docstrings for isnull and notnull (#4618)

* add docstrings to isnull and notnull

* switch isnull and notnull from monkey patched to true functions

* update whats-new.rst

* add the missing keep_attrs parameter

* add back the variable methods

* fix the variable creation

doc/whats-new.rst

@@ -109,6 +109,9 @@ Documentation
   By `Sahid Velji <https://github.com/sahidvelji>`_.
 - Update link to NumPy docstring standard in the :doc:`contributing` guide (:pull:`4558`).
   By `Sahid Velji <https://github.com/sahidvelji>`_.
+- Add docstrings to ``isnull`` and ``notnull``, and fix the displayed signature
+  (:issue:`2760`, :pull:`4618`).
+  By `Justus Magin <https://github.com/keewis>`_.
 
 Internal Changes
 ~~~~~~~~~~~~~~~~

xarray/core/common.py

@@ -1268,6 +1268,78 @@ def close(self: Any) -> None:
             self._file_obj.close()
         self._file_obj = None
 
+    def isnull(self, keep_attrs: bool = None):
+        """Test each value in the array for whether it is a missing value.
+
+        Returns
+        -------
+        isnull : DataArray or Dataset
+            Same type and shape as object, but the dtype of the data is bool.
+
+        See Also
+        --------
+        pandas.isnull
+
+        Examples
+        --------
+        >>> array = xr.DataArray([1, np.nan, 3], dims="x")
+        >>> array
+        <xarray.DataArray (x: 3)>
+        array([ 1., nan,  3.])
+        Dimensions without coordinates: x
+        >>> array.isnull()
+        <xarray.DataArray (x: 3)>
+        array([False,  True, False])
+        Dimensions without coordinates: x
+        """
+        from .computation import apply_ufunc
+
+        if keep_attrs is None:
+            keep_attrs = _get_keep_attrs(default=False)
+
+        return apply_ufunc(
+            duck_array_ops.isnull,
+            self,
+            dask="allowed",
+            keep_attrs=keep_attrs,
+        )
+
+    def notnull(self, keep_attrs: bool = None):
+        """Test each value in the array for whether it is not a missing value.
+
+        Returns
+        -------
+        notnull : DataArray or Dataset
+            Same type and shape as object, but the dtype of the data is bool.
+
+        See Also
+        --------
+        pandas.notnull
+
+        Examples
+        --------
+        >>> array = xr.DataArray([1, np.nan, 3], dims="x")
+        >>> array
+        <xarray.DataArray (x: 3)>
+        array([ 1., nan,  3.])
+        Dimensions without coordinates: x
+        >>> array.notnull()
+        <xarray.DataArray (x: 3)>
+        array([ True, False,  True])
+        Dimensions without coordinates: x
+        """
+        from .computation import apply_ufunc
+
+        if keep_attrs is None:
+            keep_attrs = _get_keep_attrs(default=False)
+
+        return apply_ufunc(
+            duck_array_ops.notnull,
+            self,
+            dask="allowed",
+            keep_attrs=keep_attrs,
+        )
+
     def isin(self, test_elements):
         """Tests each value in the array for whether it is in test elements.
 

xarray/core/ops.py

@@ -43,7 +43,6 @@
 # methods which don't modify the data shape, so the result should still be
 # wrapped in an Variable/DataArray
 NUMPY_UNARY_METHODS = ["argsort", "clip", "conj", "conjugate"]
-PANDAS_UNARY_FUNCTIONS = ["isnull", "notnull"]
 # methods which remove an axis
 REDUCE_METHODS = ["all", "any"]
 NAN_REDUCE_METHODS = [
@@ -334,10 +333,6 @@ def inject_all_ops_and_reduce_methods(cls, priority=50, array_only=True):
     for name in NUMPY_UNARY_METHODS:
         setattr(cls, name, cls._unary_op(_method_wrapper(name)))
 
-    for name in PANDAS_UNARY_FUNCTIONS:
-        f = _func_slash_method_wrapper(getattr(duck_array_ops, name), name=name)
-        setattr(cls, name, cls._unary_op(f))
-
     f = _func_slash_method_wrapper(duck_array_ops.around, name="round")
     setattr(cls, "round", cls._unary_op(f))
 

xarray/core/variable.py

@@ -2087,6 +2087,74 @@ def _coarsen_reshape(self, windows, boundary, side):
 
         return variable.data.reshape(shape), tuple(axes)
 
+    def isnull(self, keep_attrs: bool = None):
+        """Test each value in the array for whether it is a missing value.
+
+        Returns
+        -------
+        isnull : Variable
+            Same type and shape as object, but the dtype of the data is bool.
+
+        See Also
+        --------
+        pandas.isnull
+
+        Examples
+        --------
+        >>> var = xr.Variable("x", [1, np.nan, 3])
+        >>> var
+        <xarray.Variable (x: 3)>
+        array([ 1., nan,  3.])
+        >>> var.isnull()
+        <xarray.Variable (x: 3)>
+        array([False,  True, False])
+        """
+        from .computation import apply_ufunc
+
+        if keep_attrs is None:
+            keep_attrs = _get_keep_attrs(default=False)
+
+        return apply_ufunc(
+            duck_array_ops.isnull,
+            self,
+            dask="allowed",
+            keep_attrs=keep_attrs,
+        )
+
+    def notnull(self, keep_attrs: bool = None):
+        """Test each value in the array for whether it is not a missing value.
+
+        Returns
+        -------
+        notnull : Variable
+            Same type and shape as object, but the dtype of the data is bool.
+
+        See Also
+        --------
+        pandas.notnull
+
+        Examples
+        --------
+        >>> var = xr.Variable("x", [1, np.nan, 3])
+        >>> var
+        <xarray.Variable (x: 3)>
+        array([ 1., nan,  3.])
+        >>> var.notnull()
+        <xarray.Variable (x: 3)>
+        array([ True, False,  True])
+        """
+        from .computation import apply_ufunc
+
+        if keep_attrs is None:
+            keep_attrs = _get_keep_attrs(default=False)
+
+        return apply_ufunc(
+            duck_array_ops.notnull,
+            self,
+            dask="allowed",
+            keep_attrs=keep_attrs,
+        )
+
     @property
     def real(self):
         return type(self)(self.dims, self.data.real, self._attrs)