a SicX@snddlmZddlmZmZddlZddlZddlm Z m Z ddl m Z ddl mZddlmmZddlmZddlmZmZmZmZmZmZmZdd lmZmZdd l m!Z!ddl"Z#dd l"m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-dd l.m/Z/dd l0m1Z1m2Z2m3Z3m4Z4m5Z5ddl6m7Z7ddl8m9Z9ddl:m;Z;ddldddddZ?d d!Z@dld#dd$d%d&ZAdd"e d"d"d"ddd'f d'd'd(d#dd#d#d#ddd)dd* d+d,ZBdmd(dd.d/d0ZCdnd)d)dd2d3d4ZDdd5d6d7ZEdd5d8d9ZFdodd5d;d<ZGdpdd5d>d?ZHdqdd5dAdBZIdrdd5dDdEZJdsdd5dGdHZKdtdIdJZLdudddMdNdOZMd"de dKddfdddddPdQdRZNd"dd"e d"dKdKd"d"d"d"dddSfd"dKdTdddddUdVdWZOd"ddd"e d"dKdKdKd"dKd"d"dddXfddddYdZd[ZPdd5d\d]ZQdd5d^d_ZRdd5d`daZSdd5dbdcZTd#d5dddeZUdSdfdfddgdhdiZVdd5djdkZWdS)v) annotations)LiteralcastN) NoDefault no_default)is_matching_na) SparseIndex)find_stack_level)is_boolis_categorical_dtypeis_extension_array_dtypeis_interval_dtype is_numberis_numeric_dtypeneeds_i8_conversion)CategoricalDtype PandasDtype)array_equivalent) Categorical DataFrame DatetimeIndexIndex IntervalIndex MultiIndex PeriodIndex RangeIndexSeriesTimedeltaIndex)take_nd) DatetimeArrayExtensionArray IntervalArray PeriodArrayTimedeltaArray)DatetimeLikeArrayMixin) StringDtype)safe_sort_index) pprint_thingequivgh㈵>g:0yE>zbool | Literal['equiv']zbool | int | NoDefaultfloatNone) check_dtypecheck_less_precisertolatolreturncKs|tur&tjdttdt|}}t|trNt||fd|||d|nt|t rvt ||fd|||d|nt|t rt ||fd|||d|nx|rt |rt |rnBt|rt|rn0t|tjst|tjrd}nd}t|||dtj||ft|||d |d S) a Check that the left and right objects are approximately equal. By approximately equal, we refer to objects that are numbers or that contain numbers which may be equivalent to specific levels of precision. Parameters ---------- left : object right : object check_dtype : bool or {'equiv'}, default 'equiv' Check dtype if both a and b are the same type. If 'equiv' is passed in, then `RangeIndex` and `Int64Index` are also considered equivalent when doing type checking. check_less_precise : bool or int, default False Specify comparison precision. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the number of digits to compare. When comparing two numbers, if the first number has magnitude less than 1e-5, we compare the two numbers directly and check whether they are equivalent within the specified precision. Otherwise, we compare the **ratio** of the second number to the first number and check whether it is equivalent to 1 within the specified precision. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. rtol : float, default 1e-5 Relative tolerance. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. .. versionadded:: 1.1.0 The 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be removed in a future version. You can stop passing 'check_less_precise' to silence this warning. stacklevelF) check_exactexactr-r.)r3r+r-r. numpy arrayInputobj)r+r-r.N)rwarningswarn FutureWarningr _get_tol_from_less_precise isinstancerassert_index_equalrassert_series_equalrassert_frame_equalrr npndarrayassert_class_equal_testingassert_almost_equalbool)leftrightr+r,r-r.kwargsr8rJU/var/www/html/django/DPS/env/lib/python3.9/site-packages/pandas/_testing/asserters.pyrE?sv.      rEz bool | int)r,r/cCs*t|tr|rdSdSndd| SdS)ax Return the tolerance equivalent to the deprecated `check_less_precise` parameter. Parameters ---------- check_less_precise : bool or int Returns ------- float Tolerance to be used as relative/absolute tolerance. Examples -------- >>> # Using check_less_precise as a bool: >>> _get_tol_from_less_precise(False) 5e-06 >>> _get_tol_from_less_precise(True) 0.0005 >>> # Using check_less_precise as an int representing the decimal >>> # tolerance intended: >>> _get_tol_from_less_precise(2) 0.005 >>> _get_tol_from_less_precise(8) 5e-09 gMb@?gh㈵>g? N)r=rF)r,rJrJrKr<s  r<cCsZ|j}t||s.t|d|dt|dt||sVt|d|dt|ddS)a Helper method for our assert_* methods that ensures that the two objects being compared have the right type before proceeding with the comparison. Parameters ---------- left : The first object being compared. right : The second object being compared. cls : The class type to check against. Raises ------ AssertionError : Either `left` or `right` is not an instance of `cls`. z Expected type z, found z insteadN)__name__r=AssertionErrortype)rGrHclscls_namerJrJrK_check_isinstances  rRTrF) compare_keysr/cCs t||ttj|||ddS)N)rS)rRdictrDassert_dict_equal)rGrHrSrJrJrKrUs rUrz bool | strstr) rGrHr4 check_namesr,r3check_categorical check_orderr-r.r8r/c  sd} dddfdd } dd} |turHtjd ttd t|}} t||t| ||| d |j|jkr| d }|jd |}|jd |}t | |||t |t |kr| d}t |d |}t |d |}t | ||||st |}t |}|jdkrt t |}t t |}t|jD]X}| ||}| ||}d|d}t|||||| |d| |j||j|| d q2|rr||s4|j|jk}t|rt d|d}t|tdt |}| dt|dd}t | |||n&t}tj|j|j|| || ||d|rJtd||| d t|t sbt|t rrtd||| d t|t!st|t!rt"|j|jrt#|j$st#|j$rt%|j|j| dd dS)aj Check that left and right Index are equal. Parameters ---------- left : Index right : Index exact : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. If 'equiv', then RangeIndex can be substituted for Int64Index as well. check_names : bool, default True Whether to check the names attribute. check_less_precise : bool or int, default False Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. check_exact : bool, default True Whether to compare number exactly. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_order : bool, default True Whether to compare the order of index entries as well as their values. If True, both indexes must contain the same elements, in the same order. If False, both indexes must contain the same elements, but in any order. .. versionadded:: 1.2.0 rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 obj : str, default 'Index' Specify object name being compared, internally used to show appropriate assertion message. Examples -------- >>> from pandas import testing as tm >>> a = pd.Index([1, 2, 3]) >>> b = pd.Index([1, 2, 3]) >>> tm.assert_index_equal(a, b) Trr*r/cszsdSt|||dtd|||dt|jrft|jrfrbtd|||dt|j|jddStd|||ddS)N)r4r8 inferred_typer7dtype)r4)rCassert_attr_equalr r\r> categoriesrGrHr8rXr4rJrK _check_types9sz(assert_index_equal.._check_typescSs:|j|}|j|}t|j||jd}|j||j|dS)N) fill_value)name)levelscodesr_values _na_value _shallow_copynames)indexlevelunique level_codesfilledrJrJrK_get_ilevel_valuesIs  z.assert_index_equal.._get_ilevel_valuesr0r1r7z levels are different, z length are differentzMultiIndex level [])r4rWr3r-r.r8r Y@ values are different ( %))r-r.r+r8lobjZrobjrifreq categoryN)r)&rr9r:r;r r<rRrnlevelsraise_assert_detaillenr&rrranger>rdequalsrfr fillnarAsumastypeintroundrFrDrEvaluesr]r=rrassert_interval_array_equalr r\assert_categorical_equal)rGrHr4rWr,r3rXrYr-r.r8__tracebackhide__raromsg1msg2Zmsg3rkZllevelZrlevelrwmismatchdiffmsgZ exact_boolrJr`rKr>s?                 r>r6)r4r/cCspddlm}d}dd}t|t|kr,dS|dkrLt||rLt||rLdS|d}t||||||dS) z# Checks classes are equal. r) NumericIndexTcSst|tr|St|jS)N)r=rrOrM)xrJrJrK repr_classs z&assert_class_equal..repr_classNr(z classes are different)pandas.core.indexes.numericrrOr=r{)rGrHr4r8rrrrrJrJrKrCs  rC Attributes)attrr8r/c Csd}t||}t||}||us*t||r.dSz ||k}WntyPd}Yn0|tju|tjuArld}nt|ts~|}|sd|d}t||||dS)aO Check attributes are equal. Both objects must have attribute. Parameters ---------- attr : str Attribute name being compared. left : object right : object obj : str, default 'Attributes' Specify object name being compared, internally used to show appropriate assertion message TNFz Attribute "z" are different) getattrr TypeErrorpdNAr=rFallr{) rrGrHr8rZ left_attrZ right_attrresultrrJrJrKr]s"       r]rZcCsddlm}t|ttjfrX|D]0}dtt|j }t||j t fs$J|q$n.dtt|j }t||j t t fsJ|dS)NrzBone of 'objs' is not a matplotlib Axes instance, type encountered zoobjs is neither an ndarray of Artist instances nor a single ArtistArtist instance, tuple, or dict, 'objs' is a )matplotlib.pyplotpyplotr=rrArBravelreprrOrMAxesrTArtisttuple)objspltelrrJrJrK"assert_is_valid_plot_return_objects    rcCs.t|ttfr|j}t|tt|dS)z#Assert that the sequence is sorted.N)r=rrrassert_numpy_array_equalrAsortarray)seqrJrJrKassert_is_sortedsrrcCst||tt|jts$t|jtr*d}nd}|rht|j|j|d|dt|j|j||ddnzz|j}|j}Wn t y|j|j}}Yn0t|||d|dt|j |j|j |j|d|dt d|||d d S) a} Test that Categoricals are equivalent. Parameters ---------- left : Categorical right : Categorical check_dtype : bool, default True Check that integer dtype of the codes are the same. check_category_order : bool, default True Whether the order of the categories should be compared, which implies identical integer codes. If False, only the resulting values are compared. The ordered attribute is checked regardless. obj : str, default 'Categorical' Specify object name being compared, internally used to show appropriate assertion message. r(Tz .categories)r8r4z.codesr+r8z.valuesorderedr7N) rRrr=r^rr>rre sort_valuesrtaker])rGrHr+check_category_orderr8r4lcrcrJrJrKrs4     rr!cCszt||ti}|jjjdvr&d|d<t|j|jfd|di|t|j|jfd|di|td|||ddS) a Test that two IntervalArrays are equivalent. Parameters ---------- left, right : IntervalArray The IntervalArrays to compare. exact : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. If 'equiv', then RangeIndex can be substituted for Int64Index as well. obj : str, default 'IntervalArray' Specify object name being compared, internally used to show appropriate assertion message )mMF check_freqr8z.leftclosedr7N)rRr!_leftr\kind assert_equal_rightr])rGrHr4r8rIrJrJrKrHs   rr"cCs8t||tt|j|j|ddtd|||ddS)N._datar7rx)rRr"r_datar]r_rJrJrKassert_period_array_equalgs rrcCsPd}t||tt|j|j|dd|r._get_basesamez is not copyz is cs|dur|j|jkr,td|j|jd}t||D]\}}t||ds:|d7}q:|d|j}dt|dd}t|||d t|dS) Nz shapes are differentr strict_nanrqrsrtrurvr)shaper{ziprsizerArrN)rGrHerr_msgrZleft_arrZ right_arrrrr8rrJrK_raises  z(assert_numpy_array_equal.._raiserr\N) rCrRrArBrNrrr=r]) rGrHrr+r check_samer8rrrZ left_baseZ right_baserrJrrKrs$ r)r+r-r.r/c Cs,|tur&tjdttdt|}}t|ts8Jdt|tsJJd|r^td||ddt|t rt|t rt |t |krt t |jt |j|dd St |}t |} t || d |d ||jtd } || jtd } |r t | | d|d ntj| | t|||d|d d S)a Check that left and right ExtensionArrays are equal. Parameters ---------- left, right : ExtensionArray The two arrays to compare. check_dtype : bool, default True Whether to check if the ExtensionArray dtypes are identical. index_values : numpy.ndarray, default None Optional index (shared by both left and right), used in output. check_less_precise : bool or int, default False Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. check_exact : bool, default False Whether to compare number exactly. rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 Notes ----- Missing values are checked separately from valid values. A mask of missing values is computed for each and checked to match. The remaining all-valid values are cast to object dtype and checked. Examples -------- >>> from pandas import testing as tm >>> a = pd.Series([1, 2, 3, 4]) >>> b, c = a.array, a.array >>> tm.assert_extension_array_equal(b, c) r0r1zleft is not an ExtensionArrayzright is not an ExtensionArrayr\r r7rNzExtensionArray NA mask)r8r)r\)r+r-r.r8r)rr9r:r;r r<r=r r]r$rOrrAasarrayasi8isnato_numpyobjectrDrErF) rGrHr+rr,r3r-r.Zleft_naZright_naZ left_validZ right_validrJrJrKassert_extension_array_equalsR5 rr) check_index check_like)r+check_index_typer,r/c Cs d}|s|rtd|tur:tjdttdt|} }t||t|rXt |||dt |t |krt |d|j }t |d|j }t |d||| r|j |j ksJt|j dt|j |rt|j |j |||| | | ||d d |r|||}}| rPt|j ttfrP|j }|j }|j|jksPJ|j|jf|rt|jtrzt|jtrz| szntd ||d |d|rt|jrt|jr|j}|j}t|trt|trt|||t|j d nt|||t|t|j dn|rft |js,t |jrft!|j"t!|jsd|jd|jd}t#|nNt$|jrt$|jrt%|j&|j&n$t|jtst|jtrt'j(|j|j| |t)|t|t|j dnt*|jrt*|jrt|j|j| ||t|j dnt+|j|js4t+|j|jrRt|j|j|t|j d nbt |jrt |jrt|j|j|t|j d n,t'j(|j|j| |t)|t|t|j d|rtd|||d| rt|jtst|jtrt,|j|j|d| ddS)a Check that left and right Series are equal. Parameters ---------- left : Series right : Series check_dtype : bool, default True Whether to check the Series dtype is identical. check_index_type : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. check_series_type : bool, default True Whether to check the Series class is identical. check_less_precise : bool or int, default False Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare. When comparing two numbers, if the first number has magnitude less than 1e-5, we compare the two numbers directly and check whether they are equivalent within the specified precision. Otherwise, we compare the **ratio** of the second number to the first number and check whether it is equivalent to 1 within the specified precision. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. check_names : bool, default True Whether to check the Series and Index names attribute. check_exact : bool, default False Whether to compare number exactly. check_datetimelike_compat : bool, default False Compare datetime-like which is comparable ignoring dtype. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_category_order : bool, default True Whether to compare category order of internal Categoricals. .. versionadded:: 1.0.2 check_freq : bool, default True Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex. .. versionadded:: 1.1.0 check_flags : bool, default True Whether to check the `flags` attribute. .. versionadded:: 1.2.0 rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 obj : str, default 'Series' Specify object name being compared, internally used to show appropriate assertion message. check_index : bool, default True Whether to check index equivalence. If False, then compare only values. .. versionadded:: 1.3.0 check_like : bool, default False If True, ignore the order of the index. Must be False if check_index is False. Note: same labels must be with the same data. .. versionadded:: 1.5.0 Examples -------- >>> from pandas import testing as tm >>> a = pd.Series([1, 2, 3, 4]) >>> b = pd.Series([1, 2, 3, 4]) >>> tm.assert_series_equal(a, b) Tz0check_like must be False if check_index is Falser0r1r7rpzSeries length are different != .indexr4rWr3rXrYr-r.r8r\zAttributes of )r+r)r+r8rzatetimelike_compat=True] z is not equal to .)r-r.r+r8r)r-r.r+rrcry)r8rN)- ValueErrorrr9r:r;r r<rRrrCr|rjr{flagsrr> reindex_liker=rrrxr\rr]rrfr rrArrrVrrr~rNr rrrDrErFr 0is_extension_array_dtype_and_needs_i8_conversionr)rGrHr+rZcheck_series_typer,rWr3check_datetimelike_compatrXrr check_flagsr-r.r8rrrrrlidxridx left_values right_valuesrrJrJrKr?csb  *                r?r)r+rr/cCsd}|tur*tjdttdt|}}t||t|rLt|t |sLJ|j |j kr|t ||dt |j t |j |r|j |j ksJt |j dt |j t|j|j||| | | |||dd t|j|j||| | | |||dd | r|||}}|r|}|}ttt|t|D]:}||vsXJ||vsfJt||||||d qFnht|jD]\\}}|j|d d }|j|d d }t||||| || | | |d |d |d||dddqdS)a~ Check that left and right DataFrame are equal. This function is intended to compare two DataFrames and output any differences. It is mostly intended for use in unit tests. Additional parameters allow varying the strictness of the equality checks performed. Parameters ---------- left : DataFrame First DataFrame to compare. right : DataFrame Second DataFrame to compare. check_dtype : bool, default True Whether to check the DataFrame dtype is identical. check_index_type : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. check_column_type : bool or {'equiv'}, default 'equiv' Whether to check the columns class, dtype and inferred_type are identical. Is passed as the ``exact`` argument of :func:`assert_index_equal`. check_frame_type : bool, default True Whether to check the DataFrame class is identical. check_less_precise : bool or int, default False Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare. When comparing two numbers, if the first number has magnitude less than 1e-5, we compare the two numbers directly and check whether they are equivalent within the specified precision. Otherwise, we compare the **ratio** of the second number to the first number and check whether it is equivalent to 1 within the specified precision. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. check_names : bool, default True Whether to check that the `names` attribute for both the `index` and `column` attributes of the DataFrame is identical. by_blocks : bool, default False Specify how to compare internal data. If False, compare by columns. If True, compare by blocks. check_exact : bool, default False Whether to compare number exactly. check_datetimelike_compat : bool, default False Compare datetime-like which is comparable ignoring dtype. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_like : bool, default False If True, ignore the order of index & columns. Note: index labels must match their respective rows (same as in columns) - same labels must be with the same data. check_freq : bool, default True Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex. .. versionadded:: 1.1.0 check_flags : bool, default True Whether to check the `flags` attribute. rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 obj : str, default 'DataFrame' Specify object name being compared, internally used to show appropriate assertion message. See Also -------- assert_series_equal : Equivalent method for asserting Series equality. DataFrame.equals : Check DataFrame equality. Examples -------- This example shows comparing two DataFrames that are equal but with columns of differing dtypes. >>> from pandas.testing import assert_frame_equal >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]}) >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]}) df1 equals itself. >>> assert_frame_equal(df1, df1) df1 differs from df2 as column 'b' is of a different type. >>> assert_frame_equal(df1, df2) Traceback (most recent call last): ... AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different Attribute "dtype" are different [left]: int64 [right]: float64 Ignore differing dtypes in columns with check_dtype. >>> assert_frame_equal(df1, df2, check_dtype=False) Tr0r1z shape mismatchrrrz.columnsrrq)axisz .iloc[:, z] (column name="z")F) r+rr3rWrrXrr8r-r.rrN)rr9r:r;r r<rRrr=rOrr{rrr>rjcolumnsr_to_dict_of_blockslistsetkeysr@ enumerate_ixsr?)rGrHr+rZcheck_column_typeZcheck_frame_typer,rWZ by_blocksr3rrXrrrr-r.r8rZrblocksZlblocksr\icolZlcolZrcolrJrJrKr@os~   * $ r@cKsd}t|trNt||fi|t|ttfrJ|j|jksJJ|j|jfnrrrxrr?rr@r!rr"rrrr#rr rrArBrrVrE)rGrHrIrrJrJrKrQs4           rcCst||tjjt|j|jt|jts.Jt|jts>J|j}|j}| |sdt dd||nt d||t d||t| | dS)z Check that the left and right SparseArray are equal. Parameters ---------- left : SparseArray right : SparseArray zSparseArray.indexzindex are not equalrbr\N) rRrarrays SparseArrayr sp_valuesr=sp_indexrr~r{r]to_dense)rGrH left_index right_indexrJrJrKassert_sp_array_equalzs    rcCs(|D]}||vsJdt|qdS)NzDid not contain item: )r)iterableZdickrJrJrKassert_contains_allsrcKs\t||D]L\}}t||fi|dtt|dtt|d}||us J|q dS)z iter1, iter2: iterables that produce elements comparable with assert_almost_equal Checks that the elements are equal, but not the same object. (Does not check that items in sequences are also not the same object) zExpected object z and object z8 to be different objects, but they were the same object.N)rrErrO)Ziter1Ziter2Z eql_kwargsZelem1Zelem2rrJrJrK assert_copys  rcCst|ot|S)z Checks that we have the combination of an ExtensionArraydtype and a dtype that should be converted to int64 Returns ------- bool Related to issue #37609 )r r)Z left_dtypeZ right_dtyperJrJrKrs rslice)serl_slci_slcr/cCs6|j|}t|j|||js2t|||dS)z_ Check that ser.iloc[i_slc] matches ser.loc[l_slc] and, if applicable, ser[l_slc]. N)ilocr?locrj is_integer)rrrexpectedrJrJrK!assert_indexing_slices_equivalents  rcCsF|jD]:}t||d}|dur,|dus@Jq|t||dksJqdS)z: Check that ._metadata attributes are equivalent. N) _metadatar)rGrHrvalrJrJrKassert_metadata_equivalents   r)T)Tr6)r)TTr)r(r!)r")rT)r#T)NN)FTNNr5N)X __future__rtypingrrr9numpyrApandas._libs.librrpandas._libs.missingrpandas._libs.sparserZpandas._libs.testing_libstestingrDpandas.util._exceptionsr pandas.core.dtypes.commonr r r r rrrpandas.core.dtypes.dtypesrrpandas.core.dtypes.missingrpandasrrrrrrrrrrrpandas.core.algorithmsrpandas.core.arraysrr r!r"r#Zpandas.core.arrays.datetimeliker$pandas.core.arrays.string_r%pandas.core.indexes.apir&pandas.io.formats.printingr'rEr<rRrUr>rCr]rrrrrrrr{rrr?r@rrrrrrrrJrJrJrKs    $  0     o( &;' :  )Slc)!