Closed
Description
NaNs in datetime64 data values GREATLY reduce performance of Categorical(values, cats):
import pandas as pd
tmp=pd.Series(pd.DatetimeIndex(pd.np.datetime64('1995-01-01 00:00')+i for i in range(1000000)))
to = tmp.astype('category')
cats = to.cat.categorical._categories.values
%timeit pd.Categorical(tmp.values, cats)
1 loops, best of 3: 250 ms per loop
tmp[500000] = pd.NaT
%timeit pd.Categorical(tmp.values, cats)
1 loops, best of 3: 10.1 s per loop
tmp[tmp.isnull()] = pd.np.datetime64('0')
%timeit pd.Categorical(tmp.values, cats)
1 loops, best of 3: 251 ms per loop
Small issue with printing Categorical datetime64:
ds = pd.Categorical([pd.np.datetime64("2014-01-01"), pd.NaT])
>>> ds
[2014-01-01, 2014-01-01] <--- NO, -1 is NOT a category:-)
Categories (1, datetime64[ns]): [2014-01-01]
>>> ds.astype('datetime64')
array(['2014-01-01T03:00:00.000000000+0300', 'NaT'], dtype='datetime64[ns]')
Versions:
commit: None
python: 3.4.3.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.7.1
Cython: 0.23.4
numpy: 1.9.3
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.3
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: 1.2.8
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: 2.8