Python 尝试按类刮取HTML跨度值，但返回错误

返回错误：

def getDOW():
    DowURL = ["https://finance.yahoo.com/quote/%5EDJI?p=^DJI"]
    # requests data on the website(s) above
    page = requests.get(DowURL, headers=headers)

    # parses HTML text from website
    soup = BeautifulSoup(page.content, "html.parser")

    # title = soup.find(class_="D(ib) Fz(18px)").get_text()
    name = soup.find(class_= "Trsdu(0.3s) Fw(b) Fz(36px) Mb(-4px) D(ib)").get_text()
    print (name)

---

很抱歉，如果之前有人问我这个问题，但我是新来的，所以我不知道发生了什么。如果有人能帮助我，或给我建议，我将不胜感激。我试图从多个站点中提取值，但使用列表时未能做到这一点，因此创建了单独的函数（我知道这是多么冗余），并遇到了此错误。

当您查看页面的HTML源代码时，您会发现您感兴趣的对象不存在。原因可能是，只有在浏览器中加载页面后，才会加载内容。你可以用它来做这件事。但是，像这样加载数据不是很有效。我过去也这样做过，但这不是一个好的解决方案

由于您似乎对股票价格感兴趣，您可以使用以下方法：

raise InvalidSchema("No connection adapters were found for '%s' % url")
requests.exceptions.InvalidSchema: No connection adapters were found for '['https://finance.yahoo.com/quote/%5EDJI?p=^DJI']'

结果:

import yfinance as yf
import datetime

start = datetime.datetime(2019,11,15)
end = datetime.datetime(2019,11,16)
data = yf.download('^DJI', start=start, end=end)
print(data)

import datetime
import pandas as pd
import numpy as np
import pylab as pl
import datetime
from sklearn.cluster import AffinityPropagation
from sklearn import metrics
from matplotlib.collections import LineCollection
from pandas_datareader import data as wb
from sklearn import cluster, covariance, manifold


start = '2019-02-01'
end = '2020-02-01'

tickers = ['DJIA']

thelen = len(tickers)

price_data = []
for ticker in tickers:
    prices = wb.DataReader(ticker, start = start, end = end, data_source='yahoo')[['Open','Adj Close']]
    price_data.append(prices.assign(ticker=ticker)[['ticker', 'Open', 'Adj Close']])

#names = np.reshape(price_data, (len(price_data), 1))

names = pd.concat(price_data)
names.reset_index()

tickers = ['MMM',
'ABT',
'ABBV',
'ABMD',
'ACN',
'ATVI']

---

我会这样做

[*********************100%***********************]  1 of 1 downloaded
                Open      High       Low     Close  Adj Close     Volume
Date
2019-11-14  27757.20  27800.71  27676.97  27781.96   27781.96  303970000
2019-11-15  27843.54  28004.89  27843.54  28004.89   28004.89  283720000

结果:

import yfinance as yf
import datetime

start = datetime.datetime(2019,11,15)
end = datetime.datetime(2019,11,16)
data = yf.download('^DJI', start=start, end=end)
print(data)

import datetime
import pandas as pd
import numpy as np
import pylab as pl
import datetime
from sklearn.cluster import AffinityPropagation
from sklearn import metrics
from matplotlib.collections import LineCollection
from pandas_datareader import data as wb
from sklearn import cluster, covariance, manifold


start = '2019-02-01'
end = '2020-02-01'

tickers = ['DJIA']

thelen = len(tickers)

price_data = []
for ticker in tickers:
    prices = wb.DataReader(ticker, start = start, end = end, data_source='yahoo')[['Open','Adj Close']]
    price_data.append(prices.assign(ticker=ticker)[['ticker', 'Open', 'Adj Close']])

#names = np.reshape(price_data, (len(price_data), 1))

names = pd.concat(price_data)
names.reset_index()

tickers = ['MMM',
'ABT',
'ABBV',
'ABMD',
'ACN',
'ATVI']

注意：您可以输入任何您想要的票证，因此更改此行：

          Date ticker          Open     Adj Close
0   2019-02-01   DJIA  25025.310547  25063.890625
1   2019-02-04   DJIA  25062.119141  25239.369141
2   2019-02-05   DJIA  25287.929688  25411.519531
3   2019-02-06   DJIA  25371.570312  25390.300781
4   2019-02-07   DJIA  25265.810547  25169.529297
..         ...    ...           ...           ...
247 2020-01-27   DJIA  28542.490234  28535.800781
248 2020-01-28   DJIA  28594.279297  28722.849609
249 2020-01-29   DJIA  28820.529297  28734.449219
250 2020-01-30   DJIA  28640.160156  28859.439453
251 2020-01-31   DJIA  28813.039062  28256.029297

[252 rows x 4 columns]

为此：

tickers = ['DJIA']

您将获得多个股票代码的数据，如下所示

[*********************100%***********************]  1 of 1 downloaded
                Open      High       Low     Close  Adj Close     Volume
Date
2019-11-14  27757.20  27800.71  27676.97  27781.96   27781.96  303970000
2019-11-15  27843.54  28004.89  27843.54  28004.89   28004.89  283720000

结果:

import yfinance as yf
import datetime

start = datetime.datetime(2019,11,15)
end = datetime.datetime(2019,11,16)
data = yf.download('^DJI', start=start, end=end)
print(data)

import datetime
import pandas as pd
import numpy as np
import pylab as pl
import datetime
from sklearn.cluster import AffinityPropagation
from sklearn import metrics
from matplotlib.collections import LineCollection
from pandas_datareader import data as wb
from sklearn import cluster, covariance, manifold


start = '2019-02-01'
end = '2020-02-01'

tickers = ['DJIA']

thelen = len(tickers)

price_data = []
for ticker in tickers:
    prices = wb.DataReader(ticker, start = start, end = end, data_source='yahoo')[['Open','Adj Close']]
    price_data.append(prices.assign(ticker=ticker)[['ticker', 'Open', 'Adj Close']])

#names = np.reshape(price_data, (len(price_data), 1))

names = pd.concat(price_data)
names.reset_index()

tickers = ['MMM',
'ABT',
'ABBV',
'ABMD',
'ACN',
'ATVI']

---

为什么要使用URL列表？您需要提供URL（字符串）或为列表编制索引

DowURL[0]

。是的，我注意到括号是问题所在。谢谢@HTFThanks的建议！我会查一查