我正在嘗試使用 BeautifulSoup 和 pandas 來抓取和存儲一些項目。下面的代碼僅部分有效。正如您所看到的，它刮掉了“Engine426/425 HP”，而我只希望將字符串“426/425 HP”存儲在“engine”列中。我想抓取下面 HTML 中的所有 4 個 h5 字符串（請參閱下面所需的輸出）。我希望有人能幫助我，謝謝！import numpy as npimport pandas as pdfrom bs4 import BeautifulSoupimport requestsimport remain_url = "https://www.example.com/"def getAndParseURL(url): result = requests.get(url) soup = BeautifulSoup(result.text, 'html.parser') return(soup)soup = getAndParseURL(main_url)engine = []engine.append(soup.find("ul", class_ = re.compile('list-inline lot-breakdown-list')).li.text)scraped_data = pd.DataFrame({'engine': engine})scraped_data.head() engine0 Engine426/425 HP超文本標記語言<div class="lot-breakdown"> <ul class="list-inline lot-breakdown-list"> <li> <h5>Engine</h5>426/425 HP</li> <li> <h5>Trans</h5>Automatic</li> <li> <h5>Color</h5>Alpine White</li> <li> <h5>Interior</h5>Black</li> </ul></div>所需輸出scraped_data[['engine', 'trans', 'color', 'interior']] = pd.DataFrame([['426/425 HP', 'Automatic', 'Alpine White', 'Black']], index=scraped_data.index)scraped_data engine trans color interior0 426/425 HP Automatic Alpine White Black
1 回答
蝴蝶刀刀
TA貢獻1801條經驗 獲得超8個贊
您可以通過多種方式實現這一目標：
from bs4 import BeautifulSoup , NavigableString
import requests
main_url = "https://www.example.com/"
def getAndParseURL(url):
result = requests.get(url)
soup = BeautifulSoup(result.text, 'html.parser')
return(soup)
soup = getAndParseURL(main_url)
#ul = soup.select('ul[class="list-inline lot-breakdown-list"] li')
#for li in ul :
#x = li.find(text=True, recursive=False) # Will give you the text of the li skipping the text of child tag
#y = ' '.join([t for t in li.contents if type(t)== NavigableString]) # contents [<h5>Engine</h5>, '426/425 HP'] the text you want has a type of NavigableString and That's what we are returning .
ul = soup.select('ul[class="list-inline lot-breakdown-list"] li', recursive=True)
lis_e = []
for li in ul:
lis = []
lis.append(li.contents[1])
lis_e.extend(lis)
engine.append(lis_e[0])
trans.append(lis_e[1])
color.append(lis_e[2])
interior.append(lis_e[3])
scraped_data = pd.DataFrame({'engine': engine, 'transmission': trans, 'color': color, 'interior': interior})
scraped_data
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