BUG: pd.date_range not factoring in nanoseconds in freq

[galipremsagar]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
print(pd.__version__)
x = pd.date_range(end='1970-01-01 00:00:00', periods=10, freq=pd.DateOffset(**{'hours': 10, 'days': 57, 'nanoseconds': 3}), name="a")
print(x.to_numpy())

1.4.2
['1968-08-02T06:00:00.000000000' '1968-09-28T16:00:00.000000000'
 '1968-11-25T02:00:00.000000000' '1969-01-21T12:00:00.000000000'
 '1969-03-19T22:00:00.000000000' '1969-05-16T08:00:00.000000000'
 '1969-07-12T18:00:00.000000000' '1969-09-08T04:00:00.000000000'
 '1969-11-04T14:00:00.000000000' '1970-01-01T00:00:00.000000000']

Issue Description

While generating date ranges, nanoseconds specified in freq are not being factored in.

Expected Behavior

['1968-08-02T06:00:00.000000000', '1968-09-28T16:00:00.000000003',
       '1968-11-25T02:00:00.000000006', '1969-01-21T12:00:00.000000009',
       '1969-03-19T22:00:00.000000012', '1969-05-16T08:00:00.000000015',
       '1969-07-12T18:00:00.000000018', '1969-09-08T04:00:00.000000021',
       '1969-11-04T14:00:00.000000024', '1970-01-01T00:00:00.000000027']

Installed Versions

INSTALLED VERSIONS

commit : 945c9ed
python : 3.9.6.final.0
python-bits : 64
OS : Darwin
OS-release : 21.4.0
Version : Darwin Kernel Version 21.4.0: Fri Mar 18 00:45:05 PDT 2022; root:xnu-8020.101.4~15/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.22.3
pytz : 2022.1
dateutil : 2.8.2
pip : 22.0.4
setuptools : 59.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None

[RyuuOujiXS]

Issue seems to occur when offsetting the date. If only the time is offset, issue does not occur.

[simonjayhawkins]

thanks @galipremsagar for the report.

Issue seems to occur when offsetting the date. If only the time is offset, issue does not occur.

This was fixed in commit: [cd5a124] FIX BUG: Timestamp add/sub DateOffset with nanoseconds lost. (#43968)

While generating date ranges, nanoseconds specified in freq are not being factored in.

not sure why but this appears to be dropping into the relativedelta.__add__ implementation in the dateutil package (which does not support nanoseconds) whereas without the days component, say pd.DateOffset(**{'hours': 10, 'nanoseconds': 3}) I'm not sure if it does. (this was with the cli debugger without cython debugging so I could be wrong here)

The tests added in #43968, only included one component per test.

cc @tushushu

xref #43892, #36589

[Shadimrad]

take

[Shadimrad]

So, I am getting this:

"/pandas/core/arrays/datetimes.py:398: UserWarning: Discarding nonzero nanoseconds in conversion.
i8values = np.array([x.value for x in xdr], dtype=np.int64)"

and I need help regarding what is happening to proceed.

[Shadimrad]

So, I am getting this:

"/pandas/core/arrays/datetimes.py:398: UserWarning: Discarding nonzero nanoseconds in conversion. i8values = np.array([x.value for x in xdr], dtype=np.int64)"

and I need help regarding what is happening to proceed.

This happened as the change in the file mentioned below, as this code for i in range(self.n): other = other + self._offset + td_nano was replaced by for i in range(self.n): other = td_nano + other + self._offset with the hope that the td_nano would not get lost in the addition due to the fact that either other or self._offset do not support nanoseconds.

based on these results:
repr(self._offset) -> relativedelta(days=+57, hours=+10)
repr (other) -> datetime.datetime(1968, 9, 28, 16, 0)

in this file : /pandas/_libs/tslibs/offsets.pyx