Pandas to_timedelta rounds to microseconds unless unit is us or ns

Code Sample

>>> pd.to_timedelta(0.123_456_789, unit='s').components
Components(days=0, hours=0, minutes=0, seconds=0, milliseconds=123, microseconds=457, nanoseconds=0)
>>> pd.to_timedelta(123.456_789, unit='ms').components
Components(days=0, hours=0, minutes=0, seconds=0, milliseconds=123, microseconds=457, nanoseconds=0)
>>> pd.to_timedelta(123_456.789, unit='us').components
Components(days=0, hours=0, minutes=0, seconds=0, milliseconds=123, microseconds=456, nanoseconds=789)
>>> pd.to_timedelta(123_456_789.0, unit='ns').components
Components(days=0, hours=0, minutes=0, seconds=0, milliseconds=123, microseconds=456, nanoseconds=789)

Problem description

I have an array of floats that represent the number of seconds since a reference time with (roughly) nanosecond precision. I want to convert to a timedelta series, but this rounds off the nanoseconds. I can work around the behavior with something like the following:

>>> pd.to_timedelta(array_seconds * 1_000_000, unit='us')

Expected Output

>>> pd.to_timedelta(0.123_456_789, unit='s').components
Components(days=0, hours=0, minutes=0, seconds=0, milliseconds=123, microseconds=456, nanoseconds=789)
>>> pd.to_timedelta(123.456_789, unit='ms').components
Components(days=0, hours=0, minutes=0, seconds=0, milliseconds=123, microseconds=456, nanoseconds=789)

Output of pd.show_versions()

pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.6.1.final.0 python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None pandas: 0.20.1 pytest: 3.0.7 pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.12.1 scipy: 0.19.0 xarray: 0.9.6 IPython: 5.3.0 sphinx: 1.5.6 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2017.2 blosc: None bottleneck: 1.2.1 tables: 3.2.2 numexpr: 2.6.2 feather: None matplotlib: 2.0.2 openpyxl: 2.4.7 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.6 lxml: 3.7.3 bs4: 4.6.0 html5lib: 0.999 sqlalchemy: 1.1.9 pymysql: None psycopg2: None jinja2: 2.9.6 s3fs: None pandas_gbq: None pandas_datareader: None

Comment From: chris-b1

Duplicate of #14156, thanks for the report, PR welcome!