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/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes pandas/_libs/tslibs/parsing issue description traceback dtype='datetime64[ns]' personal information bug comment metadata assignees type projects latest version projects milestone triage issue pandas bug exists infer_datetime_format=true errors=coerce errors='coerce' main branch recent call dayfirst=dayfirst arr[non_nan_elements[0]] invalid offsets milestone relationships to_datetime raises issue to_datetime raising bug github timedelta strictly format _libs tslibs parsing guessing to_datetime pd timedelta timedelta sign skip jump checked reported confirmed reproducible file py line 7 line 1124 argc line 418 arg

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      headline:BUG: to_datetime raises when errors=coerce and infer_datetime_format
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python pd.to_datetime(["200622-12-31", "111111-24-11"], errors='coerce', infer_datetime_format=True) ``` ### Issue Description ``` Traceback (most recent call last): File "t.py", line 7, in <module> pd.to_datetime(["200622-12-31", "111111-24-11"], errors='coerce', infer_datetime_format=True) File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 1124, in to_datetime result = convert_listlike(argc, format) File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 418, in _convert_listlike_datetimes format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst) File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 132, in _guess_datetime_format_for_array return guess_datetime_format(arr[non_nan_elements[0]], dayfirst=dayfirst) File "pandas/_libs/tslibs/parsing.pyx", line 1029, in pandas._libs.tslibs.parsing.guess_datetime_format tokens[offset_index] = parsed_datetime.strftime("%z") ValueError: offset must be a timedelta strictly between -timedelta(hours=24) and timedelta(hours=24). ``` Note that this is different from https://github.com/pandas-dev/pandas/issues/46071, because here the error happens when pandas is still guessing the format ### Expected Behavior ``` DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None) ``` ### Installed Versions <details> Replace this line with the output of pd.show_versions() INSTALLED VERSIONS ------------------ commit : bf856a85a3b769821809ff456e288542af16310c python : 3.8.13.final.0 python-bits : 64 OS : Linux OS-release : 5.10.16.3-microsoft-standard-WSL2 Version : #1 SMP Fri Apr 2 22:23:49 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 1.6.0.dev0+145.gbf856a85a3 numpy : 1.22.4 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.3.0 pip : 22.2.2 Cython : 0.29.32 pytest : 7.1.3 hypothesis : 6.54.5 sphinx : 4.5.0 blosc : None feather : None xlsxwriter : 3.0.3 lxml.etree : 4.9.1 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : 2.9.3 jinja2 : 3.0.3 IPython : 8.5.0 pandas_datareader: 0.10.0 bs4 : 4.11.1 bottleneck : 1.3.5 brotli : fastparquet : 0.8.3 fsspec : 2021.11.0 gcsfs : 2021.11.0 matplotlib : 3.5.3 numba : 0.55.2 numexpr : 2.8.3 odfpy : None openpyxl : 3.0.10 pandas_gbq : 0.17.8 pyarrow : 9.0.0 pyreadstat : 1.1.9 pyxlsb : 1.0.9 s3fs : 2021.11.0 scipy : 1.9.1 snappy : sqlalchemy : 1.4.41 tables : 3.7.0 tabulate : 0.8.10 xarray : 2022.6.0 xlrd : 2.0.1 xlwt : 1.3.0 zstandard : 0.18.0 tzdata : None None </details>
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      headline:BUG: to_datetime raises when errors=coerce and infer_datetime_format
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python pd.to_datetime(["200622-12-31", "111111-24-11"], errors='coerce', infer_datetime_format=True) ``` ### Issue Description ``` Traceback (most recent call last): File "t.py", line 7, in <module> pd.to_datetime(["200622-12-31", "111111-24-11"], errors='coerce', infer_datetime_format=True) File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 1124, in to_datetime result = convert_listlike(argc, format) File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 418, in _convert_listlike_datetimes format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst) File "/home/marcogorelli/pandas-dev/pandas/core/tools/datetimes.py", line 132, in _guess_datetime_format_for_array return guess_datetime_format(arr[non_nan_elements[0]], dayfirst=dayfirst) File "pandas/_libs/tslibs/parsing.pyx", line 1029, in pandas._libs.tslibs.parsing.guess_datetime_format tokens[offset_index] = parsed_datetime.strftime("%z") ValueError: offset must be a timedelta strictly between -timedelta(hours=24) and timedelta(hours=24). ``` Note that this is different from https://github.com/pandas-dev/pandas/issues/46071, because here the error happens when pandas is still guessing the format ### Expected Behavior ``` DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None) ``` ### Installed Versions <details> Replace this line with the output of pd.show_versions() INSTALLED VERSIONS ------------------ commit : bf856a85a3b769821809ff456e288542af16310c python : 3.8.13.final.0 python-bits : 64 OS : Linux OS-release : 5.10.16.3-microsoft-standard-WSL2 Version : #1 SMP Fri Apr 2 22:23:49 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 1.6.0.dev0+145.gbf856a85a3 numpy : 1.22.4 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.3.0 pip : 22.2.2 Cython : 0.29.32 pytest : 7.1.3 hypothesis : 6.54.5 sphinx : 4.5.0 blosc : None feather : None xlsxwriter : 3.0.3 lxml.etree : 4.9.1 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : 2.9.3 jinja2 : 3.0.3 IPython : 8.5.0 pandas_datareader: 0.10.0 bs4 : 4.11.1 bottleneck : 1.3.5 brotli : fastparquet : 0.8.3 fsspec : 2021.11.0 gcsfs : 2021.11.0 matplotlib : 3.5.3 numba : 0.55.2 numexpr : 2.8.3 odfpy : None openpyxl : 3.0.10 pandas_gbq : 0.17.8 pyarrow : 9.0.0 pyreadstat : 1.1.9 pyxlsb : 1.0.9 s3fs : 2021.11.0 scipy : 1.9.1 snappy : sqlalchemy : 1.4.41 tables : 3.7.0 tabulate : 0.8.10 xarray : 2022.6.0 xlrd : 2.0.1 xlwt : 1.3.0 zstandard : 0.18.0 tzdata : None None </details>
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