Pandas Groupby using multiple criteria on different axis
I have a df DataFrame like :
| A | B | A_ | B_ |COMMON|
--------------------------------
0 | 1 | 3 | 0 | 1 | a |
--------------------------------
1 | 8 | 5 | 4 | 0 | a |
--------------------------------
2 | 3 | 6 | 2 | 4 | b |
--------------------------------
3 | 9 | 9 | 1 | 7 | b |
And I want to group all columns X with X_ for all letters A,B,... (let's say, the group is called X as well), and group as well using COMMON. I would like to apply later function like std() to all the grouped values.
So the result would look like:
COMMON | A | B |
---------------------------
a |std(...)|std(...)|
---------------------------
b |std(...)|std(...)|
I have been able to group either one or the other, using df.groupby(['COMMMON']) for one criteria and .groupby(mapping_function, axis=1) for the other one, but how do I use them together?
Another alternative for an intermediate step would be to concatenate individual columns so that I would get:
| A | B |COMMON|
----------------------
0 | 1 | 3 |a |
---------------------
1 | 8 | 5 |a |
---------------------
2 | 3 | 6 |b |
---------------------
3 | 9 | 9 |b |
---------------------
0 | 0 | 1 |a |
---------------------
1 | 4 | 0 |a |
---------------------
2 | 2 | 4 |b |
---------------------
3 | 1 | 7 |b |
But I also don't know how to do that.
Also as you might see, I don't really care about the index.
Thank you for your help!
python pandas pandas-groupby
asked Nov 12 at 14:22
cduguet
311214
add a comment |
I have a df DataFrame like :
| A | B | A_ | B_ |COMMON|
--------------------------------
0 | 1 | 3 | 0 | 1 | a |
--------------------------------
1 | 8 | 5 | 4 | 0 | a |
--------------------------------
2 | 3 | 6 | 2 | 4 | b |
--------------------------------
3 | 9 | 9 | 1 | 7 | b |
And I want to group all columns X with X_ for all letters A,B,... (let's say, the group is called X as well), and group as well using COMMON. I would like to apply later function like std() to all the grouped values.
So the result would look like:
COMMON | A | B |
---------------------------
a |std(...)|std(...)|
---------------------------
b |std(...)|std(...)|
I have been able to group either one or the other, using df.groupby(['COMMMON']) for one criteria and .groupby(mapping_function, axis=1) for the other one, but how do I use them together?
Another alternative for an intermediate step would be to concatenate individual columns so that I would get:
| A | B |COMMON|
----------------------
0 | 1 | 3 |a |
---------------------
1 | 8 | 5 |a |
---------------------
2 | 3 | 6 |b |
---------------------
3 | 9 | 9 |b |
---------------------
0 | 0 | 1 |a |
---------------------
1 | 4 | 0 |a |
---------------------
2 | 2 | 4 |b |
---------------------
3 | 1 | 7 |b |
But I also don't know how to do that.
Also as you might see, I don't really care about the index.
Thank you for your help!
python pandas pandas-groupby
asked Nov 12 at 14:22
cduguet
311214
add a comment |
I have a df DataFrame like :
| A | B | A_ | B_ |COMMON|
--------------------------------
0 | 1 | 3 | 0 | 1 | a |
--------------------------------
1 | 8 | 5 | 4 | 0 | a |
--------------------------------
2 | 3 | 6 | 2 | 4 | b |
--------------------------------
3 | 9 | 9 | 1 | 7 | b |
And I want to group all columns X with X_ for all letters A,B,... (let's say, the group is called X as well), and group as well using COMMON. I would like to apply later function like std() to all the grouped values.
So the result would look like:
COMMON | A | B |
---------------------------
a |std(...)|std(...)|
---------------------------
b |std(...)|std(...)|
I have been able to group either one or the other, using df.groupby(['COMMMON']) for one criteria and .groupby(mapping_function, axis=1) for the other one, but how do I use them together?
Another alternative for an intermediate step would be to concatenate individual columns so that I would get:
| A | B |COMMON|
----------------------
0 | 1 | 3 |a |
---------------------
1 | 8 | 5 |a |
---------------------
2 | 3 | 6 |b |
---------------------
3 | 9 | 9 |b |
---------------------
0 | 0 | 1 |a |
---------------------
1 | 4 | 0 |a |
---------------------
2 | 2 | 4 |b |
---------------------
3 | 1 | 7 |b |
But I also don't know how to do that.
Also as you might see, I don't really care about the index.
Thank you for your help!
python pandas pandas-groupby
asked Nov 12 at 14:22
cduguet
311214
I have a df DataFrame like :
| A | B | A_ | B_ |COMMON|
--------------------------------
0 | 1 | 3 | 0 | 1 | a |
--------------------------------
1 | 8 | 5 | 4 | 0 | a |
--------------------------------
2 | 3 | 6 | 2 | 4 | b |
--------------------------------
3 | 9 | 9 | 1 | 7 | b |
And I want to group all columns X with X_ for all letters A,B,... (let's say, the group is called X as well), and group as well using COMMON. I would like to apply later function like std() to all the grouped values.
So the result would look like:
COMMON | A | B |
---------------------------
a |std(...)|std(...)|
---------------------------
b |std(...)|std(...)|
I have been able to group either one or the other, using df.groupby(['COMMMON']) for one criteria and .groupby(mapping_function, axis=1) for the other one, but how do I use them together?
Another alternative for an intermediate step would be to concatenate individual columns so that I would get:
| A | B |COMMON|
----------------------
0 | 1 | 3 |a |
---------------------
1 | 8 | 5 |a |
---------------------
2 | 3 | 6 |b |
---------------------
3 | 9 | 9 |b |
---------------------
0 | 0 | 1 |a |
---------------------
1 | 4 | 0 |a |
---------------------
2 | 2 | 4 |b |
---------------------
3 | 1 | 7 |b |
But I also don't know how to do that.
Also as you might see, I don't really care about the index.
Thank you for your help!
python pandas pandas-groupby
python pandas pandas-groupby
asked Nov 12 at 14:22
cduguet
311214
asked Nov 12 at 14:22
cduguet
311214
asked Nov 12 at 14:22
cduguet
311214
asked Nov 12 at 14:22
cduguet
311214
asked Nov 12 at 14:22
cduguet
311214
311214
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
You can reshape first by melt with removing _ from column names (for better performance, because strip only few values) with pivot_table:
df = (df.rename(columns=lambda x: x.strip('_'))
.melt('COMMON')
.pivot_table(index='COMMON',columns='variable', values='value', aggfunc='std'))
print (df)
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:29
jezrael
319k22258336
2
Nice pivot :-) .
– W-B
Nov 12 at 14:33
add a comment |
IIUC
df.melt('COMMON').assign(variable=lambda x : x['variable'].str.rstrip('_')).
groupby(['COMMON','variable']).value.std().unstack()
Out[18]:
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:30
W-B
99.7k73163
add a comment |
Just groupby
h = lambda x: x[-1][0]
df.set_index('COMMON', append=True).stack().groupby(['COMMON', h]).std().unstack()
A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:56
piRSquared
151k22143285
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can reshape first by melt with removing _ from column names (for better performance, because strip only few values) with pivot_table:
df = (df.rename(columns=lambda x: x.strip('_'))
.melt('COMMON')
.pivot_table(index='COMMON',columns='variable', values='value', aggfunc='std'))
print (df)
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:29
jezrael
319k22258336
2
Nice pivot :-) .
– W-B
Nov 12 at 14:33
add a comment |
You can reshape first by melt with removing _ from column names (for better performance, because strip only few values) with pivot_table:
df = (df.rename(columns=lambda x: x.strip('_'))
.melt('COMMON')
.pivot_table(index='COMMON',columns='variable', values='value', aggfunc='std'))
print (df)
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:29
jezrael
319k22258336
2
Nice pivot :-) .
– W-B
Nov 12 at 14:33
add a comment |
You can reshape first by melt with removing _ from column names (for better performance, because strip only few values) with pivot_table:
df = (df.rename(columns=lambda x: x.strip('_'))
.melt('COMMON')
.pivot_table(index='COMMON',columns='variable', values='value', aggfunc='std'))
print (df)
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:29
jezrael
319k22258336
You can reshape first by melt with removing _ from column names (for better performance, because strip only few values) with pivot_table:
df = (df.rename(columns=lambda x: x.strip('_'))
.melt('COMMON')
.pivot_table(index='COMMON',columns='variable', values='value', aggfunc='std'))
print (df)
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:29
jezrael
319k22258336
edited Nov 12 at 14:45
answered Nov 12 at 14:29
jezrael
319k22258336
answered Nov 12 at 14:29
jezrael
319k22258336
answered Nov 12 at 14:29
jezrael
319k22258336
319k22258336
2
Nice pivot :-) .
– W-B
Nov 12 at 14:33
add a comment |
2
Nice pivot :-) .
– W-B
Nov 12 at 14:33
2
2
Nice pivot :-) .
– W-B
Nov 12 at 14:33
Nice pivot :-) .
– W-B
Nov 12 at 14:33
add a comment |
IIUC
df.melt('COMMON').assign(variable=lambda x : x['variable'].str.rstrip('_')).
groupby(['COMMON','variable']).value.std().unstack()
Out[18]:
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:30
W-B
99.7k73163
add a comment |
IIUC
df.melt('COMMON').assign(variable=lambda x : x['variable'].str.rstrip('_')).
groupby(['COMMON','variable']).value.std().unstack()
Out[18]:
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:30
W-B
99.7k73163
add a comment |
IIUC
df.melt('COMMON').assign(variable=lambda x : x['variable'].str.rstrip('_')).
groupby(['COMMON','variable']).value.std().unstack()
Out[18]:
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:30
W-B
99.7k73163
IIUC
df.melt('COMMON').assign(variable=lambda x : x['variable'].str.rstrip('_')).
groupby(['COMMON','variable']).value.std().unstack()
Out[18]:
variable A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:30
W-B
99.7k73163
answered Nov 12 at 14:30
W-B
99.7k73163
answered Nov 12 at 14:30
W-B
99.7k73163
answered Nov 12 at 14:30
W-B
99.7k73163
99.7k73163
add a comment |
add a comment |
Just groupby
h = lambda x: x[-1][0]
df.set_index('COMMON', append=True).stack().groupby(['COMMON', h]).std().unstack()
A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:56
piRSquared
151k22143285
add a comment |
Just groupby
h = lambda x: x[-1][0]
df.set_index('COMMON', append=True).stack().groupby(['COMMON', h]).std().unstack()
A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:56
piRSquared
151k22143285
add a comment |
Just groupby
h = lambda x: x[-1][0]
df.set_index('COMMON', append=True).stack().groupby(['COMMON', h]).std().unstack()
A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:56
piRSquared
151k22143285
Just groupby
h = lambda x: x[-1][0]
df.set_index('COMMON', append=True).stack().groupby(['COMMON', h]).std().unstack()
A B
COMMON
a 3.593976 2.217356
b 3.593976 2.081666
answered Nov 12 at 14:56
piRSquared
151k22143285
edited Nov 12 at 15:06
answered Nov 12 at 14:56
piRSquared
151k22143285
answered Nov 12 at 14:56
piRSquared
151k22143285
answered Nov 12 at 14:56
piRSquared
151k22143285
151k22143285
add a comment |
add a comment |
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