Pandas  series.rank(ascending=False) get rise to SIGSEGV  error
I read dataframe from hdf5 file and rank it. but It rise to a SIGSEGV. 
Maybe it's  a numpy or table bug.. 
#### Code Sample, a copy-pastable example if possible

import pandas as pd
def test_df_ranks(f):
    df = pd.read_hdf(f, key="t")
    print (df.shape)
    print (type(df))
    print (df)
    s=df.non_current_asset_to_total_asset
    #s.rank()     # rank() work properly 
    s.rank(ascending=False)  #rank(ascending=False) crash 
#### Expected Output

I expected work well, but rise to me  SIGSEGV error 
the stacks as follows.
#7  OBJECT_compare (ip1=0x47a3ef4b2e420, ip2=0x7f5c5413f128, __NPY_UNUSED_TAGGEDap=0x7f5cd0100760) at numpy/core/src/multiarray/arraytypes.c.src:2753
#8  0x00007f5d0142c50e in npy_aquicksort (vv=vv@entry=0x7f5c5413f060, tosort=tosort@entry=0x7f5c5413cc80, num=num@entry=52, varr=varr@entry=0x7f5cd0100760) at numpy/core/src/npysort/quicksort.c.src:480
#9  0x00007f5d0139a78a in _new_argsortlike (op=op@entry=0x7f5cd0100760, axis=0, argsort=argsort@entry=0x7f5d0142c310 <npy_aquicksort>, argpart=argpart@entry=0x0, kth=kth@entry=0x0, nkth=nkth@entry=0)

at numpy/core/src/multiarray/item_selection.c:1035
#10 0x00007f5d0139dd7b in PyArray_ArgSort (op=op@entry=0x7f5cd0100760, axis=0, which=<optimized out>) at numpy/core/src/multiarray/item_selection.c:1309
#11 0x00007f5d013dd012 in array_argsort (self=0x7f5cd0100760, args=<optimized out>, kwds=<optimized out>) at numpy/core/src/multiarray/methods.c:1278
#12 0x00007f5cf4eef28f in __Pyx_PyObject_Call (func=0x7f5cd1a1acc8, arg=0x7f5d0f900048, kw=0x0) at pandas/algos.c:201388
#13 0x00007f5cf504e006 in __pyx_pf_6pandas_5algos_8rank_1d_generic (__pyx_v_in_arr=__pyx_v_in_arr@entry=0x7f5cd0100620, __pyx_v_retry=1, __pyx_v_ties_method=0x7f5cf6999768, __pyx_v_ascending=0x7f5d0f6bd700 <_Py_FalseStruct>,

__pyx_v_na_option=<optimized out>, __pyx_v_pct=0x7f5d0f6bd700 <_Py_FalseStruct>, __pyx_self=<optimized out>) at pandas/algos.c:14942
#14 0x00007f5cf5050481 in __pyx_pw_6pandas_5algos_9rank_1d_generic (__pyx_self=<optimized out>, __pyx_args=<optimized out>, __pyx_kwds=0x7f5cd8659488) at pandas/algos.c:14439
#15 0x00007f5d0f3b9477 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#16 0x00007f5d0f3b9f3e in PyEval_EvalCodeEx () from /lib64/libpython3.4m.so.1.0
#17 0x00007f5d0f3b7a12 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#18 0x00007f5d0f3b9f3e in PyEval_EvalCodeEx () from /lib64/libpython3.4m.so.1.0
#19 0x00007f5d0f3b7a12 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#20 0x00007f5d0f3b8e40 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#21 0x00007f5d0f3b9f3e in PyEval_EvalCodeEx () from /lib64/libpython3.4m.so.1.0
#22 0x00007f5d0f3b7a12 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#23 0x00007f5d0f3b9f3e in PyEval_EvalCodeEx () from /lib64/libpython3.4m.so.1.0
#24 0x00007f5d0f32a4b3 in function_call () from /lib64/libpython3.4m.so.1.0
#25 0x00007f5d0f301dcc in PyObject_Call () from /lib64/libpython3.4m.so.1.0
#26 0x00007f5d0f3b57c9 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#### output of `pd.show_versions()`

 pd.show_versions()
## INSTALLED VERSIONS

commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 14.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.17.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: 0.6.7.None
psycopg2: None
Jinja2: None

The  h5 data in my github 
[https://github.com/HaoXJ/codefail/blob/master/data/test.h5](url)
