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  • [x] 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
from datetime import datetime, timezone

# Saving and loading without timezones (works fine)
df = pd.DataFrame({
    'timestamp': [
        datetime(2025, 8, 14, 12, 34, 56, 0), # No microseconds
        datetime(2025, 8, 14, 12, 34, 56, 1)  # With microseconds
    ]
})
df.to_csv('test.csv', index=False)
## == test.csv == ##
# timestamp
# 2025-08-14 12:34:56.000000
# 2025-08-14 12:34:56.000001
####################
df2 = pd.read_csv("test.csv", parse_dates=["timestamp"])
print(df2.dtypes["timestamp"]) # datetime64[ns]


# Saving and loading with timezones (broken)
df = pd.DataFrame({
    'timestamp': [
        datetime(2025, 8, 14, 12, 34, 56, 0, tzinfo=timezone.utc), # No microseconds
        datetime(2025, 8, 14, 12, 34, 56, 1, tzinfo=timezone.utc)  # With microseconds
    ]
})
## == test.csv == ##
# timestamp
# 2025-08-14 12:34:56+00:00
# 2025-08-14 12:34:56.000001+00:00
####################
df.to_csv('test.csv', index=False)
df2 = pd.read_csv("test.csv", parse_dates=["timestamp"])
print(df2.dtypes["timestamp"]) # object

Issue Description

When saving a datetime column with timezones to a csv, pandas uses an inconsistent format across entries depending on whether the microseconds is zero or nonzero. This means that pandas then fails to parse that column when reading in the same file.

Expected Behavior

The save behavior for dates with timezones should match the behavior for dates without timezones in this case: i.e. use a consistent datetime format that includes microseconds for all rows.

Installed Versions

INSTALLED VERSIONS ------------------ commit : c888af6d0bb674932007623c0867e1fbd4bdc2c6 python : 3.13.5 python-bits : 64 OS : Darwin OS-release : 24.3.0 Version : Darwin Kernel Version 24.3.0: Thu Jan 2 20:24:22 PST 2025; root:xnu-11215.81.4~3/RELEASE_ARM64_T6041 machine : arm64 processor : arm byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 2.3.1 numpy : 2.3.2 pytz : 2025.2 dateutil : 2.9.0.post0 pip : 25.2 Cython : None sphinx : None IPython : None adbc-driver-postgresql: None adbc-driver-sqlite : None bs4 : None blosc : None bottleneck : None dataframe-api-compat : None fastparquet : None fsspec : None html5lib : None hypothesis : None gcsfs : None jinja2 : None lxml.etree : None matplotlib : None numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None psycopg2 : None pymysql : None pyarrow : None pyreadstat : None pytest : None python-calamine : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlsxwriter : None zstandard : None tzdata : 2025.2 qtpy : None pyqt5 : None

Comment From: prazian

take

Comment From: prazian

PR https://github.com/pandas-dev/pandas/pull/62139 ready for review