+ Ŝia0t$^RIHt^RIHtHtHt^RIt^RI H t ^RI H t ^RI Ht^RIHtHt]'d^RIHt!R R ]4t] !R 4!R R ]44tRt] ] !R4!RR]444t] ] !R4!RR]444t]P2!]P44]!4]P2!]P64]!4/tR]R&R#)) annotations) TYPE_CHECKINGAnyClassVarN) set_module)register_extension_dtype)is_float_dtype) NumericArray NumericDtype)Callablec]tRt^t$Rt]P t]P!]P4t ] t R] R&RRlt]RRl4t]RR l4tR tR #) FloatingDtypea  An ExtensionDtype to hold a single size of floating dtype. These specific implementations are subclasses of the non-public FloatingDtype. For example we have Float32Dtype to represent float32. The attributes name & type are set when these subclasses are created. zCallable[[Any], bool]_checkercV^8dQhRR/#)returnztype[FloatingArray])formats"d/Users/mibo/.openclaw/workspace/.venv-ak/lib/python3.14/site-packages/pandas/core/arrays/floating.py __annotate__FloatingDtype.__annotate__(s&9c \#)zI Return the array type associated with this dtype. Returns ------- type ) FloatingArray)selfs&rconstruct_array_type"FloatingDtype.construct_array_type(s rcV^8dQhRR/#)rrdict[np.dtype, FloatingDtype]r)rs"rrr3s$$#@$rc \#)N)NUMPY_FLOAT_TO_DTYPE)clss&r_get_dtype_mapping FloatingDtype._get_dtype_mapping2s##rc(V^8dQhRRRRRRRR/#)rvaluesz np.ndarraydtypeznp.dtypecopyboolrr)rs"rrr7s(// /8/4/J/rc &VPW#R7#)zc Safely cast the values to the given dtype. "safe" in this context means the casting is lossless. )r()astype)r"r&r'r(s&&&&r _safe_castFloatingDtype._safe_cast6s}}U}..rrN)__name__ __module__ __qualname____firstlineno____doc__npnan_internal_fill_valuer'float64_default_np_dtyper r__annotations__r classmethodr#r,__static_attributes__rrrrrs^66,&4H#4$$//rrz pandas.arraysc]tRt^BtRt]tRtR#)ra* Array of floating (optional missing) values. .. warning:: FloatingArray is currently experimental, and its API or internal implementation may change without warning. Especially the behaviour regarding NaN (distinct from NA missing values) is subject to change. We represent a FloatingArray with 2 numpy arrays: - data: contains a numpy float array of the appropriate dtype - mask: a boolean array holding a mask on the data, True is missing To construct a FloatingArray from generic array-like input, use :func:`pandas.array` with one of the float dtypes (see examples). See :ref:`integer_na` for more. Parameters ---------- values : numpy.ndarray A 1-d float-dtype array. mask : numpy.ndarray A 1-d boolean-dtype array indicating missing values. copy : bool, default False Whether to copy the `values` and `mask`. Attributes ---------- None Methods ------- None Returns ------- FloatingArray See Also -------- array : Create an array. Float32Dtype : Float32 dtype for FloatingArray. Float64Dtype : Float64 dtype for FloatingArray. Series : One-dimensional labeled array capable of holding data. DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data. Examples -------- Create a FloatingArray with :func:`pandas.array`: >>> pd.array([0.1, None, 0.3], dtype=pd.Float32Dtype()) [0.1, , 0.3] Length: 3, dtype: Float32 String aliases for the dtypes are also available. They are capitalized. >>> pd.array([0.1, None, 0.3], dtype="Float32") [0.1, , 0.3] Length: 3, dtype: Float32 rN)r.r/r0r1r2r _dtype_clsr:rrrrrBs?BJrra` An ExtensionDtype for {dtype} data. This dtype uses ``pd.NA`` as missing value indicator. Attributes ---------- None Methods ------- None See Also -------- CategoricalDtype : Type for categorical data with the categories and orderedness. IntegerDtype : An ExtensionDtype to hold a single size & kind of integer dtype. StringDtype : An ExtensionDtype for string data. Examples -------- For Float32Dtype: >>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float32Dtype()) >>> ser.dtype Float32Dtype() For Float64Dtype: >>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float64Dtype()) >>> ser.dtype Float64Dtype() pandascb]tRt^t$]P tRtR]R&] PRR7t Rt R#) Float32DtypeFloat32 ClassVar[str]namefloat32r'rN) r.r/r0r1r3rCtyperBr8_dtype_docstringrr2r:rrrr?r?* ::D#D-#%%I%6Grr?cb]tRt^t$]P tRtR]R&] PRR7t Rt R#) Float64DtypeFloat64rArBr6rDrN) r.r/r0r1r3r6rErBr8rFrr2r:rrrrIrIrGrrIrr!)__conditional_annotations__ __future__rtypingrrrnumpyr3pandas.util._decoratorsrpandas.core.dtypes.baserpandas.core.dtypes.commonr pandas.core.arrays.numericr r collections.abcr rrrFr?rIr'rCr6r!r8)rKs@rrTs"" .<4 (&/L&/R OBLBBJ J H7=77  H7=77HHRZZ,.HHRZZ,.73r