Issue 1:
Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

s = pd.Series([1, 2, 3, np.nan, 5], index=pd.date_range('2017-01-01', '2017-01-05'))
print(s.rolling('3d', min_periods=1).apply(lambda x: 42))

Problem description

Output of the above code sample:

2017-01-01 42.0
2017-01-02 42.0
2017-01-03 42.0
2017-01-04 NaN
2017-01-05 42.0

It seems that the user function did not get applied to the window corresponding to the original NaN row, resulting in NaN as the result for that row. Why is that? The more reasonable output would be all 42 because by the min_periods constraint, all of the windows are valid.

Compare this to the equivalent fixed window version:

s = pd.Series([1, 2, 3, np.nan, 5])
print(s.rolling(3, min_periods=1).apply(lambda x: 42))

which gives the following output:

0 42.0
1 42.0
2 42.0
3 42.0
4 42.0

Is this inconsistency between fixed and variable size window a desired behavior?

Expected Output

2017-01-01 42.0
2017-01-02 42.0
2017-01-03 42.0
2017-01-04 42.0
2017-01-05 42.0

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None

python: 2.7.13.final.0

python-bits: 64

OS: Linux

OS-release: 4.11.9-1-ARCH

machine: x86_64

processor:

byteorder: little

LC_ALL: None

LANG: en_US.UTF-8

LOCALE: None.None

pandas: 0.21.0.dev+316.gf2b0bdc9b

pytest: None

pip: 9.0.1

setuptools: 36.2.5

Cython: 0.26

numpy: 1.13.1

scipy: None

pyarrow: None

xarray: None

IPython: 5.4.1

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: None

sqlalchemy: None

pymysql: None

psycopg2: None

jinja2: None

s3fs: None

pandas_gbq: None

pandas_datareader: None

Issue 2:
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