嗨，我有一个复杂的循环问题，因为我必须遍历交易数据的数据框（每行一个交易）。贸易数据与外部交易对手以及内部“待定”交易对手进行交易，我需要：
（a）找到三个相关行业和
（b）改变三个交易中第一个的执行时间以匹配第三个交易的执行时间。
这是三笔交易的原因是每笔交易都有执行时间和进入时间（进入时间是指交易流向更多系统，因此等于或晚于执行时间）。这必须听起来过于复杂，但这是系统工作的方式，因此是给定的。下面的代码示例中有更多详细信息。这工作但很慢（文件中有数十万笔交易）。我的python是基本的，所以我假设必须有一个更有效的方法来做这个可能与.apply或其他？有人有什么建议吗？
在下面的评论后，我已经清理了这个（道歉）并创建了一个最小的工作示例（原本应该这样做）
import pandas as pd
import numpy as np
# TradeId - unique trade id
# ExecutionTime - time trade was executed
# EntryTime - time trade entered processing system (equal to or after Execution time)
# Counterparty - counterparty name including external counterparties and internal pending
# TraderName - eg Bob Smith
# CcyPair - eg GBPUSD
# BaseTrade - notional of the trade in base currency eg 100 GBP
allTradesArrays = {
'TradeId':[101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120],
'ExecutionDateTime':['06/26/2018 12:49:23','06/26/2018 13:20:12','06/26/2018 13:20:12','06/26/2018 13:20:32','06/26/2018 13:22:19','06/26/2018 13:25:13','06/26/2018 13:26:18','06/26/2018 15:50:42','06/26/2018 15:51:12','06/26/2018 15:51:12','06/26/2018 15:54:10','06/26/2018 16:17:09','06/26/2018 18:54:06','06/26/2018 18:54:12','06/26/2018 18:54:12','06/26/2018 18:54:15','06/26/2018 19:42:05','06/26/2018 19:58:25','06/26/2018 20:13:19','06/26/2018 20:13:19'],
'EntryDateTime':['06/26/2018 12:49:23','06/26/2018 13:25:13','06/26/2018 13:25:13','06/26/2018 13:20:33','06/26/2018 13:22:19','06/26/2018 13:25:13','06/26/2018 13:26:18','06/26/2018 15:50:42','06/26/2018 15:52:01','06/26/2018 15:54:10','06/26/2018 15:54:10','06/26/2018 16:17:11','06/26/2018 18:54:07','06/26/2018 18:54:30','06/26/2018 19:58:25','06/26/2018 18:54:16','06/26/2018 19:42:05','06/26/2018 19:58:25','06/26/2018 20:13:19','06/26/2018 20:13:19'],
'Counterparty':['cpty1','PENDING','cpty2','cpty12','cpty3','PENDING','cpty6','cpty2','PENDING','cpty8','PENDING','cpty9','cpty1','PENDING','cpty8','cpty3','cpty5','PENDING','cpty6','cpty2'],
'CcyPair':['GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD','GBPUSD'],
'BaseTrade':[5,-6.94037287,-6.94037287,-11.63,-0.77222412,6.94037287,21.88,-5.2,10,10,-10,0.3931098,2.5,-670,-670,2.029,20,670,7.37097,11.056455]
}
allTrades = pd.DataFrame(allTradesArrays)
print()
print('allTrades.info()')
print(allTrades.info())
print()
print('allTrades.head()')
print(allTrades.head())
# Create a dataframe that is a subset of the trades dataframe that
# contains only trades against the pending counterparty - note the
# pending trades are still in the allTrades df as I need to keep
# allTrades complete for further processing once the time stamp changes
# are made by this looping
pendingTrades = allTrades[allTrades['Counterparty'] == 'PENDING']
print()
print('pendingTrades.info()')
print(pendingTrades.info())
print()
print('pendingTrades.head()')
print(pendingTrades.head())
# iterate over each trade in pendingTrades as explained below
for pendingTradeIndex1, pendingTrade1 in pendingTrades.iterrows():
for allTradeIndex, allTrade in allTrades.iterrows():
if (
# if we find a trade in allTrades that is:
# not the same pendingTrade1 trade that is also in the allTrades
pendingTrade1['TradeId'] != allTrade['TradeId']
# has the same CcyPair
and pendingTrade1['CcyPair'] == allTrade['CcyPair']
# has the same notional and sign
and pendingTrade1['BaseTrade'] == allTrade['BaseTrade']
# has matching execution datetimes
and pendingTrade1['ExecutionDateTime'] == allTrade['ExecutionDateTime']
# then we have found the first two of three trades
):
# To find the third trade apply similar logic
for pendingTradeIndex2, pendingTrade2 in pendingTrades.iterrows():
if (
# If we find the second trade in allTrades that is:
# not the same pendingTrade2 trade that is also in the allTrades
pendingTrade2['TradeId'] != allTrade['TradeId']
# is the same CcyPair as the devon trade
and pendingTrade2['CcyPair'] == allTrade['CcyPair']
# has the same notional but opposite sign as the devon trade
and pendingTrade2['BaseTrade'] == -1*allTrade['BaseTrade']
# has matching entry datetimes as the devon trade
and pendingTrade2['EntryDateTime'] == allTrade['EntryDateTime']
# does not have matching execution datetimes as the devon trade
and pendingTrade2['ExecutionDateTime'] != allTrade['ExecutionDateTime']
# then we should have found the third of three trades
# note this third trade is always a pending counterparty trade
):
print('pendingTrade1 ' + str(pendingTrade1['TradeId']) +
' ExTime=' +
str(allTrades.loc[allTrades['TradeId']==pendingTrade1['TradeId'],'ExecutionDateTime'].values[0]) +
' EnTime=' +
str(allTrades.loc[allTrades['TradeId']==pendingTrade1['TradeId'],'EntryDateTime'].values[0]) +
' BaseTr=' +
str(allTrades.loc[allTrades['TradeId']==pendingTrade1['TradeId'],'BaseTrade'].values[0]))
print('allTrade ' + str(allTrade['TradeId']) +
' ExTime=' +
str(allTrades.loc[allTrades['TradeId']==allTrade['TradeId'],'ExecutionDateTime'].values[0]) +
' EnTime=' +
str(allTrades.loc[allTrades['TradeId']==allTrade['TradeId'],'EntryDateTime'].values[0]) +
' BaseTr=' +
str(allTrades.loc[allTrades['TradeId']==allTrade['TradeId'],'BaseTrade'].values[0]))
print('pendingTrade2 ' + str(pendingTrade2['TradeId']) +
' ExTime=' +
str(allTrades.loc[allTrades['TradeId']==pendingTrade2['TradeId'],'ExecutionDateTime'].values[0]) +
' EnTime=' +
str(allTrades.loc[allTrades['TradeId']==pendingTrade2['TradeId'],'EntryDateTime'].values[0]) +
' BaseTr=' +
str(allTrades.loc[allTrades['TradeId']==pendingTrade2['TradeId'],'BaseTrade'].values[0]))
print('Changing ' + str(pendingTrade1['TradeId']) + ' ExTime=' +
str(allTrades.loc[allTrades['TradeId']==pendingTrade1['TradeId'],'ExecutionDateTime'].values[0]) +
' to ' + str(pendingTrade2['TradeId']) + ' ExTime=' +
str(allTrades.loc[allTrades['TradeId']==pendingTrade2['TradeId'],'ExecutionDateTime'].values[0]))
allTrades.loc[allTrades['TradeId'] == pendingTrade1['TradeId'],'ExecutionDateTime'] = \
allTrades.loc[allTrades['TradeId'] == pendingTrade2['TradeId'],'ExecutionDateTime'].values[0]
print()
试试itertuples()吧。它应该比iterrows()快得多