Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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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