o d@sddlmZddlZddlZddlZddlmZmZmZddl 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 ddZ8e8duZ9da:ddddZ;e;e dGdddZdd#d$Z? ddd'd(Z@dd.d/ZA   ddd4d5ZBdd6d7ZCddd9d:ZDdd=d>ZEddBdCZFddDdEZGddddFddGdHZHddddFddIdJZIePz$disallow.__init__..)super__init__tupler6)selfr6 __class__r2r3r=Ns zdisallow.__init__r,cCst|do t|jj|jS)Nr9)hasattr issubclassr9r7r6)r?objr2r2r3checkRszdisallow.checkfrcs"tfdd}tt|S)Nc st||}tfdd|Dr"jdd}td|dz!tjdd|i|WdWS1s._f..nanzreduction operation 'z' not allowed for this dtypeignoreinvalidr) itertoolschainvaluesany__name__replace TypeErrornperrstate ValueErrorr)argskwargsZobj_iterf_nameerFr?r2r3_fVs   (  zdisallow.__call__.._f functoolswrapsrr)r?rFr[r2rZr3__call__Us zdisallow.__call__)r6rr-r.r-r,)rFrr-r)rP __module__ __qualname__r=rEr_ __classcell__r2r2r@r3r5Ms r5c@s"eZdZd d ddZd dd ZdS) bottleneck_switchNr-r.cKs||_||_dSr/)namerW)r?rerWr2r2r3r=ns zbottleneck_switch.__init__altrc sfjpjzttWn ttfydYnwtdddd fd d }tt |S) NTaxisskipnarN np.ndarrayrhAxisInt | Nonerir,cstjdkrjD] \}}||vr|||<q |jdkr*|ddur*t||Strj|rjt|jrj|dddur]| dd|fd|i|}t |r[|f||d|}|S|f||d|}|S|f||d|}|S)Nr min_countmaskrhrg) lenrWitemssizeget_na_for_min_countr1 _bn_ok_dtyper9pop _has_infs)rNrhrikwdskr+resultrfZbn_funcZbn_namer?r2r3rFzs$  z%bottleneck_switch.__call__..f)rNrjrhrkrir,) rerPgetattrbnAttributeError NameErrorr]r^rr)r?rfrFr2ryr3r_rs  'zbottleneck_switch.__call__r/)r-r.)rfrr-r)rPrarbr=r_r2r2r2r3rdms rdr9rrestrcCst|s t|s |dvSdS)N)nansumnanprodnanmeanF)rr")r9rer2r2r3rssrsc CsPt|tjr|jdvrt|dSzt|WSt t fy'YdSw)N)f8Zf4KF) isinstancerSndarrayr9r Zhas_infsZravelisinfrOrRNotImplementedError)rxr2r2r3rus  ru fill_value Scalar | NonecCsJ|dur|St|r|durtjS|dkrtjStj S|dkr#tjStS)z9return the correct fill value for the dtype of the valuesN+inf) _na_ok_dtyperSrGinfr i8maxr )r9rfill_value_typr2r2r3_get_fill_valuesrrNrjrirmnpt.NDArray[np.bool_] | NonecCs:|durt|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)rr9rr"r%)rNrirmr2r2r3_maybe_get_masks !rrr str | NoneHtuple[np.ndarray, npt.NDArray[np.bool_] | None, np.dtype, np.dtype, Any]c Cst|sJt|dd}t|||}|j}d}t|jr&t|d}d}t|}t |||d}|rW|durW|durW| rW|sC|rO| }t |||nt |||}|}t|sat|rhttj}n t|rrttj}|||||fS)a7 Utility to get the values view, mask, dtype, dtype_max, and fill_value. If both mask and fill_value/fill_value_typ are not None and skipna is True, the values array will be copied. For input arrays of boolean or integer dtypes, copies will only occur if a precomputed mask, a fill_value/fill_value_typ, and skipna=True are provided. Parameters ---------- values : ndarray input array to potentially compute mask for skipna : bool boolean for whether NaNs should be skipped fill_value : Any value to fill NaNs with fill_value_typ : str Set to '+inf' or '-inf' to handle dtype-specific infinities mask : Optional[np.ndarray[bool]] nan-mask if known Returns ------- values : ndarray Potential copy of input value array mask : Optional[ndarray[bool]] Mask for values, if deemed necessary to compute dtype : np.dtype dtype for values dtype_max : np.dtype platform independent dtype fill_value : Any fill value used TZ extract_numpyFi8)rrN)r r(rr9r"rSZasarrayviewrrrOcopyputmaskwhererrint64rfloat64) rNrirrrmr9 datetimelikeZdtype_ok dtype_maxr2r2r3 _get_valuess0 .    rcCst|rdSt|jtj S)NF)r"rCr7rSintegerr9r2r2r3rasrnp.dtypecCs|tur |St|rL|durt}t|tjsEt|rJd||kr&tj}t|r4tdd |}nt | |}|j |dd}|S| |}|St |rt|tjs||ks_t |ritd |}|St|tjkrutdt |j |dd}|S| d |}|S) zwrap our results if neededNzExpected non-null fill_valuernsFrzoverflow in timedelta operationm8[ns])rrr rrSrr%rGZ datetime64astyperrr!isnanZ timedelta64fabsr rrU)rxr9rr2r2r3 _wrap_resultsgs8#    rfuncrcs,tdddddfd d }tt|S)z If we have datetime64 or timedelta64 values, ensure we have a correct mask before calling the wrapped function, then cast back afterwards. NTrhrirmrNrjrhrkrir,rmrcsr|}|jjdv}|r|durt|}|f|||d|}|r7t||jtd}|s7|dus0Jt||||}|S)NmMr)r)r9kindr%rr _mask_datetimelike_result)rNrhrirmrW orig_valuesrrxrr2r3new_funcs   z&_datetimelike_compat..new_func)rNrjrhrkrir,rmrr\)rrr2rr3_datetimelike_compats  rrhrkScalar | np.ndarraycCsht|r |d}t|j}|jdkr|S|dur|S|jd||j|dd}tj|||jdS)a Return the missing value for `values`. Parameters ---------- values : ndarray axis : int or None axis for the reduction, required if values.ndim > 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&r9ndimshaperSfull)rNrhrZ result_shaper2r2r3rrs    rrcs(tddd fdd}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. NrhrNrjrhrkcs|dkrT|jdkrT|jdrT|jdd|jdkrT|jtkrT|jtkrTt|ddurEdfddt t D}n fd dD}t |S|fd |iS) NrZ C_CONTIGUOUSirrmcs(g|]}|fd|iqSrmr2)r8i)arrsrrWrmr2r3 sz:maybe_operate_rowwise..newfunc..csg|] }|fiqSr2r2)r8x)rrWr2r3rsrh) rflagsrr9objectr,listrqrtrangernrSarray)rNrhrWresultsr)rrWrmr3newfuncs*    z&maybe_operate_rowwise..newfunc)rNrjrhrkr\)rrr2rr3maybe_operate_rowwises rrcC^t|jr|jjdkrtjdttdt||d|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. stacklevelFrrm) r"r9rwarningsr) FutureWarningrrrrr,rOrNrhrirm_r2r2r3nanany"  rcCr)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"r9rrr)rrrrrr,allrr2r2r3nanall*rrZM8)rhrirlrmrlintfloatc Csft||d|d\}}}}}|}t|r|}n t|r ttj}|j||d} t| |||j|d} | 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 rrrrl) rrr!rSr9rsum_maybe_null_outr) rNrhrirlrmr9rr dtype_sumthe_sumr2r2r3ras" rrx+np.ndarray | np.datetime64 | np.timedelta64npt.NDArray[np.bool_]r5np.ndarray | np.datetime64 | np.timedelta64 | NaTTypecCsTt|tjr|d|j}|j|d}t||<|S|r(tt|jS|S)Nrr) rrSrrrr9rOr r)rxrhrmrZ axis_maskr2r2r3rs  rc Cst||d|d\}}}}}|}ttj}|jdvr!ttj}nt|r,ttj}nt|r4|}|}t|j|||d} t |j ||d} |durt | ddrt tj | } tjdd  | | } Wdn1skwY| dk} | r}tj| | <| S| dkr| | ntj} | 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 rrrrNrFrIr)rrSr9rrrr _get_countsr_ensure_numericrrzrrrTrOrG) rNrhrirmr9rrrZ dtype_countcountrZthe_meanZct_maskr2r2r3rs2"     rc s"d fdd }t||dd\}}}}}t|js4z|d}Wnty3}ztt||d}~ww|dur=tj||<|j }|j dkr|dur|rwsUt |||} n7t t dd tt||} Wdn1sqwYnt|j|tjtj} n |r|||ntj} 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 Ncst|dur t|}n|}s|stjSttddtt||}Wd|S1s3wY|S)NrIAll-NaN slice encountered) r'rrSrGrcatch_warningsfilterwarningsRuntimeWarning nanmedian)rZ_maskresrir2r3 get_medians    znanmedian..get_medianr)rmrrrrIrr/)rrr9rrUrRr~rSrGrprZapply_along_axisrrrrrget_empty_reduction_resultrZfloat_r) rNrhrirmrr9rerrZnotemptyrr2rr3rs4     rrtuple[int, ...]r np.dtype | type[np.floating]cCs<t|}tt|}tj|||k|d}|||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)rSrZarangernemptyfill)rrhr9rZshpdimsretr2r2r3r8s  r values_shaperddof-tuple[float | np.ndarray, float | np.ndarray]cCst||||d}|||}t|r!||krtj}tj}||fSttj|}||k}|r?t||tjt||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) rr7r rSrGrrrOr)rrmrhrr9rdr2r2r3_get_counts_nanvarSs rrrrhrirrmc CsT|jdkr |d}|j}t|||d\}}}}}tt|||||d}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)r9rrrSsqrtnanvarr)rNrhrirrmZ orig_dtyperrxr2r2r3nanstds $  rZm8c Cst|dd}|j}t|||}t|r!|d}|dur!tj||<t|jr3t|j ||||j\}}n t|j |||\}}|rN|durN| }t ||dt |j |tjd|}|durdt||}t ||d} |durwt | |d| j |tjd|} t|r| j|dd } | 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)rhr9rFr)r(r9rrrrSrGrrrrrrrr expand_dims) rNrhrirrmr9rrZavgZsqrrxr2r2r3rs. %      rcCst|||||dt|||}t|js|d}|s&|dur&|r&tjSt|j ||||j\}}t|||||d}t |t |S)a Compute the standard error in the mean 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 : 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, 2, 3]) >>> nanops.nansem(s) 0.5773502691896258 rrN) rrrr9rrOrSrGrrr)rNrhrirrmrrvarr2r2r3nansems&   rcs2tddtdddddfdd}|S)NrG)reTrrNrjrhrkrir,rmrr-rc st|||d\}}}}}|dur|j|dks|jdkr>zt|||d}|tjWnttt fy=tj}Ynwt||}t ||||j}|S)Nrrmrr) rrrprzrrSrGr|rRrUr)rNrhrirmr9rrrxrmethr2r3 reduction2s   z_nanminmax..reduction) rNrjrhrkrir,rmrr-r)rdr)rrrr2rr3 _nanminmax1srminr)rmax-infOint | np.ndarraycC6t|dd|d\}}}}}||}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]) Trr)rZargmax_maybe_arg_null_outrNrhrirmrrxr2r2r3 nanargmaxR' r cCr )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 min 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.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)rZargminr r r2r2r3 nanargminr rc Cst|dd}t|||}t|js|d}t|j||}n t|j|||jd}|r:|dur:|}t ||dn |sG|durG| rGtj S|j |tj d|}|dur[t||}||}|rl|durlt ||d|d}||}|j |tj d} |j |tj d} t| } t| } tjddd ||d d |d| | d } Wdn1swY|j} t| r| j| d d} t| tjrt| dkd| } tj | |dk<| S| dkrdn| } |dkrtj 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 TrrrNrrrIrKdividerg?g?Fr)r(rrr9rrrrrSrrOrGrrr_zero_out_fperrrTrrr) rNrhrirmrmeanadjusted adjusted2Z adjusted3m2Zm3rxr9r2r2r3nanskewsJ '      & rcCsFt|dd}t|||}t|js|d}t|j||}n t|j|||jd}|r:|dur:|}t ||dn |sG|durG| rGtj S|j |tj d|}|dur[t||}||}|rl|durlt ||d|d}|d}|j |tj d} |j |tj d} tjddd 0d |d d|d|d } ||d |d | } |d|d | d} Wdn1swYt| } t| } t| tjs|d krtj S| dkrdStjddd | | | }Wdn1swY|j}t|r |j|d d}t|tjr!t| 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 TrrrNrrrIrrrFr)r(rrr9rrrrrSrrOrGrrrrTrrrr)rNrhrirmrrrrZ adjusted4rZm4Zadj numerator denominatorrxr9r2r2r3nankurtsV '          rcCsFt|||}|r|dur|}d||<||}t||||j|dS)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)rNrhrirlrmrxr2r2r3rks   rnp.ndarray | intcCsr|dur|S|dust|dds"|r|rdS|S|r dS|S|r*||}n||}|r7d||<|S)NrF)rzrrO)rxrhrmriZna_maskr2r2r3r s    r float | np.ndarraycCsz|dur|dur|j|}nt|}||S|dur)|j|||}n||}t|r6||S|j|ddS)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)rprrSrr7rr r)rrmrhr9nrr2r2r3rs   rnp.ndarray | float | NaTTypec Cs|dur |dkr |S|durot|tjro|dur'|j||||dk}n|||dk}|d|||dd}t||}t|rmt|rit|rW| d}n t |sb|j ddd}tj ||<|Sd||<|S|t urt |||rt|dd}t |r|d }|Stj }|S) zu Returns ------- Dtype The product of all elements on a given axis. ( NaNs are treated as 1) NrrZc16rFrr9rG)rrSrrrZ broadcast_torOrZ iscomplexobjrrrGrcheck_below_min_countrzr7) rxrhrmrrlZ null_maskZ below_countZ new_shapeZ result_dtyper2r2r3rs4           rcCs:|dkr|durt|}n|j|}||krdSdS)a Check for the `min_count` keyword. Returns True if below `min_count` (when missing value should be returned from the reduction). Parameters ---------- shape : tuple The shape of the values (`values.shape`). mask : ndarray[bool] or None Boolean numpy array (typically of same shape as `shape`) or None. min_count : int Keyword passed through from sum/prod call. Returns ------- bool rNTF)rSrrpr)rrmrlZ non_nullsr2r2r3r" s r"cCsrt|tjr*tjddtt|dkd|WdS1s#wYdSt|dkr7|jdS|S)NrIrJg+=r)rrSrrTrabsr9r7)argr2r2r3r+s $rpearson)method min_periodsabr&r r' int | NonecCspt|t|kr td|durd}t|t|@}|s&||}||}t||kr/tjSt|}|||S)z a, b: ndarrays z'Operands to nancorr must have same sizeNr)rnAssertionErrorr'rrSrG get_corr_func)r(r)r&r'validrFr2r2r3nancorr4s   r.)Callable[[np.ndarray, np.ndarray], float]csx|dkrddlmfdd}|S|dkr$ddlmfdd}|S|d kr.d d}|St|r4|Std |d ) NZkendallr kendalltauc||dSNrr2r(r)r0r2r3rWzget_corr_func..funcZspearman spearmanrcr2r3r2r4r6r2r3r^r5r%cSst||dS)Nrr)rSZcorrcoefr4r2r2r3rdszUnknown method 'z@', expected one of 'kendall', 'spearman', 'pearson', or callable)Z scipy.statsr1r7callablerU)r&rr2)r1r7r3r,Qs      r,)r'rcCsrt|t|kr td|durd}t|t|@}|s&||}||}t||kr/tjStj|||ddS)Nz&Operands to nancov must have same sizerrr8)rnr+r'rrSrGZcov)r(r)r'rr-r2r2r3nancovqs r:c Cs,t|tjrYt|st|r|tj}|St|rWz|tj}Wn)t t fyKz |tj}WY|St yJ}z t d|d|d}~wwwt t |sW|j }|St|st|st|szt|}W|St t fyzt|}WY|St y}z t d|d|d}~www|S)NzCould not convert z to numeric)rrSrrrrrrZ complex128rRrUrOimagrealrrrrcomplex)rrr2r2r3rsB     rcsfdd}|S)Ncs|t|}t|}||B}tjdd ||}Wdn1s"wY|r.fr2)r@rFr2r?r3 make_nancomps rAr c Cstjdtjftjjtj tjftjdtjftjjtjtjfi|\}}|jj dvs+J|rPt |jj tj tj fsP|}t|}|||<||dd}|||<|S||dd}|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)rSZcumprodrGmaximum accumulaterZcumsumminimumr9rrCr7rZbool_rr%)rNZ accum_funcriZmask_aZmask_bvalsrmrxr2r2r3 na_accum_funcs"    rF)T)r+r,r-r.)r9rrer~r-r,r`)NN)r9rrr)rNrjrir,rmrr-r)NNN) rNrjrir,rrrrrmrr-r)r9rr-r,r/)r9r)rrr-r)rNrjrhrkr-r) rNrjrhrkrir,rmrr-r,) rNrjrhrkrir,rlrrmrr-r) rxrrhrkrmrrrjr-r) rNrjrhrkrir,rmrr-r)rhrkrir,) rrrhr r9rrrr-rj) rrrmrrhrkrrr9rr-r)rhrkrir,rr) rNrjrhrkrir,rrrmrr-r) rNrjrhrkrir,rmrr-r) rxrjrhrkrmrrir,r-r) rrrmrrhrkr9rr-r)r) rxr!rhrkrmrrrrlrr-r!)rrrmrrlrr-r,) r(rjr)rjr&r r'r*r-r)r&r r-r/) r(rjr)rjr'r*rr*r-r)rNr rir,r-r )t __future__rr]rLoperatortypingrrrrnumpyrSZpandas._configrZ pandas._libsrrr r Zpandas._typingr r r rrrrrrZpandas.compat._optionalrZpandas.util._exceptionsrZpandas.core.dtypes.commonrrrrrrrrrrr r!r"r#Zpandas.core.dtypes.dtypesr$Zpandas.core.dtypes.missingr%r&r'Zpandas.core.constructionr(r{r0r1r4r5rdrsrurrrrrrrrrrrrrrrrr9rrrrrrZnanminZnanmaxr rrrrr rrr"rr.r,r:rrAgtZnangtgeZnangeltZnanltleZnanleeqZnaneqneZnannerFr2r2r2r3s  , @      8  / Y  ) " %:7 .? 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