BUG: to_json returns incorrect timestamps if DatetimeIndex precision is not ns

[ketakopter]

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
import numpy as np

d = pd.DataFrame({'testcol': pd.Series([12], index=[np.datetime64('2023-01-01T11:22:33.123456')])})
d.index = d.index.astype('datetime64[us]')

d.to_json(date_format='iso')

Issue Description

With a DataFrame in which the DatetimeIndex precision has been set to us, the json representation is incorrect.

With the usual initialization, the DatetimeIndex has precision ns, and it works fine:

>>> import pandas as pd
>>> d = pd.DataFrame({'testcol': pd.Series([12], index=[np.datetime64('2023-01-01T11:22:33.123456')])})
>>> d.to_json(date_format='iso')
'{"testcol":{"2023-01-01T11:22:33.123":12}}'

If the precision is changed (I tried it as a workaround of issue #53684 ), the date is incorrect in to_json:

>>> d.index = d.index.astype('datetime64[us]')
>>> d
                            testcol
2023-01-01 11:22:33.123456       12

>>> d.to_json(date_format='iso')
'{"testcol":{"1970-01-20T08:36:12.153":12}}'

The date_unit parameter doesn't help, the date is still incorrect.

Other precisions fail too:

>>> d.index = d.index.astype('datetime64[s]')
>>> d
                     testcol
2023-01-01 11:22:33       12

>>> d.to_json(date_format='iso')
'{"testcol":{"1970-01-01T00:00:01.672":12}}'

Expected Behavior

The output of to_json in iso format should be '{"testcol":{"2023-01-01T11:22:33.123456":12}}'

Installed Versions

INSTALLED VERSIONS

commit : 965ceca
python : 3.10.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.12.14-195-default
Version : #1 SMP Tue May 7 10:55:11 UTC 2019 (8fba516)
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.0.2
numpy : 1.24.3
pytz : 2023.3
dateutil : 2.8.2
setuptools : 63.2.0
pip : 22.2.2
Cython : None
pytest : 7.3.1
hypothesis : None
sphinx : 7.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.13.2
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[ketakopter]

Just tested the development version. Bug still there.

INSTALLED VERSIONS ------------------ commit : 0bc16da python : 3.10.8.final.0 python-bits : 64 OS : Linux OS-release : 4.12.14-195-default Version : #1 SMP Tue May 7 10:55:11 UTC 2019 (8fba516) machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 2.1.0.dev0+977.g0bc16da1e5
numpy : 2.0.0.dev0+84.g828fba29e
pytz : 2023.3
dateutil : 2.8.2
setuptools : 63.2.0
pip : 22.2.2
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 : 8.14.0
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[lithomas1]

Thanks for the bug report.

I think I see what's going on. In the JSON C code when converting the numpy datetimes to strings, we assume that the datetime has nanosecond resolution (non-nano support in pandas is still newish).

https://github.com/pandas-dev/pandas/blob/main/pandas/_libs/src/datetime/date_conversions.c#L48

I'll try to get at this sometime soonish.