Series.resample('1S', how='last') on series with dtype=datetime64[ns] is very slow

[yonil7]

example test:

intSeries = pd.Series(5, pd.date_range(start='2000-01-01', end='2000-01-05', freq='555000U'), dtype='int64')
timeSeries = intSeries.astype('datetime64[ns]')

time0 = time.time()
intSeries.resample('1S', how='last')
print 'resampling the int series took ', str(timedelta(seconds = time.time()-time0)), '(H:M:S)'

time0 = time.time()
timeSeries.resample('1S', how='last')
print 'resampling the datetime64[ns] series took ', str(timedelta(seconds = time.time()-time0)), '(H:M:S)'

output shows 14sec vs 0.5sec:
resampling the int series took 0:00:00.054000 (H:M:S)
resampling the datetime64[ns] series took 0:00:14.350000 (H:M:S)

[jreback]

yep...seems to be not taking a fast path. FYI, pls always post pd.show_versions(). welcome a pull-request to fix.

[yonil7]

INSTALLED VERSIONS

commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.14.0
nose: 1.3.0
Cython: 0.19.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 1.0.0
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.6
pymysql: None
psycopg2: None

[jreback]

closed by #10057