BUG: stacklevel from dayfirst warning incorrect

[MarcoGorelli]

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 io


pd.to_datetime(['13/01/2021'])  # correct stacklevel

pd.read_csv(io.StringIO('date\n01/13/2021\n'), parse_dates=['date'], dayfirst=True)  # incorrect

Issue Description

Running the above, I get

/home/megorelli/pandas-dev/t.py:5: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  pd.to_datetime(['13/01/2021'])
/home/megorelli/pandas-dev/pandas/io/parsers/base_parser.py:1061: UserWarning: Parsing dates in MM/DD/YYYY format when dayfirst=True was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  return tools.to_datetime(

Expected Behavior

Running the above, I expected to get

/home/megorelli/pandas-dev/t.py:5: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  pd.to_datetime(['13/01/2021'])
/home/megorelli/pandas-dev/t.py:7: UserWarning: Parsing dates in MM/DD/YYYY format when dayfirst=True was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  return tools.to_datetime(

Installed Versions

INSTALLED VERSIONS

commit : 23c53bb
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.114-16025-ge75506b9d98e
Version : #1 SMP PREEMPT Sat Jul 16 18:52:19 PDT 2022
machine : aarch64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.0.dev0+1212.g23c53bb6db
numpy : 1.22.4
pytz : 2022.1
dateutil : 2.8.2
setuptools : 62.1.0
pip : 22.0.4
Cython : 0.29.30
pytest : 7.1.2
hypothesis : 6.47.1
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.4.0
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : 2.8.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : 3.7.0
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
None

[tomaarsen]

This was fixed in #47998, running the provided snippet now gives:

/workspaces/pandas/demo.py:5: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  pd.to_datetime(['13/01/2021'])  # correct stacklevel
/workspaces/pandas/demo.py:7: UserWarning: Parsing dates in MM/DD/YYYY format when dayfirst=True was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  pd.read_csv(io.StringIO('date\n01/13/2021\n'), parse_dates=['date'], dayfirst=True)  # incorrect

In short, this can be closed.

[MarcoGorelli]

Thanks @tomaarsen !