EHN: Improve from_items error message (#17312)

doc/source/whatsnew/v0.22.0.txt

@@ -80,6 +80,7 @@ Other API Changes
 - :class:`IntervalIndex` constructor will raise if the ``closed`` parameter conflicts with how the input data is inferred to be closed (:issue:`18421`)
 - Inserting missing values into indexes will work for all types of indexes and automatically insert the correct type of missing value (``NaN``, ``NaT``, etc.) regardless of the type passed in (:issue:`18295`)
 - Restricted ``DateOffset`` keyword arguments. Previously, ``DateOffset`` subclasses allowed arbitrary keyword arguments which could lead to unexpected behavior. Now, only valid arguments will be accepted. (:issue:`17176`, :issue:`18226`).
+- :func:`DataFrame.from_items` provides a more informative error message when passed scalar values (:issue:`17312`)
 
 .. _whatsnew_0220.deprecations:
 

pandas/core/frame.py

@@ -54,6 +54,7 @@
     _ensure_int64,
     _ensure_platform_int,
     is_list_like,
+    is_nested_list_like,
     is_iterator,
     is_sequence,
     is_named_tuple)
@@ -1271,16 +1272,34 @@ def from_items(cls, items, columns=None, orient='columns'):
                 columns = _ensure_index(keys)
                 arrays = values
 
-            return cls._from_arrays(arrays, columns, None)
+            # GH 17312
+            # Provide more informative error msg when scalar values passed
+            try:
+                return cls._from_arrays(arrays, columns, None)
+
+            except ValueError:
+                if not is_nested_list_like(values):
+                    raise ValueError('The value in each (key, value) pair '
+                                     'must be an array, Series, or dict')
+
         elif orient == 'index':
             if columns is None:
                 raise TypeError("Must pass columns with orient='index'")
 
             keys = _ensure_index(keys)
 
-            arr = np.array(values, dtype=object).T
-            data = [lib.maybe_convert_objects(v) for v in arr]
-            return cls._from_arrays(data, columns, keys)
+            # GH 17312
+            # Provide more informative error msg when scalar values passed
+            try:
+                arr = np.array(values, dtype=object).T
+                data = [lib.maybe_convert_objects(v) for v in arr]
+                return cls._from_arrays(data, columns, keys)
+
+            except TypeError:
+                if not is_nested_list_like(values):
+                    raise ValueError('The value in each (key, value) pair '
+                                     'must be an array, Series, or dict')
+
         else:  # pragma: no cover
             raise ValueError("'orient' must be either 'columns' or 'index'")
 

pandas/tests/frame/test_constructors.py

@@ -268,13 +268,14 @@ def test_constructor_dict(self):
 
         # GH10856
         # dict with scalar values should raise error, even if columns passed
-        with pytest.raises(ValueError):
+        msg = 'If using all scalar values, you must pass an index'
+        with tm.assert_raises_regex(ValueError, msg):
             DataFrame({'a': 0.7})
 
-        with pytest.raises(ValueError):
+        with tm.assert_raises_regex(ValueError, msg):
             DataFrame({'a': 0.7}, columns=['a'])
 
-        with pytest.raises(ValueError):
+        with tm.assert_raises_regex(ValueError, msg):
             DataFrame({'a': 0.7}, columns=['b'])
 
     def test_constructor_multi_index(self):
@@ -1204,6 +1205,19 @@ def test_constructor_from_items(self):
                        columns=['one', 'two', 'three'])
         tm.assert_frame_equal(rs, xp)
 
+    def test_constructor_from_items_scalars(self):
+        # GH 17312
+        with tm.assert_raises_regex(ValueError,
+                                    'The value in each \(key, value\) '
+                                    'pair must be an array, Series, or dict'):
+            DataFrame.from_items([('A', 1), ('B', 4)])
+
+        with tm.assert_raises_regex(ValueError,
+                                    'The value in each \(key, value\) '
+                                    'pair must be an array, Series, or dict'):
+            DataFrame.from_items([('A', 1), ('B', 2)], columns=['col1'],
+                                 orient='index')
+
     def test_constructor_mix_series_nonseries(self):
         df = DataFrame({'A': self.frame['A'],
                         'B': list(self.frame['B'])}, columns=['A', 'B'])