Rolling(axis='columns').count() ignores axis= keyword

[mrocklin]

addtl example on #13753

Code Sample, a copy-pastable example if possible

In [1]: import pandas as pd

In [2]: df = pd.DataFrame({'x': range(5), 'y': range(5)})

In [3]: df
Out[3]: 
   x  y
0  0  0
1  1  1
2  2  2
3  3  3
4  4  4

In [4]: df.rolling(2, axis='columns').sum()  # meets expectations
Out[4]: 
    x    y
0 NaN  0.0
1 NaN  2.0
2 NaN  4.0
3 NaN  6.0
4 NaN  8.0

In [5]: df.rolling(2, axis='columns').count()  # appears to be the same as axis='rows'
Out[5]: 
     x    y
0  1.0  1.0
1  2.0  2.0
2  2.0  2.0
3  2.0  2.0
4  2.0  2.0

In [6]: df.rolling(2, axis='rows').count()  # yeah, exactly the same
Out[6]: 
     x    y
0  1.0  1.0
1  2.0  2.0
2  2.0  2.0
3  2.0  2.0
4  2.0  2.0

output of pd.show_versions()

In [7]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None

Arose from tests in dask/dask#1280

[jreback]

yes .count() is one of the few methods that is implemented in a special way so it takes a different path and is not using the .axis arg :<

[mrocklin]

Should I consider this a bug or is count(axis=) not supported?

[jreback]

no, its a bug.

[jorisvandenbossche]

Actually, using axis=1 seems broken for the other methods (which use _apply) as well if you have different dtypes:

In [49]: df = pd.DataFrame({'x': range(5), 'y': np.arange(0, 1, 0.2)})

In [50]: df
Out[50]:
   x    y
0  0  0.0
1  1  0.2
2  2  0.4
3  3  0.6
4  4  0.8

In [51]: df.rolling(2, axis=1).sum()
Out[51]:
    x   y
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN

Because the _apply does this for each block of uniform dtype, which is fine for axis=0, but not for axis=1.

[yhaque1213]

I'm interested in helping out with this! Can I work on it?