Python Beautifulsoup HTML解析器不起作用

发布于 2025-02-09 20:13:03 字数 342 浏览 2 评论 0原文

在这里，我正在尝试阅读页面并分别使用列创建CSV。但是我无法阅读解析数据以使用查找功能。汤数据没有网页中存在的数据

import requests
import pandas as pd
from bs4 import BeautifulSoup
url = "https://www.fancraze.com/marketplace/sales/mornemorkel1?tab=latest-sales"
r = requests.get(url)
soup = BeautifulSoup(r.content, "html.parser")

原文

Here i'm trying to read the page and create a csv with columns respectively. But i'm unable to read the parsed data to use find function. The soup data doesn't have the data present in webpage

import requests
import pandas as pd
from bs4 import BeautifulSoup
url = "https://www.fancraze.com/marketplace/sales/mornemorkel1?tab=latest-sales"
r = requests.get(url)
soup = BeautifulSoup(r.content, "html.parser")

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明天过后 2025-02-16 20:13:03

站点使用API获取数据，因此您可以处理

import pandas as pd
import requests

url = 'https://api.faze.app/v1/latestSalesInAGroup/mornemorkel1'
result = []
response = requests.get(url=url)
for data in response.json()['data']:
    data = {
        'id': data['momentId']['id'],
        'seller': data['sellerAddress']['userName'],
        'buyer': data['buyerAddress']['userName'],
        'price': data['price'],
        'created': data['createdAt']
    }
    result.append(data)
df = pd.DataFrame(result)
print(df)

输出：

      id                     seller  ... price                   created
0   1882                   singal22  ...     8  2022-06-22T14:34:39.403Z
1   1737           olive_creepy2343  ...     7  2022-06-22T14:09:32.070Z
2   1256          tomato_wicked3294  ...    10  2022-06-22T13:49:20.895Z
3   1931  aquamarine_productive9244  ...     6  2022-06-22T13:41:49.153Z
4   1603  aquamarine_productive9244  ...     9  2022-06-22T13:28:01.624Z
..   ...                        ...  ...   ...                       ...
95  1026           olive_creepy2343  ...     7  2022-04-16T18:00:00.662Z
96  1719                 Hhassan136  ...     5  2022-04-14T23:14:12.037Z
97  2054                 Cricket101  ...     5  2022-04-14T21:30:13.185Z
98  1961                  emzeden_9  ...     6  2022-04-14T18:02:05.194Z
99  1194       amaranth_curious1871  ...     5  2022-04-14T17:45:25.266Z

Site use API to get data, so you can handle it

import pandas as pd
import requests

url = 'https://api.faze.app/v1/latestSalesInAGroup/mornemorkel1'
result = []
response = requests.get(url=url)
for data in response.json()['data']:
    data = {
        'id': data['momentId']['id'],
        'seller': data['sellerAddress']['userName'],
        'buyer': data['buyerAddress']['userName'],
        'price': data['price'],
        'created': data['createdAt']
    }
    result.append(data)
df = pd.DataFrame(result)
print(df)

OUTPUT:

      id                     seller  ... price                   created
0   1882                   singal22  ...     8  2022-06-22T14:34:39.403Z
1   1737           olive_creepy2343  ...     7  2022-06-22T14:09:32.070Z
2   1256          tomato_wicked3294  ...    10  2022-06-22T13:49:20.895Z
3   1931  aquamarine_productive9244  ...     6  2022-06-22T13:41:49.153Z
4   1603  aquamarine_productive9244  ...     9  2022-06-22T13:28:01.624Z
..   ...                        ...  ...   ...                       ...
95  1026           olive_creepy2343  ...     7  2022-04-16T18:00:00.662Z
96  1719                 Hhassan136  ...     5  2022-04-14T23:14:12.037Z
97  2054                 Cricket101  ...     5  2022-04-14T21:30:13.185Z
98  1961                  emzeden_9  ...     6  2022-04-14T18:02:05.194Z
99  1194       amaranth_curious1871  ...     5  2022-04-14T17:45:25.266Z

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