MhzSSKrSSKrSSKJr SSKJrJr \RS5r\RS5r \RS5r \RS5r \R"SS /S 9S 5r \RS 5r \RS 5r\RS5r\RS5r\RS5r\RS5r\R"SS/S 9S5r\R"SSSS//SQS9S5r\R"SS/S 9S5r\R"SS/S 9S5r\R"SS/S 9S5r\R"SS /S 9S!5r\R"SS/S 9S"5r\RS#5r\RS$\4S%j5rg)&N) _get_option)Seriesoptionsc[e)z3A fixture providing the ExtensionDtype to validate.NotImplementedErrorQ/var/www/html/env/lib/python3.13/site-packages/pandas/tests/extension/conftest.pydtyper  r c[e)z} Length-100 array for this type. * data[0] and data[1] should both be non missing * data[0] and data[1] should not be equal rr r r datar  r cUR(d)URS:Xd[R"US35 [e)z} Length-100 array in which all the elements are two. Call pytest.skip in your fixture if the dtype does not support divmod. mz is not a numeric dtype) _is_numerickindpytestskiprr s r data_for_twosrs4   s!2  ug456 r c[e)zLength-2 array with [NA, Valid]rr r r data_missingr-r r rr)paramscLURS:XaU$URS:XaU$g)z5Parametrized fixture giving 'data' and 'data_missing'rrNparam)requestrrs r all_datar 3s,}} . ( )r c^U4SjnU$)z Generate many datasets. Parameters ---------- data : fixture implementing `data` Returns ------- Callable[[int], Generator]: A callable that takes a `count` argument and returns a generator yielding `count` datasets. c3:># [U5HnTv M g7fN)range)count_rs r gendata_repeated..genLsuAJsr )rr's` r data_repeatedr)<s  Jr c[e)z Length-3 array with a known sort order. This should be three items [B, C, A] with A < B < C For boolean dtypes (for which there are only 2 values available), set B=C=True rr r r data_for_sortingr+Ss  r c[e)zk Length-3 array with a known sort order. This should be three items [B, NA, A] with A < B and NA missing. rr r r data_missing_for_sortingr-arr c"[R$)z Binary operator for comparing NA values. Should return a function of two arguments that returns True if both arguments are (scalar) NA for your type. By default, uses ``operator.is_`` )operatoris_r r r na_cmpr1ls <<r cUR$)z The scalar missing value for this type. Default dtype.na_value. TODO: can be removed in 3.x (see https://github.com/pandas-dev/pandas/pull/54930) )na_valuers r r3r3ys >>r c[e)z Data for factorization, grouping, and unique tests. Expected to be like [B, B, NA, NA, A, A, B, C] Where A < B < C and NA is missing. If a dtype has _is_boolean = True, i.e. only 2 unique non-NA entries, then set C=B. rr r r data_for_groupingr5s  r TFcUR$)z#Whether to box the data in a Seriesrrs r box_in_seriesr8s ==r cgNr xs r r>s!r c S/[U5-$r:)lenr<s r r>r>s1#A,r c2[S/[U5-5$r:)rr@r<s r r>r>s&!s1v&r cU$r#r r<s r r>r>s!r )scalarlistseriesobject)ridscUR$)z$ Functions to test groupby.apply(). rr7s r groupby_apply_oprIs ==r cUR$)zM Boolean fixture to support Series and Series.to_frame() comparison testing. rr7s r as_framerK ==r cUR$)zD Boolean fixture to support arr and Series(arr) comparison testing. rr7s r as_seriesrNrLr cUR$)zX Boolean fixture to support comparison testing of ExtensionDtype array and numpy array. rr7s r use_numpyrP ==r ffillbfillcUR$)zo Parametrized fixture giving method parameters 'ffill' and 'bfill' for Series.fillna(method=) testing. rr7s r fillna_methodrUrQr cUR$)zJ Boolean fixture to support ExtensionDtype _from_sequence method testing. rr7s r as_arrayrWrLr c4[R[5$)z A scalar that *cannot* be held by this ExtensionArray. The default should work for most subclasses, but is not guaranteed. If the array can hold any item (i.e. object dtype), then use pytest.skip. )rF__new__)rs r invalid_scalarrZs >>& !!r returncb[RRSL=(a [SSS9S:H$)z/ Fixture to check if Copy-on-Write is enabled. Tzmode.data_manager)silentblock)rmode copy_on_writerr r r using_copy_on_writeras1  ""d* E +D 9W Dr )r/rpandas._config.configrpandasrrfixturer rrrr r)r+r-r1r3r5r8rIrKrNrPrUrWrZboolrar r r rfsV -     /01,      e}%& &   /e}%&e}%&e}%&)*+e}%&""Tr