}).find('a')['href']:(lambda x:None if not x else [group_categories(c) for c in x])(i.find_all('div', {'class':'CategoryTreeChildren'})) for i in source.find_all('div', {'class':'CategoryTreeSection'})}
from collections import namedtuple
page = namedtuple('page', ['pages', 'children'])
def subcategory_pages(d, depth, current = 0):
r = {}
for a, b in d.items():
all_pages_listing = get_pages(requests.get(f'https://en.wikipedia.org{a}').text)
print(f'page number for {a}: {len(all_pages_listing)}')
r[a] = page(all_pages_listing, None if current==depth else [subcategory_pages(i, depth, current+1) for i in b])
return r
print(subcategory_pages(full_dict, 2))
请注意，为了使用subcategory_pages，必须使用_group_categories代替group_categories。