Description
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