from __future__ import annotations
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import numpy as np
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from pandas.core.algorithms import unique1d
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from pandas.core.arrays.categorical import (
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Categorical,
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CategoricalDtype,
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recode_for_categories,
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)
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def recode_for_groupby(
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c: Categorical, sort: bool, observed: bool
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) -> tuple[Categorical, Categorical | None]:
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"""
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Code the categories to ensure we can groupby for categoricals.
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If observed=True, we return a new Categorical with the observed
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categories only.
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If sort=False, return a copy of self, coded with categories as
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returned by .unique(), followed by any categories not appearing in
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the data. If sort=True, return self.
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This method is needed solely to ensure the categorical index of the
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GroupBy result has categories in the order of appearance in the data
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(GH-8868).
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Parameters
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----------
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c : Categorical
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sort : bool
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The value of the sort parameter groupby was called with.
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observed : bool
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Account only for the observed values
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Returns
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-------
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Categorical
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If sort=False, the new categories are set to the order of
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appearance in codes (unless ordered=True, in which case the
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original order is preserved), followed by any unrepresented
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categories in the original order.
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Categorical or None
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If we are observed, return the original categorical, otherwise None
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"""
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# we only care about observed values
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if observed:
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# In cases with c.ordered, this is equivalent to
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# return c.remove_unused_categories(), c
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unique_codes = unique1d(c.codes)
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take_codes = unique_codes[unique_codes != -1]
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if sort:
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take_codes = np.sort(take_codes)
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# we recode according to the uniques
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categories = c.categories.take(take_codes)
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codes = recode_for_categories(c.codes, c.categories, categories)
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# return a new categorical that maps our new codes
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# and categories
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dtype = CategoricalDtype(categories, ordered=c.ordered)
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return Categorical(codes, dtype=dtype, fastpath=True), c
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# Already sorted according to c.categories; all is fine
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if sort:
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return c, None
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# sort=False should order groups in as-encountered order (GH-8868)
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# xref GH:46909: Re-ordering codes faster than using (set|add|reorder)_categories
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all_codes = np.arange(c.categories.nunique())
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# GH 38140: exclude nan from indexer for categories
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unique_notnan_codes = unique1d(c.codes[c.codes != -1])
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if sort:
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unique_notnan_codes = np.sort(unique_notnan_codes)
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if len(all_codes) > len(unique_notnan_codes):
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# GH 13179: All categories need to be present, even if missing from the data
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missing_codes = np.setdiff1d(all_codes, unique_notnan_codes, assume_unique=True)
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take_codes = np.concatenate((unique_notnan_codes, missing_codes))
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else:
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take_codes = unique_notnan_codes
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return Categorical(c, c.unique().categories.take(take_codes)), None
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