result from groupby / nlargest with data frame with one row does not include the groupby key in the resulting index

Code Sample, a copy-pastable example if possible

In [1]: df = pandas.DataFrame([["Dog", 1], ["Dog", 2]], columns=["animal", "value"])

In [2]: df.groupby("animal").value.nlargest(5)
Out[2]: 
animal   
Dog     1    2
        0    1
Name: value, dtype: int64
In [3]: df = pandas.DataFrame([["Dog", 1]], columns=["animal", "value"])
In [4]: df.groupby("animal").value.nlargest(5)
Out[4]: 
0    1
Name: value, dtype: int64

Problem description

Expected Output

In [3]: df = pandas.DataFrame([["Dog", 1]], columns=["animal", "value"])
In [4]: df.groupby("animal").value.nlargest(5)
Out[4]: 
animal
Dog      0    1
Name: value, dtype: int64

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None python: 3.5.3.final.0 python-bits: 64 OS: Linux machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8

pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.12.1 scipy: None statsmodels: None xarray: None IPython: 6.0.0 sphinx: None patsy: None dateutil: 2.6.0 pytz: 2017.2 blosc: None bottleneck: None tables: None numexpr: None matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: 4.5.3 html5lib: 0.999 httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.9.6 boto: None pandas_datareader: None

[phofl]

Hi, @jreback @mroeschke

I looked into this a bit. I found the reason, why the column is dropped sometimes and sometimes not. For functions like nlarges and apply (not head) the column is always dropped, if the input DataFrame equals the output DataFrame (sorting too!). It is checked, if the Index was changed. Depending on the result, different code is executed. While I tried to fix this, I ran into some issues with existing unittests.

If we do something like:

`` df = pd.DataFrame({“key”: [“b”] * 10, “value”: 2})

actual = df.groupby("key")["value"].cumprod()

``

Should the resulting Series have the column b in the Index or is

0    2
1    4
2    8
3    16
4    32
5    64
6    128
7    256
8    512
9    1024

Name: value, dtype: int64

the desired output?

Similar question: If we execute

`` base_df = pd.DataFrame({“A”: [1, 1, 1, 1, 2, 2, 2, 2], “B”: [np.nan] * 8})

expected = pd.DataFrame({"B": [np.nan] * 8})
result = base_df.groupby("A").cummax()

``

should the output Series look like

B
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN

or should the column A be part of the Index?

I would really appreciate an answert about the output format of these two functions. Depending on that I may have found a way to fix this issue and the issues related with this (#29129 for example). If the columns should not be part of the Index, the solution is more complex.

Thanks very much.

[rhshadrach]

@phofl - transformers should return the index of the original DataFrame.