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Keywords {🔍}

groupby, support, mask, nullable, enh, dtypes, mentioned, jorisvandenbossche, masked, sign, arrays, cython, kleene, anyall, bug, pull, requests, projects, algos, issue, extension, add, logic, cummin, cummax, minmax, perf, milestone, mzeitlin, navigation, actions, security, algorithms, array, float, passing, improve, correctly, pass, perfbug, algo, cumprod, sum, grouplast, added, enhancement, maskedarrays, related, pdna, jbrockmendel,

Topics {✒️}

correctly pass mask masked array-specific support nullable extension array implement kleene versions fix groupby std casting 19-digit integer comment metadata assignees support masked arrays groupby min/max personal information enh cython algos nullable dtypes type projects kleene logic projects milestone groupby cummin cython algorithm min/max groupby support add support numpy array groupby operations specific case masked algo support mask groupby algorithms perf/bug milestone relationships na normal reductions missing values resulting dtype int64 leads precision loss github groupby add ohlc perf enh mask support float bug algorithms skip jump sign investigate starting

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      headline:ENH: support masked arrays in groupby cython algos
      articleBody:Similarly as for normal reductions (eg https://github.com/pandas-dev/pandas/pull/30982), we should investigate having masked array-specific support in the groupby algorithms. Currently, when starting from a nullable extension array, they get converted to a numpy array (eg integers with missing values will typically get cast to float with nan) before passing to the cython algorithm. Having support for passing a mask to the cython algos can improve the groupby support for nullable dtypes. * [x] `any` / `all` (correctly pass mask + add Kleene logic) -> https://github.com/pandas-dev/pandas/pull/40819 * [x] `cummin` / `cummax` -> https://github.com/pandas-dev/pandas/pull/40651 * [x] `cumsum` / `cumprod` -> https://github.com/pandas-dev/pandas/pull/48070, https://github.com/pandas-dev/pandas/pull/48138 * [x] `sum` / `prod` / `var` / `mean` * [x] `min` / `max` -> https://github.com/pandas-dev/pandas/pull/42567 * [x] `median` * [x] `ohlc` (#48081) * [x] `quantile` (correctly pass mask) * [x] `last` / `nth` (last #46107) * [x] `rank` (#46932)
      author:
         url:https://github.com/jorisvandenbossche
         type:Person
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      datePublished:2020-10-29T19:23:02.000Z
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      url:https://github.com/37493/pandas/issues/37493
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      headline:ENH: support masked arrays in groupby cython algos
      articleBody:Similarly as for normal reductions (eg https://github.com/pandas-dev/pandas/pull/30982), we should investigate having masked array-specific support in the groupby algorithms. Currently, when starting from a nullable extension array, they get converted to a numpy array (eg integers with missing values will typically get cast to float with nan) before passing to the cython algorithm. Having support for passing a mask to the cython algos can improve the groupby support for nullable dtypes. * [x] `any` / `all` (correctly pass mask + add Kleene logic) -> https://github.com/pandas-dev/pandas/pull/40819 * [x] `cummin` / `cummax` -> https://github.com/pandas-dev/pandas/pull/40651 * [x] `cumsum` / `cumprod` -> https://github.com/pandas-dev/pandas/pull/48070, https://github.com/pandas-dev/pandas/pull/48138 * [x] `sum` / `prod` / `var` / `mean` * [x] `min` / `max` -> https://github.com/pandas-dev/pandas/pull/42567 * [x] `median` * [x] `ohlc` (#48081) * [x] `quantile` (correctly pass mask) * [x] `last` / `nth` (last #46107) * [x] `rank` (#46932)
      author:
         url:https://github.com/jorisvandenbossche
         type:Person
         name:jorisvandenbossche
      datePublished:2020-10-29T19:23:02.000Z
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      url:https://github.com/37493/pandas/issues/37493
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      url:https://github.com/jorisvandenbossche
      name:jorisvandenbossche
      url:https://github.com/jorisvandenbossche
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