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Keywords {🔍}

categories, categorical, groupby, sortfalse, order, mroeschke, rhshadrach, performance, result, grouping, issue, values, commented, member, codes, sign, code, reorder, type, mentioned, projects, unordered, daskdask, comment, categoricalindex, orderedfalse, groups, cat, data, reordering, libs, labels, returned, navigation, open, pandas, pull, requests, actions, security, apiperf, dont, categoricals, sorttrue, resulting, dfgroupbycol, observedfalsefirstindex, dtypecategory, namerange, sorted,

Topics {✒️}

pandas/pandas/core/groupby/categorical personal information api/perf rhshadrach edits member respect sort=true/false nice performance benefit recode categorical codes type projects assess issue discussion requires discussion extra time cost _libs groupby code groupby result disagrees comment metadata assignees rhshadrach mentioned categorical codes [2 unordered categorical called labels projects milestone perf difference groupby code data core team grouping labels sort=true sort=false pandas ] = categorical categorical observed=false ordered=false extra work info clarification behavior needed sorting imposes ~2x slower ~10x slower perf slap grouper incorrect results desired output milestone relationships categories unordered integer categories string categories codes[cat encountered order reorder categoricals dtype='category' take_codes = np github

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• Do we have an idea what kind of extra time cost the sorting imposes for something sensible, like 10 000 rows and a 100 categories?

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DiscussionForumPosting:
      context:https://schema.org
      headline:API/PERF: Don't reorder categoricals when grouping by an unordered categorical and `sort=False`
      articleBody:xref: https://github.com/dask/dask/pull/9486#issue-1372066649 TLDR: When calling `df.groupby(key=categocial<order=False>, sort=True, observed=False)` the resulting `CategoricalIndex` will have it's values _and categories_ unordered. ``` In [1]: df = DataFrame( ...: [ ...: ["(7.5, 10]", 10, 10], ...: ["(7.5, 10]", 8, 20], ...: ["(2.5, 5]", 5, 30], ...: ["(5, 7.5]", 6, 40], ...: ["(2.5, 5]", 4, 50], ...: ["(0, 2.5]", 1, 60], ...: ["(5, 7.5]", 7, 70], ...: ], ...: columns=["range", "foo", "bar"], ...: ) In [2]: col = "range" In [3]: df["range"] = Categorical(df["range"], ordered=False) In [4]: df.groupby(col, sort=True, observed=False).first().index Out[4]: CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', '(7.5, 10]'], categories=['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', '(7.5, 10]'], ordered=False, dtype='category', name='range') In [5]: df.groupby(col, sort=False, observed=False).first().index Out[5]: CategoricalIndex(['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', '(0, 2.5]'], categories=['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', '(0, 2.5]'], ordered=False, dtype='category', name='range') ``` It's reasonable that the values are not sorted, but a lot of extra work can be spent un-ordering the _categories_ in: https://github.com/pandas-dev/pandas/blob/44a4f1619ff5031e59a970a61fac94c3745e4433/pandas/core/groupby/categorical.py#L77-L92 May have been an outcome of fixing https://github.com/pandas-dev/pandas/issues/8868, but if grouping and `sort=False` the values can be achieved without reordering the categories, there would probably be a nice performance benefit.
      author:
         url:https://github.com/mroeschke
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      datePublished:2022-09-23T20:45:55.000Z
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      url:https://github.com/48749/pandas/issues/48749
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      headline:API/PERF: Don't reorder categoricals when grouping by an unordered categorical and `sort=False`
      articleBody:xref: https://github.com/dask/dask/pull/9486#issue-1372066649 TLDR: When calling `df.groupby(key=categocial<order=False>, sort=True, observed=False)` the resulting `CategoricalIndex` will have it's values _and categories_ unordered. ``` In [1]: df = DataFrame( ...: [ ...: ["(7.5, 10]", 10, 10], ...: ["(7.5, 10]", 8, 20], ...: ["(2.5, 5]", 5, 30], ...: ["(5, 7.5]", 6, 40], ...: ["(2.5, 5]", 4, 50], ...: ["(0, 2.5]", 1, 60], ...: ["(5, 7.5]", 7, 70], ...: ], ...: columns=["range", "foo", "bar"], ...: ) In [2]: col = "range" In [3]: df["range"] = Categorical(df["range"], ordered=False) In [4]: df.groupby(col, sort=True, observed=False).first().index Out[4]: CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', '(7.5, 10]'], categories=['(0, 2.5]', '(2.5, 5]', '(5, 7.5]', '(7.5, 10]'], ordered=False, dtype='category', name='range') In [5]: df.groupby(col, sort=False, observed=False).first().index Out[5]: CategoricalIndex(['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', '(0, 2.5]'], categories=['(7.5, 10]', '(2.5, 5]', '(5, 7.5]', '(0, 2.5]'], ordered=False, dtype='category', name='range') ``` It's reasonable that the values are not sorted, but a lot of extra work can be spent un-ordering the _categories_ in: https://github.com/pandas-dev/pandas/blob/44a4f1619ff5031e59a970a61fac94c3745e4433/pandas/core/groupby/categorical.py#L77-L92 May have been an outcome of fixing https://github.com/pandas-dev/pandas/issues/8868, but if grouping and `sort=False` the values can be achieved without reordering the categories, there would probably be a nice performance benefit.
      author:
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