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Keywords {🔍}

issue, warning, format, bug, inferdatetimeformattrue, pandas, rhshadrach, array, userwarning, parsing, provide, member, marcogorelli, date, sign, inferdatetimeformat, pdtodatetime, moojen, ddmmyyyy, added, commented, datetimedate, datetime, projects, lacking, consistent, behavior, removed, benoit, leads, object, guessed, navigation, code, issues, pull, requests, actions, security, closed, description, version, confirmed, exists, main, reproducible, provided, triage, reviewed, team,

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dtype='datetime64[ns]' dd/mm/yyyy format date stdlib datetime mm/dd/yyyy dd/mm/yyyy personal information bug installed versions activity removed datetime issue description set infer_datetime_format=true pass infer_datetime_format=true infer_datetime_format option takes latest version pandas type date support assess issue bug exists df['date'] = pd type projects projects milestone rhshadrach removed locally changed triage issue pandas df['date'] trickier issues infer_datetime_format=true guessed format lacking infer_datetime_format unique date utc=true [/-] [+] bug errors='ignore' expected behavior info clarification behavior needed conversion function internal error recommend scoping added milestone relationships provide format df = pd main branch consistent parsing initial guess github entire array large array nan object

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• Can you provide a small DataFrame that reproduces this warning?

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DiscussionForumPosting:
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      headline:BUG: UserWarning about lacking infer_datetime_format with pd.to_datetime
      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 df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, utc=True, errors='ignore') ``` ### Issue Description When I run this code, I get the following warning: "UserWarning: Parsing '15/09/1979' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing." Which is strange, because I already have set infer_datetime_format=True. ### Expected Behavior You would expect no warning at all, since "infer_datetime_format=True" is already provided as an argument. ### Installed Versions <details> pymysql : None html5lib : None lxml.etree : 4.8.0 xlsxwriter : None feather : None blosc : None sphinx : None hypothesis : None pytest : 6.2.5 Cython : None setuptools : 58.1.0 pip : 22.0.3 dateutil : 2.8.2 pytz : 2021.3 numpy : 1.22.2 pandas : 1.4.1 </details>
      author:
         url:https://github.com/moojen
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      datePublished:2022-03-03T12:53:26.000Z
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      url:https://github.com/46210/pandas/issues/46210
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      headline:BUG: UserWarning about lacking infer_datetime_format with pd.to_datetime
      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 df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, utc=True, errors='ignore') ``` ### Issue Description When I run this code, I get the following warning: "UserWarning: Parsing '15/09/1979' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing." Which is strange, because I already have set infer_datetime_format=True. ### Expected Behavior You would expect no warning at all, since "infer_datetime_format=True" is already provided as an argument. ### Installed Versions <details> pymysql : None html5lib : None lxml.etree : 4.8.0 xlsxwriter : None feather : None blosc : None sphinx : None hypothesis : None pytest : 6.2.5 Cython : None setuptools : 58.1.0 pip : 22.0.3 dateutil : 2.8.2 pytz : 2021.3 numpy : 1.22.2 pandas : 1.4.1 </details>
      author:
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      url:https://github.com/46210/pandas/issues/46210
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