+ ܳjoRt^RIHt^RIHt^RIt^RIHt^RI H t H t ]'d ^RI H t ^RIHtRR ltR R ltR R ltR#)zH Module containing utilities for NDFrame.sample() and .GroupBy.sample() ) annotations) TYPE_CHECKINGN)lib) ABCDataFrame ABCSeries)AxisInt)NDFramec$V^8dQhRRRRRR/#)objraxisrreturn np.ndarray)formats"S/home/luca/screen-checking/.venv/lib/python3.14/site-packages/pandas/core/sample.py __annotate__rs!66G6G6 6cN\V\4'd#VPVPV,4p\V\4'd>\V\ 4'dV^8Xd W,pM\R4h\R4h\V\4'dVPpM VPpV!VRR7Pp\V4VPV,8wd \R4h\P!V4'd \R4hV^8P4'd \R 4h\ P"!V4pVP4'dVP%4p^W&V# \ dp\ R4ThRp?ii;i) z Process and validate the `weights` argument to `NDFrame.sample` and `.GroupBy.sample`. Returns `weights` as an ndarray[np.float64], validated except for normalizing weights (because that must be done groupwise in groupby sampling). z+String passed to weights not a valid columnNzLStrings can only be passed to weights when sampling from rows on a DataFramez@Strings cannot be passed as weights when sampling from a Series.float64)dtypez5Weights and axis to be sampled must be of same lengthz*weight vector may not include `inf` valuesz.weight vector many not include negative values) isinstancerreindexaxesstrrKeyError ValueError _constructor_constructor_sliced_valueslenshaperhas_infsanynpisnancopy)r weightsr errfuncmissings&&& rpreprocess_weightsr+sd'9%%//#((4.1'3 c< ( (qy!lG !" R #y!!&&7),44G 7|syy&PQQ ||GEFF! IJJhhwG{{}},,. NI "Es-F F$ FF$c(V^8dQhRRRRRRRR/#)r nz int | Nonefracz float | Nonereplaceboolr r)rs"rrrQs," " " %" 04" " rc Vf Vf^pV#VeVe \R4hVe/V^8d \R4hV^,^8wd \R4hV#VfQhV^8dV'g \R4hV^8d \R4hV#)z Process and validate the `n` and `frac` arguments to `NDFrame.sample` and `.GroupBy.sample`. Returns None if `frac` should be used (variable sampling sizes), otherwise returns the constant sampling size. z0Please enter a value for `frac` OR `n`, not bothz=A negative number of rows requested. Please provide `n` >= 0.z$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)r-r.r/s&&&rprocess_sampling_sizer2Qs yT\ , H+ 4+KLL  q5O  q5A:CD D  H !8G8  !8R  Hrc 0V^8dQhRRRRRRRRRR R R /#) r obj_lenintsizer/r0r'znp.ndarray | None random_statez+np.random.RandomState | np.random.Generatorr rr)rs"rrrvsD-- - -- - > -  -rc,Ve`VP4pV^8wd W5, pM \R4hVfQhV'g'WP4,^8d \R4hVPWW#R7P \ P RR7#)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] z$Invalid weights: weights sum to zerozWeighted sampling cannot be achieved with replace=False. Either set replace=True or use smaller weights. See the docstring of sample for details.)r6r/pF)r&)sumrmaxchoiceastyper$intp)r4r6r/r'r7 weight_sums&&&&& rsampler@vs8[[] ?*GCD D"""4++-/!3&    w7  N U U e V r)__doc__ __future__rtypingrnumpyr$ pandas._libsrpandas.core.dtypes.genericrrpandas._typingrpandas.core.genericrr+r2r@rrrrIs9#  &+6r" J-r