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series, numericonly, rhshadrach, ops, dtype, columns, float, mroeschke, commented, member, raise, consistency, docstring, api, behavior, axis, author, sign, seriesgroupby, raises, numeric, discussion, nuisance, type, pandas, projects, issue, closed, notimplementederror, meannumericonlytrue, view, option, added, include, dataframe, imo, reduction, functions, mentioned, kwargs, navigation, pull, requests, actions, security, rolling, window, passed, printsergroupby, meannumericonlyfalse,

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deprecate unused args/kwargs july dev meeting personal information api activity rhshadrach added boolean columns docstring boolean columns docstring imo numeric_only appears discussion requires discussion completed itholic mentioned api/behavior comment metadata assignees rhshadrach closed projects milestone 1 allowing numeric_only=true object type series rhshadrach mentioned 1d columns type projects ops ignore numeric_only numeric_only=true/false seriesgroupby behavior ops raise notimplementederror apply type series behavior seriesgroupby ops reduction functions columns api pandas 2 numeric_only=true implement numeric_only numeric_only=false interpret numeric_only numeric_only topic handle numeric_only ops raise view passing make sense slightly odd core team convert ['foobar'] quick check circle back dove deeply incorrect view makes sense pun intended enabling tests 100% complete relationships window ops

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      headline:API: numeric_only in Series ops behavior
      articleBody:Part of #46560 For Series and SeriesGroupBy ops (and perhaps others like resample, rolling, window, expanding, ewm), there is an inconsistency when numeric_only is passed to ops that have it as an argument: ``` ser = pd.Series([1, 2, 3]) print(ser.mean(numeric_only=True)) # Raises `NotImplementedError: Series.mean does not implement numeric_only.` print(ser.mean(numeric_only=False)) # 2.0 print(ser.groupby([0, 0, 1]).mean(numeric_only=True)) # 0 1.5 # 1 3.0 # dtype: float64 print(ser.groupby([0, 0, 1]).mean(numeric_only=False)) # 0 1.5 # 1 3.0 # dtype: float64 ``` I see three possible options: 1. Ops raise NotImplementedError if numeric_only is truthy (the Series behavior) 2. Ops ignore numeric_only when the dtype is numeric (the SeriesGroupBy behavior) 3. Ops raise if anything is passed (I don't know of any ops that act like this currently) I am in favor of 1 - I view passing in `numeric_only=True` as erroneous; it doesn't make sense to request this from a Series even if it is a no-op. On the other hand, I view passing in `numeric_only=False` as "requesting nothing", though perhaps it is slightly odd. Option 1 would also allow the default to be False across all ops (in pandas 2.0).
      author:
         url:https://github.com/rhshadrach
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      datePublished:2022-06-24T19:43:43.000Z
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      headline:API: numeric_only in Series ops behavior
      articleBody:Part of #46560 For Series and SeriesGroupBy ops (and perhaps others like resample, rolling, window, expanding, ewm), there is an inconsistency when numeric_only is passed to ops that have it as an argument: ``` ser = pd.Series([1, 2, 3]) print(ser.mean(numeric_only=True)) # Raises `NotImplementedError: Series.mean does not implement numeric_only.` print(ser.mean(numeric_only=False)) # 2.0 print(ser.groupby([0, 0, 1]).mean(numeric_only=True)) # 0 1.5 # 1 3.0 # dtype: float64 print(ser.groupby([0, 0, 1]).mean(numeric_only=False)) # 0 1.5 # 1 3.0 # dtype: float64 ``` I see three possible options: 1. Ops raise NotImplementedError if numeric_only is truthy (the Series behavior) 2. Ops ignore numeric_only when the dtype is numeric (the SeriesGroupBy behavior) 3. Ops raise if anything is passed (I don't know of any ops that act like this currently) I am in favor of 1 - I view passing in `numeric_only=True` as erroneous; it doesn't make sense to request this from a Series even if it is a no-op. On the other hand, I view passing in `numeric_only=False` as "requesting nothing", though perhaps it is slightly odd. Option 1 would also allow the default to be False across all ops (in pandas 2.0).
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      datePublished:2022-06-24T19:43:43.000Z
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      url:https://github.com/47500/pandas/issues/47500
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