h PddlmZddlZddlmZddlmZmZm Z  ddZ y)) annotationsN)unique1d) CategoricalCategoricalDtyperecode_for_categoriesc |rt|j}||dk7}|rtj|}|jj |}t |j|j|}t||j}tj|||fS|r|dfStj|jj}t|j|jdk7} |rtj| } t|t| kDr0tj|| d} tj| | f}n| }t||j!jj |dfS)a= Code the categories to ensure we can groupby for categoricals. If observed=True, we return a new Categorical with the observed categories only. If sort=False, return a copy of self, coded with categories as returned by .unique(), followed by any categories not appearing in the data. If sort=True, return self. This method is needed solely to ensure the categorical index of the GroupBy result has categories in the order of appearance in the data (GH-8868). Parameters ---------- c : Categorical sort : bool The value of the sort parameter groupby was called with. observed : bool Account only for the observed values Returns ------- Categorical If sort=False, the new categories are set to the order of appearance in codes (unless ordered=True, in which case the original order is preserved), followed by any unrepresented categories in the original order. Categorical or None If we are observed, return the original categorical, otherwise None )ordered)dtypeNT) assume_unique)rcodesnpsort categoriestakerrr r _simple_newarangenuniquelen setdiff1d concatenateunique) crobserved unique_codes take_codesrr r all_codesunique_notnan_codes missing_codess DD:\jyotish\venv\Lib\site-packages\pandas/core/groupby/categorical.pyrecode_for_groupbyr! sPH ( !,""45 ,J\\&&z2 %aggq||ZH!QYY?&&uE:A== $w  !,,..01I"177177b=#9:  gg&9: 9~/00 Y0CSWX ^^%8-$HI ( q!((*//44Z@ A4 GG)rrrboolrr#returnz&tuple[Categorical, Categorical | None]) __future__rnumpyrpandas.core.algorithmsrpandas.core.arrays.categoricalrrrr!r"r r*sG"+JHJHJH*.JH+JHr"