我在下面有df：
Hour 1 2
CU0111-012379-H Output Energy, (Wh/h) 2.0 3.0
Lights (Wh) 4.0 5.0
Lights+Media (Wh) 0.0 0.0
Total Usage (h) 0.0 2.0
Lights (h) 0.0 1.0
Light+Media (h) 0.0 0.0
Battery Voltage, (V) 13.5 13.7
Max Watt, W 7.5 4.5
并且我在末尾添加了总计列：
col_list= list(df)
df['Total'] = df[col_list].sum(axis=1)
Hour 1 2 Total
CU0111-012379-H Output Energy, (Wh/h) 2.0 3.0 5.0
Lights (Wh) 4.0 5.0 9.0
Lights+Media (Wh) 0.0 0.0 0.0
Total Usage (h) 0.0 2.0 2.0
Lights (h) 0.0 1.0 1.0
Light+Media (h) 0.0 0.0 0.0
Battery Voltage, (V) 13.5 13.7 27.2
Max Watt, W 7.5 4.5 12.0
然而。我想获得总计1列中轴1的最大值，而不是总和：
Battery Voltage, (V)
Max Watt, W
这样df将是：
Hour 1 2 Total/Max
CU0111-012379-H Output Energy, (Wh/h) 2.0 3.0 5.0
Lights (Wh) 4.0 5.0 9.0
Lights+Media (Wh) 0.0 0.0 0.0
Total Usage (h) 0.0 2.0 2.0
Lights (h) 0.0 1.0 1.0
Light+Media (h) 0.0 0.0 0.0
Battery Voltage, (V) 13.5 13.7 13.7 <-max
Max Watt, W 7.5 4.5 7.5 <-max
一个恶魔般的初学者尝试可能看起来像这样：
df3['Total/Max'] = d3[col_list].sum(axis=1).df3.groupby(level=1).df3['Battery Voltage, (v)'].transform(max)
最佳答案
您可以将numpy.where与isin和get_level_values一起使用，以检查级别是否具有某些值，然后检查max和sum：
L = ['Battery Voltage, (V)','Max Watt, W']
print (df.index.get_level_values(1).isin(L))
[False False False False False False True True]
df['Total/Max'] = np.where(df.index.get_level_values(1).isin(L),
df.max(axis=1),
df.sum(axis=1))
print (df)
1 2 Total/Max
Hour
CU0111-012379-H Output Energy, (Wh/h) 2.0 3.0 5.0
Lights (Wh) 4.0 5.0 9.0
Lights+Media (Wh) 0.0 0.0 0.0
Total Usage (h) 0.0 2.0 2.0
Lights (h) 0.0 1.0 1.0
Light+Media (h) 0.0 0.0 0.0
Battery Voltage, (V) 13.5 13.7 13.7
Max Watt, W 7.5 4.5 7.5
使用
loc进行mask选择并应用max和sum的另一种解决方案，还需要~来反转boolean array：L = ['Battery Voltage, (V)','Max Watt, W']
mask = df.index.get_level_values(1).isin(L)
df.loc[mask, 'Total/Max'] = df[mask].max(axis=1)
df.loc[~mask, 'Total/Max'] = df[~mask].sum(axis=1)
print (df)
1 2 Total/Max
Hour
CU0111-012379-H Output Energy, (Wh/h) 2.0 3.0 5.0
Lights (Wh) 4.0 5.0 9.0
Lights+Media (Wh) 0.0 0.0 0.0
Total Usage (h) 0.0 2.0 2.0
Lights (h) 0.0 1.0 1.0
Light+Media (h) 0.0 0.0 0.0
Battery Voltage, (V) 13.5 13.7 13.7
Max Watt, W 7.5 4.5 7.5
通过评论编辑：需要带有另一个遮罩的双
numpy.where。L = ['Battery Voltage, (V)','Max Watt, W']
mask1 = df.index.get_level_values(1).isin(L)
mask2 = df.index.get_level_values(1) == 'Lights (h)'
df['Total/Max/Min'] = np.where(mask1, df.max(axis=1),
np.where(mask2, df.min(axis=1), df.sum(axis=1)))
print (df)
1 2 Total/Max/Min
Hour
CU0111-012379-H Output Energy, (Wh/h) 2.0 3.0 5.0
Lights (Wh) 4.0 5.0 9.0
Lights+Media (Wh) 0.0 0.0 0.0
Total Usage (h) 0.0 2.0 2.0
Lights (h) 0.0 1.0 0.0
Light+Media (h) 0.0 0.0 0.0
Battery Voltage, (V) 13.5 13.7 13.7
Max Watt, W 7.5 4.5 7.5
关于python - Pandas 在新列中横穿轴线1的总和和最大值，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/42115431/