174 lines
4.2 KiB
Python
174 lines
4.2 KiB
Python
from __future__ import annotations
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from typing import ClassVar
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import numpy as np
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from pandas.core.dtypes.base import register_extension_dtype
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from pandas.core.dtypes.common import is_float_dtype
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from pandas.core.arrays.numeric import (
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NumericArray,
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NumericDtype,
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)
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class FloatingDtype(NumericDtype):
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"""
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An ExtensionDtype to hold a single size of floating dtype.
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These specific implementations are subclasses of the non-public
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FloatingDtype. For example we have Float32Dtype to represent float32.
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The attributes name & type are set when these subclasses are created.
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"""
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_default_np_dtype = np.dtype(np.float64)
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_checker = is_float_dtype
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@classmethod
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def construct_array_type(cls) -> type[FloatingArray]:
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"""
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Return the array type associated with this dtype.
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Returns
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-------
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type
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"""
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return FloatingArray
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@classmethod
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def _get_dtype_mapping(cls) -> dict[np.dtype, FloatingDtype]:
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return NUMPY_FLOAT_TO_DTYPE
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@classmethod
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def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray:
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"""
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Safely cast the values to the given dtype.
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"safe" in this context means the casting is lossless.
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"""
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# This is really only here for compatibility with IntegerDtype
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# Here for compat with IntegerDtype
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return values.astype(dtype, copy=copy)
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class FloatingArray(NumericArray):
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"""
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Array of floating (optional missing) values.
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.. warning::
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FloatingArray is currently experimental, and its API or internal
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implementation may change without warning. Especially the behaviour
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regarding NaN (distinct from NA missing values) is subject to change.
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We represent a FloatingArray with 2 numpy arrays:
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- data: contains a numpy float array of the appropriate dtype
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- mask: a boolean array holding a mask on the data, True is missing
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To construct an FloatingArray from generic array-like input, use
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:func:`pandas.array` with one of the float dtypes (see examples).
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See :ref:`integer_na` for more.
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Parameters
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----------
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values : numpy.ndarray
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A 1-d float-dtype array.
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mask : numpy.ndarray
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A 1-d boolean-dtype array indicating missing values.
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copy : bool, default False
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Whether to copy the `values` and `mask`.
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Attributes
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----------
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None
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Methods
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-------
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None
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Returns
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-------
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FloatingArray
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Examples
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--------
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Create an FloatingArray with :func:`pandas.array`:
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>>> pd.array([0.1, None, 0.3], dtype=pd.Float32Dtype())
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<FloatingArray>
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[0.1, <NA>, 0.3]
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Length: 3, dtype: Float32
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String aliases for the dtypes are also available. They are capitalized.
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>>> pd.array([0.1, None, 0.3], dtype="Float32")
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<FloatingArray>
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[0.1, <NA>, 0.3]
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Length: 3, dtype: Float32
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"""
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_dtype_cls = FloatingDtype
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# The value used to fill '_data' to avoid upcasting
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_internal_fill_value = np.nan
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# Fill values used for any/all
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# Incompatible types in assignment (expression has type "float", base class
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# "BaseMaskedArray" defined the type as "<typing special form>")
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_truthy_value = 1.0 # type: ignore[assignment]
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_falsey_value = 0.0 # type: ignore[assignment]
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_dtype_docstring = """
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An ExtensionDtype for {dtype} data.
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This dtype uses ``pd.NA`` as missing value indicator.
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Attributes
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----------
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None
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Methods
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-------
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None
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Examples
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--------
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For Float32Dtype:
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>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float32Dtype())
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>>> ser.dtype
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Float32Dtype()
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For Float64Dtype:
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>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float64Dtype())
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>>> ser.dtype
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Float64Dtype()
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"""
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# create the Dtype
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@register_extension_dtype
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class Float32Dtype(FloatingDtype):
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type = np.float32
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name: ClassVar[str] = "Float32"
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__doc__ = _dtype_docstring.format(dtype="float32")
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@register_extension_dtype
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class Float64Dtype(FloatingDtype):
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type = np.float64
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name: ClassVar[str] = "Float64"
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__doc__ = _dtype_docstring.format(dtype="float64")
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NUMPY_FLOAT_TO_DTYPE: dict[np.dtype, FloatingDtype] = {
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np.dtype(np.float32): Float32Dtype(),
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np.dtype(np.float64): Float64Dtype(),
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}
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