Resampling on index and column after groupby give different results

[danguetta]

Code Sample, a copy-pastable example if possible

This should be copy-pastable and self-contained:

import pandas as pd

get_df = lambda : pd.DataFrame( {'DATETIME' : pd.to_datetime(['2018-01-01 11:25:00', '2018-01-01 11:50:00', '2018-01-03 10:30:00'
                                                    , '2018-01-04 10:25:00']*2),
                                 'GROUP'    : ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                                 'FILTER'   : [True, True, True, True, False, False, True, True],
                                 'X'        : [1, 2, 3, 4, 5, 6, 7, 8]} )

df = get_df()
df = df.set_index('DATETIME')
df.groupby('GROUP').resample('D').X.sum()                               # <-- LINE A
# Returns
# -------
# GROUP  DATETIME  
# A      2018-01-01     3
#        2018-01-02     0
#        2018-01-03     3
#        2018-01-04     4
# B      2018-01-01    11
#        2018-01-02     0
#        2018-01-03     7
#        2018-01-04     8
# Name: X, dtype: int64

df = get_df()
df.groupby('GROUP').resample('D', on = 'DATETIME').X.sum()               # <-- LINE B
# Returns
# -------
# GROUP  DATETIME  
# A      2018-01-01    10
# B      2018-01-03    11
#        2018-01-04    15
# Name: X, dtype: int64

df = get_df()
df = df.set_index('DATETIME')
df[df.FILTER].groupby('GROUP').resample('D').X.sum()                     # <-- LINE C
# Returns
# -------
# GROUP  DATETIME  
# A      2018-01-01    3
#        2018-01-02    0
#        2018-01-03    3
#        2018-01-04    4
# B      2018-01-03    7
#        2018-01-04    8
# Name: X, dtype: int64

df = get_df()
df[df.FILTER].groupby('GROUP').resample('D', on = 'DATETIME').X.sum()   # <-- LINE D
# Error
# -----
#    IndexError: index 6 is out of bounds for size 6

Problem description

Lines A and B are identical except that one does a resample on an index, and one does it on an identical column.

Lines C and D are identical except that one does a resample on an index, and one does it on an identical column.

Yet, they behave differently!

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None

pandas : 0.25.3
numpy : 1.16.2
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.0
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8

Thanks!!

[rhshadrach]

A & B now give the same output on main (line A above); D still raises.