groupby agg with rank and parameter return does not reduce
Sample with **Multiindex** in columns:

    df = pd.DataFrame({('r', 'c'): {(1, '2016-11-01 00:00:00+00:00', 3121): 143, (1, '2016-11-01 00:00:00+00:00', 4880): 12, (1, '2016-11-01 00:00:00+00:00', 3953): 4, (1, '2016-11-01 00:00:00+00:00', 3923): 11}})
    df.index.names = ['z','x','y']  
    print (df)
                                        r
                                        c
    z x                         y        
    1 2016-11-01 00:00:00+00:00 3121  143
                                3923   11
                                3953    4
                                4880   12
                                
    x = 'x'
    y = 'y'
    
    #works perfect
    print (df.groupby(level=[x, y]).agg({('r', 'c'): 'rank'}))
    
    print (df.groupby(level=[x, y]).agg({('r', 'c'): lambda x: x.rank(ascending=False)}))
    #ValueError: Function does not reduce

Problem is with **non MultiIndex** columns also:

    df = pd.DataFrame({'A':[1,1,3,3],
                       'B':[4,5,6,1]})
    
    print (df)
       A  B
    0  1  4
    1  1  5
    2  3  6
    3  3  1
    
    print (df.groupby('A').agg({'B': 'rank'}))
         B
    0  1.0
    1  2.0
    2  2.0
    3  1.0
    print (df.groupby('A').agg({'B': lambda x: x.rank()}))
    #Exception: Must produce aggregated value
    print (df.groupby('A').agg({'B': lambda x: x.rank(ascending=False)}))
    #Exception: Must produce aggregated value

Problem is how can I use function with parameter in `agg` function? [SO question](http://stackoverflow.com/questions/40785476/pandas-rank-specify-order)

    print (pd.show_versions())
    INSTALLED VERSIONS
    ------------------
    commit: None
    python: 3.5.1.final.0
    python-bits: 64
    OS: Windows
    OS-release: 7
    machine: AMD64
    processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
    byteorder: little
    LC_ALL: None
    LANG: sk_SK
    LOCALE: None.None
    
    pandas: 0.19.1
    nose: 1.3.7
    pip: 8.1.1
    setuptools: 20.3
    Cython: 0.23.4
    numpy: 1.11.0
    scipy: 0.17.0
    statsmodels: 0.6.1
    xarray: None
    IPython: 4.1.2
    sphinx: 1.3.1
    patsy: 0.4.0
    dateutil: 2.5.1
    pytz: 2016.2
    blosc: None
    bottleneck: 1.0.0
    tables: 3.2.2
    numexpr: 2.5.2
    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: 0.999
    httplib2: None
    apiclient: None
    sqlalchemy: 1.0.12
    pymysql: None
    psycopg2: None
    jinja2: 2.8
    boto: 2.39.0
    pandas_datareader: 0.2.1
    None
    
Thank you for pandas and for your perfect documentation.