Rolling apply on DataFrame with Datetime index returns NaN

[cafischer]

Code Sample, a copy-pastable example if possible

import pandas as pd

df = pd.DataFrame({'A': [0, 1, 2, 3, 4]},
                  index=[pd.Timestamp('20130101 09:00:00'),
                         pd.Timestamp('20130101 09:00:01'),
                         pd.Timestamp('20130101 09:00:02'),
                         pd.Timestamp('20130101 09:00:03'),
                         pd.Timestamp('20130101 09:00:04')])
rolled = df.rolling('3s', 3)

print rolled.sum()
print rolled.apply(sum)

outputs

                       A
2013-01-01 09:00:00  NaN
2013-01-01 09:00:01  NaN
2013-01-01 09:00:02  3.0
2013-01-01 09:00:03  6.0
2013-01-01 09:00:04  9.0

                      A
2013-01-01 09:00:00 NaN
2013-01-01 09:00:01 NaN
2013-01-01 09:00:02 NaN
2013-01-01 09:00:03 NaN
2013-01-01 09:00:04 NaN

Problem description

rolled.apply.sum() should give the same output as rolled.apply(sum) but instead only returns nan.

Expected Output

same as rolled.sum()

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 2.7.12.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-59-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None

pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 20.7.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: 2.38.0
pandas_datareader: None

[jreback]

well .apply rolling on time-specified windows isn't tested very well :<

The issue is that the counts computation is using the fixed interval one (see a few lines above). Instead it needs to bascially call counts = roll_count(...) more or les.

see https://github.com/pandas-dev/pandas/blob/master/pandas/window.pyx#L1362

pull-requests are welcome!.

note needs testing with multiple min_periods as well.

[staftermath]

The above link is broken.
see https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/window.pyx#L1412