MhƉhSrSSKJr SSKJr SSKJrJrJrJ r J r SSK r SSK JrJrJr SSKJrJrJrJrJr SSKJr SS KJr SS KJrJrJrJrJ r J!r! SS K"J#r# SS K$J%r%J&r&J'r' \(aSS K(J)r) S4Sjr*S5Sjr+\ SS.S6Sjj5r,\ S7Sj5r,SS.S8Sjjr,/SQr-/SQr.S9Sjr/S:Sjr0S;Sjr1SSjr4S?S@Sjjr5SASjr6SBSCS jjr7SDSES!jjr8SFSGS"jjr9SHSIS#jjr:SJSKS$jjr;SLS%jr<SMSNS&jjr=SOSPS'jjr>SQS(jr?\?SRSSS)jj5r@\?SRSSS*jj5rA\?SRSTS+jj5rB\?SRSUS,jj5rCSVS-jrDSVS.jrE\@\AS/.rFSWSXS0jjrGSYS1jrHSZS2jrIS[S3jrJg)\z$ Routines for filling missing data. ) annotations)wraps) TYPE_CHECKINGAnyLiteralcastoverloadN)NaTalgoslib) ArrayLikeAxisIntF ReindexMethodnpt)import_optional_dependency)infer_dtype_from) is_array_like is_bool_dtypeis_numeric_dtypeis_numeric_v_string_likeis_object_dtypeneeds_i8_conversion)DatetimeTZDtype)is_valid_na_for_dtypeisnana_value_for_dtypeIndexc[U5(a-[U5U:wa[S[U5SU35eXnU$)zB Validate the size of the values passed to ExtensionArray.fillna. z'Length of 'value' does not match. Got (z ) expected )rlen ValueError)valuemasklengths E/var/www/html/env/lib/python3.13/site-packages/pandas/core/missing.pycheck_value_sizer'3sQU u: 9#e*F#H&   Lcj[U5up![U[R5(a[R"XS9nO>UR 5n[ R"U5(dU/nURXSS9nSn[UR5(aSn[U5)n[U5nX)n[R"UR[S9n[UR5(a5[UR5(d[UR5(aO[UR5(a5[UR5(a[UR5(dOUHn [!X 5(aMU(a8[R"UR[R"S9n UWU :HX'O7X :Hn [U [R$5(dU R'[SS9n X-nM UR)5(aU[U5-nU$)z Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True Parameters ---------- arr : ArrayLike values_to_mask: list, tuple, or scalar Returns ------- np.ndarray[bool] )dtypeF)r*copyT)r*na_value)r isinstancenpr*arrayconstruct_array_typer is_list_like_from_sequencerrzerosshapeboolrrrbool_ndarrayto_numpyany) arrvalues_to_maskr*cls potential_naarr_maskna_masknonnar$xnew_masks r& mask_missingrCBs"-^<E%"".>((*//,-N++Ne+TLsyy!! I:>"G 8 $E 88CIIT *D##cii(( %++ & & cii  U[[ ) )ekk** A'//!xx BH),X!);H&"xH%h ;;#+#4#44%#4#P  {{}} S  Kr(. allow_nearestcgNmethodrEs r&clean_fill_methodrK r(cgrGrHrIs r&rKrKrLr(Fc[U[5(a!UR5nUS:XaSnOUS:XaSnSS/nSnU(aURS5 SnX;a[ SUS U35eU$) Nffillpadbfillbackfillzpad (ffill) or backfill (bfill)nearestz(pad (ffill), backfill (bfill) or nearestzInvalid fill method. Expecting z. Got )r-strlowerappendr")rJrE valid_methods expectings r&rKrKs &# W F w FJ'M1IY'>  ":9+VF8TUU Mr()lineartimeindexvalues)rSzeroslinear quadraticcubic barycentrickroghspline polynomialfrom_derivativespiecewise_polynomialpchipakima cubicsplinec URS5nUS;aUc [S5e[[-nX;a[SUSUS35eUS;aUR(d[US35eU$) Norder)rcrdz7You must specify the order of the spline or polynomial.zmethod must be one of z. Got 'z ' instead.)rbrfrgz4 interpolation requires that the index be monotonic.)getr" NP_METHODS SP_METHODSis_monotonic_increasing)rJr[kwargsrkvalids r&clean_interp_methodrrs JJw E ))emRSS  #E 1%xzRSS ;;,,(NO  Mr(cUS;de[U5S:XagURS:XaURSS9nUS:XaUSSR5nO+US:Xa%[U5S- USSS 2R5- nUWnU(dgU$) z Retrieves the positional index of the first valid value. Parameters ---------- how : {'first', 'last'} Use this parameter to change between the first or last valid index. is_valid: np.ndarray Mask to find na_values. Returns ------- int or None )firstlastrNaxisrtru)r!ndimr9argmax)howis_valididxpos chk_notnas r&find_valid_indexrs # ## # 8}}}<b B  r(cbUb+SS/nUR5nX;a[SUSUS35eU$)Ninsideoutsidez%Invalid limit_area: expecting one of z, got .r) limit_areavalid_limit_areass r&validate_limit_arearsS%y1%%'  .78I7J&,a!  r(cUcUS;aSnU$SnU$US;aUS:wa[SUS35eUS;aUS:wa[SUS35eU$)N)rRrQrr)rPrOz0`limit_direction` must be 'forward' for method ``z1`limit_direction` must be 'backward' for method `)r")rrJs r&infer_limit_directionr#s * *(O (O  % %/Y*FB6(!L  * **/LCF81M  r(cUS:Xa,SSKJn U"[R"[ U555nO}1Skn[ UR 5=(dB [UR [5=(d! [R"UR S5nX;aU(d[SUS35e[U5R5(a [S5eU$) NrYrr>rZr[r\rSmMz9Index column must be numeric or datetime type when using z_ method other than linear. Try setting a numeric or datetime index column before interpolating.zkInterpolation with NaNs in the index has not been implemented. Try filling those NaNs before interpolating.)pandasrr.aranger!rr*r-rr is_np_dtyper"rr9NotImplementedError)rJr[rmethodsis_numeric_or_datetimes r&get_interp_indexr8s  biiE +,8 U[[ ) 2%++7 2u{{D1   )?!!!  E{! /  Lr(c ^^^^^^ ^ ^ [TU40T D6 [TUR5(a[URSS9mTS:Xa'[ UR5(d [ S5eSm[ T5m[U5m [R"STS9m[UT5m S UU U UU UUU4Sjjn [R"XU5 g) z Column-wise application of _interpolate_1d. Notes ----- Alters 'data' in-place. The signature does differ from _interpolate_1d because it only includes what is needed for Block.interpolate. F)compatrZzStime-weighted interpolation only works on Series or DataFrames with a DatetimeIndexr\N)nobslimitc 0>[STUTTTTTSTS. TD6 g)NF) indicesyvaluesrJrrr fill_value bounds_errorr$rH)_interpolate_1d) rrrrprlimit_area_validatedrr$rJs r&func$interpolate_2d_inplace..funcs7  ++!  r()r np.ndarrayreturnNone) rrrr*rrr"rrr validate_limit_index_to_interp_indicesr.apply_along_axis) datar[ryrJrrrrr$rprrrs ``` ``` @@r&interpolate_2d_inplacerWs.00Z44' 5A  "5;;//   .?O.z:  d% 8E&uf5G  ,D)r(c\URn[UR5(aURS5nUS:XaUn[ [ R U5nU$[ R"U5nUS;a4UR[ R:Xa[R"U5nU$)z= Convert Index to ndarray of indices to pass to NumPy/SciPy. i8rY)r\r[) _valuesrr*viewrr.r7asarrayobject_r maybe_convert_objects)r[rJxarrindss r&rrs ==D4::&&yy BJJ% K zz$ ( (zzRZZ'006 Kr(c  U bU n O [U5n U )n U R5(dgU R5(ag[[R "U 55n [ SU S9nUcSn[[U55n[ SU S9nUc [U5n[[SU-[U 555nUS:XaU[[XS55-nO5US:XaU[[U SU55-nO[[XU55nUS :Xa UUU--nOUS :Xa X- U- nUU-n[U5nURRS ;nU(aURS 5nU[;a?[R"X 5n[R "X X UXU5X'O[#X XX 4UUUUS .U D6X'U b SU SS&SU U'gU(a[$R&UU'g[R(UU'g)z Logic for the 1-d interpolation. The input indices and yvalues will each be 1-d arrays of the same length. Bounds_error is currently hardcoded to False since non-scipy ones don't take it as an argument. Notes ----- Fills 'yvalues' in-place. Nrtr}r~rrurwrrrrrr)rJrrrkFT)rr9allsetr. flatnonzerorranger! _interp_limitsortedr*kindrrmargsortinterp_interpolate_scipy_wrapperr r#nan)rrrJrrrrrrkr$rpinvalidrqall_nansfirst_valid_index start_nanslast_valid_indexend_nans preserve_nansmid_nansis_datetimelikeindexers r&rrs70 w- HE 99;; yy{{2>>'*+H(WuE U,-.J'FUCw<5--s5z:;H)#"Swq)I%JJ J & 3}Wa'G#HH M'%@A Xh.. y (83! =)Mmm((D0O,,t$ **W^,99  gnW5w~g7N 6 N N    !%     Q"]  !$  "$  r(c US3n[SUS9 SSKJn [R"U5nU R U R [[[[U RS.n /SQn X;;a&US:XaUn OUn U RXXUS 9n U "U5nU$US :XaC[U5(dUS::a[S U35eU R"X4S U0UD6n U "U5nU$URR (dUR#5nURR (dUR#5nURR (dUR#5nXn U "XU40UD6nU$) z Passed off to scipy.interpolate.interp1d. method is scipy's kind. Returns an array interpolated at new_x. Add any new methods to the list in _clean_interp_method. z interpolation requires SciPy.scipy)extrar interpolate)rarbrerfrirhrg)rSr]r^r_r`rdrd)rrrrcz;order needs to be specified and greater than 0; got order: k)rrrr.rbarycentric_interpolatekrogh_interpolate_from_derivatives_cubicspline_interpolate_akima_interpolatepchip_interpolateinterp1drr"UnivariateSplineflags writeabler+)rAynew_xrJrrrkrprr alt_methodsinterp1d_methodsrterpnew_ys r&rr%sph4 5Ewe4! JJu E#::..- 1/#..K! \ !DD## t$ U ( L' 8  ;;5A:MeWU ++ADEDVDU  Lww  Aww  A{{$$JJLE"Q5+F+ Lr(cxSSKJn URRnU"XR SS5X5S9nU"U5$)a/ Convenience function for interpolate.BPoly.from_derivatives. Construct a piecewise polynomial in the Bernstein basis, compatible with the specified values and derivatives at breakpoints. Parameters ---------- xi : array-like sorted 1D array of x-coordinates yi : array-like or list of array-likes yi[i][j] is the j-th derivative known at xi[i] order: None or int or array-like of ints. Default: None. Specifies the degree of local polynomials. If not None, some derivatives are ignored. der : int or list How many derivatives to extract; None for all potentially nonzero derivatives (that is a number equal to the number of points), or a list of derivatives to extract. This number includes the function value as 0th derivative. extrapolate : bool, optional Whether to extrapolate to ouf-of-bounds points based on first and last intervals, or to return NaNs. Default: True. See Also -------- scipy.interpolate.BPoly.from_derivatives Returns ------- y : scalar or array-like The result, of length R or length M or M by R. rrrzrw)orders extrapolate)rrBPolyrereshape) xiyirArkderrrrJms r&rrls;R"   / /Fr::b!$ULA Q4Kr(c:SSKJn URXUS9nU"X#S9$)a Convenience function for akima interpolation. xi and yi are arrays of values used to approximate some function f, with ``yi = f(xi)``. See `Akima1DInterpolator` for details. Parameters ---------- xi : np.ndarray A sorted list of x-coordinates, of length N. yi : np.ndarray A 1-D array of real values. `yi`'s length along the interpolation axis must be equal to the length of `xi`. If N-D array, use axis parameter to select correct axis. x : np.ndarray Of length M. der : int, optional How many derivatives to extract; None for all potentially nonzero derivatives (that is a number equal to the number of points), or a list of derivatives to extract. This number includes the function value as 0th derivative. axis : int, optional Axis in the yi array corresponding to the x-coordinate values. See Also -------- scipy.interpolate.Akima1DInterpolator Returns ------- y : scalar or array-like The result, of length R or length M or M by R, rrrx)nu)rrAkima1DInterpolator)rrrArryrPs r&rrs'T"''T':A Q<r(c@SSKJn URXX4US9nU"U5$)ak Convenience function for cubic spline data interpolator. See `scipy.interpolate.CubicSpline` for details. Parameters ---------- xi : np.ndarray, shape (n,) 1-d array containing values of the independent variable. Values must be real, finite and in strictly increasing order. yi : np.ndarray Array containing values of the dependent variable. It can have arbitrary number of dimensions, but the length along ``axis`` (see below) must match the length of ``x``. Values must be finite. x : np.ndarray, shape (m,) axis : int, optional Axis along which `y` is assumed to be varying. Meaning that for ``x[i]`` the corresponding values are ``np.take(y, i, axis=axis)``. Default is 0. bc_type : string or 2-tuple, optional Boundary condition type. Two additional equations, given by the boundary conditions, are required to determine all coefficients of polynomials on each segment [2]_. If `bc_type` is a string, then the specified condition will be applied at both ends of a spline. Available conditions are: * 'not-a-knot' (default): The first and second segment at a curve end are the same polynomial. It is a good default when there is no information on boundary conditions. * 'periodic': The interpolated functions is assumed to be periodic of period ``x[-1] - x[0]``. The first and last value of `y` must be identical: ``y[0] == y[-1]``. This boundary condition will result in ``y'[0] == y'[-1]`` and ``y''[0] == y''[-1]``. * 'clamped': The first derivative at curves ends are zero. Assuming a 1D `y`, ``bc_type=((1, 0.0), (1, 0.0))`` is the same condition. * 'natural': The second derivative at curve ends are zero. Assuming a 1D `y`, ``bc_type=((2, 0.0), (2, 0.0))`` is the same condition. If `bc_type` is a 2-tuple, the first and the second value will be applied at the curve start and end respectively. The tuple values can be one of the previously mentioned strings (except 'periodic') or a tuple `(order, deriv_values)` allowing to specify arbitrary derivatives at curve ends: * `order`: the derivative order, 1 or 2. * `deriv_value`: array-like containing derivative values, shape must be the same as `y`, excluding ``axis`` dimension. For example, if `y` is 1D, then `deriv_value` must be a scalar. If `y` is 3D with the shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2D and have the shape (n0, n1). extrapolate : {bool, 'periodic', None}, optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. If None (default), ``extrapolate`` is set to 'periodic' for ``bc_type='periodic'`` and to True otherwise. See Also -------- scipy.interpolate.CubicHermiteSpline Returns ------- y : scalar or array-like The result, of shape (m,) References ---------- .. [1] `Cubic Spline Interpolation `_ on Wikiversity. .. [2] Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978. rr)rybc_typer)rr CubicSpline)rrrAryrrrrs r&rrs/Z" T   A Q4Kr(c.[U5nU)nUR5(dr[SUS9nUcSn[SUS9nUc [U5n[ UUUUS9 US:XaSXFUS -&OUS :Xa S=USU&XGS -S&O [ S 5e[ RX'gg) ao Apply interpolation and limit_area logic to values along a to-be-specified axis. Parameters ---------- values: np.ndarray Input array. method: str Interpolation method. Could be "bfill" or "pad" limit: int, optional Index limit on interpolation. limit_area: {'inside', 'outside'} Limit area for interpolation. Notes ----- Modifies values in-place. rtrNrru)rJrrrFrwrz*limit_area should be 'inside' or 'outside')rrrr!pad_or_backfill_inplacer"r.r)r\rJrrrr~rtrus r&_interpolate_with_limit_arear%s26lGxH ;;== Wx@ =EFX> <v;D !    !(-GD1H % 9 $49 9GFUOgQhj1IJ J&&- r(cUS:XaSOSnURS:Xa8US:wa [S5eUR[SUR-55n[ U5nU"U5n[ USS9nU"XcUS 9 g ) a Perform an actual interpolation of values, values will be make 2-d if needed fills inplace, returns the result. Parameters ---------- values: np.ndarray Input array. method: str, default "pad" Interpolation method. Could be "bfill" or "pad" axis: 0 or 1 Interpolation axis limit: int, optional Index limit on interpolation. limit_area: str, optional Limit area for interpolation. Can be "inside" or "outside" Notes ----- Modifies values in-place. rcU$rGrHrAs r&)pad_or_backfill_inplace..vsr(cUR$rG)Trs r&rrvsr(rwz0cannot interpolate on a ndim == 1 with axis != 0rwrv)r{)rrN)r{AssertionErrorrtupler4rK get_fill_func)r\rJryrrtransftvaluesrs r&rrZsw8#aikmF{{a 19 !ST TdV\\&9 :; v &FVnG a (D*5r(c"Uc [U5nU$rG)r)r\r$s r& _fillna_prepr s  |F| Kr(c`^[T5SSU4Sjjj5n[[U5$)z6 Wrapper to handle datetime64 and timedelta64 dtypes. c>[UR5(aDUc [U5nT"URS5XUS9upCURUR5U4$T"XX#S9$)Nr)rrr$)rr*rr)r\rrr$resultrs r&new_func&_datetimelike_compat..new_funcse v|| , ,|F| D!DLF;;v||,d2 2FJJJr(NNN)r int | Noner#Literal['inside', 'outside'] | None)rrr)rr s` r&_datetimelike_compatrsK  4[!:>  KK8KK$ 8 r(c[X5nUb UR5(d [X25 [R"XUS9 X4$N)r)r r_fill_limit_area_1dr pad_inplacer\rrr$s r&_pad_1drs>  %DdhhjjD- f%0 <r(c[X5nUb UR5(d [X25 [R"XUS9 X4$r)r rrr backfill_inplacers r& _backfill_1drs>  %DdhhjjD- 6u5 <r(c[X5nUb [X25 UR(a[R"XUS9 X4$X4$r)r _fill_limit_area_2dsizer pad_2d_inplacers r&_pad_2dr sJ  %DD- {{ V7 < <r(c[X5nUb [X25 UR(a[R"XUS9 X4$X4$r)r rrr backfill_2d_inplacers r& _backfill_2dr#sJ  %DD- {{ !!&e< < <r(cU)nUR5n[U5USSS2R5- S- nUS:Xa SUSU&SXS-S&gUS:XaSXS-U&gg)aPrepare 1d mask for ffill/bfill with limit_area. Caller is responsible for checking at least one value of mask is False. When called, mask will no longer faithfully represent when the corresponding are NA or not. Parameters ---------- mask : np.ndarray[bool, ndim=1] Mask representing NA values when filling. limit_area : { "outside", "inside" } Whether to limit filling to outside or inside the outer most non-NA value. NrzrwrFr)r|r!)r$rneg_maskrtrus r&rrsw uH OO E x=8DbD>002 2Q 6DXVe  AXZ y !&QY !r(cpUR)nUS:XaJ[RRUSS9[RRUSSS2SS9SSS2-nOK[RRUSS9)[RRUSSS2SS9SSS2)-nSXR'g)agPrepare 2d mask for ffill/bfill with limit_area. When called, mask will no longer faithfully represent when the corresponding are NA or not. Parameters ---------- mask : np.ndarray[bool, ndim=1] Mask representing NA values when filling. limit_area : { "outside", "inside" } Whether to limit filling to outside or inside the outer most non-NA value. rrrxNrzF)rr.maximum accumulate)r$rr%la_masks r&rrswHY JJ ! !( ! 3jj##HTrTN#;DbDA B ZZ " "8! " 4 4zz$$Xdd^!$[UT5n[XS-5RS5n[[R "U5SU-5[[R "USUS-)R 5S:H5S5-nU$)Nrwr)min_rolling_windowrrr.wherecumsum)rrwindowedidxNs r&inner_interp_limit..inner\sE1 "7AI6::1="((8$Q'%/03 HHw{++335: ;A >4   r(Nrrzrw)rint)r!rr.r3listr)rfw_limitbw_limitf_idxb_idxr8 b_idx_invr7s @r&rr7sB G A EE EE q=)!,-E',E q=LU74R4=(; [ [True, True], [True, False], [False, True], [True, False], ] Nrzrw)r4strides)r4rBr.r stride_tricks as_strided)awindowr4rBs r&r2r2xsh GGCRLAGGBK&014f= =Eii199R=**G 66   * *17 * KKr()r$npt.NDArray[np.bool_]r%r:)r:r rrG)rJz,Literal['ffill', 'pad', 'bfill', 'backfill']rEzLiteral[False]rLiteral['pad', 'backfill'])rJ7Literal['ffill', 'pad', 'bfill', 'backfill', 'nearest']rEz Literal[True]r%Literal['pad', 'backfill', 'nearest'])rJrIrEr5rrJ)rJrTr[rrrT)r}rTr~rGrr)rrTrz&Literal['forward', 'backward', 'both'])r str | Nonerr)rz-Literal['backward', 'forward', 'both'] | NonerJrTrz&Literal['backward', 'forward', 'both'])r[rrr)rYNrNNN)rrr[rryrrJrTrrrrTrrKr Any | Nonerr)r[rrJrTrr)rYNrNNFNN)rrrrrJrTrrrrTrrrrLrr5rkrrr)NFN) rArrrrrrJrTrr5)NrF) rrrrrArrint | list[int] | Nonerr5)rr) rrrrrArrrMryr)rz not-a-knotN) rrrrrArryrrzstr | tuple[Any, Any]) r\rrJrHrrrzLiteral['inside', 'outside']rr)rPrNN) r\rrJrHryrrrrrrrrG)r$npt.NDArray[np.bool_] | NonerrG)rrrrr) r\rrrrrr$rNrz(tuple[np.ndarray, npt.NDArray[np.bool_]])r\rrrrrr$rN)rrrrr$rN)r$rGrzLiteral['outside', 'inside']rrr)r{r:)rzReindexMethod | None)rrGr<rr=r)rErGrFr:rrG)K__doc__ __future__r functoolsrtypingrrrrr numpyr. pandas._libsr r r pandas._typingr rrrrpandas.compat._optionalrpandas.core.dtypes.castrpandas.core.dtypes.commonrrrrrrpandas.core.dtypes.dtypesrpandas.core.dtypes.missingrrrrrr'rCrKrmrnrrrrrrrrrrrrrrrrr rrrr r#rrr,rr.rr2rHr(r&r[s# ?46  FR %( 8"    C!+    C+ 43  $&#L  +  BLO+*F$!! C* C* C* C*  C*  C*  C*C*C* C*L2$6:! o o o  o   o  o 4 o o o  o  o n DDD D  D  DV "# //// /  /l#$ .... .  .j%1 SSSS  S # Sl2! 2! &2! 2!- 2!  2!n*/6: )6 )6 &)6 )6  )6 4 )6  )6Z26.66:)-     4  '  .  6:)-     4  '  .  6:)-   4 ' $6:)-  4 ' $' '-I' '4 -I > \: >9 > ">.8>DN>BLr(