Rolling Data Frames Don't Include Index Values

[ffanatic13]

t = pd.DataFrame(np.random.uniform(0,1,[100,100]),index=np.random.uniform(0,1,100)).rolling(10).apply(lambda x: x.argmax()).tail()``
The above code shows only integer values. However, the index defined is solely in floats (no integers).

      0    1    2    3    4    5    6    7    8    9  ...

0.886683 1.0 4.0 1.0 7.0 3.0 6.0 2.0 7.0 5.0 4.0 ...
0.932133 9.0 3.0 0.0 6.0 2.0 9.0 1.0 6.0 4.0 3.0 ...
0.555330 8.0 2.0 4.0 5.0 9.0 8.0 0.0 5.0 3.0 2.0 ...
0.369954 7.0 1.0 3.0 4.0 8.0 7.0 5.0 4.0 2.0 1.0 ...
0.793682 6.0 0.0 2.0 3.0 7.0 9.0 4.0 3.0 1.0 0.0 ...

The reason here is that what gets passed into the rolling data frame is a ndarray and not a data frame so access to the index isn't possible. This limits the rolling function's usefulness especially when dealing with time series data where the index can matter a lot.

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

pandas: 0.20.3
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[jreback]

not really sure what you are asking can you update the top post to be more clear, showing what it does now and what you think it should do

[ffanatic13]

Is this clearer? Am I expecting too much from this?

[jreback]

duplicate of #5071 and in 0.23 via #20584