努力使用硒刮桌子答案

【问题标题】：Struggling to scrape a table using selenium努力使用硒刮桌子
【发布时间】：2021-01-05 15:52:03
【问题描述】：

所以我很期待对这个link中出现的表格进行抓取。

为了刮，我决定使用硒。

在我的第一次尝试中，我所做的是：

driver = webdriver.Chrome(ChromeDriverManager().install())
driver.get(url)
html_source = self.driver.page_source
self.driver.quit()
BeautifulSoup(html_source, "html5lib")
table = soup.find('table', {'class': 'heavy-table ncpulse-fav-table ncpulse-sortable compressed-table'})
df = pd.read_html(str(table), flavor='html5lib', header=0, thousands='.', decimal=',')

但是它输出错误

'no tables found'

然后我尝试使用 expected_conditions 类，因为当我在 SO 中查找时，可能“页面源甚至在子元素完全呈现之前就被拉出”

因此我尝试了这样的事情：

driver.get(route)
element_present = expected_conditions.presence_of_element_located(
    (By.CLASS_NAME, 'heavy-table ncpulse-fav-table ncpulse-sortable compressed-table'))
WebDriverWait(driver, 20).until(element_present)
html_source = driver.page_source 
driver.quit()

但是这次它输出：

selenium.common.exceptions.TimeoutException: Message

因此我的问题是：我怎样才能获得所需的输出？我在使用 expected_conditions 类时做错了什么？到底是什么问题/前端技术让刮桌子变得如此困难？

【问题讨论】：

标签： python selenium xpath css-selectors webdriverwait

【解决方案1】：

CLASSNAME 选择器不处理复合类名，但您可以通过 css 选择器或 xpath 获取它。 CSS_SELECTOR 比 XPATH 更高效

element_present = expected_conditions.presence_of_element_located(
            (By.CSS_SELECTOR, "table[class='heavy-table ncpulse-fav-table ncpulse-sortable compressed-table']"))
    #or by xpath
element_present = expected_conditions.presence_of_element_located(
            (By.XPATH, "//table[@class='heavy-table ncpulse-fav-table ncpulse-sortable compressed-table']"))

【讨论】：

【解决方案2】：

对于多个类名，使用最右边。

element_present = WebDriverWait(driver, 20).until(EC.presence_of_element_located((By.CLASS_NAME, 'compressed-table')))
print(element_present.text)

输出

AKTIE +/- +/-% SENESTE ÅTD% VOLUMEN OMSÆTNING MARKEDSVÆRDI
Abn Amro Bank N.V. -0,32 -4,08% 7,48 -53,90% 7,9 mio 59,0 mio -
Adyen 81,00 5,62% 1523,00 108% 954 082 1,5 mia -
Aegon -0,08 -3,49% 2,16 -45,47% 17,4 mio 37,5 mio -
Ahold Del 0,25 0,98% 25,65 19,74% 8,0 mio 204,1 mio -
Akzo Nobel 0,14 0,16% 85,86 -3,16% 1,1 mio 90,6 mio -
Arcelormittal Sa 0,08 0,66% 11,53 -26,26% 11,9 mio 137,3 mio -

转成英文

options = Options()
prefs = {
  "translate_whitelists": {"da":"en"},
  "translate":{"enabled":"true"}
}
options.add_experimental_option("prefs", prefs)
driver = webdriver.Chrome(ChromeDriverManager().install(),options=options)


SHARES +/- + / -% MOST RECENT ÅTD% VOLUME TURNOVER MARKET VALUE
Abn Amro Bank NV -0.32 -4.08% 7.48 -53.90% 7.9 million 59.0 million -
Adyen 81.00 5.62% 1523.00 108% 954 082 1.5 billion -
Aegon -0.08 -3.49% 2.16 -45.47% 17.4 million 37.5 million -
Ahold Del 0.25 0.98% 25.65 19.74% 8.0 million 204.1 million -
Akzo Nobel 0.14 0.16% 85.86 -3.16% 1.1 million 90.6 million -

导入

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

【讨论】：

【解决方案3】：

获取表信息WebDriverWait() 和visibility_of_element_located() 并关注css selector

driver.get("https://borsen.dk/investor/kurser/eur-aktier/?filter=aex25")
WebDriverWait(driver,20).until(EC.visibility_of_element_located((By.CSS_SELECTOR,".compressed-table")))
html_source =driver.page_source
driver.quit()
soup=BeautifulSoup(html_source, "html5lib")
table=soup.select_one(".compressed-table")
df = pd.read_html(str(table), flavor='html5lib', header=0, thousands='.', decimal=',')
print(df[0])

输出：

  Unnamed: 0               Aktie    +/-  ... Markedsværdi  Sektor    Tid
0          NaN  Abn Amro Bank N.V.  -0.32  ...            -     NaN  16:39
1          NaN               Adyen  81.00  ...            -     NaN  16:39
2          NaN               Aegon  -0.08  ...            -     NaN  16:35
3          NaN           Ahold Del   0.25  ...            -     NaN  16:35
4          NaN          Akzo Nobel   0.14  ...            -     NaN  16:36
5          NaN    Arcelormittal Sa   0.08  ...            -     NaN  16:39
6          NaN   Asm International   0.35  ...            -     NaN  16:37
7          NaN        Asml Holding   1.50  ...            -     NaN  16:35
8          NaN       Asr Nederland  -0.22  ...            -     NaN  16:35
9          NaN             Dsm Kon   2.25  ...            -     NaN  16:39
10         NaN           Galapagos  -1.45  ...            -     NaN  16:35
11         NaN            Heineken   0.74  ...            -     NaN  16:35
12         NaN                Imcd   1.85  ...            -     NaN  16:35
13         NaN      Ing Groep N.V.  -0.19  ...            -     NaN  16:38
14         NaN   Just Eat Takeaway   0.08  ...            -     NaN  16:39
15         NaN             Kpn Kon  -0.03  ...            -     NaN  16:35
16         NaN            Nn Group  -0.35  ...            -     NaN  16:35
17         NaN         Philips Kon  -0.08  ...            -     NaN  16:35
18         NaN              Prosus  -1.52  ...            -     NaN  16:39
19         NaN         Randstad Nv  -0.98  ...            -     NaN  16:35
20         NaN                Relx   0.00  ...            -     NaN  16:36
21         NaN  Royal Dutch Shella  -0.24  ...            -     NaN  16:37
22         NaN  Unibail-Rodamco-We  -3.79  ...            -     NaN  16:37
23         NaN            Unilever  -1.04  ...            -     NaN  16:38
24         NaN      Wolters Kluwer  -0.04  ...            -     NaN  16:35

[25 rows x 13 columns]

你也可以使用find()

driver.get("https://borsen.dk/investor/kurser/eur-aktier/?filter=aex25")
WebDriverWait(driver,20).until(EC.visibility_of_element_located((By.CSS_SELECTOR,".compressed-table")))
html_source =driver.page_source
driver.quit()
soup=BeautifulSoup(html_source, "html5lib")
df=pd.read_html(str(soup.find('table',class_='heavy-table ncpulse-fav-table ncpulse-sortable compressed-table')))[0]
print(df)

【讨论】：

【解决方案4】：

要从表中提取内容，因为<table> 是基于Angular 的元素，使用Selenium 和python 而不是presence_of_element_located()，您必须为visibility_of_element_located() 诱导WebDriverWait，您可以使用以下任一Locator Strategies:

使用CSS_SELECTOR：

print(WebDriverWait(driver, 20).until(EC.visibility_of_element_located((By.CSS_SELECTOR, "table.heavy-table.ncpulse-fav-table.ncpulse-sortable.compressed-table"))).text)

使用XPATH：

print(WebDriverWait(driver, 20).until(EC.visibility_of_element_located((By.XPATH, "//table[@class='heavy-table ncpulse-fav-table ncpulse-sortable compressed-table']"))).text)

注意：您必须添加以下导入：

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

控制台输出：

AKTIE +/- +/-% SENESTE ÅTD% BUD UDBUD VOLUMEN OMSÆTNING MARKEDSVÆRDI TID
Abn Amro Bank N.V. -0,32 -4,08% 7,48 -53,90% - - 7,9 mio 59,0 mio - 21:09
Adyen 81,00 5,62% 1523,00 108% - - 954 082 1,5 mia - 21:09
Aegon -0,08 -3,49% 2,16 -45,47% - - 17,4 mio 37,5 mio - 21:05
Ahold Del 0,25 0,98% 25,65 19,74% - - 8,0 mio 204,1 mio - 21:05
Akzo Nobel 0,14 0,16% 85,86 -3,16% - - 1,1 mio 90,6 mio - 21:06
Arcelormittal Sa 0,08 0,66% 11,53 -26,26% - - 11,9 mio 137,3 mio - 21:09
Asm International 0,35 0,29% 119,10 21,23% - - 403 117 48,0 mio - 21:07
Asml Holding 1,50 0,49% 308,45 17,56% - - 2,3 mio 712,7 mio - 21:05
Asr Nederland -0,22 -0,73% 29,76 -4,97% - - 740 781 22,0 mio - 21:05
Dsm Kon 2,25 1,66% 138,20 21,52% - - 680 867 94,1 mio - 21:09
Galapagos -1,45 -1,22% 117,70 -36,89% - - 475 793 56,0 mio - 21:05
Heineken 0,74 0,94% 79,10 -15,50% - - 1,1 mio 88,0 mio - 21:05
Imcd 1,85 1,80% 104,85 36,23% - - 922 391 96,7 mio - 21:05
Ing Groep N.V. -0,19 -2,80% 6,60 -38,24% - - 43,4 mio 286,2 mio - 21:08
Just Eat Takeaway 0,08 0,09% 91,70 11,56% - - 1,1 mio 100,2 mio - 21:09
Kpn Kon -0,03 -1,54% 2,11 -15,04% - - 21,4 mio 45,1 mio - 21:05
Nn Group -0,35 -1,06% 32,80 3,82% - - 2,4 mio 79,6 mio - 21:05
Philips Kon -0,08 -0,20% 39,42 -9,42% - - 5,2 mio 205,9 mio - 21:05
Prosus -1,52 -1,89% 78,74 18,35% - - 15,0 mio 1,2 mia - 21:09
Randstad Nv -0,98 -2,09% 45,93 -15,63% - - 698 496 32,1 mio - 21:05
Relx 0,00 0,03% 19,64 -10,24% - - 1,9 mio 36,6 mio - 21:06
Royal Dutch Shella -0,24 -2,07% 11,45 -54,58% - - 21,1 mio 241,2 mio - 21:07
Unibail-Rodamco-We -3,79 -10,53% 32,20 -75,02% - - 6,6 mio 213,2 mio - 21:07
Unilever -1,04 -2,00% 50,98 2,00% - - 8,2 mio 417,5 mio - 21:08
Wolters Kluwer -0,04 -0,05% 72,88 14,19% - - 803 644 58,6 mio - 21:05

【讨论】：