astype(self, dtype: 'AstypeArg | None' = None, copy: 'bool' = True)
The output will always be a SparseArray. To convert to a dense ndarray with a certain dtype, use numpy.asarray
.
For SparseDtype, this changes the dtype of self.sp_values
and the self.fill_value
.
For other dtypes, this only changes the dtype of self.sp_values
.
Whether to ensure a copy is made, even if not necessary.
Change the dtype of a SparseArray.
>>> arr = pd.arrays.SparseArray([0, 0, 1, 2])This example is valid syntax, but we were not able to check execution
... arr [0, 0, 1, 2] Fill: 0 IntIndex Indices: array([2, 3], dtype=int32)
>>> arr.astype(np.dtype('int32')) [0, 0, 1, 2] Fill: 0 IntIndex Indices: array([2, 3], dtype=int32)
Using a NumPy dtype with a different kind (e.g. float) will coerce just self.sp_values
.
>>> arr.astype(np.dtype('float64'))
... # doctest: +NORMALIZE_WHITESPACE [0.0, 0.0, 1.0, 2.0] Fill: 0.0 IntIndex Indices: array([2, 3], dtype=int32)
Use a SparseDtype if you wish to be change the fill value as well.
This example is valid syntax, but we were not able to check execution>>> arr.astype(SparseDtype("float64", fill_value=np.nan))See :
... # doctest: +NORMALIZE_WHITESPACE [nan, nan, 1.0, 2.0] Fill: nan IntIndex Indices: array([2, 3], dtype=int32)
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