Add docstring examples

xarray/core/dataarray.py

@@ -6767,6 +6767,48 @@ def groupby(
         Coordinates:
           * dayofyear  (dayofyear) int64 3kB 1 2 3 4 5 6 7 ... 361 362 363 364 365 366
 
+        >>> da = xr.DataArray(
+        ...     data=np.arange(12).reshape((4, 3)),
+        ...     dims=("x", "y"),
+        ...     coords={"x": [10, 20, 30, 40], "letters": ("x", list("abba"))},
+        ... )
+
+        Grouping by a single variable is easy
+        >>> da.groupby("letters")
+        <DataArrayGroupBy, grouped over 1 grouper(s), 2 groups in total:
+            'letters': 2 groups with labels 'a', 'b'>
+
+        Execute a reduction
+        >>> da.groupby("letters").sum()
+        <xarray.DataArray (letters: 2, y: 3)> Size: 48B
+        array([[ 9., 11., 13.],
+               [ 9., 11., 13.]])
+        Coordinates:
+          * letters  (letters) object 16B 'a' 'b'
+        Dimensions without coordinates: y
+
+        Grouping by multiple variables
+        >>> da.groupby(["letters", "x"])
+        <DataArrayGroupBy, grouped over 2 grouper(s), 8 groups in total:
+            'letters': 2 groups with labels 'a', 'b'
+            'x': 4 groups with labels 10, 20, 30, 40>
+
+        Use Grouper objects to express more complicated GroupBy operations
+        >>> from xarray.groupers import BinGrouper, UniqueGrouper
+        >>>
+        >>> da.groupby(x=BinGrouper(bins=[5, 15, 25]), letters=UniqueGrouper()).sum()
+        <xarray.DataArray (x_bins: 2, letters: 2, y: 3)> Size: 96B
+        array([[[ 0.,  1.,  2.],
+                [nan, nan, nan]],
+        <BLANKLINE>
+               [[nan, nan, nan],
+                [ 3.,  4.,  5.]]])
+        Coordinates:
+          * x_bins   (x_bins) object 16B (5, 15] (15, 25]
+          * letters  (letters) object 16B 'a' 'b'
+        Dimensions without coordinates: y
+
+
         See Also
         --------
         :ref:`groupby`

xarray/core/dataset.py

@@ -10373,7 +10373,7 @@ def groupby(
         Grouping by a single variable is easy
         >>> ds.groupby("letters")
         <DatasetGroupBy, grouped over 1 grouper(s), 2 groups in total:
-                'letters': 2 groups with labels 'a', 'b'>
+            'letters': 2 groups with labels 'a', 'b'>
 
         Execute a reduction
         >>> ds.groupby("letters").sum()
@@ -10388,13 +10388,21 @@ def groupby(
         Grouping by multiple variables
         >>> ds.groupby(["letters", "x"])
         <DatasetGroupBy, grouped over 2 grouper(s), 8 groups in total:
-                'letters': 2 groups with labels 'a', 'b'
-                'x': 4 groups with labels 10, 20, 30, 40>
+            'letters': 2 groups with labels 'a', 'b'
+            'x': 4 groups with labels 10, 20, 30, 40>
 
         Use Grouper objects to express more complicated GroupBy operations
-        >>> from xarray.groupers import BinGrouper
+        >>> from xarray.groupers import BinGrouper, UniqueGrouper
         >>>
         >>> ds.groupby(x=BinGrouper(bins=[5, 15, 25]), letters=UniqueGrouper()).sum()
+        <xarray.Dataset> Size: 128B
+        Dimensions:  (y: 3, x_bins: 2, letters: 2)
+        Coordinates:
+          * x_bins   (x_bins) object 16B (5, 15] (15, 25]
+          * letters  (letters) object 16B 'a' 'b'
+        Dimensions without coordinates: y
+        Data variables:
+            foo      (y, x_bins, letters) float64 96B 0.0 nan nan 3.0 ... nan nan 5.0
 
         See Also
         --------

xarray/core/groupby.py

@@ -671,7 +671,7 @@ def __repr__(self) -> str:
         for grouper in self.groupers:
             coord = grouper.unique_coord
             labels = ", ".join(format_array_flat(coord, 30).split())
-            text += f"\n\t{grouper.name!r}: {coord.size} groups with labels {labels}"
+            text += f"\n    {grouper.name!r}: {coord.size} groups with labels {labels}"
         return text + ">"
 
     def _iter_grouped(self) -> Iterator[T_Xarray]:

xarray/tests/test_groupby.py

@@ -583,7 +583,7 @@ def test_groupby_repr(obj, dim) -> None:
     N = len(np.unique(obj[dim]))
     expected = f"<{obj.__class__.__name__}GroupBy"
     expected += f", grouped over 1 grouper(s), {N} groups in total:"
-    expected += f"\n\t{dim!r}: {N} groups with labels "
+    expected += f"\n    {dim!r}: {N} groups with labels "
     if dim == "x":
         expected += "1, 2, 3, 4, 5>"
     elif dim == "y":
@@ -600,7 +600,7 @@ def test_groupby_repr_datetime(obj) -> None:
     actual = repr(obj.groupby("t.month"))
     expected = f"<{obj.__class__.__name__}GroupBy"
     expected += ", grouped over 1 grouper(s), 12 groups in total:\n"
-    expected += "\t'month': 12 groups with labels "
+    expected += "    'month': 12 groups with labels "
     expected += "1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>"
     assert actual == expected