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For some cases, this will preserve extension types of arrays by calling the ufunc blockwise. ```python In [1]: import pandas as pd; import numpy as np In [2]: df = pd.DataFrame({"A": pd.array([0, 1], dtype="Sparse")}) In [3]: np.sin(df).dtypes Out[3]: A Sparse[float64, nan] dtype: object ``` We don't currently handle the multi-input case well (aside from ufuncs that are implemented as dunder ops like `np.add`). For these, we fall back to the old implementation of converting to an ndarray.
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