from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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Any,
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)
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import numpy as np
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from pandas._libs import lib
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from pandas.errors import LossySetitemError
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from pandas.core.dtypes.cast import np_can_hold_element
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from pandas.core.dtypes.common import is_numeric_dtype
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if TYPE_CHECKING:
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from pandas._typing import (
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ArrayLike,
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npt,
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)
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def to_numpy_dtype_inference(
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arr: ArrayLike, dtype: npt.DTypeLike | None, na_value, hasna: bool
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) -> tuple[npt.DTypeLike, Any]:
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if dtype is None and is_numeric_dtype(arr.dtype):
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dtype_given = False
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if hasna:
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if arr.dtype.kind == "b":
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dtype = np.dtype(np.object_)
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else:
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if arr.dtype.kind in "iu":
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dtype = np.dtype(np.float64)
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else:
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dtype = arr.dtype.numpy_dtype # type: ignore[union-attr]
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if na_value is lib.no_default:
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na_value = np.nan
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else:
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dtype = arr.dtype.numpy_dtype # type: ignore[union-attr]
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elif dtype is not None:
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dtype = np.dtype(dtype)
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dtype_given = True
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else:
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dtype_given = True
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if na_value is lib.no_default:
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if dtype is None or not hasna:
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na_value = arr.dtype.na_value
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elif dtype.kind == "f": # type: ignore[union-attr]
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na_value = np.nan
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elif dtype.kind == "M": # type: ignore[union-attr]
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na_value = np.datetime64("nat")
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elif dtype.kind == "m": # type: ignore[union-attr]
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na_value = np.timedelta64("nat")
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else:
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na_value = arr.dtype.na_value
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if not dtype_given and hasna:
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try:
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np_can_hold_element(dtype, na_value) # type: ignore[arg-type]
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except LossySetitemError:
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dtype = np.dtype(np.object_)
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return dtype, na_value
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