python - Pandas, groupby where column value is greater than x -

i have table this

    timestamp   avg_hr  hr_quality  avg_rr  rr_quality  activity    sleep_summary_id      1422404668  66      229             0       0           13              78     1422404670  64      223             0       0           20              78     1422404672  64      216             0       0           11              78     1422404674  66      198             0       40          9               78     1422404676  65      184             0       30          3               78     1422404678  64      173             0       10          17              78     1422404680  66      199             0       20          118             78 

i'm trying group data timestamp,sleep id , rr_quality, rr_quality > 0

i've tried following , none of them seems work

 df3 = df2.groupby([df2.index.hour,'sleep_summary_id',df2['rr_quality']>0])   df3 = df2.groupby([df2.index.hour,'sleep_summary_id','rr_quality'>0])   df3 = df2.groupby([df2.index.hour,'sleep_summary_id',['rr_quality']>0]) 

all of them returns keyerror.

edit:

also can't seem able pass more 1 filter @ time. tried following:

df2[df2['rr_quality'] >= 150, df2['hr_quality'] > 200] df2[df2['rr_quality'] >= 150, ['hr_quality'] > 200] df2[[df2['rr_quality'] >= 150, ['hr_quality'] > 200]] 

returns: typeerror: 'series' objects mutable, cannot hashed

the simplest thing here filter df first , perform groupby:

df2[df2['rr_quality'] > 0]].groupby([df2.index.hour,'sleep_summary_id') 

edit

if you're intending assign original df:

df2.loc[df2['rr_quality'] > 0, 'avg_hr'] = df2[df2['rr_quality'] >= 150].groupby([df2.index.hour,'emfit_sleep_summary_id'])['avg_hr'].transform('mea‌​n') 

the loc call mask lhs result of transform aligns correctly

to filter using multiple conditions need use array comparision operators &, | , ~ and, or , not respectively, additionally need wrap conditions in parentheses due operator precedence:

df2[(df2['rr_quality'] >= 150) & (df2['hr_quality'] > 200)]