此网页（'*https://www.nseindia.com/market-data/top-gainers-losers*'）有 2 个表格（'gainers' 和 'losers'）。
我想要一个代码来读取网页的内容并将这两个表下载到两个单独的数据框中。我如何实现这一目标？
此页面使用
JavaScriptJavaScript但是有按钮
Download .csvCSV但它没有
URLonclick="downloadCSVFile('loosers')"downloadCSVFile('loosers')Selenium但是我在 Firefox 中下载了这个文件，然后在 Firefox 中打开了
Download ManagerCopy Download Link获胜者：
https://www.nseindia.com/api/live-analysis-variations?index=gainers&type=NIFTY&csv=true
失败者：
https://www.nseindia.com/api/live-analysis-variations?index=loosers&type=NIFTY&csv=true
现在我测试是否可以使用
requestsimport requests
session = requests.Session()
headers = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:125.0) Gecko/20100101 Firefox/125.0'}
url = 'https://www.nseindia.com/market-data/top-gainers-losers'
response = session.get(url, headers=headers)
#print(response.text)
url = 'https://www.nseindia.com/api/live-analysis-variations?index=gainers&type=NIFTY&csv=true'
response = session.get(url, headers=headers)
response.encoding = "utf-8-sig"
print(response.text)
首先：它需要标头
User-Agent第二：下载之前需要获取主页 - 可能是为了获取一些cookie。 第三：它给出开头带有
Byte Order Markutf-8-sig这给了我：
Symbol","Open","High","Low","Prev. Close","LTP","%chng","Volume","Value","CA "
"BAJFINANCE",6840.05,7150,6810.05,6893.2,7110,3.15,1218375,8520583725,"30-Jun-2023"
"M&M",2034,2087,1998.2,2024.95,2084,2.92,3253248,6691117824,"14-Jul-2023"
"HDFCBANK",1486.55,1534.95,1480.25,1494.7,1534.2,2.64,17288217,26183869057.35,"16-May-2023"
"MARUTI",12399.9,12759.4,12225,12405,12690,2.3,635535,7949392531.65,"03-Aug-2023"
"JSWSTEEL",842.85,867.3,833.2,844.8,864,2.27,3157898,2697823840.38,"11-Jul-2023"
"BHARTIARTL",1280,1296.5,1253.35,1265.75,1289.95,1.91,13103862,16751846142.18,"11-Aug-2023"
"GRASIM",2220,2290.75,2201.35,2226.05,2266.9,1.84,1061064,2394651677.76,"10-Jan-2024"
"WIPRO",440,453.9,437,444.35,452.1,1.74,10235053,4572612278.28,"24-Jan-2024"
"BAJAJFINSV",1587,1628.75,1568.7,1593.9,1618.6,1.55,1242066,1990398344.34,"30-Jun-2023"
"APOLLOHOSP",6140,6199,6050,6074.15,6155,1.33,560178,3437179384.86,"20-Feb-2024"
"ITC",418,426.25,416,418.85,424,1.23,16582634,7025067067.76,"08-Feb-2024"
"ICICIBANK",1052.95,1072,1048.1,1055.45,1068,1.19,11284433,11984406378.99,"09-Aug-2023"
"ADANIPORTS",1280,1316,1270,1295.55,1310.8,1.18,3899281,5057640406.67,"28-Jul-2023"
"TATASTEEL",160,162.5,157.3,160.05,161.9,1.16,60078229,9640753407.63,"22-Jun-2023"
"TECHM",1163.05,1204.85,1162.95,1179.65,1192.5,1.09,2572144,3057173194.08,"02-Nov-2023"
"TITAN",3525.1,3571.2,3478.25,3525.1,3562,1.05,1507940,5329859168.2,"13-Jul-2023"
"AXISBANK",1015,1036.95,995.7,1024,1031.7,0.75,21598007,21821762352.52,"07-Jul-2023"
"SBIN",734.5,752,732.05,744.8,750,0.7,10886554,8092302184.82,"31-May-2023"
"HINDUNILVR",2220,2243.75,2196,2214.8,2230,0.69,2337694,5205834145.54,"02-Nov-2023"
"INDUSINDBK",1466,1490.25,1444.4,1474.4,1483,0.58,4311650,6341790402.5,"02-Jun-2023"
使用
iopandasimport pandas as pd
import io
df = pd.read_csv(io.StringIO(response.text))
print(df)
结果：
0 BAJFINANCE 6840.05 7150.00 6810.05 6893.20 7110.00 3.15 1218375 8.520584e+09 30-Jun-2023
1 M&M 2034.00 2087.00 1998.20 2024.95 2084.00 2.92 3253248 6.691118e+09 14-Jul-2023
2 HDFCBANK 1486.55 1534.95 1480.25 1494.70 1534.20 2.64 17288217 2.618387e+10 16-May-2023
3 MARUTI 12399.90 12759.40 12225.00 12405.00 12690.00 2.30 635535 7.949393e+09 03-Aug-2023
4 JSWSTEEL 842.85 867.30 833.20 844.80 864.00 2.27 3157898 2.697824e+09 11-Jul-2023
5 BHARTIARTL 1280.00 1296.50 1253.35 1265.75 1289.95 1.91 13103862 1.675185e+10 11-Aug-2023
6 GRASIM 2220.00 2290.75 2201.35 2226.05 2266.90 1.84 1061064 2.394652e+09 10-Jan-2024
7 WIPRO 440.00 453.90 437.00 444.35 452.10 1.74 10235053 4.572612e+09 24-Jan-2024
8 BAJAJFINSV 1587.00 1628.75 1568.70 1593.90 1618.60 1.55 1242066 1.990398e+09 30-Jun-2023
9 APOLLOHOSP 6140.00 6199.00 6050.00 6074.15 6155.00 1.33 560178 3.437179e+09 20-Feb-2024
10 ITC 418.00 426.25 416.00 418.85 424.00 1.23 16582634 7.025067e+09 08-Feb-2024
11 ICICIBANK 1052.95 1072.00 1048.10 1055.45 1068.00 1.19 11284433 1.198441e+10 09-Aug-2023
12 ADANIPORTS 1280.00 1316.00 1270.00 1295.55 1310.80 1.18 3899281 5.057640e+09 28-Jul-2023
13 TATASTEEL 160.00 162.50 157.30 160.05 161.90 1.16 60078229 9.640753e+09 22-Jun-2023
14 TECHM 1163.05 1204.85 1162.95 1179.65 1192.50 1.09 2572144 3.057173e+09 02-Nov-2023
15 TITAN 3525.10 3571.20 3478.25 3525.10 3562.00 1.05 1507940 5.329859e+09 13-Jul-2023
16 AXISBANK 1015.00 1036.95 995.70 1024.00 1031.70 0.75 21598007 2.182176e+10 07-Jul-2023
17 SBIN 734.50 752.00 732.05 744.80 750.00 0.70 10886554 8.092302e+09 31-May-2023
18 HINDUNILVR 2220.00 2243.75 2196.00 2214.80 2230.00 0.69 2337694 5.205834e+09 02-Nov-2023
19 INDUSINDBK 1466.00 1490.25 1444.40 1474.40 1483.00 0.58 4311650 6.341790e+09 02-Jun-2023