jnddlmZddlmZddlZddlmZmZer ddl m Z m Z m Z dd Z ddZddZddZdS)) annotations) TYPE_CHECKINGN)isnana_value_for_dtype) ArrayLikeScalarnptvaluesrqsnpt.NDArray[np.float64] interpolationstrreturnct|tjr8t|jd}t |}t |||||S|||S)a Compute the quantiles of the given values for each quantile in `qs`. Parameters ---------- values : np.ndarray or ExtensionArray qs : np.ndarray[float64] interpolation : str Returns ------- np.ndarray or ExtensionArray F)compat) isinstancenpndarrayrdtyperquantile_with_mask _quantile)r r r fill_valuemasks tC:\Users\Terasoftware\OneDrive\Desktop\faahhh\fyndo\fyndo\venv\Lib\site-packages\pandas/core/array_algos/quantile.pyquantile_compatrsc &"*%%3' UCCC F||!&$ B NNNM222 np.ndarrayrnpt.NDArray[np.bool_]cV|j|jksJ|jdkrCtj|}tj|}t |||||}|dS|jdksJ|jddk}|rvtj|gt |z}tj|t |t |t |}n2t||dz|||}tj |}|j }|S)a Compute the quantiles of the given values for each quantile in `qs`. Parameters ---------- values : np.ndarray For ExtensionArray, this is _values_for_factorize()[0] mask : np.ndarray[bool] mask = isna(values) For ExtensionArray, this is computed before calling _value_for_factorize fill_value : Scalar The value to interpret fill NA entries with For ExtensionArray, this is _values_for_factorize()[1] qs : np.ndarray[float64] interpolation : str Type of interpolation Returns ------- np.ndarray Notes ----- Assumes values is already 2D. For ExtensionArray this means np.atleast_2d has been called on _values_for_factorize()[0] Quantile is computed along axis=1. rgY@)na_valuerr ) shapendimr atleast_2drarraylenrepeatreshape_nanpercentileasarrayT) r rrr r res_valuesis_emptyflatresults rrr,s(F <4: %%%% {av&&}T""'j"mTT !} ;! |A!#Hx s2ww.//4V--55c&kk3r77KK  J'    F## Mrr"rScalar | np.ndarrayc||}t|dkr"tjt||Stj|||S)a Wrapper for np.percentile that skips missing values, specialized to 1-dimensional case. Parameters ---------- values : array over which to find quantiles mask : ndarray[bool] locations in values that should be considered missing qs : np.ndarray[float64] of quantile indices to find na_value : scalar value to return for empty or all-null values interpolation : str Returns ------- quantiles : scalar or array r)method)r'rfull percentile)r rr r"r s r_nanpercentile_1dr6os`4TE]F 6{{a*ws2ww))) =     rc6|jjdvrTt|dd|}||jS|r|j|jksJfdtt|t|D}|jjdkr!tj ||jj }ntj |j }|j|jkrb| sN|||jdk r||jd}|Stj |d  S) a Wrapper for np.percentile that skips missing values. Parameters ---------- values : np.ndarray[ndim=2] over which to find quantiles qs : np.ndarray[float64] of quantile indices to find na_value : scalar value to return for empty or all-null values mask : np.ndarray[bool] locations in values that should be considered missing interpolation : str Returns ------- quantiles : scalar or array mMi8)r r"rr c >g|]\}}t||S))r )r6).0valmr r"r s r z"_nanpercentile..s@   a c1b(- P P P   rf)rF)copyr )axisr3)rkindr*viewastypeanyr#ziplistrr+r,allr5)r r r"rr r0s `` ` rr*r*s4|D + KK  ]]4(('    }}V\*** xxzz zV\))))      V d4jj99    <  # AZfl;;;=FFZ'')F  , A  Av}}V\}FFFKKMM A v|%@@ }  !    r)r rr r r rrr) r rrrr r r rrr) r rrrr r r"rr rrr1)r rr r rrr r) __future__rtypingrnumpyrpandas.core.dtypes.missingrrpandas._typingrrr rrr6r*rrrOs""""""  33330@@@@F))))XG G G G G G r