hddlmZddlmZddlZddlmZmZer ddl m Z m Z m Z  d dZ d dZ d dZ d d Zy)) annotations) TYPE_CHECKINGN)isnana_value_for_dtype) ArrayLikeScalarnptct|tjr1t|jd}t |}t |||||S|j||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)valuesqs interpolation fill_valuemasks ED:\jyotish\venv\Lib\site-packages\pandas/core/array_algos/quantile.pyquantile_compatrsQ &"**%' UC F|!&$ B NNM22cR|j|jk(sJ|jdk(r>tj|}tj|}t |||||}|dS|jdk(sJ|jddk(}|retj |gt |z}tj|t |jt |t |}|St||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) rrrrr res_valuesis_emptyflatresults rrr,sF <<4:: %% % {{av&}}T"'j"mT !} ;;!  ||A!#Hxx s2w./4V-55c&k3r7K M  J'  F# Mrc||}t|dk(rtjt||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"r full percentile)rrrrrs r_nanpercentile_1dr0osN4TE]F 6{awws2w)) ==   rc |jjdvrIt|jd||jd||}|j |jS|j r4|j |j k(sJtt|t|Dcgc]\}}t|||||}}}|jjdk(r-tj||jj}|Stj|j}|j|jk7r[|jsK||j |jdk(jr|j |jd}|Stj||d | Scc}}w) 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)rrrr)rf)rF)copyr)axisr-)rkindr%viewastypeanyrziplistr0r r&r'allr/)rrrrrr+valms rr%r%s~4||D  KK ]]4('  }}V\\** xxzzzV\\))) V d4j9 9a c1b(- P9   <<   #ZZfll;==F ZZ'))F  , v}}V\\}FFKKM  v||%@ }}  !  ) s/G)rrrnpt.NDArray[np.float64]rstrreturnr) r np.ndarrayrnpt.NDArray[np.bool_]rr@rrArBrC) rrCrrDrr@rrrrArBzScalar | np.ndarray)rrCrr@rrDrrA) __future__rtypingrnumpyr pandas.core.dtypes.missingrrpandas._typingrrr rrr0r%rrrKs"  3 323CF330@ @ @ @  @  @F) ) ) ) )  )  )XG G G G  G r