I'm trying 3.6b4 on my mac (OS X 10.10.5) and when I run the following code  (after doing pip install numpy and pandas) I see a 'SystemError':

>>> import pandas as pd
>>> pd.to_datetime('10/31/2016')
ValueError: Error parsing datetime string "10/31/2016" at position 2

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/schouten/virtualenvs/tmp/lib/python3.6/site-packages/pandas/util/decorators.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/Users/schouten/virtualenvs/tmp/lib/python3.6/site-packages/pandas/tseries/tools.py", line 430, in to_datetime
    return _convert_listlike(np.array([arg]), box, format)[0]
  File "/Users/schouten/virtualenvs/tmp/lib/python3.6/site-packages/pandas/tseries/tools.py", line 401, in _convert_listlike
    require_iso8601=require_iso8601
  File "pandas/tslib.pyx", line 2302, in pandas.tslib.array_to_datetime (pandas/tslib.c:43278)
  File "pandas/tslib.pyx", line 2458, in pandas.tslib.array_to_datetime (pandas/tslib.c:41773)
  File "pandas/tslib.pyx", line 2416, in pandas.tslib.array_to_datetime (pandas/tslib.c:41009)
  File "pandas/src/datetime.pxd", line 141, in datetime._string_to_dts (pandas/tslib.c:90460)
SystemError: <class 'str'> returned a result with an error set
>>>

sys.version says:

>>> import sys
>>> sys.version
'3.6.0b4 (v3.6.0b4:18496abdb3d5, Nov 21 2016, 20:44:47) \n[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]'
>>> 



With 3.5 it works as expected:

>>> import sys
>>> sys.version
'3.5.1 (v3.5.1:37a07cee5969, Dec  5 2015, 21:12:44) \n[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]'
>>> import pandas as pd
>>> pd.to_datetime('10/31/2016')
Timestamp('2016-10-31 00:00:00')
>>> 


I know it looks like pandas might be at fault, but given that the only thing I've changed is the Python version, I'm thinking it might be exposing a 3.6 bug (or vice versa, I suppose).


My apologies if I'm doing this all wrong, I'm new around here.

Thanks for the report. This problem was reported previously in Issue28636 where it was determined that the problem is in Pandas (https://github.com/pandas-dev/pandas/issues/14561).  It looks like the Pandas project has pushed a fix for it.

Thanks for the quick response!