BUG: .nlargest with unsigned integers

Code Sample, a copy-pastable example if possible

pd.Series(np.array([0, 0, 0, 100, 1000, 10000, 100], dtype='uint32')).nlargest(5) 
0        0
1        0
2        0
5    10000
4     1000

Problem description

nlargest favours 0 above positive values. Common to both uint32 and uint64 types and possibly others.

Expected Output

5    10000
4     1000
3      100
6      100
0        0
dtype: uint32

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None python: 3.6.2.final.0 python-bits: 64 OS: Linux OS-release: 3.10.0-327.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8

pandas: 0.20.3 pytest: 3.2.1 pip: 9.0.1 setuptools: 36.4.0 Cython: None numpy: 1.13.1 scipy: 0.19.1 xarray: None IPython: 6.1.0 sphinx: None patsy: 0.4.1 dateutil: 2.6.1 pytz: 2017.2 blosc: None bottleneck: None tables: 3.4.2 numexpr: 2.6.2 feather: None matplotlib: 2.0.2 openpyxl: 2.4.8 xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.9999999 sqlalchemy: 1.1.13 pymysql: 0.7.9.None psycopg2: None jinja2: 2.9.6 s3fs: None pandas_gbq: None pandas_datareader: None

[jschendel]

I suspect the issue is with this block of code: https://github.com/pandas-dev/pandas/blob/480790531ffcc4329f280ddf6877d028d08e969f/pandas/core/algorithms.py#L1136-L1138

Specifically for uint data, I don’t think -arr behaves as intended:

In [2]: arr = np.array([0, 0, 0, 100, 1000, 10000, 100], dtype='uint64')

In [3]: -arr
Out[3]:
array([                   0,                    0,                    0,
       18446744073709551516, 18446744073709550616, 18446744073709541616,
       18446744073709551516], dtype=uint64)

[gfyoung]

Sigh…that’s symptomatic of the same overflow issue presented with uint. Good catch!