]jk*6SSKJr SSKJrJr SSKrSSKJrJ r SSK J r J r SSKJr SSKJr SSKJr SS KJrJrJrJrJrJrJrJrJr SS KJr SS K J!r!J"r" SS K#J$r$ SS K%J&r& \(a SSK'J(r(J)r)J*r* \"S5SS\RV4SSjj5r,g)) annotations) TYPE_CHECKINGLiteralN)libmissing) Timedelta Timestamp) set_module)check_dtype_backend)maybe_downcast_numeric) ensure_object is_bool_dtype is_decimalis_integer_dtype is_numberis_numeric_dtype is_scalaris_string_dtypeneeds_i8_conversion) ArrowDtype)ABCIndex ABCSeries)BaseMaskedArray) StringDtype)DateTimeErrorChoices DtypeBackendnptpandasraisec  US;a [S5eUS;a [S5e[U5 SnSnSn[U[5(aSnURnGO[U[ 5(a;Sn[ UR5(aURS5nOURnO[U[[45(a[R"USS 9nO[U5(am[U5(a [U5$[!U5(aU$[U["[$45(a UR&$Sn[R"U/SS 9nO[)US S 5S :a [+S 5eUnS n[U[,5(aUR.nUR0U)n[)USS 5n [U [25(a.UR55nUR75R95nS n [;U 5(aO[<R>"U S5(a UR[R@5nO{[CU5nUS:gn [<RD"U[G5U U[<RHL=(d3 [U [J5=(a U RL[NRPLS9upzU bXz)nOuU[<RHLaU b2[U [J5(aJU RL[NRPLa-[RR"URT[RVS 9n UGb=[;UR5(Ga"S n US;a[RXSn OUS:Xa>[[U5(a[R\"U5S:a[RXSn O\US:XaV[RXSn [R"[R^5R`n U RcU 5nXS n U bcU H]n[R"U5nURdURRd::dM?[gUU5nURU:XdM] O UcU Gb [iUR5(GdUbU bU RTURT:XaU nOURk5n[U[Rl5(de[RR"URTURS 9nUUU)'SSK7J8nJ9nJ:nJ;n [yUR5(aUnO[{UR5(aUnOUnU"UU5nUS:Xd[U [25(aU"UR}55nU(a$URXpRbURS9$U(aSSKAJBn U"XpRS9$U(aUS$U$)a Convert argument to a numeric type. If the input is already of a numeric dtype, the dtype will be preserved. For non-numeric inputs, the default return dtype is `float64` or `int64` depending on the data supplied. Use the `downcast` parameter to obtain other dtypes. Please note that precision loss may occur if really large numbers are passed in. Due to the internal limitations of `ndarray`, if numbers smaller than `-9223372036854775808` (np.iinfo(np.int64).min) or larger than `18446744073709551615` (np.iinfo(np.uint64).max) are passed in, it is very likely they will be converted to float so that they can be stored in an `ndarray`. These warnings apply similarly to `Series` since it internally leverages `ndarray`. Parameters ---------- arg : scalar, list, tuple, 1-d array, or Series Argument to be converted. errors : {'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception. - If 'coerce', then invalid parsing will be set as NaN. downcast : str, default None Can be 'integer', 'signed', 'unsigned', or 'float'. If not None, and if the data has been successfully cast to a numerical dtype (or if the data was numeric to begin with), downcast that resulting data to the smallest numerical dtype possible according to the following rules: - 'integer' or 'signed': smallest signed int dtype (min.: np.int8) - 'unsigned': smallest unsigned int dtype (min.: np.uint8) - 'float': smallest float dtype (min.: np.float32) As this behaviour is separate from the core conversion to numeric values, any errors raised during the downcasting will be surfaced regardless of the value of the 'errors' input. In addition, downcasting will only occur if the size of the resulting data's dtype is strictly larger than the dtype it is to be cast to, so if none of the dtypes checked satisfy that specification, no downcasting will be performed on the data. dtype_backend : {'numpy_nullable', 'pyarrow'} Back-end data type applied to the resultant :class:`DataFrame` (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed object * ``"pyarrow"``: returns with pyarrow-backed nullable object .. versionadded:: 2.0 Returns ------- ret Numeric if parsing succeeded. Return type depends on input. Series if Series, otherwise ndarray. Raises ------ ValueError If the input contains non-numeric values and `errors='raise'`. TypeError If the input is not list-like, 1D, or scalar convertible to numeric, such as nested lists or unsupported input types (e.g., dict). See Also -------- DataFrame.astype : Cast argument to a specified dtype. to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. numpy.ndarray.astype : Cast a numpy array to a specified type. DataFrame.convert_dtypes : Convert dtypes. Examples -------- Take separate series and convert to numeric, coercing when told to >>> s = pd.Series(["1.0", "2", -3]) >>> pd.to_numeric(s) 0 1.0 1 2.0 2 -3.0 dtype: float64 >>> pd.to_numeric(s, downcast="float") 0 1.0 1 2.0 2 -3.0 dtype: float32 >>> pd.to_numeric(s, downcast="signed") 0 1 1 2 2 -3 dtype: int8 >>> s = pd.Series(["apple", "1.0", "2", -3]) >>> pd.to_numeric(s, errors="coerce") 0 NaN 1 1.0 2 2.0 3 -3.0 dtype: float64 Downcasting of nullable integer and floating dtypes is supported: >>> s = pd.Series([1, 2, 3], dtype="Int64") >>> pd.to_numeric(s, downcast="integer") 0 1 1 2 2 3 dtype: Int8 >>> s = pd.Series([1.0, 2.1, 3.0], dtype="Float64") >>> pd.to_numeric(s, downcast="float") 0 1.0 1 2.1 2 3.0 dtype: Float32 )Nintegersignedunsignedfloatz#invalid downcasting method provided)rcoercezinvalid error value specifiedFTi8O)dtypendimz/arg must be a list, tuple, 1-d array, or SeriesNr(mMr)coerce_numericconvert_to_masked_nullable)r!r"Integerr#rUnsignedIntegerr$Float)ArrowExtensionArray BooleanArray FloatingArray IntegerArraypyarrow)indexname)Index)r7)C ValueErrorr isinstancervaluesrrr(viewlisttuplenparrayrrr$rrr _valuegetattr TypeErrorr_mask_datarisnadropnato_numpyrr is_np_dtypeint64r maybe_convert_numericset no_defaultrna_value libmissingNAzerosshapebool_ typecodeslenminfloat32charr6itemsizer rcopyndarraypandas.core.arraysr1r2r3r4rr__arrow_array__ _constructorr7rr8)argerrorsdowncast dtype_backend is_seriesis_index is_scalarsr;mask values_dtypenew_maskr,rT float_32_char float_32_indtypecoder(datar1r2r3r4klassr8s V/home/mande/repo/quber/.venv/lib/python3.13/site-packages/pandas/core/tools/numeric.py to_numericro2sBGG>?? 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