Code Sample, a copy-pastable example if possible
import pandas as pd import numpy as np indices = pd.date_range('2016-01-01 00:00:00+0200', freq='S', periods=10) np.split(indices, indices_or_sections=[]) Out: [DatetimeIndex(['2015-12-31 20:00:00+02:00', '2015-12-31 20:00:01+02:00', '2015-12-31 20:00:02+02:00', '2015-12-31 20:00:03+02:00', '2015-12-31 20:00:04+02:00', '2015-12-31 20:00:05+02:00', '2015-12-31 20:00:06+02:00', '2015-12-31 20:00:07+02:00', '2015-12-31 20:00:08+02:00', '2015-12-31 20:00:09+02:00'], dtype='datetime64[ns, pytz.FixedOffset(120)]', freq='S')]
Expected Output
[DatetimeIndex(['2016-01-01 00:00:00+02:00', '2016-01-01 00:00:01+02:00', '2016-01-01 00:00:02+02:00', '2016-01-01 00:00:03+02:00', '2016-01-01 00:00:04+02:00', '2016-01-01 00:00:05+02:00', '2016-01-01 00:00:06+02:00', '2016-01-01 00:00:07+02:00', '2016-01-01 00:00:08+02:00', '2016-01-01 00:00:09+02:00'], dtype='datetime64[ns, pytz.FixedOffset(120)]', freq='S')]
output of pd.show_versions()
In [16]: pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.6.final.0 python-bits: 64 OS: Linux OS-release: 4.2.0-38-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.18.1 nose: 1.3.7 pip: 8.1.2 setuptools: 25.2.0 Cython: 0.24.1 numpy: 1.11.1 scipy: 0.18.0 statsmodels: None xarray: None IPython: 1.2.1 sphinx: None patsy: None dateutil: 2.5.3 pytz: 2016.6.1 blosc: None bottleneck: None tables: 3.1.1 numexpr: 2.6.1 matplotlib: 1.3.1 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.999 httplib2: 0.8 apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None boto: 2.42.0 pandas_datareader: None
