How can I get grouped windows in pandas? I.e. like WINDOW OVER … PARTITION BY … from SQL
Pandas window functions i.e. rolling work great. However coming from SQL I know, that windows can also be PARTITIONED BY some group.
How can I get grouped windows in pandas?
A:
df.groupby(['group']).rolling('10s').mean()
fails with:
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'
And if df.rolling('10s', on='group') is used, it only works in case 'group' is int i.e. in SQL some concrete number of preceding/following rows. How can I preserve the window by time?
edit minimal sample:
import random
groups = ['A', 'B']
df = pd.DataFrame({'value': range(60), 'group': [random.choice(groups) for i in range(60)]},index=pd.DatetimeIndex(pd.date_range(start='20160101', end='20160229')))
df.head()
The following works, but does not consider the groups:
df[['value']].rolling('2d').mean().head()
The following does not work for time windows:
df[['group','value']].rolling(3, on='group').mean().head()
and
df.rolling('2D', on='group').mean().head()
fails with: window must be an integer when trying to use a time window.
python pandas pandas-groupby window-functions partition-by
asked Nov 12 at 5:47
Georg Heiler
4,950551125
add a comment |
Pandas window functions i.e. rolling work great. However coming from SQL I know, that windows can also be PARTITIONED BY some group.
How can I get grouped windows in pandas?
A:
df.groupby(['group']).rolling('10s').mean()
fails with:
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'
And if df.rolling('10s', on='group') is used, it only works in case 'group' is int i.e. in SQL some concrete number of preceding/following rows. How can I preserve the window by time?
edit minimal sample:
import random
groups = ['A', 'B']
df = pd.DataFrame({'value': range(60), 'group': [random.choice(groups) for i in range(60)]},index=pd.DatetimeIndex(pd.date_range(start='20160101', end='20160229')))
df.head()
The following works, but does not consider the groups:
df[['value']].rolling('2d').mean().head()
The following does not work for time windows:
df[['group','value']].rolling(3, on='group').mean().head()
and
df.rolling('2D', on='group').mean().head()
fails with: window must be an integer when trying to use a time window.
python pandas pandas-groupby window-functions partition-by
asked Nov 12 at 5:47
Georg Heiler
4,950551125
add a comment |
Pandas window functions i.e. rolling work great. However coming from SQL I know, that windows can also be PARTITIONED BY some group.
How can I get grouped windows in pandas?
A:
df.groupby(['group']).rolling('10s').mean()
fails with:
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'
And if df.rolling('10s', on='group') is used, it only works in case 'group' is int i.e. in SQL some concrete number of preceding/following rows. How can I preserve the window by time?
edit minimal sample:
import random
groups = ['A', 'B']
df = pd.DataFrame({'value': range(60), 'group': [random.choice(groups) for i in range(60)]},index=pd.DatetimeIndex(pd.date_range(start='20160101', end='20160229')))
df.head()
The following works, but does not consider the groups:
df[['value']].rolling('2d').mean().head()
The following does not work for time windows:
df[['group','value']].rolling(3, on='group').mean().head()
and
df.rolling('2D', on='group').mean().head()
fails with: window must be an integer when trying to use a time window.
python pandas pandas-groupby window-functions partition-by
asked Nov 12 at 5:47
Georg Heiler
4,950551125
Pandas window functions i.e. rolling work great. However coming from SQL I know, that windows can also be PARTITIONED BY some group.
How can I get grouped windows in pandas?
A:
df.groupby(['group']).rolling('10s').mean()
fails with:
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'
And if df.rolling('10s', on='group') is used, it only works in case 'group' is int i.e. in SQL some concrete number of preceding/following rows. How can I preserve the window by time?
edit minimal sample:
import random
groups = ['A', 'B']
df = pd.DataFrame({'value': range(60), 'group': [random.choice(groups) for i in range(60)]},index=pd.DatetimeIndex(pd.date_range(start='20160101', end='20160229')))
df.head()
The following works, but does not consider the groups:
df[['value']].rolling('2d').mean().head()
The following does not work for time windows:
df[['group','value']].rolling(3, on='group').mean().head()
and
df.rolling('2D', on='group').mean().head()
fails with: window must be an integer when trying to use a time window.
python pandas pandas-groupby window-functions partition-by
python pandas pandas-groupby window-functions partition-by
asked Nov 12 at 5:47
Georg Heiler
4,950551125
asked Nov 12 at 5:47
Georg Heiler
4,950551125
edited Nov 12 at 6:23
asked Nov 12 at 5:47
Georg Heiler
4,950551125
asked Nov 12 at 5:47
Georg Heiler
4,950551125
asked Nov 12 at 5:47
Georg Heiler
4,950551125
4,950551125
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Drop the index so you can group by. I think the issue with the index column.
df.groupby(['col2','col3'], as_index=False)
answered Nov 12 at 5:53
AmilaMGunawardana
645
but if I drop the index, then I no longer can aggregate by time. I.e. your solution throws:window must be an integeras the independent no longer contains time information.
– Georg Heiler
Nov 12 at 5:58
then uses.groupby(level=['first','second'])in level specify the index columns
– AmilaMGunawardana
Nov 12 at 6:02
This does not work, as initially it is not a multi index, but only a time based index. i.e. The second column/levelgroupis not in the index.
– Georg Heiler
Nov 12 at 6:10
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Drop the index so you can group by. I think the issue with the index column.
df.groupby(['col2','col3'], as_index=False)
answered Nov 12 at 5:53
AmilaMGunawardana
645
but if I drop the index, then I no longer can aggregate by time. I.e. your solution throws:window must be an integeras the independent no longer contains time information.
– Georg Heiler
Nov 12 at 5:58
then uses.groupby(level=['first','second'])in level specify the index columns
– AmilaMGunawardana
Nov 12 at 6:02
This does not work, as initially it is not a multi index, but only a time based index. i.e. The second column/levelgroupis not in the index.
– Georg Heiler
Nov 12 at 6:10
add a comment |
Drop the index so you can group by. I think the issue with the index column.
df.groupby(['col2','col3'], as_index=False)
answered Nov 12 at 5:53
AmilaMGunawardana
645
but if I drop the index, then I no longer can aggregate by time. I.e. your solution throws:window must be an integeras the independent no longer contains time information.
– Georg Heiler
Nov 12 at 5:58
then uses.groupby(level=['first','second'])in level specify the index columns
– AmilaMGunawardana
Nov 12 at 6:02
This does not work, as initially it is not a multi index, but only a time based index. i.e. The second column/levelgroupis not in the index.
– Georg Heiler
Nov 12 at 6:10
add a comment |
Drop the index so you can group by. I think the issue with the index column.
df.groupby(['col2','col3'], as_index=False)
answered Nov 12 at 5:53
AmilaMGunawardana
645
Drop the index so you can group by. I think the issue with the index column.
df.groupby(['col2','col3'], as_index=False)
answered Nov 12 at 5:53
AmilaMGunawardana
645
answered Nov 12 at 5:53
AmilaMGunawardana
645
answered Nov 12 at 5:53
AmilaMGunawardana
645
answered Nov 12 at 5:53
AmilaMGunawardana
645
645
but if I drop the index, then I no longer can aggregate by time. I.e. your solution throws:window must be an integeras the independent no longer contains time information.
– Georg Heiler
Nov 12 at 5:58
then uses.groupby(level=['first','second'])in level specify the index columns
– AmilaMGunawardana
Nov 12 at 6:02
This does not work, as initially it is not a multi index, but only a time based index. i.e. The second column/levelgroupis not in the index.
– Georg Heiler
Nov 12 at 6:10
add a comment |
but if I drop the index, then I no longer can aggregate by time. I.e. your solution throws:window must be an integeras the independent no longer contains time information.
– Georg Heiler
Nov 12 at 5:58
then uses.groupby(level=['first','second'])in level specify the index columns
– AmilaMGunawardana
Nov 12 at 6:02
This does not work, as initially it is not a multi index, but only a time based index. i.e. The second column/levelgroupis not in the index.
– Georg Heiler
Nov 12 at 6:10
but if I drop the index, then I no longer can aggregate by time. I.e. your solution throws:
window must be an integer as the independent no longer contains time information.– Georg Heiler
Nov 12 at 5:58
but if I drop the index, then I no longer can aggregate by time. I.e. your solution throws:
window must be an integer as the independent no longer contains time information.– Georg Heiler
Nov 12 at 5:58
then use
s.groupby(level=['first','second']) in level specify the index columns– AmilaMGunawardana
Nov 12 at 6:02
then use
s.groupby(level=['first','second']) in level specify the index columns– AmilaMGunawardana
Nov 12 at 6:02
This does not work, as initially it is not a multi index, but only a time based index. i.e. The second column/level
group is not in the index.– Georg Heiler
Nov 12 at 6:10
This does not work, as initially it is not a multi index, but only a time based index. i.e. The second column/level
group is not in the index.– Georg Heiler
Nov 12 at 6:10
add a comment |
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