无法再在 tbody 中找到 tr答案

【问题标题】：Can no longer find tr inside a tbody无法再在 tbody 中找到 tr
【发布时间】：2021-09-20 11:32:43
【问题描述】：

这个 sn-p 上周运行良好，因为我正在使用它。当我今天尝试运行它时，它显示错误 trs = t.find('tbody').find_all('tr')。

import requests
from bs4 import BeautifulSoup

url = 'https://bscscan.com/token/generic-tokenholders2?m=normal&a=0xe56842ed550ff2794f010738554db45e60730371'
r = requests.get(url)
soup = BeautifulSoup(r.text, 'lxml')
t = soup.find('table', class_='table table-md-text-normal table-hover')
trs = t.find('tbody').find_all('tr')
for tr in trs[:5]:
    print(list(tr.stripped_strings))

电流输出：

trs = t.find('tbody').find_all('tr')
AttributeError: 'NoneType' object has no attribute 'find'

【问题讨论】：

t 是 None ，因此 t.find 会引发此错误。原因t 是none，因为您尝试查找的元素可能不存在或稍后由js 加载。

标签： python python-3.x beautifulsoup

【解决方案1】：

尝试在您的请求中添加User-Agent HTTP 标头：

import requests
from bs4 import BeautifulSoup

headers = {
    "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:92.0) Gecko/20100101 Firefox/92.0",
}

url = "https://bscscan.com/token/generic-tokenholders2?m=normal&a=0xe56842ed550ff2794f010738554db45e60730371&s=1000000000000000000000000&sid=68a003f6aa7123d1ce19faee148486b1&p=1%27"
r = requests.get(url, headers=headers)
soup = BeautifulSoup(r.content, "lxml")
t = soup.find("table", class_="table table-md-text-normal table-hover")
trs = t.find("tbody").find_all("tr")
for tr in trs[:5]:
    print(list(tr.stripped_strings))

打印：

['1', '0xbbda05ea467ad348212dade5c38c11910c14e83e', '202,543,296.764175197290329227', '20,254.3297%', '$3,054,207.08']
['2', 'UniCrypt: Token Vesting', '150,000,000', '15,000.0000%', '$2,261,892.00']
['3', 'PancakeSwap V2: BIN 17', '99,217,566.546415684406759104', '9,921.7567%', '$1,496,129.47']
['4', 'Binemon: BIN Token', '27,010,296.006072937294814033', '2,701.0296%', '$407,295.82']
['5', '0x81da471feb4a45438053dc05e709be056ec26c39', '14,865,062', '1,486.5062%', '$224,154.43']

【讨论】：

【解决方案2】：

实际上，表格不是动态的，这就是为什么您只能使用 pandas 来做到这一点。

代码：

import requests
import pandas as pd

headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.131 Safari/537.36'}

url = "https://bscscan.com/token/generic-tokenholders2?m=normal&a=0xe56842ed550ff2794f010738554db45e60730371&s=1000000000000000000000000&sid=68a003f6aa7123d1ce19faee148486b1&p=1%27"

req = requests.get(url,headers=headers)

wiki_table = pd.read_html(req.text, attrs = {"class":"table table-md-text-normal table-hover"} )

df = wiki_table[0]#.to_csv('score.csv',index = False)

print(df)

输出：

Rank                                     Address  ...          Value Analytics
0      1  0xbbda05ea467ad348212dade5c38c11910c14e83e  ...  $3,054,206.03       NaN
1      2                     UniCrypt: Token Vesting  ...  $2,261,892.00       NaN
2      3                      PancakeSwap V2: BIN 17  ...  $1,496,520.20       NaN
3      4                          Binemon: BIN Token  ...    $407,340.87       NaN
4      5  0x81da471feb4a45438053dc05e709be056ec26c39  ...    $224,172.53       NaN
5      6  0x40bd49e1aa2adaa6595e7372e843ba9483fc4832  ...    $127,352.26       NaN
6      7  0xdcdee0f73b9d573de05b93128a84caa7df675956  ...    $112,479.07       NaN
7      8  0xd98cad569d4e24e5f1a157cee76a47bdfb026418  ...    $107,714.46       NaN
8      9  0xe32b97f5b8ba0d716607e555c8d6099fffb5cdc6  ...    $102,848.74       NaN
9     10  0xb1ec3521e766aec113883a984cab330fab4beba5  ...     $89,142.96       NaN
10    11  0x0d9b1e53cbb251572d982d9f96520e8d40d22bb0  ...     $88,967.75       NaN
11    12  0x18c9cf7f86437398aca31f45365b6168298a5b51  ...     $86,177.31       NaN
12    13  0xb96ee668b63c475b86d5b12d3b5bca86d9317f93  ...     $85,513.83       NaN
13    14  0xffd2df7ba9a04672933748dc86ce71f131ff4d50  ...     $71,639.51       NaN
14    15  0xb6957013a430e4cf509a4c51002073c1b24356e2  ...     $69,995.87       NaN
15    16  0xaaa4b610f3c48b427a3ccdc7c18fe70242c30578  ...     $69,259.03       NaN
16    17  0x4b0005c7bba3e10820b5b3a2863821e00701b383  ...     $68,916.24       NaN
17    18  0x43e5959343cd9154080c235c16fbb4bbd7f83e70  ...     $64,840.90       NaN
18    19  0x1349fee2589914fff5460d1998afe09584208b73  ...     $61,881.68       NaN
19    20  0xa3da6c54a7451770eb2f7014366490bddeb37d30  ...     $61,619.12       NaN
20    21  0x9db651920039b49ba7988848e060d6203e497993  ...     $60,642.64       NaN
21    22  0xe02752824b6b11e027080e75f692bd22b3dc7091  ...     $59,651.84       NaN
22    23  0x1bb45ab9d1a9af4b7017889ab4f3ef9539d63514  ...     $57,265.40       NaN
23    24  0x0eb207b525dc856c3bad5bfd7a7a4aae781e1757  ...     $54,586.99       NaN
24    25  0x63ae09700a236fde03b4634fefd39d4bbf8c2332  ...     $53,890.98       NaN
25    26  0x8453a1c9eb46769f2620ab05dc6da715ffc5c670  ...     $53,815.76       NaN
26    27  0x7c9453d05f91738bfa4175caeaa7bf0b72785670  ...     $53,180.22       NaN
27    28  0x0511b95fa08a648bc320c25ae92e65aae807fd4f  ...     $52,623.27       NaN
28    29  0xd919b251059f8e2262cf6ef90cb8ca602254a9a8  ...     $52,563.11       NaN
29    30  0xfd38f35a89b9662c92655f3c4c0366d246116fd6  ...     $52,528.32       NaN
30    31  0xd7668f2d5303c4c5202a405c32b1df28051e56ca  ...     $51,482.65       NaN
31    32  0xbe58dfc5a66fd5ea833f8c6494b5d7e0d68acac5  ...     $51,040.69       NaN
32    33  0xa7ae530d3cf06b8245bb38d865a94f3c82d3d041  ...     $51,026.66       NaN
33    34  0xd9f12f3e93fe799373f18662d6220860ba46c567  ...     $50,478.77       NaN
34    35  0x3ab7822d0c6916953cbde33e1236b329d11e9bb6  ...     $47,099.69       NaN
35    36  0x9de94914be7719bd26ae7d33574e154cd6723756  ...     $46,120.94       NaN
36    37  0xbdc8e2eb753952013ad07ea248e4445513223e97  ...     $46,049.96       NaN
37    38  0x26c7085a67600e85ce0102a8e2ea282e3e56e707  ...     $45,884.58       NaN
38    39  0x1fa68bacd8543e0aa7dc95d21f210753c967ef12  ...     $45,056.99       NaN
39    40  0x8a6245af6da9447201db7f5611d40ea6e41e7b7c  ...     $45,020.06       NaN
40    41  0xd7c5d49cf4c4360f6a528cbc5a027c9d7eb632db  ...     $43,428.15       NaN
41    42  0x40d081b46e08f5c2385cbbdebc31558330ec09f4  ...     $42,054.96       NaN
42    43  0x28d18f32c2e175eaed385ec517d1f9ec7bb7b0b0  ...     $41,927.94       NaN
43    44  0x59c23a309a3be9ba3a17b2fe3128ed7c947c2990  ...     $41,806.24       NaN
44    45  0x790b9a3fe8dc7e59cc1030e0ccc4f415b6c643a2  ...     $41,785.23       NaN
45    46  0xd621cf052f5d816043f0def74202bdfcb3401db3  ...     $40,096.69       NaN
46    47  0x1ea6ac69ddb06e0eb15b235203bc92673d93c7e5  ...     $34,802.68       NaN
47    48  0x9bbfa4001cfbd9f95d3fa25ca2f505f1148649c0  ...     $34,042.52       NaN
48    49  0x44b17a9305c565309c32eac1789dfb7e362a65ff  ...     $33,834.53       NaN
49    50  0x7e9a9d12caa9440ef43b4f2e4b3a42ec3f5f97cd  ...     $33,477.25       NaN

[50 rows x 6 columns]

【讨论】：