Python BeautifulSoup-使用给定URL的iframe抓取多个网页

问题描述

我们有以下代码(感谢Cody和Alex Tereshenkov):

We have this code (thanks to Cody and Alex Tereshenkov):

import pandas as pd
import requests
from bs4 import BeautifulSoup

pd.set_option('display.width', 1000)
pd.set_option('display.max_columns', 50)

url = "https://www.aliexpress.com/store/feedback-score/1665279.html"
s = requests.Session()
r = s.get(url)

soup = BeautifulSoup(r.content, "html.parser")
iframe_src = soup.select_one("#detail-displayer").attrs["src"]

r = s.get(f"https:{iframe_src}")

soup = BeautifulSoup(r.content, "html.parser")
rows = []
for row in soup.select(".history-tb tr"):
    #print("\t".join([e.text for e in row.select("th, td")]))
    rows.append([e.text for e in row.select("th, td")])
#print

df = pd.DataFrame.from_records(
    rows,
    columns=['Feedback', '1 Month', '3 Months', '6 Months'],
)

# remove first row with column names
df = df.iloc[1:]
df['Shop'] = url.split('/')[-1].split('.')[0]

pivot = df.pivot(index='Shop', columns='Feedback')
pivot.columns = [' '.join(col).strip() for col in pivot.columns.values]

column_mapping = dict(
    zip(pivot.columns.tolist(), [col[:12] for col in pivot.columns.tolist()]))
# column_mapping
# {'1 Month Negative (1-2 Stars)': '1 Month Nega',
#  '1 Month Neutral (3 Stars)': '1 Month Neut',
#  '1 Month Positive (4-5 Stars)': '1 Month Posi',
#  '1 Month Positive feedback rate': '1 Month Posi',
#  '3 Months Negative (1-2 Stars)': '3 Months Neg',
#  '3 Months Neutral (3 Stars)': '3 Months Neu',
#  '3 Months Positive (4-5 Stars)': '3 Months Pos',
#  '3 Months Positive feedback rate': '3 Months Pos',
#  '6 Months Negative (1-2 Stars)': '6 Months Neg',
#  '6 Months Neutral (3 Stars)': '6 Months Neu',
#  '6 Months Positive (4-5 Stars)': '6 Months Pos',
#  '6 Months Positive feedback rate': '6 Months Pos'}
pivot.columns = [column_mapping[col] for col in pivot.columns]

pivot.to_excel('Report.xlsx')

代码为给定的URL(位于iframe中)提取反馈历史记录"表，并将所有表数据转换为1行，就像这样:

The code extracts the "Feedback History" table for the given URL (which is inside an iframe) and transforms all the table data into 1 line, exactly like this:

另一方面，我们在同一项目文件夹("urls.txt")中具有50个网址，如下所示:

And on the other hand we have file in the same project folder ("urls.txt") with 50 URLS like this:

https://www.aliexpress.com/store/feedback-score/4385007.html
https://www.aliexpress.com/store/feedback-score/1473089.html
https://www.aliexpress.com/store/feedback-score/3085095.html
https://www.aliexpress.com/store/feedback-score/2793002.html
https://www.aliexpress.com/store/feedback-score/4656043.html
https://www.aliexpress.com/store/feedback-score/4564021.html

我们只需要提取文件中所有URL的相同数据即可.

We just need to extract the same data for all the URLs in the file.

我们该怎么做?

推荐答案

由于网址数量约为50，因此您只需将网址读取到列表中，然后向每个网址发送请求即可.我刚刚测试了这6个网址，该解决方案适用于它们.但是您可能想要添加一些try-except，否则可能会发生任何异常.

Since the number of urls is ~ 50, you could just read the urls into a list and then send a request to each of the urls. I have just tested these 6 urls and the solution works for them. But you may want to add some try-except for any exceptions that may occur.

import pandas as pd
import requests
from bs4 import BeautifulSoup
with open('urls.txt','r') as f:
    urls=f.readlines()
master_list=[]
for idx,url in enumerate(urls):
    s = requests.Session()
    r = s.get(url)
    soup = BeautifulSoup(r.content, "html.parser")
    iframe_src = soup.select_one("#detail-displayer").attrs["src"]
    r = s.get(f"https:{iframe_src}")
    soup = BeautifulSoup(r.content, "html.parser")
    rows = []
    for row in soup.select(".history-tb tr"):
        rows.append([e.text for e in row.select("th, td")])
    df = pd.DataFrame.from_records(
        rows,
        columns=['Feedback', '1 Month', '3 Months', '6 Months'],
    )

    df = df.iloc[1:]
    shop=url.split('/')[-1].split('.')[0]
    df['Shop'] = shop
    pivot = df.pivot(index='Shop', columns='Feedback')
    master_list.append([shop]+pivot.values.tolist()[0])
    if idx == len(urls) - 1: #last item in the list
        final_output=pd.DataFrame(master_list)
        pivot.columns = [' '.join(col).strip() for col in pivot.columns.values]
        column_mapping = dict(zip(pivot.columns.tolist(), [col[:12] for col in pivot.columns.tolist()]))
        final_output.columns = ['Shop']+[column_mapping[col] for col in pivot.columns]
        final_output.set_index('Shop', inplace=True)
final_output.to_excel('Report.xlsx')

输出:

也许可以考虑的更好的解决方案是完全避免使用熊猫.在获取数据之后，您可以操纵它以获取列表并使用 XlsxWriter .

Perhaps a better solution that you could consider is avoiding the use of pandas at all. After you get the data, you could manipulate it to get a list and use XlsxWriter.

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