When creating a daterange with a decimal number of minutes, pandas truncates everything before the decimal point. This leads to odd results e.g. if you're expecting to generate something every half minute and instead end up with something every 5 minutes, and even odder results if you have a float that is a whole number.

A less contrived example is if pandas is used to process the results of some API call where you don't know what unit you'll have to go to to get to a whole number. It would be safe, I guess, to convert everything to nanoseconds, but not especially readable.

A small, complete example of the issue

>>> import pandas as pd
>>> pd.date_range(end='2016-10-07T21:08:58.490180', periods=3, freq='0.5min')
DatetimeIndex(['2016-10-07 20:58:58.490180', '2016-10-07 21:03:58.490180',
               '2016-10-07 21:08:58.490180'],
              dtype='datetime64[ns]', freq='5T')
>>> pd.date_range(end='2016-10-07T21:08:58.490180', periods=3, freq='10080.0min')
DatetimeIndex([], dtype='datetime64[ns]', freq='0T')

Expected Output

For the first example: DatetimeIndex(['2016-10-07 21:07:58.490180', '2016-10-07 21:08:28.490180', '2016-10-07 21:08:58.490180'], dtype='datetime64[ns]', freq='30S')

For the second: DatetimeIndex(['2016-09-23 21:08:58.490180', '2016-09-30 21:08:58.490180', '2016-10-07 21:08:58.490180'], dtype='datetime64[ns]', freq='10080T')

Output of pd.show_versions()

## INSTALLED VERSIONS commit: None python: 2.7.6.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-38-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.18.1 nose: 1.3.1 pip: 1.5.4 setuptools: 26.0.0 Cython: None numpy: 1.11.1 scipy: 0.17.1 statsmodels: None xarray: None IPython: 5.1.0 sphinx: None patsy: None dateutil: 2.5.3 pytz: 2016.4 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.3.1 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.999 httplib2: 0.9.2 apiclient: None sqlalchemy: 1.0.15 pymysql: None psycopg2: 2.6.2 (dt dec pq3 ext lo64) jinja2: 2.8 boto: 2.41.0 pandas_datareader: None

Comment From: jisantuc

After following traceback I don't think this would be hard to fix, so I'm happy to do that if it's agreed that what I expected is a reasonable expectation.

Comment From: jreback

this is a duplicate of https://github.com/pydata/pandas/issues/8419

welcome a fix for that (you can use your example and the other as examples).