如何在Python中使用带条件的Groupby
我有一个数据帧，叫做合并的能量如何在Python中使用带条件的Groupby,python,pandas,dataframe,group-by,conditional-statements,Python,Pandas,Dataframe,Group By,Conditional Statements,我有一个数据帧，叫做合并的能量 merged_df_energy.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 11232 entries, 0 to 11231 Data columns (total 17 columns): TIMESTAMP 11232 non-null datetime64[ns] P_ACT_KW
merged_df_energy.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 11232 entries, 0 to 11231
Data columns (total 17 columns):
TIMESTAMP 11232 non-null datetime64[ns]
P_ACT_KW 11232 non-null int64
PERIODE_TARIF 11232 non-null object
P_SOUSCR 11232 non-null int64
high_energy 11232 non-null int64
medium_energy 11232 non-null int64
low_energy 11232 non-null int64
0ACT_TIME_ETA_PRG_P2REF_RM 11232 non-null int64
0ACT_TIME_ETA_PRG_VDES_RM 11232 non-null int64
0ACT_TIME_ETA_PRG_P3REF_RM 11232 non-null int64
0ACT_TIME_ETA_POMP_RECIRC_N1 11232 non-null int64
0ACT_TIME_ETA_POMP_RECIRC_N2 11232 non-null int64
0ACT_TIME_ETA_POMP_RECIRC_N3 11232 non-null int64
0ACT_TIME_ETA_SURPRES_AIR_N1 11232 non-null int64
0ACT_TIME_ETA_SURPRES_AIR_N2 11232 non-null int64
0ACT_TIME_ETA_SURPRES_AIR_N3 11232 non-null int64
class_energy 11232 non-null object
dtypes: datetime64[ns](1), int64(14), object(2)
memory usage: 1.5+ MB
df_F1 = (merged_df_energy.groupby(by=['class_energy'], as_index=False)['0ACT_TIME_ETA_PRG_P2REF_RM', '0ACT_TIME_ETA_PRG_VDES_RM','0ACT_TIME_ETA_PRG_P3REF_RM','0ACT_TIME_ETA_POMP_RECIRC_N1','0ACT_TIME_ETA_POMP_RECIRC_N2', '0ACT_TIME_ETA_POMP_RECIRC_N3', '0ACT_TIME_ETA_SURPRES_AIR_N1', '0ACT_TIME_ETA_SURPRES_AIR_N2', '0ACT_TIME_ETA_SURPRES_AIR_N3' ].sum())
它工作正常，但我想知道在这种情况下（如果PERIODE_TARIF='HP'）如何做到这一点？我认为您需要在
groupbycols = merged_df_energy.columns[7:16]
print (cols)
Index(['0ACT_TIME_ETA_PRG_P2REF_RM', '0ACT_TIME_ETA_PRG_VDES_RM',
'0ACT_TIME_ETA_PRG_P3REF_RM', '0ACT_TIME_ETA_POMP_RECIRC_N1',
'0ACT_TIME_ETA_POMP_RECIRC_N2', '0ACT_TIME_ETA_POMP_RECIRC_N3',
'0ACT_TIME_ETA_SURPRES_AIR_N1', '0ACT_TIME_ETA_SURPRES_AIR_N2',
'0ACT_TIME_ETA_SURPRES_AIR_N3'],
dtype='object')
merged_df_energy1 = merged_df_energy[merged_df_energy.PERIODE_TARIF == 'HP']
cols = ['0ACT_TIME_ETA_PRG_P2REF_RM',
'0ACT_TIME_ETA_PRG_VDES_RM',
'0ACT_TIME_ETA_PRG_P3REF_RM',
'0ACT_TIME_ETA_POMP_RECIRC_N1',
'0ACT_TIME_ETA_POMP_RECIRC_N2',
'0ACT_TIME_ETA_POMP_RECIRC_N3',
'0ACT_TIME_ETA_SURPRES_AIR_N1',
'0ACT_TIME_ETA_SURPRES_AIR_N2',
'0ACT_TIME_ETA_SURPRES_AIR_N3']
df_F1 = (merged_df_energy1.groupby(by=['class_energy'], as_index=False)[cols].sum())
print (df_F1)
class_energy 0ACT_TIME_ETA_PRG_P2REF_RM 0ACT_TIME_ETA_PRG_VDES_RM \
0 high 100 0
0ACT_TIME_ETA_PRG_P3REF_RM 0ACT_TIME_ETA_POMP_RECIRC_N1 \
0 100 0
0ACT_TIME_ETA_POMP_RECIRC_N2 0ACT_TIME_ETA_POMP_RECIRC_N3 \
0 0 0
0ACT_TIME_ETA_SURPRES_AIR_N1 0ACT_TIME_ETA_SURPRES_AIR_N2 \
0 0 0
0ACT_TIME_ETA_SURPRES_AIR_N3
0 0
cols = merged_df_energy.columns[7:16]
print (cols)
Index(['0ACT_TIME_ETA_PRG_P2REF_RM', '0ACT_TIME_ETA_PRG_VDES_RM',
'0ACT_TIME_ETA_PRG_P3REF_RM', '0ACT_TIME_ETA_POMP_RECIRC_N1',
'0ACT_TIME_ETA_POMP_RECIRC_N2', '0ACT_TIME_ETA_POMP_RECIRC_N3',
'0ACT_TIME_ETA_SURPRES_AIR_N1', '0ACT_TIME_ETA_SURPRES_AIR_N2',
'0ACT_TIME_ETA_SURPRES_AIR_N3'],
dtype='object')