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timedelta, jbrockmendel, dataframe, type, data, anyall, operations, datetime, consistency, sign, api, logical, operation, issue, jorisvandenbossche, reductions, index, typeerror, dtype, mentioned, projects, datetimelike, dtypes, enable, inconsistency, series, internal, simply, reduction, matching, added, commented, member, milestone, navigation, pandas, pull, requests, actions, security, time, bit, array, similar, tdany, perform, works, call, fails, columnwise,

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dta/tda give bool-dtype enable column-wise reductions numeric data operations make sense personal information api array/index/series case enable dataframe reductions jreback added dataframe reduction differen result leading comment metadata assignees type projects timedelta64/datetime64 dtypes internal version dataframe reductions nanops behavior index type consistency data projects milestone released pandas call pandas timedelta array true dtype object-dtype timedelta64[ns] logical functions column-wise logical operation datetime datetime datetime64[ns] ufunc logical_or timedelta milestone relationships simply enable issue dtype dtype= current master nan-aware work fine failing tests implementation detail numbering starting enh/perf wip/depr series loop matching time cleaning

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• @jbrockmendel did #38723 close this?
• Already have an account?
• But for datetimes this might make less sense?
• But for timedeltas, that might rather be an issue of "never got implemented" rather than "deliberately not implemented" ?
• The question is thus: do we think that the any() and all() operations make sense for datetime-like data?

Schema {🗺️}

DiscussionForumPosting:
      context:https://schema.org
      headline:API: any/all logical operation for datetime-like dtypes
      articleBody:In my attempt to enable DataFrame reductions for ExtensionArrays (and at the same time cleaning up `DataFrame._reduce` a bit) at https://github.com/pandas-dev/pandas/pull/32867, I am running into the following inconsistency. Consider a Timedelta array, and how it behaves for the `any` and `all` logical functions (on current master, bit similar for released pandas): ```python >>> td = pd.timedelta_range(0, periods=3) # Index >>> td.any() ... TypeError: cannot perform any with this index type: TimedeltaIndex # Series >>> pd.Series(td).any() ... TypeError: cannot perform any with type timedelta64[ns] # DataFrame >>> pd.DataFrame({'a': td}).any() a True dtype: bool ``` (and exactly the same pattern for datetime64[ns]) For DataFrame, it works because there we call `pandas.core.nanops.nanany()`, and our internal version of nan-aware `any`/`all` work fine with timedelta64/datetime64 dtypes. The reason it fails for Index and Series is because we simply up front say: this operation is not possible with this dtype (we never get to call `nanany()` in those cases). But in the DataFrame reduction, we simply try it on `df.values`, and it works. This is clearly an inconsistency that we should solve. And especially for my PR https://github.com/pandas-dev/pandas/pull/32867 this gives problems, as I want to rely on the array (column-wise) ops for DataFrame reductions, but thus those give a differen result leading to failing tests. ---- The question is thus: **do we think that the `any()` and `all()` operations make sense for datetime-like data?** (and if yes, we can simply enable them for the array/index/series case) - For timedelta, I think you can say there is a concept of "zero" and "non-zero" (which is what any/all are testing in the end, for non-boolean, numeric data) - But for datetimes this might make less sense? The fact that 1970-01-01 00:00:00 would be "False" is just an implementation detail of the numbering starting at that point in time. As reference, this operation fails in numpy for both timedelta and datetime: ```python >>> np.array([0, 1], dtype="datetime64[ns]").any() ... TypeError: No loop matching the specified signature and casting was found for ufunc logical_or >>> np.array([0, 1], dtype="timedelta64[ns]").any() ... TypeError: No loop matching the specified signature and casting was found for ufunc logical_or ``` But for timedeltas, that might rather be an issue of "never got implemented" rather than "deliberately not implemented" ? (there was a similar conversation about "mean" for timedelta) cc @shoyer
      author:
         url:https://github.com/jorisvandenbossche
         type:Person
         name:jorisvandenbossche
      datePublished:2020-05-30T06:58:51.000Z
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      url:https://github.com/34479/pandas/issues/34479
      context:https://schema.org
      headline:API: any/all logical operation for datetime-like dtypes
      articleBody:In my attempt to enable DataFrame reductions for ExtensionArrays (and at the same time cleaning up `DataFrame._reduce` a bit) at https://github.com/pandas-dev/pandas/pull/32867, I am running into the following inconsistency. Consider a Timedelta array, and how it behaves for the `any` and `all` logical functions (on current master, bit similar for released pandas): ```python >>> td = pd.timedelta_range(0, periods=3) # Index >>> td.any() ... TypeError: cannot perform any with this index type: TimedeltaIndex # Series >>> pd.Series(td).any() ... TypeError: cannot perform any with type timedelta64[ns] # DataFrame >>> pd.DataFrame({'a': td}).any() a True dtype: bool ``` (and exactly the same pattern for datetime64[ns]) For DataFrame, it works because there we call `pandas.core.nanops.nanany()`, and our internal version of nan-aware `any`/`all` work fine with timedelta64/datetime64 dtypes. The reason it fails for Index and Series is because we simply up front say: this operation is not possible with this dtype (we never get to call `nanany()` in those cases). But in the DataFrame reduction, we simply try it on `df.values`, and it works. This is clearly an inconsistency that we should solve. And especially for my PR https://github.com/pandas-dev/pandas/pull/32867 this gives problems, as I want to rely on the array (column-wise) ops for DataFrame reductions, but thus those give a differen result leading to failing tests. ---- The question is thus: **do we think that the `any()` and `all()` operations make sense for datetime-like data?** (and if yes, we can simply enable them for the array/index/series case) - For timedelta, I think you can say there is a concept of "zero" and "non-zero" (which is what any/all are testing in the end, for non-boolean, numeric data) - But for datetimes this might make less sense? The fact that 1970-01-01 00:00:00 would be "False" is just an implementation detail of the numbering starting at that point in time. As reference, this operation fails in numpy for both timedelta and datetime: ```python >>> np.array([0, 1], dtype="datetime64[ns]").any() ... TypeError: No loop matching the specified signature and casting was found for ufunc logical_or >>> np.array([0, 1], dtype="timedelta64[ns]").any() ... TypeError: No loop matching the specified signature and casting was found for ufunc logical_or ``` But for timedeltas, that might rather be an issue of "never got implemented" rather than "deliberately not implemented" ? (there was a similar conversation about "mean" for timedelta) cc @shoyer
      author:
         url:https://github.com/jorisvandenbossche
         type:Person
         name:jorisvandenbossche
      datePublished:2020-05-30T06:58:51.000Z
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      url:https://github.com/34479/pandas/issues/34479
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      url:https://github.com/jorisvandenbossche
      name:jorisvandenbossche
      url:https://github.com/jorisvandenbossche
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