I have a dataframe merged_df_energy:
+------------------------+------------------------+------------------------+--------------+ | ACT_TIME_AERATEUR_1_F1 | ACT_TIME_AERATEUR_1_F3 | ACT_TIME_AERATEUR_1_F5 | class_energy | +------------------------+------------------------+------------------------+--------------+ | 63.333333 | 63.333333 | 63.333333 | low | | 0 | 0 | 0 | high | | 45.67 | 0 | 55.94 | high | | 0 | 0 | 23.99 | low | | 0 | 20 | 23.99 | medium | +------------------------+------------------------+------------------------+--------------+ I would like to create for each ACT_TIME_AERATEUR_1_Fx (ACT_TIME_AERATEUR_1_F1, ACT_TIME_AERATEUR_1_F3 and ACT_TIME_AERATEUR_1_F5) a dataframe which contains these columns: class_energy and sum_time
For example for the dataframe corresponding to ACT_TIME_AERATEUR_1_F1:
+-----------------+-----------+ | class_energy | sum_time | +-----------------+-----------+ | low | 63.333333 | | medium | 0 | | high | 45.67 | +-----------------+-----------+ I thing to do I should use the group by like this:
data.groupby(by=['class_energy'])['sum_time'].sum() How can I do this?
1 Answer
You can add all columns to [] for aggregating:
print (df.groupby(by=['class_energy'])['ACT_TIME_AERATEUR_1_F1', 'ACT_TIME_AERATEUR_1_F3','ACT_TIME_AERATEUR_1_F5'].sum()) ACT_TIME_AERATEUR_1_F1 ACT_TIME_AERATEUR_1_F3 \ class_energy high 45.670000 0.000000 low 63.333333 63.333333 medium 0.000000 20.000000 ACT_TIME_AERATEUR_1_F5 class_energy high 55.940000 low 87.323333 medium 23.990000 You can use also parameter as_index=False:
print (df.groupby(by=['class_energy'], as_index=False)['ACT_TIME_AERATEUR_1_F1', 'ACT_TIME_AERATEUR_1_F3','ACT_TIME_AERATEUR_1_F5'].sum()) class_energy ACT_TIME_AERATEUR_1_F1 ACT_TIME_AERATEUR_1_F3 \ 0 high 45.670000 0.000000 1 low 63.333333 63.333333 2 medium 0.000000 20.000000 ACT_TIME_AERATEUR_1_F5 0 55.940000 1 87.323333 2 23.990000 If need aggregate only first 3 columns:
print (df.groupby(by=['class_energy'], as_index=False)[df.columns[:3]].sum()) class_energy ACT_TIME_AERATEUR_1_F1 ACT_TIME_AERATEUR_1_F3 \ 0 high 45.670000 0.000000 1 low 63.333333 63.333333 2 medium 0.000000 20.000000 ACT_TIME_AERATEUR_1_F5 0 55.940000 1 87.323333 2 23.990000 ...or all columns without last:
print (df.groupby(by=['class_energy'], as_index=False)[df.columns[:-1]].sum()) class_energy ACT_TIME_AERATEUR_1_F1 ACT_TIME_AERATEUR_1_F3 \ 0 high 45.670000 0.000000 1 low 63.333333 63.333333 2 medium 0.000000 20.000000 ACT_TIME_AERATEUR_1_F5 0 55.940000 1 87.323333 2 23.990000 
