BUG: not reproducible error FloatingPointError: overflow encountered in multiply in the following sequence: read_csv followed by to_datetime with pandas version 2.2.2

[kapytaine]

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Reproducible Example

import pandas as pd

for ii in range(10000):
    df = pd.read_csv("data.csv", dtype={"ts": float})   # data.csv provided in an attached file
    pd.to_datetime(df["ts"], unit="s", errors="coerce")

Issue Description

I, sometimes, get the following error with pandas 2.2.2 (I don't have this error with pandas 2.1.4):

Exception has occurred: FloatingPointError
overflow encountered in multiply
File ".../main.py", line 218, in
pd.to_datetime(df["ts"], unit="s", errors="coerce")
FloatingPointError: overflow encountered in multiply

The error is not repeatable, hence the loop. I tried to reduce as much as possible the input file while keeping the raised error, this is why I provided a csv file with 200 rows, attached to this issue. I don't know if the issue is due to the read_csv (I got the same problem with read_parquet) or due to to_datetime. If the read_csv is outside the loop and I make a deepcopy at the beginning of each loop, I don't have the problem, so my hunch is that this is linked to the reading process (read_csv in the example).

Expected Behavior

I expect the loop content to have the same behaviour, works every time or fails every time.

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.11.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-105-generic
Version : #115~20.04.1-Ubuntu SMP Mon Apr 15 17:33:04 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 65.5.0
pip : 24.0
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
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 16.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

data.csv

[SUCM]

I also see this FloatingPointError: overflow encountered in multiply error from time to time and it's always with pd.to_datetime where errors=coerce or ignore

so far I have done code like below and it would always work even when encountering the random FloatingPointError confirmed by the prints. basically a retry would work

try:
    c=df["a"].copy()
    df["a"] = pd.to_datetime(df["a"], ... errors="coerce")
except FloatingPointError:
    print("pandas FloatingPointError on column a")
    df["a"] = pd.to_datetime(c, ... errors="coerce")

you may want to try using Python pickle to preserve Python objects. I find pandas's csv and excel read/write to be unreliable

as an example, having 2 columns of str, one with phone numbers where area codes all starts with 0 and the other column are empty string, then pandas to/read csv and excel would save them to file without problem but on reading back you would get 2 columns of timestamps and/or other non-str objects