BUG: Rolling count aggregation produces unexpected results

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>>> pd.__version__
'1.1.0.dev0+1690.g70d7c04ff'
>>> pd.Series([1,1,1,None]).rolling(2, min_periods=2, center=True).count()
0    NaN
1    2.0
2    2.0
3    1.0
dtype: float64

Problem description

Apologies if this is expected behavior and not a bug. I noticed that prior to version 1.0, rolling.count ignored the min_periods parameter, as discussed in https://github.com/pandas-dev/pandas/pull/30923. However I’m having trouble understanding this output. Shouldn’t the last element of the output be NaN, given that the final window includes the last and second to last elements, of which only one is valid?

Expected Output

0    NaN
1    2.0
2    2.0
3    NaN
dtype: float64

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 70d7c04ff585de361622e4fe1788480a7a4526b5 python : 3.8.3.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-76-generic Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.1.0.dev0+1690.g70d7c04ff numpy : 1.18.1 pytz : 2020.1 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.4.0.post20200518 Cython : 0.29.17 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None

[bashtage]

Minimum number of observations in window required to have a value

Having a value means not-missing, e.g, not null.

[toobaz]

I’m in favor of consistency with other methods. I understand that

Keeping this behavior would give more useful results for users, especially considering count method is meant for counting non-NA values.

… but counting non-NA values, is as easy as setting min_periods=0, right? Vice-versa, counting the number of non-NA values whenever there are at least a minimum number of non-NA values is the goal of min_periods, and it is more annoying to do otherwise.