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    "body": "#### Code Sample, a copy-pastable example if possible\r\n\r\nFirst noted in discussion here: https://github.com/quantopian/zipline/pull/1190/files/fe5a2a888a498d838c3cc43de2c33ac08b20d2a7#r62506342.  This most often matters when passing a value typed in Cython as something like `int64_t[:]`.\r\n\r\nSimple repro cases:\r\n\r\nDatetimeIndex construction fails (looks like trying to parse strings?), and vanilla Index construction returns an array of unicode strings.\r\n```\r\nIn [6]: import pandas as pd; import numpy as np\r\n\r\nIn [7]: pd.__version__, np.__version__\r\nOut[7]: ('0.18.1+20.gaf7bdd3', '1.11.0')\r\n\r\nIn [8]: m = memoryview(np.arange(5))\r\n\r\nIn [9]: pd.DatetimeIndex(m)\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-9-09b855a663a4> in <module>()\r\n----> 1 pd.DatetimeIndex(m)\r\n\r\n/home/ssanderson/clones/pandas/pandas/util/decorators.pyc in wrapper(*args, **kwargs)\r\n     89                 else:\r\n     90                     kwargs[new_arg_name] = new_arg_value\r\n---> 91             return func(*args, **kwargs)\r\n     92         return wrapper\r\n     93     return _deprecate_kwarg\r\n\r\n/home/ssanderson/clones/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)\r\n    283                 data = tslib.parse_str_array_to_datetime(data, freq=freq,\r\n    284                                                          dayfirst=dayfirst,\r\n--> 285                                                          yearfirst=yearfirst)\r\n    286             else:\r\n    287                 data = tools.to_datetime(data, errors='raise')\r\n\r\n/home/ssanderson/clones/pandas/pandas/tslib.pyx in pandas.tslib.parse_str_array_to_datetime (pandas/tslib.c:39610)()\r\n   2175                         except ValueError:\r\n   2176                             if is_coerce:\r\n-> 2177                                 iresult[i] = NPY_NAT\r\n   2178                                 continue\r\n   2179                             raise\r\n\r\nValueError:\r\n\r\nIn [10]: pd.Index(m)\r\nOut[10]: Index([u'', u'', u'', u'', u''], dtype='object')\r\n```\r\n\r\n#### Expected Output\r\n\r\nI'd expect this to either error immediately or provide the same result as coercing the provided memoryview into a numpy array.\r\n\r\n#### output of ``pd.show_versions()``\r\n\r\n```\r\nIn [6]: pd.show_versions()\r\n\r\nINSTALLED VERSIONS\r\n------------------\r\ncommit: af7bdd3883c8d61e9d9388d3aa699930eee7fff8\r\npython: 2.7.10.final.0\r\npython-bits: 64\r\nOS: Linux\r\nOS-release: 4.2.0-16-generic\r\nmachine: x86_64\r\nprocessor: x86_64\r\nbyteorder: little\r\nLC_ALL: None\r\nLANG: en_US.UTF-8\r\n\r\npandas: 0.18.1+20.gaf7bdd3\r\nnose: None\r\npip: 8.1.0\r\nsetuptools: 20.2.2\r\nCython: 0.24\r\nnumpy: 1.11.0\r\nscipy: None\r\nstatsmodels: None\r\nxarray: None\r\nIPython: 4.2.0\r\nsphinx: None\r\npatsy: None\r\ndateutil: 2.5.3\r\npytz: 2016.3\r\nblosc: None\r\nbottleneck: None\r\ntables: None\r\nnumexpr: None\r\nmatplotlib: None\r\nopenpyxl: None\r\nxlrd: None\r\nxlwt: None\r\nxlsxwriter: None\r\nlxml: None\r\nbs4: None\r\nhtml5lib: None\r\nhttplib2: None\r\napiclient: None\r\nsqlalchemy: None\r\npymysql: None\r\npsycopg2: None\r\njinja2: None\r\nboto: None\r\npandas_datareader: None\r\n```\r\n\r\ncc @jbredeche"

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    "body": "From SO: http://stackoverflow.com/questions/37106810/bug-in-python-pandas-pandas-series-resample-vs-pandas-dataframe-resample\r\n\r\n```\r\nIn [7]: import datetime as dt\r\n\r\nIn [8]: df = pd.DataFrame([[dt.datetime(2016,1,31,15)], [dt.datetime(2016,2,7,17)]], index=[dt.datetime(2016,1,31,15), dt.datetime(2016,2,7,17)], columns=['timestamp'])\r\n\r\nIn [10]: df\r\nOut[10]:\r\n                              timestamp\r\n2016-01-31 15:00:00 2016-01-31 15:00:00\r\n2016-02-07 17:00:00 2016-02-07 17:00:00\r\n\r\nIn [11]: df.resample('D').first().timestamp\r\nOut[11]:\r\n2016-01-31   2016-01-31 15:00:00\r\n2016-02-01                   NaT\r\n2016-02-02                   NaT\r\n2016-02-03                   NaT\r\n2016-02-04                   NaT\r\n2016-02-05                   NaT\r\n2016-02-06                   NaT\r\n2016-02-07   2016-02-07 17:00:00\r\nFreq: D, Name: timestamp, dtype: datetime64[ns]\r\n\r\nIn [12]: df.timestamp.resample('D').first()\r\nOut[12]:\r\n2016-01-31    1.454252e+18\r\n2016-02-01             NaN\r\n2016-02-02             NaN\r\n2016-02-03             NaN\r\n2016-02-04             NaN\r\n2016-02-05             NaN\r\n2016-02-06             NaN\r\n2016-02-07    1.454864e+18\r\nFreq: D, Name: timestamp, dtype: float64\r\n\r\n```\r\n\r\nTested it with latest master"

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