如果以上正是您想要做的，那么可以通过创建一个包含要发送的ticker语法的列，然后将该列作为一个序列通过blpapi传递来实现

df['ISIN_NEW'] = '/isin/' + df['ISIN']
isin_new = pd.unique(df['ISIN_NEW'].dropna())
mktsec_df = blp.bdp(tickers = isin_new, flds = ['market_sector_des'])

然后，您可以将新创建的df连接到现有的df，以便完整地获得列中的数字。

newdf = pd.merge(df, mktsec_df, how='left', left_on = 'ISIN_NEW', right_index = True )

这应该会导致一个单一的电话，这将理想地下降到不到一分钟的速度。如果这工作让我知道。

你可以使用multiprocessing来并行化API调用。将你的Series划分为THREAD块，然后每个块运行一个进程：
main.py

import multiprocessing as mp
import pandas as pd
import numpy as np
import parallel_tickers

THREADS = mp.cpu_count() - 1

# df = your_dataframe_here
split = np.array_split(df['ISIN'], THREADS)
with mp.Pool(THREADS) as pool:
    data = pool.map(proxy_func, split)

df['new_column'] = pd.concat(data)

parallel_tickers.py

import pandas as pd
from xbbg import blp

def market_sector_des(isin):
    isin = '/isin/' + isin
    return blp.bdp(tickers = isin, flds = ['market_sector_des']).iloc[0]

def proxy_func(sr):
    return pd.Series([market_sector_des(isin) for isin in sr], index=sr.index)

编辑：使用另一个模块的mp功能

这可以使用asyncio语法来完成。工作代码如下：

import asyncio
async def market_sector_des_async(isin):
    isin = '/isin/' + isin
    return blp.bdp(tickers = isin, flds=['market_sector_des']).iloc[0]['market_sector_des']

async def main():
    tasks = [asyncio.create_task(market_sector_des_async(isin)) for isin in df['ISIN']]
    results = await asyncio.gather(*tasks)
    df['market_sector_des'] = results

await main()