.unique fails with read-only input.

[allComputableThings]

Code Sample, a copy-pastable example if possible

arr = np.arange(10)
arr.setflags(write=False)
ser = pd.Series(arr, index=arr)
print ser.unique()

...

  File "/usr/local/lib/python2.7/dist-packages/pandas/core/algorithms.py", line 364, in unique
    uniques = table.unique(values)
  File "pandas/_libs/hashtable_class_helper.pxi", line 973, in pandas._libs.hashtable.Int64HashTable.unique
  File "stringsource", line 646, in View.MemoryView.memoryview_cwrapper
  File "stringsource", line 347, in View.MemoryView.memoryview.__cinit__
ValueError: buffer source array is read-only

Problem description

For no good reason, unique tries to modify the input and fails if the input data is read only.

Perhaps should simply delegate to np.unique which does not modify the input.

Expected Output

Operation should not modify the input. Is rude to the caller and will doubtless lead to inexplicable concurrency issues.

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 36.0.1
Cython: 0.27.3
numpy: 1.13.3
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: 0.2.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[jschendel]

This works on master for me, and I suspect it was fixed by #18825

In [2]: arr = np.arange(10)

In [3]: arr.setflags(write=False)

In [4]: ser = pd.Series(arr, index=arr)

In [5]: ser.unique()
Out[5]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)

In [6]: pd.__version__
Out[6]: '0.23.0.dev0+98.g8acdf80'

[jreback]

yep this is in 0.23.0

[jreback]

@stuz5000 note that this is actually a bug in cython
which pandas works around

[gabomgp]

I'll put this here, delete if is inappropiate. But, the question is related with this bug:

https://stackoverflow.com/questions/49619588/how-to-prevent-bug-https-jiasu.xzqcsaa.nyc.mn-pandas-dev-pandas-issues-19195-with-dese