`Series.resample().nlargest` produces incorrect output

Code Sample, a copy-pastable example if possible

With this setup:

import numpy as np
n = 1000
dates = pd.date_range(start='2010-01-01', periods=n)
rain_random = pd.Series(data=np.random.uniform(size=n), index=dates)

these two operations given different results:

rain_random.groupby(rain_random.index.year).nlargest(3)

rain_random.resample('A').nlargest(3)

Problem description

The Series.resample().nlargest() operation is inconsistent with DataFrame.resample()[column].nlargest() and the groupby equivalent. It emits a warning

Output:

/Users/schofield/miniconda/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 
.resample() is now a deferred operation
You called nlargest(...) on this deferred object which materialized it into a series
by implicitly taking the mean.  Use .resample(...).mean() instead
  """Entry point for launching an IPython kernel.
Out[427]:
2010-12-31    0.507550
2012-12-31    0.490082
2011-12-31    0.478356
dtype: float64

Expected output:

Date        Date      
1930-12-31  1930-10-06      288.135370
            1930-10-05      285.587734
            1930-10-07      259.439935
            1930-10-08      227.587389
            1930-10-09      190.054844
1931-12-31  1931-01-26     3052.104566
            1931-01-25     2839.126102
            1931-01-29     2196.167129
            1931-02-01     1953.331709
            1931-01-27     1893.975328
1932-12-31  1932-01-19     9526.953864
            1932-01-20     4278.291105
            1932-03-03     2952.348903
            1932-03-02     2946.385433
            1932-03-04     2098.108897

pd.show_versions() output:

INSTALLED VERSIONS

commit: None python: 3.6.1.final.0 python-bits: 64 OS: Darwin OS-release: 16.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_AU.UTF-8 LOCALE: en_AU.UTF-8

pandas: 0.20.1 pytest: 3.0.7 pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.12.1 scipy: 0.19.0 xarray: None IPython: 5.3.0 sphinx: 1.6.3 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2017.2 blosc: None bottleneck: 1.2.1 tables: 3.4.2 numexpr: 2.6.2 feather: None matplotlib: 2.0.2 openpyxl: 2.4.7 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.6 lxml: 3.7.3 bs4: 4.6.0 html5lib: 0.999 sqlalchemy: 1.1.9 pymysql: None psycopg2: None jinja2: 2.9.6 s3fs: None pandas_gbq: None pandas_datareader: 0.5.0

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