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BUG: Wrong timestamp resolution when parsing timestamp string with comma separated milliseconds #59256

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3 tasks done
rocodero opened this issue Jul 16, 2024 · 5 comments
Open
3 tasks done

BUG: Wrong timestamp resolution when parsing timestamp string with comma separated milliseconds #59256

rocodero opened this issue Jul 16, 2024 · 5 comments
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@Anurag-Varma
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Bug Timestamp pd.Timestamp and associated methods

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@rocodero
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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

pd.Timestamp('2024-06-17T18:57:43,567')
pd.Timestamp('2024-06-17T18:57:43,567').unit
pd.Timestamp('2024-06-17T18:57:43,567') - pd.Timedelta(0)

pd.Timestamp('2024-06-17T18:57:43.567')
pd.Timestamp('2024-06-17T18:57:43.567').unit
pd.Timestamp('2024-06-17T18:57:43.567') - pd.Timedelta(0)

Issue Description

When parsing a timestamp string that separates the milliseconds with a comma instead of a dot, the timestamp representation shows microseconds, but the actual stored resolution / unit that is used for any calculations with the timestamp is only seconds.

The problem does not occur with more then three digits after the comma or when using a dot as a separator.

Expected Behavior

Timestamp resolution is properly parsed to the millisecond

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.12.4.final.0
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.22621
machine : AMD64
processor : Intel64 Family 6 Model 141 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.5.1
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.2.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.25.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : 1.3.7
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.4
numba : None
numexpr : 2.8.7
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.13.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
@rocodero rocodero added Bug Needs Triage Issue that has not been reviewed by a pandas team member labels Jul 16, 2024
@Anurag-Varma
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take

@Anurag-Varma
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Hi @rocodero

You can use the below code to format comma between seconds and milliseconds.

By default Python ISO 8601 Expects a .
But as you have a , you can parse the datetime

from datetime import datetime

# Sample datetime string
datetime_str = "2024-06-17T18:57:43,567"

# Define the format
datetime_format = "%Y-%m-%dT%H:%M:%S,%f"

# Parse the datetime string
parsed_datetime = datetime.strptime(datetime_str, datetime_format)

@Anurag-Varma
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You can also follow this link

And use their suggested similar alternative:

date_string.replace(',', '.'))

@rocodero
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Hi @Anurag-Varma,

thank you for the suggestions, plenty of options there, I know. I was trying to focus on the bug itself and keep it clean, but if workarounds are appreciated I can add them in the future.

What I forgot to add, though: It's only present since pandas 2.2. Version 2.1.4 parses the comma-separated milliseconds without problems.

@MarcoGorelli
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MarcoGorelli commented Jul 25, 2024 •
edited

thanks @rocodero for the report

aside from whether it should parse or not, it looks pretty wild to me that

In [6]: t
Out[6]: Timestamp('2024-06-17 18:57:43.567000')

In [7]: t.unit
Out[7]: 's'

In [8]: t.microsecond
Out[8]: 567000

it records non-zero microseconds but has 's' unit

looks like it's going down the dateutil path

In [9]: pd.to_datetime(['2024-06-17T18:57:43,567']*2)
<ipython-input-9-ad2044f68d63>:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  pd.to_datetime(['2024-06-17T18:57:43,567']*2)
Out[9]: DatetimeIndex(['2024-06-17 18:57:43', '2024-06-17 18:57:43'], dtype='datetime64[ns]', freq=None)

@MarcoGorelli MarcoGorelli added Timestamp pd.Timestamp and associated methods and removed Needs Triage Issue that has not been reviewed by a pandas team member labels Jul 25, 2024
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@MarcoGorelli @Anurag-Varma @rocodero