Question

我正在尝试编写代码以在不使用pandas或Numpy的情况下从nasdaq页面上的表格中对数据进行网络爬虫分析，但我一直在尝试各种方法，并且属性错误不断发生

我尝试观看教程并使用不同的解析器，但是没有用

import bs4
import requests
from bs4 import BeautifulSoup as soup
#setting webpage
my_url = requests.get("https://www.nasdaq.com/market- 
activity/stocks/rshpf/historical").text

#grabbing webpage and opening the connect

#does html parser
page_soup = soup(my_url, "xml")
table = soup.find("table")

我希望看到表格的html文本

Answer 1

页面是动态的，并且在拉动html之后呈现数据，这意味着表标记为空。

您可以从API获取数据，然后将其转换为数据框：

import pandas as pd
import requests

url = 'https://api.nasdaq.com/api/quote/RSHPF/historical'

payload = {
'assetclass': 'stocks',
'fromdate': '2019-10-06',
'limit': '100',
'todate': '2019-11-06'}

jsonData = requests.get(url, params=payload).json()

df = pd.DataFrame(jsonData['data']['tradesTable']['rows'])

输出：

print (df.to_string())
    close        date   high    low   open  volume
0   $1.97  11/05/2019  $1.97  $1.97  $1.97     N/A
1   $1.97  11/04/2019  $1.97  $1.97  $1.97     N/A
2   $1.97  11/01/2019  $1.97  $1.97  $1.97  12,600
3   $1.96  10/31/2019  $1.96  $1.96  $1.96     N/A
4   $1.96  10/30/2019  $1.96  $1.96  $1.96     N/A
5   $1.96  10/29/2019  $1.96  $1.96  $1.96     N/A
6   $1.96  10/28/2019  $1.96  $1.96  $1.96     N/A
7   $1.96  10/25/2019  $1.96  $1.96  $1.96     N/A
8   $1.96  10/24/2019  $1.96  $1.96  $1.96     N/A
9   $1.96  10/23/2019  $1.96  $1.96  $1.96     N/A
10  $1.96  10/22/2019  $1.96  $1.96  $1.96     N/A
11  $1.96  10/21/2019  $1.96  $1.96  $1.96     N/A
12  $1.96  10/18/2019  $1.96  $1.96  $1.96     N/A
13  $1.96  10/17/2019  $1.96  $1.96  $1.96     N/A
14  $1.96  10/16/2019  $1.96  $1.96  $1.96     N/A
15  $1.96  10/15/2019  $1.96  $1.96  $1.96   7,650
16     $2  10/14/2019     $2     $2     $2     N/A
17     $2  10/11/2019     $2     $2     $2     N/A
18     $2  10/10/2019     $2     $2     $2     250
19     $2  10/09/2019     $2     $2     $2     250
20     $2  10/08/2019     $2     $2     $2     N/A
21     $2  10/07/2019     $2     $2     $2     200

AttributeError：'str'对象没有属性'find_all

1 个答案: