我们如何导航到一个网页，刮取数据，移动到下一页，然后再做一次？

Question

我做了两次尝试，让我的代码导航到一个网页，将数据从一个表导入到一个数据框，然后移动到下一个页面，再次执行同样的操作。下面是我测试的一些示例代码。现在我被卡住了；我不知道怎么继续

# first attempt
import requests
from bs4 import BeautifulSoup
import pandas as pd
from selenium import webdriver
from time import sleep

lst = []
url = "https://www.nasdaq.com/market-activity/stocks/screener"

for numb in (1, 10):
    url = "https://www.nasdaq.com/market-activity/stocks/screener"
    r = requests.get(url)
    html = r.text
    soup = BeautifulSoup(html, "html.parser")
    table = soup.find_all('table')
    df = pd.DataFrame(table)
    lst.append(df)
    
    def get_cpf():
        driver = webdriver.Chrome("C:/Utility/chromedriver.exe")
        driver.get(url)
        driver.find_element_by_class('pagination__page" data-page="'' +  numb + ''').click()
        sleep(10)
        text=driver.find_element_by_id('texto_cpf').text
        print(text)
    get_cpf()
    get_cpf.click
    

### second attempt
#import BeautifulSoup
from bs4 import BeautifulSoup
import pandas as pd
import requests
from selenium import webdriver
from time import sleep

lst = []

for numb in (1, 10):
    r=requests.get('https://www.nasdaq.com/market-activity/stocks/screener')
    data = r.text
    soup = BeautifulSoup(data, "html.parser")
    table = soup.find( "table", {"class":"nasdaq-screener__table"} )
    
    for row in table.findAll("tr"):
        for cell in row("td"):
            data = cell.get_text().strip()
            df = pd.DataFrame(data)
            lst.append(df)
            
    def get_cpf():
        driver = webdriver.Chrome("C:/Utility/chromedriver.exe")
        driver.get(url)
        driver.find_element_by_class('pagination__page" data-page="'' +  numb + ''').click()
        sleep(10)
        text=driver.find_element_by_id('texto_cpf').text
        print(text)
    get_cpf()
    get_cpf.click


### third attempt
from bs4 import BeautifulSoup
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait 
from selenium.webdriver.support import expected_conditions as EC
from selenium import webdriver
import time
import requests
import pandas as pd

lst = []

url="https://www.nasdaq.com/market-activity/stocks/screener"
driver = webdriver.Chrome("C:/Utility/chromedriver.exe")
wait = WebDriverWait(driver, 10)
driver.get(url)
wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,"#_evh-ric-c"))).click()
for pages in range(1,9):
    try:
        print(pages)
        r = requests.get(url)
        html = r.text
        soup = BeautifulSoup(html, "html.parser")
        table = soup.find_all('table')
        df = pd.DataFrame(table)
        lst.append(df)
        wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,"button.pagination__next"))).click()
        time.sleep(1)
    except:
        break

这是一个屏幕截图的HTML背后的表，我正试图刮

因此，在第一页中，我想从以下内容中删除所有内容：

AAPL    Apple Inc. Common Stock $127.79 6.53    5.385%  2,215,538,678,600

致：

ASML    ASML Holding N.V. New York Registry Shares  $583.55 16.46   2.903%  243,056,764,541

然后，移到第2页，做同样的事情，移到第3页，做同样的事情，等等，等等。我不确定仅使用BeautifulSoup是否可行。或者我需要Selenium，用于按钮单击事件。我愿意做这里最简单的事。谢谢

Answer 1

不会处理API，因为Nuran只会按照用户的要求处理

下面是浏览前10页的示例。首先，我们删除通知。然后等待“下一步”按钮可交互并单击它

wait = WebDriverWait(driver, 10)
driver.get("https://www.nasdaq.com/market-activity/stocks/screener")
wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,"#_evh-ric-c"))).click()
#Currently you start on the 1st page and say we want to click 9 times for the 10th page
for pages in range(1,10):
    try:
        print(pages)
        #Get your data from this page
        wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,"button.pagination__next"))).click()
        #This is just here to slow everything so it may be removeable. 
        time.sleep(5)
    except:
        break

进口

from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait 
from selenium.webdriver.support import expected_conditions as EC

你可以这样做

html=driver.page_source
soup=BeautifulSoup(html,'html.parser')
div=soup.select_one("table.nasdaq-screener__table")
table=pd.read_html(str(div))
print(table[0])