只有在浏览器中打开主页 (url) 的另一个 URL 时，才能从浏览器下载来自 URL (url 1) 的 csv 文件。如何在 python 中实施

Question

如果 url https://www.nseindia.com/companies-listing/corporate-filings-announcements is open in a tab of browser, I can download the CSV file using another url https://www.nseindia.com/api/corporate-announcements?index=equities&from_date=14-01-2022&to_date=20-01-2022&csv=true 来自同一浏览器中的另一个选项卡。 Else Not 并显示“找不到资源”。如何使用 pandas.

在 python 中实现它

Answer 1

此页面使用 Cookie 来检查文件是否从第一页打开。

您将必须使用 requests 和 Session 来获取第一页和 cookie，接下来使用 requests 和 Session（使用先前请求的 cookie）来获取文件 csv，最后你必须使用 io 将数据发送到 pandas，它在内存中模拟文件。

顺便说一句：它似乎用 BOM (Byte Order Mark) 发送文件所以我从 r.content 读取字节数据而不是从 r.text 和 pandas 将跳过 BOM

import requests
import pandas as pd
import io

# --- create Session with User-Agent from real browser ---

headers = {
    'User-Agent': 'Mozilla/5.0'
}

s = requests.Session()
s.headers.update(headers)

# --- get first page to get cookies --- 

url = 'https://www.nseindia.com/companies-listing/corporate-filings-announcements'
r = s.get(url)

# --- get file ---

url = 'https://www.nseindia.com/api/corporate-announcements?index=equities&from_date=14-01-2022&to_date=20-01-2022&csv=true'
r = s.get(url)

print(r.text[:100])  # code `ï»¿` at the beginning means BOM
                     # so I will use `r.content` instead of `r.text`

# --- read file from memory ---

#df = pd.read_csv(io.StringIO(r.text))   # it doesn't remove BOM
df = pd.read_csv(io.BytesIO(r.content))  # it removes BOM

# --- show it ---

print(df.head())

结果：

ï»¿"SYMBOL","COMPANY NAME","SUBJECT","DETAILS","BROADCAST DATE/TIME","RECEIPT","DISSEMINATION","DIFF


      SYMBOL  ... DIFFERENCE
0  TATAELXSI  ...   00:00:08
1       RIIL  ...   00:00:10
2       ERIS  ...   00:00:06
3       RIIL  ...   00:00:09
4  INGERRAND  ...   00:00:09

[5 rows x 8 columns]

只有在浏览器中打开主页 (url) 的另一个 URL 时，才能从浏览器下载来自 URL (url 1) 的 csv 文件。如何在 python 中实施

Fom browser a csv file from URL (url 1) can be downloaded only if another URL of main page (url) is open in browser. How to implement in python

python

browser

csv

url

pandas