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Description
xref to #7307
Converting from datetime64[ns] to datetime64[m] is disallowed, as mentioned in issue #3416, apparently because of a numpy bug. It looks to me like numpy does this correctly, however. It would be nice if this could work without resorting to raw numpy arrays:
In [89]: s = pd.Series(['20140330','19740821'])
In [90]: pd.to_datetime(s)
Out[90]:
0 2014-03-30
1 1974-08-21
dtype: datetime64[ns]
In [91]: pd.to_datetime(s).values
Out[91]:
array(['2014-03-30T03:00:00.000000000+0300',
'1974-08-21T03:00:00.000000000+0300'], dtype='datetime64[ns]')
In [92]: pd.to_datetime(s).values.astype('datetime64[h]')
Out[92]: array(['2014-03-30T03+0300', '1974-08-21T03+0300'], dtype='datetime64[h]')My other workaround, which is slightly less clumsy, is do just convert manually, since casting to int64 is permitted:
In [103]: pd.to_datetime(s).astype('int64').div(6e10) # minutes since epochBut it'd be nicer not to have to do all that.
In [93]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20
numpy: 1.8.0
scipy: None
statsmodels: None
IPython: 1.2.1
sphinx: 1.1.3
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: 0.7.4
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: 0.999
bq: None
apiclient: NoneMetadata
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Datetime data dtypeDatetime data dtypeUnexpected or buggy dtype conversionsUnexpected or buggy dtype conversionsTimedelta data typeTimedelta data type