ttjddlmZddlZddlZddlmZmZmZddlZddl Z ddl m Z ddl mZmZmZmZddlmZmZmZmZmZmZmZmZmZddlmZddlmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&dd l'm(Z(m)Z)m*Z*erdd l+m,Z,ed d Z-e-duZ.da/dddZ0e0e dGddZ1GddZ2ddZ3ddZ4 ddd"Z5dd(Z6 ddd-Z7dd/Z8dd0Z9ddd1Z:dd4Z;dd8Zdddd:dd<Z?e1d=e;e=ddddd>ddBZ@ddHZAe2e;dddd:ddJZBe2dddd:ddLZCddPZDe jEe jFfddTZGe2dUVdddUddWddXZHe1d=dYe2dUVdddUddWddZZIe1d=dYdddUddWdd[ZJd\ZKeKd]d^_ZLeKd`da_ZMdddd:ddcZNdddd:dddZOe1d=dYe=dddd:ddeZPe1d=dYe=dddd:ddfZQe1d=dYe=ddddd>ddgZRddiZSe jEe jFfddlZT dddpZUddqZVddsZWe1d=dYdtddudd|ZXdd~ZYe1d=dYddUdddZZdZ[ddZ\dS)) annotationsN) TYPE_CHECKINGAnycast) get_option)NaTNaTTypeiNaTlib) ArrayLikeAxisIntCorrelationMethodDtypeDtypeObjFScalarShapenpt)import_optional_dependency) is_complexis_floatis_float_dtype is_integeris_numeric_dtypeis_object_dtypeneeds_i8_conversion pandas_dtype)isnana_value_for_dtypenotna)Callable bottleneckwarn)errorsFTvboolreturnNonectr|adSdSN)_BOTTLENECK_INSTALLED_USE_BOTTLENECK)r%s P/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/pandas/core/nanops.pyset_use_bottleneckr.;szcompute.use_bottleneckc0eZdZd fd Zd dZd d ZxZS)disallowdtypesrr'r(cttd|D|_dS)Nc3>K|]}t|jVdSr*)rtype).0dtypes r- z$disallow.__init__..Hs-IIL//4IIIIIIr/)super__init__tupler2)selfr2 __class__s r-r:zdisallow.__init__Fs; II&IIIII r/r&c`t|dot|jj|jS)Nr7)hasattr issubclassr7r5r2)r<objs r-checkzdisallow.checkJs'sG$$PCINDK)P)PPr/frcptjfd}tt|S)Ncftj||}tfd|Dr.jdd}t d|d |i|S#t$r+}t|drt ||d}~wwxYw)Nc3BK|]}|VdSr*)rB)r6rAr<s r-r8z0disallow.__call__.._f..Qs-77s4::c??777777r/nanzreduction operation 'z' not allowed for this dtyper) itertoolschainvaluesany__name__replace TypeError ValueErrorr)argskwargsobj_iterf_nameerCr<s r-_fzdisallow.__call__.._fNs tV]]__==H7777h77777 ++E266PFPPP q$)&)))    #47++.#A,,A- s3A;; B0&B++B0 functoolswrapsrr)r<rCrVs`` r-__call__zdisallow.__call__MsG           $Ar{{r/)r2rr'r(r'r&)rCrr'r)rM __module__ __qualname__r:rBrZ __classcell__)r=s@r-r1r1EsoJJJJJJQQQQr/r1c eZdZdd dZd dZdS) bottleneck_switchNr'r(c "||_||_dSr*)namerR)r<rbrRs r-r:zbottleneck_switch.__init__es  r/altrcjpj ttn#tt f$rdYnwxYwt jdddd fd }tt|S) NTaxisskipnarK np.ndarrayrfAxisInt | Nonergr&ct jdkr( jD]\}}||vr|||<|jdkr%|dt ||St rw|rut|j r`|dd=| dd|fd|i|}t|r |f||d|}n|f||d|}n |f||d|}|S)Nr min_countmaskrfre) lenrRitemssizeget_na_for_min_countr, _bn_ok_dtyper7pop _has_infs) rKrfrgkwdskr%resultrcbn_funcbn_namer<s r-rCz%bottleneck_switch.__call__..fqs^4;!## K--//$$DAq}}"#Q{aDHH[$9$9$A)666 G6 Gl6<.Q.Q G88FD))1HHVT***$WV??$?$??F!((O!$V!N$v!N!N!N!N SJd6JJTJJFFVF$vFFFFMr/)rKrhrfrirgr&) rbrMgetattrbnAttributeError NameErrorrXrYrr)r<rcrCrxrys`` @@r-rZzbottleneck_switch.__call__is)+s| b'**GG *   GGG    $( % % % % % % % % % %   % NAqzzs*AAr*)r'r()rcrr'r)rMr\r]r:rZr/r-r`r`dsA000000r/r`r7rrbstrcB|tkrt|s|dvSdS)N)nansumnanprodnanmeanF)objectr)r7rbs r-rrrrs+ 2599;;; 5r/ct|tjr0|jdvr't j|dS tj|S#ttf$rYdSwxYw)N)f8f4KF) isinstancenpndarrayr7r has_infsravelisinfrLrONotImplementedError)rws r-rtrts&"*%%3 << ' '< S 1 122 2x##%%% * +uus %A22BB fill_value Scalar | Nonec||St|r-| tjS|dkr tjStj S|dkrtjt jStjtS)z9return the correct fill value for the dtype of the valuesN+inf) _na_ok_dtyperrGinfint64r i8maxr )r7rfill_value_typs r-_get_fill_valuersuE  !6M v % %6MF7N 6 ! ! x """x~~r/rKrhrgrlnpt.NDArray[np.bool_] | Nonech|/|jjdvrdS|s|jjdvrt|}|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]] NbiumM)r7kindr)rKrgrls r-_maybe_get_maskrsFB | <  % %4  V\&$..<.new_funcs |(D0  DL< 1. Returns ------- result : scalar or ndarray For 1-D values, returns a scalar of the correct missing type. For 2-D values, returns a 1-D array where each element is missing. iufcbrNr)r7rrrndimshaperfull)rKrfr result_shapes r-rqrqs"|G##y))#FL11J {a |ETE*V\$(**-EE w|Zv|DDDDr/cttjdddfd}tt|S) z NumPy operations on C-contiguous ndarrays with axis=1 can be very slow if axis 1 >> axis 0. Operate row-by-row and concatenate the results. NrfrKrhrfric|dkr|jdkr|jdr|jddz |jdkr|jtt fvrt |dAdfdttD}nfdD}tj |S|fd |iS) Nr C_CONTIGUOUSrrlc>g|]}|fd|iSrlr~)r6iarrsrrRrls r- z:maybe_operate_rowwise..newfunc..sE>?DDa99tAw9&99r/c"g|] }|fi Sr~r~)r6xrrRs r-rz:maybe_operate_rowwise..newfunc..s+;;;44,,V,,;;;r/rf) rflagsrr7rr&listrprsrangermrarray)rKrfrRresultsrrlrs ` @@r-newfuncz&maybe_operate_rowwise..newfuncs AII q   ^,!a4'6<?:: VTN22<B11111111, 7  r/rc4|jjdvr|||S|jjdkrtdt ||d|\}}|jt kr|t}||S)a Check if any elements along an axis evaluate to True. Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : bool Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, 2]) >>> nanops.nanany(s.values) np.True_ >>> from pandas.core import nanops >>> s = pd.Series([np.nan]) >>> nanops.nanany(s.values) np.False_ iubNrz0datetime64 type does not support operation 'any'Frrl)r7rrLrOrrrr&rKrfrgrl_s r-nananyrsD|E!!dlzz$ |CJKKKFFu4HHHIFA|vt$$ ::d  r/c4|jjdvr|||S|jjdkrtdt ||d|\}}|jt kr|t}||S)a Check if all elements along an axis evaluate to True. Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : bool Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, 2, np.nan]) >>> nanops.nanall(s.values) np.True_ >>> from pandas.core import nanops >>> s = pd.Series([1, 0]) >>> nanops.nanall(s.values) np.False_ rNrz0datetime64 type does not support operation 'all'Tr)r7rallrOrrrr&rs r-nanallrsD|E!!dlzz$ |CJKKKFFt$GGGIFA|vt$$ ::d  r/M8)rfrgrkrlrkint*npt.NDArray[np.floating] | float | NaTTypec,|j}t||d|\}}t|}|jdkr|}n)|jdkrt jtj}|||}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.values) np.float64(3.0) rrrCrrrk) r7rrrrrsum_maybe_null_outr)rKrfrgrkrlr7 dtype_sumthe_sums r-rrUsD LEvv!$GGGLFDu%%I zS s  HRZ(( jjYj//GgtT6<9UUUG Nr/rw+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) rrrrrr7rLr r)rwrfrlr axis_masks r-rrs &"*%%6t$$))+*;<<HH$H''  y 6x~~"";#4555 Mr/floatc|jtkr/t|dkr|t|dd|||j}t ||d|\}}t |}t jt j}|jdvrt jt j}n7|jdvrt jt j}n|jdkr|}|}t|j ||| }| || }t|}|t|d d rvtt j|}t jd 5||z } dddn #1swxYwY|dk} | rt j| | <n|dkr||z n t j} | 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.values) np.float64(1.5) rNrerrriurCrrFignore)r)r7rrmrrrrrr _get_countsrr_ensure_numericrzrrerrstaterLrG) rKrfrgrlr7r dtype_countcountrthe_meanct_masks r-rrsB|v#f++"5"5$,uu D8888 LEvv!$GGGLFDu%%I(2:&&K zTHRZ(( t  HRZ(( s     dD D D DEjjYj//Gg&&G GGVU;;RZ'' [X & & & ' 'H ' ' ' ' ' ' ' ' ' ' ' ' ' ' '1* ;;== ' "HW &+aii7U??RV Os)E;;E?E?float | np.ndarraycB|jjdko|du}ddfd }|j}t||d\}}|jjdkr|jtkr+t j|}|dvrt d|d  |d }n/#t$r"}t t||d}~wwxYw|s1|/|j j s| }tj||<|j} |jd kr|| r͉stj|||} nt%j5t%jd d t*|jd d kr|dks|jdd kr/|d kr)tjtj|d} ntj||} dddn #1swxYwYn0t3|j|} n| r |||n tj} t5| |S)a> Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : float | ndarray 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.values) 2.0 >>> s = pd.Series([np.nan, np.nan, np.nan]) >>> nanops.nanmedian(s.values) nan rCNrrhcl|t|}n|}s |s tjSt j5t jddtt jddttj||}dddn #1swxYwY|S)NrAll-NaN slice encounteredzMean of empty slice) r rrrGwarningscatch_warningsfilterwarningsRuntimeWarning nanmedian)r_maskresrgs r- get_medianznanmedian..get_medians =!HHEEFE eiikk 6M  $ & & ) )  #5~     #H.C^ T T T,qx((C  ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) s AB))B-0B-)rlrstringmixedzCannot convert to numericrrrr rT)keepdimsrr*)rrh)r7rrrr infer_dtyperOrrPrr writeablerrrGrorapply_along_axisrrrrrrsqueeze_get_empty_reduction_resultr) rKrfrgrlusing_nan_sentinelrr7inferrederrnotemptyrs ` r-rrsB *c1Bddl LEvvDTJJJLFD |C <6 ! !v..H... E& E E EFFF /]]4((FF / / /CHH%%3 . / $"2|% #[[]]Fvt {H {Q4+  B >)*dFCC,.. > >+ "=~ Q1,, Q1,,!l2:f+=+=MMM l6=== > > > > > > > > > > > > > > >$.flDAACC+3>jj&&& e $ $$s+B C&CCB GGGrrr ctj|}tjt|}tj|||ktj}|tj|S)z The result from a reduction on an empty ndarray. Parameters ---------- shape : Tuple[int, ...] axis : int Returns ------- np.ndarray r)rrarangermemptyrfillrG)rrfshpdimsrets r-rrFs^ (5//C 9SZZ D (3tt|$BJ 7 7 7CHHRV Jr/ values_shapeddof-tuple[float | np.ndarray, float | np.ndarray]ct||||}|||z }t|r||krtj}tj}ntt tj|}||k}|r@tj||tjtj||tj||fS)a: Get the count of non-null values along an axis, accounting for degrees of freedom. Parameters ---------- values_shape : Tuple[int, ...] shape tuple from values ndarray, used if mask is None mask : Optional[ndarray[bool]] locations in values that should be considered missing axis : Optional[int] axis to count along ddof : int degrees of freedom dtype : type, optional type to use for count Returns ------- count : int, np.nan or np.ndarray d : int, np.nan or np.ndarray r) rr5rrrGrrrLr)r+rlrfr,r7rds r-_get_counts_nanvarr0]s:  dD > > >E  4   A , D==FEARZ''} 88:: , Jq$ ' ' ' JudBF + + + !8Or/rr,rfrgr,rlc 6|jjdkr8tj|jd}|d|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.values) 1.0 rrzm8[]rr2) r7rrrrrsqrtnanvarr)rKrfrgr,rlr orig_dtyperws r-nanstdr8sH|C --a0]4]]]++JvvD999LFD WVFf4dSSS T TF  , ,,r/m8cz|j}t|||}|jdvr'|d}|tj||<n>|jdkr3t |j||||t |j||||zS|jjdkr!t|j ||||j\}}nt|j |||\}}|r,|*| }t j ||dt||tj|z }|t j||}t||z d z} |t j | |d| |tj|z } |jdkr| |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.values) 1.0 rrNcr2rCr)rfr7rFr)r7rrrrrGr6realimagr0rrrrrr expand_dims) rKrfrgr,rlr7rr/avgsqrrws r-r6r6sJ LE 664 0 0D zTt$$  6F4L s   Kd64   6;T&t$ O O OP P|C%flD$flSSqq%flD$EEq $$" 64### &**$bj*AA B BU JC nS$'' 3>> from pandas.core import nanops >>> s = pd.Series([1, np.nan, 2, 3]) >>> nanops.nansem(s.values) np.float64(0.5773502691896258) r2fcrNrC) r6rr7rrrLrrGrr0rr5) rKrfrgr,rlrrrvars r-nansemrDsL 6V$TBBBB 664 0 0D |$$t$$ d&488::&v (2:&&K |Cl !&,dD+NNHE1 d64 H H HC 73<<"'%.. ((r/cntdtdddddfd }|S)NrG)rbTrrKrhrfrirgr&rlrc|jdkrt||S|j}t|||\}}t ||}t ||||j|jdv}|S)Nrrrlr)r)rorqr7rrzrrr)rKrfrgrlr7rwrmeths r- reductionz_nanminmax..reductionKs ;!  $VT22 2 " F>   '&&t,, D$ 5:;M    r/r)r`r)rHrrIs`` r- _nanminmaxrJJskL$LL))) $-1 *)( r/minr)rmax-infint | np.ndarrayct|dd|\}}||}t||||}|S)a Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : int or ndarray[int] The index/indices of max value in specified axis or -1 in the NA case Examples -------- >>> from pandas.core import nanops >>> arr = np.array([1, 2, 3, np.nan, 4]) >>> nanops.nanargmax(arr) np.int64(4) >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3) >>> arr[2:, 2] = np.nan >>> arr array([[ 0., 1., 2.], [ 3., 4., 5.], [ 6., 7., nan], [ 9., 10., nan]]) >>> nanops.nanargmax(arr, axis=1) array([2, 2, 1, 1]) TrMrG)rargmax_maybe_arg_null_outrKrfrgrlrws r- nanargmaxrShJLvtFNNNLFD ]]4 F!tV < >> from pandas.core import nanops >>> arr = np.array([1, 2, 3, np.nan, 4]) >>> nanops.nanargmin(arr) np.int64(0) >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3) >>> arr[2:, 0] = np.nan >>> arr array([[ 0., 1., 2.], [ 3., 4., 5.], [nan, 7., 8.], [nan, 10., 11.]]) >>> nanops.nanargmin(arr, axis=1) array([0, 0, 1, 1]) TrrG)rargminrQrRs r- nanargminrWrTr/ct|||}|jjdkr,|d}t |j||}nt |j|||j}|r-|+|}tj||dn$|s"| | r tj Stj dd5| |tj |z }dddn #1swxYwY|tj||}||z }|r|tj||d|dz}||z}| |tj } | |tj } tj||d } tj| jj} | | zdz|z} | | zd z|z}t'| | } t'| |} tj dd5||d z d zz|dz z | | dzz z}dddn #1swxYwY|j}|jdkr||d}t)|tjr.tj| dkd|}tj ||d k<n/| dkr|dn|}|d kr 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.values) np.float64(1.7320508075688787) rCrrNrrinvaliddividerinitialrg?g?Fr)rr7rrrrrrrrLrGrrrr>absrLfinfoeps_zero_out_fperrrrrr5)rKrfrgrlrmeanadjusted adjusted2 adjusted3m2m3max_absrbconstant_tolerance2constant_tolerance3rwr7s r-nanskewrms{J 664 0 0D |Ct$$FL$55FL$FLIII $" 64#### (TXXZZ(v Xh 7 7 7::zz$bjz11E9::::::::::::::: ~dD))}H &$" 8T1%%%! IH$I t2: . .B t2: . .BfVnn  s 33G (28   C'Ma/58'Ma/58 0 1 1B 0 1 1B Xh 7 7 7MM519,, :rBG|LMMMMMMMMMMMMMMM LE zSu511&"*%%"'1f--Fuqy"$''Av 1996M Ms$%DD  D %I  IIct|||}|jjdkr,|d}t |j||}nt |j|||j}|r-|+|}tj||dn$|s"| | r tj Stj dd5| |tj |z }dddn #1swxYwY|tj||}||z }|r|tj||d|dz}|dz}| |tj } | |tj } tj||d } tj| jj} | | zdz|z} | | zd z|z}t'| | } t'| |} tj dd5d |d z dzz|dz |d z zz }||d zz|d z z| z}|dz |d z z| dzz}dddn #1swxYwYt)|tjs2|d kr tj S|dkr|jdStj dd5||z |z }dddn #1swxYwY|j}|jdkr||d}t)|tjr-tj|dkd|}tj ||d k<|S)a Compute the sample excess kurtosis The statistic computed here is the adjusted Fisher-Pearson standardized moment coefficient G2, computed directly from the second and fourth central moment. Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True mask : ndarray[bool], optional nan-mask if known Returns ------- result : float64 Unless input is a float array, in which case use the same precision as the input array. Examples -------- >>> from pandas.core import nanops >>> s = pd.Series([1, np.nan, 1, 3, 2]) >>> nanops.nankurt(s.values) np.float64(-1.2892561983471076) rCrrNrrrYrr\r]r_rFr)rr7rrrrrrrrLrGrrrr>r`rLrarbrcrrr5r)rKrfrgrlrrdrerf adjusted4rhm4rjrbrkconstant_tolerance4adj numerator denominatorrwr7s r-nankurtrvs1J 664 0 0D |Ct$$FL$55FL$FLIII $" 64#### (TXXZZ(v Xh 7 7 7::zz$bjz11E9::::::::::::::: ~dD))}H &$" 8T1%%%! I1 I t2: . .B t2: . .B0fVnn  s 33G (28   C'Ma/58'Ma/58 0 1 1B 0 1 1B Xh 7 7 788519""uqyUQY&?@UQY'5195: qyUQY/"a%7 888888888888888 k2: . .( 1996M !  <$$Q'' ' Xh 7 7 7//[(3./////////////// LE zSu511&"*%%#+*Av66Fuqy Ms6%DD  D %:I++I/2I/ K--K14K1ct|||}|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.values) np.float64(6.0) Nrr)rrprodrr)rKrfrgrkrlrws r-rrsp@ 664 0 0D $"t [[  F dFLI   r/np.ndarray | intc||S|t|ddsK|r#|rtd|s#|rtdnp|r6||rtd|s6||rtd|S)NrFzEncountered all NA valuesz)Encountered an NA value with skipna=False)rzrrPrL)rwrfrlrgs r-rQrQs |  |766599|  Jdhhjj J899 9 JDHHJJ JHII I FDHHTNN&&((F4555 F**,,FDEEE Mr/np.dtype[np.floating]&np.floating | npt.NDArray[np.floating]ch|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)rorrrxr5rrr)r+rlrfr7nrs r-rrs0 |   DHHJJ&AA %%Azz!}}  4 488D>>1T"%!zz%   <<E< * **r/np.ndarray | float | NaTTypetuple[int, ...]rc ||dkr|S|t|tjr|+|j|||z |z dk}n<|||z dk}|d|||dzdz}tj||}tj|r|r t||<nt|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) Nrrc16rFrr7rG)rrrrr broadcast_torLr r iscomplexobjrrrGrcheck_below_min_countrzr5) rwrfrlrrkr null_mask below_count new_shape result_dtypes r-rrs | Q  Jvrz::  D)DHHTNN:YF!KII + 1A5Kete uTAXZZ'88I Y??I 6)   ) )$(y!!!&)) )?6**=#]]511FF'//=#]]4e]<r/pearson)method min_periodsabrrr int | Nonect|t|krtd|d}t|t|z}|s||}||}t||kr tjSt |}t |}t|}|||S)z a, b: ndarrays z'Operands to nancorr must have same sizeNr)rmAssertionErrorr rrrGr get_corr_func)rrrrvalidrCs r-nancorrr[s 1vvQFGGG !HHuQxx E 99;; eH eH 1vv v AAfA 1Q77Nr/)Callable[[np.ndarray, np.ndarray], float]c|dkr ddlmfd}|S|dkr ddlmfd}|S|dkrd }|St|r|St d |d ) Nkendallr) kendalltauc(||dSrr~)rrrs r-rzget_corr_func..funcs:a##A& &r/spearman) spearmanrc(||dSrr~)rrrs r-rzget_corr_func..funcs9Q??1% %r/rc8tj||dS)Nrr)rcorrcoef)rrs r-rzget_corr_func..funcs;q!$$T* *r/zUnknown method 'z@', expected one of 'kendall', 'spearman', 'pearson', or callable) scipy.statsrrcallablerP)rrrrs @@r-rr{s****** ' ' ' ' ' :  )))))) & & & & & 9   + + + &    86 8 8 8  r/)rr,ct|t|krtd|d}t|t|z}|s||}||}t||kr tjSt |}t |}t j|||dS)Nz&Operands to nancov must have same sizerr1r)rmrr rrrGrcov)rrrr,rs r-nancovrs 1vvQEFFF !HHuQxx E 99;; eH eH 1vv v AA 6!QT " " "4 ((r/ct|tjr|jjdvr!|tj}n|jtkrtj |}|dvrtd|d |tj }tj tj |s|j}n#ttf$rJ |tj}n&#t$r}td|d|d}~wwxYwYnwxYwnt!|st#|st%|st|t&rtd|d t)|}nN#ttf$r: t+|}n&#t$r}td|d|d}~wwxYwYnwxYw|S)NrrzCould not convert rzCould not convert string 'z ' to numeric)rrrr7rrrrr rrO complex128rLr=r<rPrrrrrcomplex)rr!r"s r-rrs6!RZ  N 7<5 $$AA W  q))H... CQ C C CDDD HHR]++vbgajj))Az* R R RR,,AA!RRR#$G$G$G$GHHcQRA R qkk NZ]] Njmm N a   JHHHHII I NaAA:& N N N NAJJ N N N CQ C C CDD#M N N HslCD0'DD0 D*D%%D**D0/D0 FG%,F<;G%< GGGG%$G%r c .tjdtjftjjtj tjftjdtjftjjtjtjfi|\}}|jj dvsJ|rkt|jj tj tj fs;|}t|}|||<||d}|||<n ||d}|S)a Cumulative function with skipna support. Parameters ---------- values : np.ndarray or ExtensionArray accum_func : {np.cumprod, np.maximum.accumulate, np.cumsum, np.minimum.accumulate} skipna : bool Returns ------- np.ndarray or ExtensionArray g?r\rrr)rcumprodrGmaximum accumulatercumsumminimumr7rr@r5rbool_rr)rK accum_funcrgmask_amask_bvalsrlrws r- na_accum_funcrs S"&M 0 C= /   NFF < D ( ( ( (,j!2RZ4JKK,{{}}DzzT Dq)))t F+++ Mr/)T)r%r&r'r()r7rrbrr'r&r[)NN)r7rrr)rKrhrgr&rlrr'r)NNN) rKrhrgr&rrrrrlrr'r)r7rr'r)r7rr'r&r*)r7r)rrr'r)rKrhrfrir'r) rKrhrfrirgr&rlrr'r&) rKrhrfrirgr&rkrrlrr'r) rwrrfrirlrrrhr'r) rKrhrfrirgr&rlrr'r)rKrhrfrirgr&r'r )rrrfr r'rh) r+rrlrrfrir,rr7rr'r-)rfrirgr&r,r)rKrhrfrirgr&r,r) rKrhrfrirgr&r,rrlrr'r) rKrhrfrirgr&rlrr'rN) rKrhrfrirgr&rkrrlrr'r) rwrhrfrirlrrgr&r'ry) r+rrlrrfrir7r{r'r|)rF)rwrrfrirlrrrrkrrr&r'r)rrrlrrkrr'r&)rr ) rrhrrhrrrrr'r)rrr'r) rrhrrhrrr,rr'r)rKr rgr&r'r )] __future__rrXrItypingrrrrnumpyrpandas._configr pandas._libsrr r r pandas._typingr r rrrrrrrpandas.compat._optionalrpandas.core.dtypes.commonrrrrrrrrpandas.core.dtypes.missingrrr collections.abcr!r{r+r,r.r1r`rrrtrrrrrrrrqrrrrrrrrr7rr0r8r6rDrJnanminnanmaxrSrWrmrvrrQrrrrcrrrrrr~r/r-rs """""" %%%%%%                      ?>>>>>                     )(((((( V<<<$::677888>55555555p(    GK.))))^!%)- DDDDDN    2222 '''''TDEEEE>H )- 555555v )- 555555p $ )- ******Z      )- @@@@@@F26tRVh%h%h%h%h%h%V8bhrz** /////d  +-+-+-+-+-+-\ $  NNNNNNb $ )- 5)5)5)5)5)5)p4 E& 1 1 1 E& 1 1 1  )- ++++++b )- ++++++\ $ )- TTTTTTn $ )- rrrrrrj $ )- ((((((V4$,28BJ#7#7 &+&+&+&+&+\ 11111h>???? $ !*" >@ $ # ))))))6! ! ! 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