d8ddlmZddlZddlZddlZddlmZmZmZddl Z ddl Z ddl m Z ddlmZmZmZmZddlmZmZmZmZmZmZmZmZmZddlmZddlm Z dd l!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/dd l0m1Z1dd l2m3Z3m4Z4m5Z5dd l6m7Z7ed dZ8e8duZ9da:dddZ;e;e dGddZ<GddZ=dd Z>dd!Z? ddd$Z@dd*ZA ddd/ZBdd0ZCddd2ZDdd5ZEdd9ZFdd:ZGdddd;dd<ZHdddd;dd=ZIeeEeGddddd?ddCZJddIZKedZe=dVWdddVddXdd[ZSedZdddVddXdd\ZTd]ZUeUd^d_`ZVeUdadb`ZWedZeGdddd;ddgZZedZeGdddd;ddhZ[edZeGddddd?ddiZ\ddkZ]e jOe jPfddmZ^ dddoZ_ddpZ`dqZaedZdrddsddzZbdd|ZcedZddVd}dd~ZddZedZfefejgZhefejiZjefejkZlefejmZnefejoZpefejqZrddZsdS)) annotationsN)AnyCallablecast) get_option)NaTNaTTypeiNaTlib) ArrayLikeAxisIntCorrelationMethodDtypeDtypeObjFScalarShapenpt)import_optional_dependency)find_stack_level)is_any_int_dtype is_bool_dtype is_complexis_datetime64_any_dtypeis_floatis_float_dtype is_integeris_integer_dtypeis_numeric_dtypeis_object_dtype is_scalaris_timedelta64_dtypeneeds_i8_conversion pandas_dtype) PeriodDtype)isnana_value_for_dtypenotna) extract_array bottleneckwarn)errorsFTvboolreturnNonectr|adSdSN)_BOTTLENECK_INSTALLED_USE_BOTTLENECK)r-s N/var/www/html/t/fyr/venv311/lib/python3.11/site-packages/pandas/core/nanops.pyset_use_bottleneckr6Cszcompute.use_bottleneckc0eZdZd fd Zd dZd d ZxZS)disallowdtypesrr/r0cttd|D|_dS)Nc3>K|]}t|jVdSr2)r$type).0dtypes r5 z$disallow.__init__..Ps-IIL//4IIIIIIr7)super__init__tupler:)selfr: __class__s r5rBzdisallow.__init__Ns; II&IIIII r7r.c`t|dot|jj|jS)Nr?)hasattr issubclassr?r=r:)rDobjs r5checkzdisallow.checkRs'sG$$PCINDK)P)PPr7frcptjfd}tt|S)Nctj||}tfd|Dr.jdd}t d|d tjd5|i|cdddS#1swxYwYdS#t$r+}t|drt ||d}~wwxYw) Nc3BK|]}|VdSr2)rJ)r>rIrDs r5r@z0disallow.__call__.._f..Ys-77s4::c??777777r7nanzreduction operation 'z' not allowed for this dtypeignoreinvalidr) itertoolschainvaluesany__name__replace TypeErrornperrstate ValueErrorr )argskwargsobj_iterf_nameerKrDs r5_fzdisallow.__call__.._fVsL tV]]__==H7777h77777 ++E266PFPPP [222..1d-f--..................    #47++.#A,,A- s<3B*B B*B!!B*$B!%B** C4&CC functoolswrapsrr)rDrKrcs`` r5__call__zdisallow.__call__UsG           &Ar{{r7)r:rr/r0r/r.)rKrr/r)rX __module__ __qualname__rBrJrg __classcell__)rEs@r5r9r9MsoJJJJJJQQQQr7r9c eZdZdd dZd dZdS) bottleneck_switchNr/r0c "||_||_dSr2)namer_)rDror_s r5rBzbottleneck_switch.__init__ns  r7altrcjpj ttn#tt f$rdYnwxYwt jdddd fd }tt|S) NTaxisskipnarV np.ndarrayrsAxisInt | Nonertr.ct jdkr( jD]\}}||vr|||<|jdkr%|dt ||St rw|rut|j r`|dd=| dd|fd|i|}t|r |f||d|}n|f||d|}n |f||d|}|S)Nr min_countmaskrsrr) lenr_itemssizeget_na_for_min_countr4 _bn_ok_dtyper?pop _has_infs) rVrsrtkwdskr-resultrpbn_funcbn_namerDs r5rKz%bottleneck_switch.__call__..fzs^4;!## K--//$$DAq}}"#Q{aDHH[$9$9$A)666 G6 Gl6<.Q.Q G88FD))1HHVT***$WV??$?$??F!((O!$V!N$v!N!N!N!N SJd6JJTJJFFVF$vFFFFMr7)rVrursrvrtr.) rorXgetattrbnAttributeError NameErrorrerfrr)rDrprKrrs`` @@r5rgzbottleneck_switch.__call__rs)+s| b'**GG *   GGG    $( % % % % % % % % % %   % NAqzzs*AAr2)r/r0)rprr/r)rXrirjrBrgr7r5rmrmmsA000000r7rmr?rrostrcJt|st|s|dvSdS)N)nansumnanprodnanmeanF)r r#)r?ros r5rrs5 5 ! !<*=e*D*D<;;; 5r7ct|tjr0|jdvr't j|dS tj|S#ttf$rYdSwxYw)N)f8f4KF) isinstancer[ndarrayr?r has_infsravelisinfrWrZNotImplementedError)rs r5rrs&"*%%3 << ' '< S 1 122 2x##%%% * +uus %A22BB fill_value Scalar | Nonec||St|r-| tjS|dkr tjStj S|dkr tjSt S)z9return the correct fill value for the dtype of the valuesN+inf) _na_ok_dtyper[rOinfr i8maxr )r?rfill_value_typs r5_get_fill_valuers`E   !6M''v w V # #9 Kr7rVrurtrynpt.NDArray[np.bool_] | Nonec|Ot|jst|jrdS|st|jrt |}|S)a Compute a mask if and only if necessary. This function will compute a mask iff it is necessary. Otherwise, return the provided mask (potentially None) when a mask does not need to be computed. A mask is never necessary if the values array is of boolean or integer dtypes, as these are incapable of storing NaNs. If passing a NaN-capable dtype that is interpretable as either boolean or integer data (eg, timedelta64), a mask must be provided. If the skipna parameter is False, a new mask will not be computed. The mask is computed using isna() by default. Setting invert=True selects notna() as the masking function. Parameters ---------- values : ndarray input array to potentially compute mask for skipna : bool boolean for whether NaNs should be skipped mask : Optional[ndarray] nan-mask if known Returns ------- Optional[np.ndarray[bool]] N)rr?rr#r&)rVrtrys r5_maybe_get_maskrs]B |  & & *:6<*H*H 4  (66 <.new_funcs |(J6  DL< 1. Returns ------- result : scalar or ndarray For 1-D values, returns a scalar of the correct missing type. For 2-D values, returns a 1-D array where each element is missing. rNr)rrr'r?ndimshaper[full)rVrsr result_shapes r5r~r~s"*y))#FL11J {a |ETE*V\$(**-EE w|Zv|DDDDr7cttjdddfd}tt|S) z NumPy operations on C-contiguous ndarrays with axis=1 can be very slow if axis 1 >> axis 0. Operate row-by-row and concatenate the results. NrsrVrursrvc|dkr|jdkr|jdr|jddz |jdkr|jtkr|jt krt |dAdfdttD}nfdD}tj |S|fd |iS) Nr C_CONTIGUOUSirryc>g|]}|fd|iSryr)r>iarrsrr_rys r5 z:maybe_operate_rowwise..newfunc..sE>?DDa99tAw9&99r7c"g|] }|fi Srr)r>xrr_s r5rz:maybe_operate_rowwise..newfunc..s+;;;44,,V,,;;;r7rs) rflagsrr?objectr.listr}rrangerzr[array)rVrsr_resultsrryrs ` @@r5newfuncz&maybe_operate_rowwise..newfuncs% AII q   ^,!a4'6<?:: && $$<B11111111. 7  r7rcHt|jr8|jjdkr(tjdt t t||d|\}}}}}t|r| t}| |S)a Check if any elements along an axis evaluate to True. Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : bool Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, 2]) >>> nanops.nanany(s) True >>> from pandas.core import nanops >>> s = pd.Series([np.nan]) >>> nanops.nanany(s) False rzz'any' with datetime64 dtypes is deprecated and will raise in a future version. Use (obj != pd.Timestamp(0)).any() instead. stacklevelFrry) r#r?rwarningsr+ FutureWarningrrr rr.rWrVrsrtry_s r5nananyrsD6<(( V\->#-E-E  J '))     %VVDQQQFAq!Qv%t$$ ::d  r7cHt|jr8|jjdkr(tjdt t t||d|\}}}}}t|r| t}| |S)a Check if all elements along an axis evaluate to True. Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : bool Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, 2, np.nan]) >>> nanops.nanall(s) True >>> from pandas.core import nanops >>> s = pd.Series([1, 0]) >>> nanops.nanall(s) False rzz'all' with datetime64 dtypes is deprecated and will raise in a future version. Use (obj != pd.Timestamp(0)).all() instead.rTr) r#r?rrr+rrrr rr.allrs r5nanallr*sD6<(( V\->#-E-E  J '))     %VV4PPPFAq!Qv%t$$ ::d  r7M8)rsrtrxryrxintfloatct||d|\}}}}}|}t|r|}n-t|rtjtj}|||} t| |||j|} | S)a Sum the elements along an axis ignoring NaNs Parameters ---------- values : ndarray[dtype] axis : int, optional skipna : bool, default True min_count: int, default 0 mask : ndarray[bool], optional nan-mask if known Returns ------- result : dtype Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, 2, np.nan]) >>> nanops.nansum(s) 3.0 rrrrx) rrr"r[r?rsum_maybe_null_outr) rVrsrtrxryr?rr dtype_sumthe_sums r5rrasD)414)))%FD%AIe) e $ $)HRZ(( jjYj//GgtT6<9UUUG Nr7r+np.ndarray | np.datetime64 | np.timedelta64npt.NDArray[np.bool_]r5np.ndarray | np.datetime64 | np.timedelta64 | NaTTypec`t|tjrN|d|j}||}t||<nE|r1tjt|jS|S)Nrr) rr[rrrr?rWr r)rrsryr axis_masks r5rrs &"*%% :t$$))+*;<<HH$H'' !y 88:: :8D>>&&{'899 9 Mr7ct||d|\}}}}}|}tjtj}|jdvrtjtj}nAt |rtjtj}nt |r|}|}t|j|||} t| ||} |t| ddrvttj | } tjd 5| | z } dddn #1swxYwY| dk} | rtj| | <n| dkr| | z n tj} | S) a Compute the mean of the element along an axis ignoring NaNs Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- float Unless input is a float array, in which case use the same precision as the input array. Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, 2, np.nan]) >>> nanops.nanmean(s) 1.5 rrrrNrFrQr)rr[r?rrrr _get_countsr_ensure_numericrrrrr\rWrO) rVrsrtryr?rrr dtype_countcountrthe_meanct_masks r5rrsD)414)))%FD%AI(2:&&K zZHRZ(( % HRZ((      dD D D DEfjjYj??@@G GGVU;;RZ'' [X & & & ' 'H ' ' ' ' ' ' ' ' ' ' ' ' ' ' '1* ;;== ' "HW &+aii7U??RV Os!D33D7:D7cd fd }t||d\}}}}}t|jsF |d}n/#t$r"}t t ||d}~wwxYw|tj||<|j }|j dkr||rustj |||} ntj 5tjddttj||} dddn #1swxYwYnFt#|j|tjtj} n|r |||n tj} t)| |S) a Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : float Unless input is a float array, in which case use the same precision as the input array. Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, np.nan, 2, 2]) >>> nanops.nanmedian(s) 2.0 Nc6|t|}n|}s |s tjSt j5t jddttj||}dddn #1swxYwY|S)NrQAll-NaN slice encountered) r(rr[rOrcatch_warningsfilterwarningsRuntimeWarning nanmedian)r_maskresrts r5 get_medianznanmedian..get_medians =!HHEEFE eiikk 6M  $ & & ) )  #5~   ,qx((C  ) ) ) ) ) ) ) ) ) ) ) ) ) ) )  s 6BBBr)ryrrrrQrr2)rrr?rr]rZrr[rOr|rapply_along_axisrrrrrget_empty_reduction_resultrfloat_r) rVrsrtryrr?rerrnotemptyrs ` r5rrs2      !,FFRS T T TFD%A &, ' '/ /]]4((FF / / /CHH%%3 . / vt {H{Q4+  T 5)*dFCC,..55+ "=~,vt44C 555555555555555-V\4BFSSCC+3>jj&&& e $ $$s)A A8A33A81D  DDrtuple[int, ...]r np.dtype | type[np.floating]ctj|}tjt|}tj|||k|}|||S)z The result from a reduction on an empty ndarray. Parameters ---------- shape : Tuple[int] axis : int dtype : np.dtype fill_value : Any Returns ------- np.ndarray r)r[rarangerzemptyfill)rrsr?rshpdimsrets r5rr8sZ( (5//C 9SZZ D (3tt|$E 2 2 2CHHZ Jr7 values_shaperddof-tuple[float | np.ndarray, float | np.ndarray]ct||||}|||z }t|r||krtj}tj}ntt tj|}||k}|r@tj||tjtj||tj||fS)a: Get the count of non-null values along an axis, accounting for degrees of freedom. Parameters ---------- values_shape : Tuple[int, ...] shape tuple from values ndarray, used if mask is None mask : Optional[ndarray[bool]] locations in values that should be considered missing axis : Optional[int] axis to count along ddof : int degrees of freedom dtype : type, optional type to use for count Returns ------- count : int, np.nan or np.ndarray d : int, np.nan or np.ndarray r) r r=r!r[rOrrrWr)r'ryrsr(r?r ds r5_get_counts_nanvarr,Ss:  dD > > >E  4   A , D==FEARZ''} 88:: , Jq$ ' ' ' JudBF + + + !8Or7rr(rsrtr(ryc |jdkr|d}|j}t|||\}}}}}tjt |||||}t ||S)a Compute the standard deviation along given axis while ignoring NaNs Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. mask : ndarray[bool], optional nan-mask if known Returns ------- result : float Unless input is a float array, in which case use the same precision as the input array. Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, np.nan, 2, 3]) >>> nanops.nanstd(s) 1.0 zM8[ns]rrr.)r?rrr[sqrtnanvarr)rVrsrtr(ry orig_dtyperrs r5nanstdr3s{H|xX&&J'TBBBFD!Q WVFf4dSSS T TF  , ,,r7m8c:t|d}|j}t|||}t|r&|d}|t j||<t|jr!t|j ||||j\}}nt|j |||\}}|r,|*| }t j ||dt| |t j|z }|t j||}t||z dz} |t j | |d| |t j|z } t|r| |d } | S) a Compute the variance along given axis while ignoring NaNs Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. mask : ndarray[bool], optional nan-mask if known Returns ------- result : float Unless input is a float array, in which case use the same precision as the input array. Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, np.nan, 2, 3]) >>> nanops.nanvar(s) 1.0 TrrNr)rsr?rFr)r)r?rrrr[rOrr,rrrr rr expand_dims) rVrsrtr(ryr?r r+avgsqrrs r5r1r1sJ6 6 6 6F LE 664 0 0D"t$$  6F4Lfl##F%flD$flSSqq%flD$EEq $$" 64### &**$bj*AA B BU JC nS$'' 3>> from pandas.core import nanops >>> s = pd.Series([1, np.nan, 2, 3]) >>> nanops.nansem(s) 0.5773502691896258 r.r) r1rrr?rrWr[rOr,rr0)rVrsrtr(ryr rvars r5nansemr;sL 6V$TBBBB 664 0 0D &, ' '%t$$ d&488::&v !&,dD&,OOHE1 d64 H H HC 73<<"'%.. ((r7cntdtdddddfd}|S)NrO)roTrrVrursrvrtr.ryrr/rct|||\}}}}}||j|dks |jdkre t| ||}|t jnB#tttf$rt j}YnwxYwt| |}t||||j}|S)Nrryrr) rrr|rr#r[rOrrZr]r) rVrsrtryr?rrrrmeths r5 reductionz_nanminmax..reduction2s6A F>6 6 6 2eY   d!3q!8!8V[A=M=M ...t9EEE BF####"Iz:    +WVT**400F tV\BB s:A55#BB) rVrursrvrtr.ryrr/r)rmr)r?rr@s`` r5 _nanminmaxrA1skL$LL))) $-1 *). r7minr)rmax-infOint | np.ndarrayct|dd|\}}}}}||}t||||}|S)a Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : int or ndarray[int] The index/indices of max value in specified axis or -1 in the NA case Examples -------- >>> from pandas.core import nanops >>> arr = np.array([1, 2, 3, np.nan, 4]) >>> nanops.nanargmax(arr) 4 >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3) >>> arr[2:, 2] = np.nan >>> arr array([[ 0., 1., 2.], [ 3., 4., 5.], [ 6., 7., nan], [ 9., 10., nan]]) >>> nanops.nanargmax(arr, axis=1) array([2, 2, 1, 1]) TrDr>)rargmax_maybe_arg_null_outrVrsrtryrrs r5 nanargmaxrKRPN(VRVWWWFD!Q ]]4 F tV < >> from pandas.core import nanops >>> arr = np.array([1, 2, 3, np.nan, 4]) >>> nanops.nanargmin(arr) 0 >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3) >>> arr[2:, 0] = np.nan >>> arr array([[ 0., 1., 2.], [ 3., 4., 5.], [nan, 7., 8.], [nan, 10., 11.]]) >>> nanops.nanargmin(arr, axis=1) array([0, 0, 1, 1]) Trr>)rargminrIrJs r5 nanargminrOrLr7ct|d}t|||}t|js,|d}t |j||}nt |j|||j}|r-|+|}tj ||dn$|s"| | r tj S| |tj |z }|tj||}||z }|r|tj ||d|dz}||z}| |tj } | |tj } t| } t| } tjdd 5||d z d zz|dz z | | d zz z} dddn #1swxYwY|j} t| r| | d } t#| tjr.tj| dkd| } tj | |dk<n| dkrdn| } |dkr tj S| S)a Compute the sample skewness. The statistic computed here is the adjusted Fisher-Pearson standardized moment coefficient G1. The algorithm computes this coefficient directly from the second and third central moment. Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : float64 Unless input is a float array, in which case use the same precision as the input array. Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, np.nan, 1, 2]) >>> nanops.nanskew(s) 1.7320508075688787 TrrrNrrrQrSdividerg?g?Fr)r)rrr?rr rrr[rrWrOrrr6_zero_out_fperrr\rrr) rVrsrtryr meanadjusted adjusted2 adjusted3m2m3rr?s r5nanskewr[sN6 6 6 6F 664 0 0D &, ' 'Jt$$FL$55FL$FLIII $" 64#### (TXXZZ(v ::d"*: - - 5D ~dD))}H &$" 8T1%%%! IH$I t2: . .B t2: . .B   B   B Xh 7 7 7MM519,, :rBG|LMMMMMMMMMMMMMMM LEe2u511&"*%%"'1f--FuqyAgg6 1996M Ms-GGGct|d}t|||}t|js,|d}t |j||}nt |j|||j}|r-|+|}tj ||dn$|s"| | r tj S| |tj |z }|tj||}||z }|r|tj ||d|dz}|dz}| |tj } | |tj } tjdd 5d |d z dzz|dz |d z zz } ||d zz|d z z| z} |dz |d z z| dzz} dddn #1swxYwYt!| } t!| } t#| tjs|d kr tj S| dkrdStjdd 5| | z | z }dddn #1swxYwY|j}t|r||d }t#|tjr-tj| dkd|}tj ||d k<|S)a Compute the sample excess kurtosis The statistic computed here is the adjusted Fisher-Pearson standardized moment coefficient G2, computed directly from the second and fourth central moment. Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : float64 Unless input is a float array, in which case use the same precision as the input array. Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, np.nan, 1, 3, 2]) >>> nanops.nankurt(s) -1.2892561983471076 TrrrNrrrQrQrSrFr)r)rrr?rr rrr[rrWrOrrr6r\rTrrr)rVrsrtryr rUrVrW adjusted4rYm4adj numerator denominatorrr?s r5nankurtrcsjN6 6 6 6F 664 0 0D &, ' 'Jt$$FL$55FL$FLIII $" 64#### (TXXZZ(v ::d"*: - - 5D ~dD))}H &$" 8T1%%%! I1 I t2: . .B t2: . .B Xh 7 7 788519""uqyUQY&?@UQY'5195: qyUQY/"a%7 888888888888888 **I!+..K k2: . . 1996M !  1 Xh 7 7 7//[(3./////////////// LEe2u511&"*%%#+*Av66Fuqy Ms$:GGG II!$I!ct|||}|r||}d||<||}t||||j|S)a Parameters ---------- values : ndarray[dtype] axis : int, optional skipna : bool, default True min_count: int, default 0 mask : ndarray[bool], optional nan-mask if known Returns ------- Dtype The product of all elements on a given axis. ( NaNs are treated as 1) Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, 2, 3, np.nan]) >>> nanops.nanprod(s) 6.0 Nrr)rrprodrr)rVrsrtrxryrs r5rrksp@ 664 0 0D $"t [[  F dFLI   r7np.ndarray | intc ||S|t|dds0|r|rdSn]|rdSnF|r||}n||}|rd||<|S)NrF)rrrW)rrsryrtna_masks r5rIrIs |  |766599|  xxzz r xxzz r   %hhtnnGGhhtnnG ;;== ! F7O Mr7float | np.ndarraych|H||j|z }ntj|}||S|$|j|||z }n||}t |r||S||dS)a Get the count of non-null values along an axis Parameters ---------- values_shape : tuple of int shape tuple from values ndarray, used if mask is None mask : Optional[ndarray[bool]] locations in values that should be considered missing axis : Optional[int] axis to count along dtype : type, optional type to use for count Returns ------- count : scalar or array NFr)r|rr[rer=rr!r)r'ryrsr?nr s r5r r s0 |   DHHJJ&AA %%Azz!}}  4 488D>>1T"!zz%   <<E< * **r7np.ndarray | float | NaTTypec||dkr|S| t|tjr|+|j|||z |z dk}n<|||z dk}|d|||dzdz}tj||}tj|rtt|r`tj|r| d}n&t|s| dd}tj ||<nbd||<n\|turSt|||rBt|dd}t|r|d }n tj }|S) zu Returns ------- Dtype The product of all elements on a given axis. ( NaNs are treated as 1) Nrrc16rFrr?rO)rr[rrr broadcast_torWr iscomplexobjrrrOrcheck_below_min_countrr=) rrsryrrx null_mask below_count new_shape result_dtypes r5rrs | Q  Jvrz::  D)DHHTNN:YF!KII + 1A5Kete uTAXZZ'88I Y??I 6)   )'' )?6**=#]]511FF'//=#]]4e]<.funcWs:a##A& &r7spearman) spearmanrc(||dSrr)rrrs r5rzget_corr_func..func^s9Q??1% %r7r|c8tj||dS)Nrr)r[corrcoef)rrs r5rzget_corr_func..funcds;q!$$T* *r7zUnknown method 'z@', expected one of 'kendall', 'spearman', 'pearson', or callable) scipy.statsrrcallabler])r}rrrs @@r5rrQs****** ' ' ' ' ' :  )))))) & & & & & 9   + + + &    86 8 8 8  r7)r~r(cft|t|krtd|d}t|t|z}|s||}||}t||kr tjSt j|||dS)Nz&Operands to nancov must have same sizerr-r)rzrr(rr[rOcov)rrr~r(rs r5nancovrqs 1vvQEFFF !HHuQxx E 99;; eH eH 1vv v 6!QT " " "4 ((r7cHt|tjrt|st |r!|tj}nHt|r |tj}tj tj |s|j }n#ttf$rJ |tj}n&#t$r}td|d|d}~wwxYwYnwxYwnt|s}t|snt!|s_ t#|}nN#ttf$r: t%|}n&#t$r}td|d|d}~wwxYwYnwxYw|S)NzCould not convert z to numeric)rr[rrrrrr complex128rWimagrealrZr]rrrrcomplex)rrs r5r r s!RZ  N A   -"2"2 $$AA Q    HHR]++vbgajj))Az* R R RR,,AA!RRR#$G$G$G$GHHcQRA R qkk NZ]] Njmm N NaAA:& N N N NAJJ N N N CQ C C CDD#M N N Hsl*B77D C)(D) D 3DD  DDEF&E65F6 FFFFFcfd}|S)Ncpt|}t|}||z}tjd5||}dddn #1swxYwY|rDt |r|d}tj||tj|S)NrQrrE)r&r[r\rWrrrrO)ryxmaskymaskryrops r5rKzmake_nancomp..fsQQu} [X & & &  R1XXF                88:: -V$$ ,s++ JvtRV , , , s AAAr)rrKs` r5 make_nancomprs#      Hr7r c .tjdtjftjjtj tjftjdtjftjjtjtjfi|\}}|jj dvsJ|rkt|jj tj tj fs;|}t|}|||<||d}|||<n ||d}|S)a Cumulative function with skipna support. Parameters ---------- values : np.ndarray or ExtensionArray accum_func : {np.cumprod, np.maximum.accumulate, np.cumsum, np.minimum.accumulate} skipna : bool Returns ------- np.ndarray or ExtensionArray g?grrr)r[cumprodrOmaximum accumulatercumsumminimumr?rrHr=rbool_rr&)rV accum_funcrtmask_amask_bvalsryrs r5 na_accum_funcrs S"&M 0 C= /   NFF < J . . . .,j!2RZ4JKK,{{}}DzzT Dq)))t F+++ Mr7)T)r-r.r/r0)r?rrorr/r.rh)NN)r?rrr)rVrurtr.ryrr/r)NNN) rVrurtr.rrrrryrr/r)r?rr/r.r2)r?r)rrr/r)rVrursrvr/r) rVrursrvrtr.ryrr/r.) rVrursrvrtr.rxrryrr/r) rrrsrvryrrrur/r) rVrursrvrtr.ryrr/r)rsrvrtr.) rrrsr r?rrrr/ru) r'rryrrsrvr(rr?rr/r))rsrvrtr.r(r) rVrursrvrtr.r(rryrr/r) rVrursrvrtr.ryrr/rF) rrursrvryrrtr.r/rf) r'rryrrsrvr?rr/rj)r) rrmrsrvryrrrrxrr/rm)rrryrrxrr/r.) rrurrur}rr~rr/r)r}rr/r) rrurrur~rr(rr/r)rVr rtr.r/r )t __future__rrerToperatortypingrrrrnumpyr[pandas._configr pandas._libsrr r r pandas._typingr r rrrrrrrpandas.compat._optionalrpandas.util._exceptionsrpandas.core.dtypes.commonrrrrrrrrrr r!r"r#r$pandas.core.dtypes.dtypesr%pandas.core.dtypes.missingr&r'r(pandas.core.constructionr)rr3r4r6r9rmrrrrrrrrr~rrrrrrrrr?rr,r3r1r;rAnanminnanmaxrKrOr[rcrrIr rrrrTrrrr rgtnangtgenangeltnanltlenanleeqnaneqnenannerrr7r5rs """""" %%%%%%                      ?>>>>>444444 211111 322222 V<<<$::677888@55555555p(    GK,))))^!%)- V6V6V6V6V6r2222 &&&&&RDEEEE>J )- 444444t )- 444444n $ )- ++++++\& + )- <<<<<<~04TPTM%M%M%M%M%M%`@bhrz** ,,,,,^  *-*-*-*-*-*-Z $  GGGGGGT $ )- 1)1)1)1)1)1)h: E& 1 1 1 E& 1 1 1 # )- ******Z # )- ******Z $ )- UUUUUUp $ )- ^^^^^^B $ )- ((((((V>bhrz** &+&+&+&+&+\ -----`>AAA $ !*" 8@ $ # ))))))0   @   &  X[!! 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