Infer names in MultiIndex.from_product

doc/source/whatsnew/v1.0.0.rst

@@ -28,7 +28,7 @@ Enhancements
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
 
--
+- :meth:`MultiIndex.from_product` infers level names from inputs when not explicitly provided (:issue:`27292`)
 -
 
 .. _whatsnew_1000.api_breaking:

pandas/core/indexes/multi.py

@@ -508,7 +508,9 @@ def from_product(cls, iterables, sortorder=None, names=None):
             Level of sortedness (must be lexicographically sorted by that
             level).
         names : list / sequence of str, optional
-            Names for the levels in the index.
+            Names for the levels in the index. If not provided, names
+            will be inferred from iterables if the elements of iterables
+            have a name attribute.
 
         Returns
         -------
@@ -526,6 +528,18 @@ def from_product(cls, iterables, sortorder=None, names=None):
         >>> colors = ['green', 'purple']
         >>> pd.MultiIndex.from_product([numbers, colors],
         ...                            names=['number', 'color'])
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1,  'green'),
+                    (1, 'purple'),
+                    (2,  'green'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+
+        >>> numbers = pd.Series([0, 1, 2], name='number')
+        >>> colors = pd.Series(['green', 'purple'], name='color')
+        >>> pd.MultiIndex.from_product([numbers, colors])
+        ...
         MultiIndex([(0,  'green'),
                     (0, 'purple'),
                     (1,  'green'),
@@ -541,6 +555,13 @@ def from_product(cls, iterables, sortorder=None, names=None):
         elif is_iterator(iterables):
             iterables = list(iterables)
 
+        # Infer names from iterable if attribute is available
+        if names is None:
+            names = [getattr(idx, "name", None) for idx in iterables]
+
+            if all(name is None for name in names):
+                names = None
+
         codes, levels = _factorize_from_iterables(iterables)
         codes = cartesian_product(codes)
         return MultiIndex(levels, codes, sortorder=sortorder, names=names)

pandas/tests/indexes/multi/test_constructor.py

@@ -473,6 +473,29 @@ def test_from_product_datetimeindex():
     tm.assert_numpy_array_equal(mi.values, etalon)
 
 
+@pytest.mark.parametrize(
+    "a, b, expected_names",
+    [
+        (
+            pd.Series([1, 2, 3], name="foo"),
+            pd.Series(["a", "b"], name="bar"),
+            ["foo", "bar"],
+        ),
+        (pd.Series([1, 2, 3]), pd.Series(["a", "b"]), None),
+        (pd.Series([1, 2, 3], name="foo"), ["a", "b"], ["foo", None]),
+        ([1, 2, 3], ["a", "b"], None),
+    ],
+)
+def test_from_product_infers_partial_names(a, b, expected_names):
+    result = MultiIndex.from_product([a, b])
+    expected = MultiIndex(
+        levels=[[1, 2, 3], ["a", "b"]],
+        codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
+        names=expected_names,
+    )
+    tm.assert_index_equal(result, expected)
+
+
 @pytest.mark.parametrize("ordered", [False, True])
 @pytest.mark.parametrize("f", [lambda x: x, lambda x: pd.Series(x), lambda x: x.values])
 def test_from_product_index_series_categorical(ordered, f):