Adding a grand total when using the groupedby function on a DataFrame
1
Suppose I have the following DataFrame
>>> d = {'col1': ['A', 'B', 'A', 'A', 'B'], 'col2': ['q1', 'q2', 'q1', 'q2', 'q1'],
'col3':[1, 2, 1, 4, 5]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2 col3
0 A q1 1
1 B q2 2
2 A q1 1
3 A q2 4
4 B q1 5
Now I would like to group this by col1 and col2, and also add the grand total below
So my result would be:
col3
col1 col2
A q1 2
A q2 4
total_A 6
B q1 5
B q2 2
total_B 7
Any ideas on how to add this total value while keeping grouped columns 1 and 2? Preferably not manually since my actual DataFrame is quite a bit larger
python pandas dataframe
edited Dec 27 '18 at 15:32
coldspeed
120k19119194
asked Dec 27 '18 at 15:31
Whizkid95
16010
add a comment |
1
Suppose I have the following DataFrame
>>> d = {'col1': ['A', 'B', 'A', 'A', 'B'], 'col2': ['q1', 'q2', 'q1', 'q2', 'q1'],
'col3':[1, 2, 1, 4, 5]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2 col3
0 A q1 1
1 B q2 2
2 A q1 1
3 A q2 4
4 B q1 5
Now I would like to group this by col1 and col2, and also add the grand total below
So my result would be:
col3
col1 col2
A q1 2
A q2 4
total_A 6
B q1 5
B q2 2
total_B 7
Any ideas on how to add this total value while keeping grouped columns 1 and 2? Preferably not manually since my actual DataFrame is quite a bit larger
python pandas dataframe
edited Dec 27 '18 at 15:32
coldspeed
120k19119194
asked Dec 27 '18 at 15:31
Whizkid95
16010
add a comment |
1
1
1
Suppose I have the following DataFrame
>>> d = {'col1': ['A', 'B', 'A', 'A', 'B'], 'col2': ['q1', 'q2', 'q1', 'q2', 'q1'],
'col3':[1, 2, 1, 4, 5]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2 col3
0 A q1 1
1 B q2 2
2 A q1 1
3 A q2 4
4 B q1 5
Now I would like to group this by col1 and col2, and also add the grand total below
So my result would be:
col3
col1 col2
A q1 2
A q2 4
total_A 6
B q1 5
B q2 2
total_B 7
Any ideas on how to add this total value while keeping grouped columns 1 and 2? Preferably not manually since my actual DataFrame is quite a bit larger
python pandas dataframe
edited Dec 27 '18 at 15:32
coldspeed
120k19119194
asked Dec 27 '18 at 15:31
Whizkid95
16010
Suppose I have the following DataFrame
>>> d = {'col1': ['A', 'B', 'A', 'A', 'B'], 'col2': ['q1', 'q2', 'q1', 'q2', 'q1'],
'col3':[1, 2, 1, 4, 5]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2 col3
0 A q1 1
1 B q2 2
2 A q1 1
3 A q2 4
4 B q1 5
Now I would like to group this by col1 and col2, and also add the grand total below
So my result would be:
col3
col1 col2
A q1 2
A q2 4
total_A 6
B q1 5
B q2 2
total_B 7
Any ideas on how to add this total value while keeping grouped columns 1 and 2? Preferably not manually since my actual DataFrame is quite a bit larger
python pandas dataframe
python pandas dataframe
edited Dec 27 '18 at 15:32
coldspeed
120k19119194
asked Dec 27 '18 at 15:31
Whizkid95
16010
edited Dec 27 '18 at 15:32
coldspeed
120k19119194
asked Dec 27 '18 at 15:31
Whizkid95
16010
edited Dec 27 '18 at 15:32
coldspeed
120k19119194
edited Dec 27 '18 at 15:32
coldspeed
120k19119194
edited Dec 27 '18 at 15:32
coldspeed
120k19119194
120k19119194
asked Dec 27 '18 at 15:31
Whizkid95
16010
asked Dec 27 '18 at 15:31
Whizkid95
16010
asked Dec 27 '18 at 15:31
Whizkid95
16010
16010
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
2
Compute the totals separately and concatenate them together:
# First step: GroupBy and sum.
u = df.groupby(['col1', 'col2']).sum()
# Second step: Compute the totals and set a MultiIndex for easy concatenation.
v = u.groupby(level=0).sum()
v.index = pd.MultiIndex.from_arrays([v.index, ['total'] * len(v)])
# Final step: Concat the intermediate results.
pd.concat([v, u]).sort_index(level=0, axis=0)
col3
col1
A q1 2
q2 4
total 6
B q1 5
q2 2
total 7
answered Dec 27 '18 at 15:38
coldspeed
120k19119194
add a comment |
2
You can try this also:
d = df.groupby(['col1','col2']).sum()
pd.concat([d,
d.sum(level=0).assign(col2='')
.rename(index=lambda x: x+' total')
.set_index('col2', append=True)]).sort_index()
Output:
col3
col1 col2
A q1 2
q2 4
A total 6
B q1 5
q2 2
B total 7
answered Dec 27 '18 at 16:15
Scott Boston
51.3k72955
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
Compute the totals separately and concatenate them together:
# First step: GroupBy and sum.
u = df.groupby(['col1', 'col2']).sum()
# Second step: Compute the totals and set a MultiIndex for easy concatenation.
v = u.groupby(level=0).sum()
v.index = pd.MultiIndex.from_arrays([v.index, ['total'] * len(v)])
# Final step: Concat the intermediate results.
pd.concat([v, u]).sort_index(level=0, axis=0)
col3
col1
A q1 2
q2 4
total 6
B q1 5
q2 2
total 7
answered Dec 27 '18 at 15:38
coldspeed
120k19119194
add a comment |
2
Compute the totals separately and concatenate them together:
# First step: GroupBy and sum.
u = df.groupby(['col1', 'col2']).sum()
# Second step: Compute the totals and set a MultiIndex for easy concatenation.
v = u.groupby(level=0).sum()
v.index = pd.MultiIndex.from_arrays([v.index, ['total'] * len(v)])
# Final step: Concat the intermediate results.
pd.concat([v, u]).sort_index(level=0, axis=0)
col3
col1
A q1 2
q2 4
total 6
B q1 5
q2 2
total 7
answered Dec 27 '18 at 15:38
coldspeed
120k19119194
add a comment |
2
2
2
Compute the totals separately and concatenate them together:
# First step: GroupBy and sum.
u = df.groupby(['col1', 'col2']).sum()
# Second step: Compute the totals and set a MultiIndex for easy concatenation.
v = u.groupby(level=0).sum()
v.index = pd.MultiIndex.from_arrays([v.index, ['total'] * len(v)])
# Final step: Concat the intermediate results.
pd.concat([v, u]).sort_index(level=0, axis=0)
col3
col1
A q1 2
q2 4
total 6
B q1 5
q2 2
total 7
answered Dec 27 '18 at 15:38
coldspeed
120k19119194
Compute the totals separately and concatenate them together:
# First step: GroupBy and sum.
u = df.groupby(['col1', 'col2']).sum()
# Second step: Compute the totals and set a MultiIndex for easy concatenation.
v = u.groupby(level=0).sum()
v.index = pd.MultiIndex.from_arrays([v.index, ['total'] * len(v)])
# Final step: Concat the intermediate results.
pd.concat([v, u]).sort_index(level=0, axis=0)
col3
col1
A q1 2
q2 4
total 6
B q1 5
q2 2
total 7
answered Dec 27 '18 at 15:38
coldspeed
120k19119194
answered Dec 27 '18 at 15:38
coldspeed
120k19119194
answered Dec 27 '18 at 15:38
coldspeed
120k19119194
answered Dec 27 '18 at 15:38
coldspeed
120k19119194
120k19119194
add a comment |
add a comment |
2
You can try this also:
d = df.groupby(['col1','col2']).sum()
pd.concat([d,
d.sum(level=0).assign(col2='')
.rename(index=lambda x: x+' total')
.set_index('col2', append=True)]).sort_index()
Output:
col3
col1 col2
A q1 2
q2 4
A total 6
B q1 5
q2 2
B total 7
answered Dec 27 '18 at 16:15
Scott Boston
51.3k72955
add a comment |
2
You can try this also:
d = df.groupby(['col1','col2']).sum()
pd.concat([d,
d.sum(level=0).assign(col2='')
.rename(index=lambda x: x+' total')
.set_index('col2', append=True)]).sort_index()
Output:
col3
col1 col2
A q1 2
q2 4
A total 6
B q1 5
q2 2
B total 7
answered Dec 27 '18 at 16:15
Scott Boston
51.3k72955
add a comment |
2
2
2
You can try this also:
d = df.groupby(['col1','col2']).sum()
pd.concat([d,
d.sum(level=0).assign(col2='')
.rename(index=lambda x: x+' total')
.set_index('col2', append=True)]).sort_index()
Output:
col3
col1 col2
A q1 2
q2 4
A total 6
B q1 5
q2 2
B total 7
answered Dec 27 '18 at 16:15
Scott Boston
51.3k72955
You can try this also:
d = df.groupby(['col1','col2']).sum()
pd.concat([d,
d.sum(level=0).assign(col2='')
.rename(index=lambda x: x+' total')
.set_index('col2', append=True)]).sort_index()
Output:
col3
col1 col2
A q1 2
q2 4
A total 6
B q1 5
q2 2
B total 7
answered Dec 27 '18 at 16:15
Scott Boston
51.3k72955
answered Dec 27 '18 at 16:15
Scott Boston
51.3k72955
answered Dec 27 '18 at 16:15
Scott Boston
51.3k72955
answered Dec 27 '18 at 16:15
Scott Boston
51.3k72955
51.3k72955
add a comment |
add a comment |
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