问题
I am aware of this link but I didn't manage to solve my problem.
I have this below DataFrame from pandas.DataFrame.groupby().sum():
Value
Level Company Item
1 X a 100
b 200
Y a 35
b 150
c 35
2 X a 48
b 100
c 50
Y a 80
and would like to add total rows for each level of index that I have to get:
Value
Level Company Item
1 X a 100
b 200
Total 300
Y a 35
b 150
c 35
Total 520
Total 820
2 X a 48
b 100
c 50
Total 198
Y a 80
Total 80
Total 278
Total 1098
As request
level = list(map(int, list('111112222')))
company = list('XXYYYXXXY')
item = list('ababcabca')
value = [100,200,35,150,35,48,100,50,80]
col = ['Level', 'Company', 'Item', 'Value']
df = pd.DataFrame([level,company,item,value]).T
df.columns = col
df.groupby(['Level', 'Company', 'Item']).sum()
回答1:
You can try stacking it one level at a time:
m = df.groupby(['Level','Company','Item'])['Value'].sum().unstack(level=['Company','Item'])
m = m.assign(total=m.sum(1))
m = m.stack(level='Company')
m = m.assign(total=m.sum(1))
m = m.stack(level='Item')
The output has duplicate totals though:
Level Company Item
1 X a 100.0
b 200.0
total 300.0
Y a 35.0
b 150.0
c 35.0
total 220.0
total 520.0
total 520.0
2 X a 48.0
b 100.0
c 50.0
total 198.0
Y a 80.0
total 80.0
total 278.0
total 278.0
dtype: float64
回答2:
You can use:
m=df.groupby(['Level','Company','Item'])['Value'].sum().unstack()
m.assign(total=m.sum(1)).stack().to_frame('Value')
Value
Level Company Item
1 X a 100.0
b 200.0
total 300.0
Y a 35.0
b 150.0
c 35.0
total 220.0
2 X a 48.0
b 100.0
c 50.0
total 198.0
Y a 80.0
total 80.0
回答3:
Try this: Basically, this is creating two new dfs from the using the sum of the two groups and concating the three data frames
level = list(map(int, list('111112222')))
company = list('XXYYYXXXY')
item = list('ababcabca')
value = [100,200,35,150,35,48,100,50,80]
col = ['Level', 'Company', 'Item', 'Value']
df = pd.DataFrame([level,company,item,value]).T
df.columns = col
df1 = (df.groupby(['Level', 'Company', 'Item'])['Value'].sum())
df2 = (df1.sum(level=0).to_frame().assign(Company='total').set_index('Company', append=True))
df3 = (df1.groupby(['Level','Company']).sum().to_frame().assign(Item='total').set_index('Item', append=True))
dfx = pd.concat([df1.to_frame().reset_index(),
df2.reset_index(),
df3.reset_index()],sort=False)
print(dfx)
Output:
Level Company Item Value
0 1 X a 100
1 1 X b 200
2 1 Y a 35
3 1 Y b 150
4 1 Y c 35
5 2 X a 48
6 2 X b 100
7 2 X c 50
8 2 Y a 80
0 1 total NaN 520
1 2 total NaN 278
0 1 X total 300
1 1 Y total 220
2 2 X total 198
3 2 Y total 80
This is not sorted though as you expect. If I concat the 3 dfs without resetting the index I'm getting the expected sort order, but the index is a multi-index column
dfx = pd.concat([df1.to_frame(), df2, df3]).sort_index()
Output
Value
(1, X, a) 100
(1, X, b) 200
(1, X, total) 300
(1, Y, a) 35
(1, Y, b) 150
(1, Y, c) 35
(1, Y, total) 220
(1, total) 520
(2, X, a) 48
(2, X, b) 100
(2, X, c) 50
(2, X, total) 198
(2, Y, a) 80
(2, Y, total) 80
(2, total) 278
I am not sure how to convert this to columns as in your df.
来源：https://stackoverflow.com/questions/59326903/adding-total-row-to-pandas-dataframe-groupby