以下是函数的line_profiler记录：
Wrote profile results to FM_CORE.py.lprof
Timer unit: 2.79365e-07 s
File: F:\FM_CORE.py
Function: _rpt_join at line 1068
Total time: 1.87766 s
Line # Hits Time Per Hit % Time Line Contents
==============================================================
1068 @profile
1069 def _rpt_join(dfa, dfb, join_type='inner'):
1070 ''' join two dataframe together by ('STK_ID','RPT_Date') multilevel index.
1071 'join_type' can be 'inner' or 'outer'
1072 '''
1073
1074 2 56 28.0 0.0 try: # ('STK_ID','RPT_Date') are normal column
1075 2 2936668 1468334.0 43.7 rst = pd.merge(dfa, dfb, how=join_type, on=['STK_ID','RPT_Date'], left_index=True, right_index=True)
1076 except: # ('STK_ID','RPT_Date') are index
1077 rst = pd.merge(dfa, dfb, how=join_type, left_index=True, right_index=True)
1078
1079
1080 2 81 40.5 0.0 try: # handle 'STK_Name
1081 2 426472 213236.0 6.3 name_combine = pd.concat([dfa.STK_Name, dfb.STK_Name])
1082
1083
1084 2 900584 450292.0 13.4 nameseries = name_combine[-Series(name_combine.index.values, name_combine.index).duplicated()]
1085
1086 2 1138140 569070.0 16.9 rst.STK_Name_x = nameseries
1087 2 596768 298384.0 8.9 rst = rst.rename(columns={'STK_Name_x': 'STK_Name'})
1088 2 722293 361146.5 10.7 rst = rst.drop(['STK_Name_y'], axis=1)
1089 except:
1090 pass
1091
1092 2 94 47.0 0.0 return rst
这两条线让我感到惊讶：
1087 2 596768 298384.0 8.9 rst = rst.rename(columns={'STK_Name_x': 'STK_Name'})
1088 2 722293 361146.5 10.7 rst = rst.drop(['STK_Name_y'], axis=1)
为什么简单的数据框列"rename"和"drop"操作会花费很多时间（8.9％+ 10.7％）？无论如何，"merge"操作仅花费43.7％，并且“重命名”/“丢弃”看起来不像计算密集型操作。怎么改进呢？