]joSrSSKJr SSKJr SSKrSSKJr SSK J r J r \(a SSK J r SSKJr S S jrS S jrSS jrg)zH Module containing utilities for NDFrame.sample() and .GroupBy.sample() ) annotations) TYPE_CHECKINGN)lib) ABCDataFrame ABCSeries)AxisInt)NDFramec[U[5(aURURU5n[U[5(a7[U[ 5(aUS:XaXnO[S5e[S5e[U[5(a URnO URnU"USS9Rn[U5URU:wa [S5e[R"U5(a [S 5eUS:R5(a [S 5e[ R""U5nUR5(aUR%5nSX'U$![ an[ S5UeSnAff=f) z Process and validate the `weights` argument to `NDFrame.sample` and `.GroupBy.sample`. Returns `weights` as an ndarray[np.float64], validated except for normalizing weights (because that must be done groupwise in groupby sampling). rz+String passed to weights not a valid columnNzLStrings can only be passed to weights when sampling from rows on a DataFramez@Strings cannot be passed as weights when sampling from a Series.float64)dtypez5Weights and axis to be sampled must be of same lengthz*weight vector may not include `inf` valuesz.weight vector many not include negative values) isinstancerreindexaxesstrrKeyError ValueError _constructor_constructor_sliced_valueslenshaperhas_infsanynpisnancopy)objweightsaxiserrfuncmissings O/home/mande/repo/quber/.venv/lib/python3.13/site-packages/pandas/core/sample.pypreprocess_weightsr$sd'9%%//#((4.1'3 c< ( (qy!lG !" R #y!!&&7),44G 7|syy&PQQ ||GEFF! IJJhhwG{{}},,. NI "Es%E00 F : FF cUcUcSnU$UbUb [S5eUb(US:a [S5eUS-S:wa [S5eU$UceUS:aU(d [S5eUS:a [S5eU$)z Process and validate the `n` and `frac` arguments to `NDFrame.sample` and `.GroupBy.sample`. Returns None if `frac` should be used (variable sampling sizes), otherwise returns the constant sampling size. z0Please enter a value for `frac` OR `n`, not bothrz=A negative number of rows requested. Please provide `n` >= 0.z$Only integers accepted as `n` valueszJReplace has to be set to `True` when upsampling the population `frac` > 1.z@A negative number of rows requested. Please provide `frac` >= 0.)r)nfracreplaces r#process_sampling_sizer*Qs yT\ , H+ 4+KLL  q5O  q5A:CD D  H !8G8  !8R  HcUbSUR5nUS:waX5- nO [S5eUceU(d!XR5-S:a [S5eURXX#S9R [ R SS9$)a Randomly sample `size` indices in `np.arange(obj_len)`. Parameters ---------- obj_len : int The length of the indices being considered size : int The number of values to choose replace : bool Allow or disallow sampling of the same row more than once. weights : np.ndarray[np.float64] or None If None, equal probability weighting, otherwise weights according to the vector normalized random_state: np.random.RandomState or np.random.Generator State used for the random sampling Returns ------- np.ndarray[np.intp] rz$Invalid weights: weights sum to zeror&zWeighted sampling cannot be achieved with replace=False. Either set replace=True or use smaller weights. See the docstring of sample for details.)sizer)pF)r)sumrmaxchoiceastyperintp)obj_lenr-r)r random_state weight_sums r#sampler7vs8[[] ?*GCD D"""4++-/!3&    w7  N U U e V r+)rr rrreturn np.ndarray)r' int | Noner(z float | Noner)boolr8r:) r4intr-r<r)r;rznp.ndarray | Noner5z+np.random.RandomState | np.random.Generatorr8r9)__doc__ __future__rtypingrnumpyr pandas._libsrpandas.core.dtypes.genericrrpandas._typingrpandas.core.genericr r$r*r7r+r#rFs#  &+6r" " %" 04" " J- - -- - > -  -r+