BUG: time grouper differs when passes as a list and as a scalar · Issue #17530 · pandas-dev/pandas (original) (raw)

Code Sample, a copy-pastable example if possible

data_frame.groupby(pd.TimeGrouper('D', convention='start', key=time_column)).count() data_frame.groupby([pd.TimeGrouper('D', convention='start', key=time_column)]).count()

give below two different outputs

            location  price  profit  type
time                                     
2017-07-23         1      1       1     1
2017-07-24         0      0       0     0
2017-07-25         1      1       1     1
2017-07-26         1      1       1     1
2017-07-27         3      3       3     3
2017-07-28         6      6       6     6

            location  price  profit  type
time                                     
2017-07-23         1      1       1     1
2017-07-25         1      1       1     1
2017-07-26         1      1       1     1
2017-07-27         3      3       3     3
2017-07-28         6      6       6     6

Problem description

The origin data frame doesn't contain a record in 2017-07-24, you can see if I use time grouper with convention='start' lonely, the 2017-07-24 item has been filled, but if by group list, that item hasn't been filled

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 36.2.0
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_gbq: None
pandas_datareader: None