Closed
Description
So our internal astype_nansafe has the following behaviour:
In [53]: pd.core.dtypes.cast.astype_nansafe(np.array([1, 2, 3]), np.dtype("float64"))
Out[53]: array([1., 2., 3.])
In [54]: pd.core.dtypes.cast.astype_nansafe(np.array([1, 2, 3]), np.dtype("float64"), copy=False)
Out[54]: array([4.9e-324, 9.9e-324, 1.5e-323])
where it seems it assumes it can take a view if copy=False, which of course depends on the dtype it is casting to:
pandas/pandas/core/dtypes/cast.py
Line 990 in 043b609
I am not fully sure about the intent of astype_nansafe or whether this was intentional (I would say it is a bug, as I would expect that function to take care of whether copy=False is possible or not).
This also causes a bug in SparseArray's astype with copy=False:
In [59]: pd.arrays.SparseArray([1, 2, 3]).astype(float, copy=False)
Out[59]:
[5e-324, 1e-323, 1.5e-323]
Fill: 0.0
IntIndex
Indices: array([0, 1, 2], dtype=int32)