Add __array_ufunc__ to Series / Array

doc/source/development/extending.rst

@@ -208,6 +208,25 @@ will
 2. call ``result = op(values, ExtensionArray)``
 3. re-box the result in a ``Series``
 
+.. _extending.extension.ufunc:
+
+NumPy Universal Functions
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:class:`Series` implements ``__array_ufunc__``. As part of the implementation,
+pandas unboxes the ``ExtensionArray`` from the :class:`Series`, applies the ufunc,
+and re-boxes it if necessary.
+
+If applicable, we highly recommend that you implement ``__array_ufunc__`` in your
+extension array to avoid coercion to an ndarray. See
+`the numpy documentation <https://docs.scipy.org/doc/numpy/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html>`__
+for an example.
+
+As part of your implementation, we require that you defer to pandas when a pandas
+container (:class:`Series`, :class:`DataFrame`, :class:`Index`) is detected in ``inputs``.
+If any of those is present, you should return ``NotImplemented``. Pandas will take care of
+unboxing the array from the container and re-calling the ufunc with the unwrapped input.
+
 .. _extending.extension.testing:
 
 Testing extension arrays

doc/source/getting_started/dsintro.rst

@@ -731,28 +731,62 @@ DataFrame interoperability with NumPy functions
 .. _dsintro.numpy_interop:
 
 Elementwise NumPy ufuncs (log, exp, sqrt, ...) and various other NumPy functions
-can be used with no issues on DataFrame, assuming the data within are numeric:
+can be used with no issues on Series and DataFrame, assuming the data within
+are numeric:
 
 .. ipython:: python
 
    np.exp(df)
    np.asarray(df)
 
-The dot method on DataFrame implements matrix multiplication:
+DataFrame is not intended to be a drop-in replacement for ndarray as its
+indexing semantics and data model are quite different in places from an n-dimensional
+array.
+
+:class:`Series` implements ``__array_ufunc__``, which allows it to work with NumPy's
+`universal functions <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_.
+
+The ufunc is applied to the underlying array in a Series.
 
 .. ipython:: python
 
-   df.T.dot(df)
+   ser = pd.Series([1, 2, 3, 4])
+   np.exp(ser)
 
-Similarly, the dot method on Series implements dot product:
+Like other parts of the library, pandas will automatically align labeled inputs
+as part of a ufunc with multiple inputs. For example, using :meth:`numpy.remainder`
+on two :class:`Series` with differently ordered labels will align before the operation.
 
 .. ipython:: python
 
-   s1 = pd.Series(np.arange(5, 10))
-   s1.dot(s1)
+   ser1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
+   ser2 = pd.Series([1, 3, 5], index=['b', 'a', 'c'])
+   ser1
+   ser2
+   np.remainder(ser1, ser2)
 
-DataFrame is not intended to be a drop-in replacement for ndarray as its
-indexing semantics are quite different in places from a matrix.
+As usual, the union of the two indices is taken, and non-overlapping values are filled
+with missing values.
+
+.. ipython:: python
+
+   ser3 = pd.Series([2, 4, 6], index=['b', 'c', 'd'])
+   ser3
+   np.remainder(ser1, ser3)
+
+When a binary ufunc is applied to a :class:`Series` and :class:`Index`, the Series
+implementation takes precedence and a Series is returned.
+
+.. ipython:: python
+
+   ser = pd.Series([1, 2, 3])
+   idx = pd.Index([4, 5, 6])
+
+   np.maximum(ser, idx)
+
+NumPy ufuncs are safe to apply to :class:`Series` backed by non-ndarray arrays,
+for example :class:`SparseArray` (see :ref:`sparse.calculation`). If possible,
+the ufunc is applied without converting the underlying data to an ndarray.
 
 Console display
 ~~~~~~~~~~~~~~~

doc/source/user_guide/computation.rst

@@ -5,6 +5,7 @@
 Computational tools
 ===================
 
+
 Statistical functions
 ---------------------
 

doc/source/whatsnew/v0.25.0.rst

@@ -886,6 +886,7 @@ Sparse
 - Introduce a better error message in :meth:`Series.sparse.from_coo` so it returns a ``TypeError`` for inputs that are not coo matrices (:issue:`26554`)
 - Bug in :func:`numpy.modf` on a :class:`SparseArray`. Now a tuple of :class:`SparseArray` is returned (:issue:`26946`).
 
+
 Build Changes
 ^^^^^^^^^^^^^
 
@@ -896,6 +897,7 @@ ExtensionArray
 
 - Bug in :func:`factorize` when passing an ``ExtensionArray`` with a custom ``na_sentinel`` (:issue:`25696`).
 - :meth:`Series.count` miscounts NA values in ExtensionArrays (:issue:`26835`)
+- Added ``Series.__array_ufunc__`` to better handle NumPy ufuncs applied to Series backed by extension arrays (:issue:`23293`).
 - Keyword argument ``deep`` has been removed from :meth:`ExtensionArray.copy` (:issue:`27083`)
 
 Other

pandas/core/arrays/base.py

@@ -107,6 +107,17 @@ class ExtensionArray:
     attributes called ``.values`` or ``._values`` to ensure full compatibility
     with pandas internals. But other names as ``.data``, ``._data``,
     ``._items``, ... can be freely used.
+
+    If implementing NumPy's ``__array_ufunc__`` interface, pandas expects
+    that
+
+    1. You defer by raising ``NotImplemented`` when any Series are present
+       in `inputs`. Pandas will extract the arrays and call the ufunc again.
+    2. You define a ``_HANDLED_TYPES`` tuple as an attribute on the class.
+       Pandas inspect this to determine whether the ufunc is valid for the
+       types present.
+
+    See :ref:`extending.extension.ufunc` for more.
     """
     # '_typ' is for pandas.core.dtypes.generic.ABCExtensionArray.
     # Don't override this.

pandas/core/arrays/categorical.py

@@ -26,6 +26,7 @@
 from pandas.core.dtypes.inference import is_hashable
 from pandas.core.dtypes.missing import isna, notna
 
+from pandas.core import ops
 from pandas.core.accessor import PandasDelegate, delegate_names
 import pandas.core.algorithms as algorithms
 from pandas.core.algorithms import factorize, take, take_1d, unique1d
@@ -1292,6 +1293,20 @@ def __array__(self, dtype=None):
             ret = np.asarray(ret)
         return ret
 
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        # for binary ops, use our custom dunder methods
+        result = ops.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs)
+        if result is not NotImplemented:
+            return result
+
+        # for all other cases, raise for now (similarly as what happens in
+        # Series.__array_prepare__)
+        raise TypeError("Object with dtype {dtype} cannot perform "
+                        "the numpy op {op}".format(
+                            dtype=self.dtype,
+                            op=ufunc.__name__))
+
     def __setstate__(self, state):
         """Necessary for making this object picklable"""
         if not isinstance(state, dict):

pandas/core/arrays/integer.py

@@ -1,3 +1,4 @@
+import numbers
 import sys
 from typing import Type
 import warnings
@@ -17,7 +18,7 @@
 from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
 from pandas.core.dtypes.missing import isna, notna
 
-from pandas.core import nanops
+from pandas.core import nanops, ops
 from pandas.core.arrays import ExtensionArray, ExtensionOpsMixin
 from pandas.core.tools.numeric import to_numeric
 
@@ -344,6 +345,52 @@ def __array__(self, dtype=None):
         """
         return self._coerce_to_ndarray()
 
+    _HANDLED_TYPES = (np.ndarray, numbers.Number)
+
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        # For IntegerArray inputs, we apply the ufunc to ._data
+        # and mask the result.
+        if method == 'reduce':
+            # Not clear how to handle missing values in reductions. Raise.
+            raise NotImplementedError("The 'reduce' method is not supported.")
+        out = kwargs.get('out', ())
+
+        for x in inputs + out:
+            if not isinstance(x, self._HANDLED_TYPES + (IntegerArray,)):
+                return NotImplemented
+
+        # for binary ops, use our custom dunder methods
+        result = ops.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs)
+        if result is not NotImplemented:
+            return result
+
+        mask = np.zeros(len(self), dtype=bool)
+        inputs2 = []
+        for x in inputs:
+            if isinstance(x, IntegerArray):
+                mask |= x._mask
+                inputs2.append(x._data)
+            else:
+                inputs2.append(x)
+
+        def reconstruct(x):
+            # we don't worry about scalar `x` here, since we
+            # raise for reduce up above.
+
+            if is_integer_dtype(x.dtype):
+                m = mask.copy()
+                return IntegerArray(x, m)
+            else:
+                x[mask] = np.nan
+            return x
+
+        result = getattr(ufunc, method)(*inputs2, **kwargs)
+        if isinstance(result, tuple):
+            tuple(reconstruct(x) for x in result)
+        else:
+            return reconstruct(result)
+
     def __iter__(self):
         for i in range(len(self)):
             if self._mask[i]:

pandas/core/arrays/sparse.py

@@ -38,6 +38,7 @@
 from pandas.core.base import PandasObject
 import pandas.core.common as com
 from pandas.core.missing import interpolate_2d
+import pandas.core.ops as ops
 
 import pandas.io.formats.printing as printing
 
@@ -1665,42 +1666,11 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
             if not isinstance(x, self._HANDLED_TYPES + (SparseArray,)):
                 return NotImplemented
 
-        special = {'add', 'sub', 'mul', 'pow', 'mod', 'floordiv', 'truediv',
-                   'divmod', 'eq', 'ne', 'lt', 'gt', 'le', 'ge', 'remainder'}
-        aliases = {
-            'subtract': 'sub',
-            'multiply': 'mul',
-            'floor_divide': 'floordiv',
-            'true_divide': 'truediv',
-            'power': 'pow',
-            'remainder': 'mod',
-            'divide': 'div',
-            'equal': 'eq',
-            'not_equal': 'ne',
-            'less': 'lt',
-            'less_equal': 'le',
-            'greater': 'gt',
-            'greater_equal': 'ge',
-        }
-
-        flipped = {
-            'lt': '__gt__',
-            'le': '__ge__',
-            'gt': '__lt__',
-            'ge': '__le__',
-            'eq': '__eq__',
-            'ne': '__ne__',
-        }
-
-        op_name = ufunc.__name__
-        op_name = aliases.get(op_name, op_name)
-
-        if op_name in special and kwargs.get('out') is None:
-            if isinstance(inputs[0], type(self)):
-                return getattr(self, '__{}__'.format(op_name))(inputs[1])
-            else:
-                name = flipped.get(op_name, '__r{}__'.format(op_name))
-                return getattr(self, name)(inputs[0])
+        # for binary ops, use our custom dunder methods
+        result = ops.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs)
+        if result is not NotImplemented:
+            return result
 
         if len(inputs) == 1:
             # No alignment necessary.