IMP: more verbose error handling for merge method use cases

q2_metadata/_merge.py

@@ -19,21 +19,46 @@ def merge(metadata1: qiime2.Metadata,
     n_overlapping_ids = len(overlapping_ids)
     n_overlapping_columns = len(overlapping_columns)
 
-    if len(overlapping_ids) > 0 and len(overlapping_columns) > 0:
-        raise ValueError(f"Merging can currently handle overlapping ids "
-                         f"or overlapping columns, but not both. "
-                         f"{n_overlapping_ids} overlapping ids were "
-                         f"identified ({', '.join(overlapping_ids)}) and"
-                         f"{n_overlapping_columns} overlapping columns "
-                         f"were identified {', '.join(overlapping_columns)}.")
+    if n_overlapping_ids and n_overlapping_columns:
+        raise ValueError(
+            "Merging can currently handle overlapping ids or overlapping "
+            f"columns but not both. {n_overlapping_ids} overlapping ids were "
+            f"identified ({', '.join(overlapping_ids)}) and "
+            f"{n_overlapping_columns} overlapping columns were identified "
+            f"({', '.join(overlapping_columns)})."
+        )
 
     df1 = metadata1.to_dataframe()
     df2 = metadata2.to_dataframe()
 
-    if n_overlapping_columns == 0:
+    if df1.index.name != df2.index.name:
+        raise ValueError(
+            "Metadata files contain different ID column names. "
+            f"Metadata1 file contains '{df1.index.name}' and metadata2 "
+            f"contains '{df2.index.name}'. These column names must match."
+        )
+
+    if not n_overlapping_columns:
         result = pd.merge(df1, df2, how='outer', left_index=True,
                           right_index=True)
-    else:  # i.e., n_overlapping_ids == 0
+
+    else:
+        for column in overlapping_columns:
+            if df1[column].dtype != df2[column].dtype:
+                column_type1 = type(
+                    qiime2.Metadata(df1[[column]]).get_column(column))
+                column_type2 = type(
+                    qiime2.Metadata(df2[[column]]).get_column(column))
+                raise ValueError(
+                    f"Metadata files contain the shared column '{column}' "
+                    "with different type designations. "
+                    f"In 'metadata1', the column '{column}' is of type "
+                    f"'{column_type1.__name__}', "
+                    f"and in 'metadata2', it is of type "
+                    f"'{column_type2.__name__}'. These type designations must "
+                    "match."
+                )
+
         result = pd.merge(df1, df2, how='outer', left_index=True,
                           right_index=True, suffixes=('', '_'))
         for c in overlapping_columns:

q2_metadata/tests/test_merge.py

@@ -122,8 +122,6 @@ def test_merge_all_columns_overlapping(self):
                                            columns=['col1', 'col2', 'col3']))
 
         obs1 = merge(md1, md2)
-        print(obs1.to_dataframe())
-
         index_exp1 = pd.Index(['sample1', 'sample2', 'sample3',
                                'sample4', 'sample5', 'sample6'], name='id')
         data_exp1 = [['a', 'd', 'h'],
@@ -135,8 +133,6 @@ def test_merge_all_columns_overlapping(self):
         exp1 = qiime2.Metadata(
             pd.DataFrame(data_exp1, index=index_exp1, dtype=object,
                          columns=['col1', 'col2', 'col3']))
-
-        print(exp1.to_dataframe())
         self.assertEqual(obs1, exp1)
 
     def test_merge_some_columns_overlapping(self):
@@ -201,3 +197,58 @@ def test_merge_no_samples_or_columns_overlapping(self):
                                   'col4', 'col5', 'col6']))
 
         self.assertEqual(obs1, exp1)
+
+    def test_merge_mismatched_columnID_names_in_error_message(self):
+        index1 = pd.Index(['sample1', 'sample2', 'sample3'], name='id')
+        data1 = [['a', 'd', 'h'],
+                 ['b', 'e', 'i'],
+                 ['c', 'f', 'j']]
+        md1 = qiime2.Metadata(pd.DataFrame(data1, index=index1, dtype=object,
+                                           columns=['col1', 'col2', 'col3']))
+
+        index2 = pd.Index(['sample4', 'sample5', 'sample6'], name='sample-id')
+        data2 = [['k', 'n', 'q'],
+                 ['l', 'o', 'r'],
+                 ['m', 'p', 's']]
+        md2 = qiime2.Metadata(pd.DataFrame(data2, index=index2, dtype=object,
+                                           columns=['col4', 'col5', 'col6']))
+
+        with self.assertRaisesRegex(
+            ValueError,
+            "Metadata files contain different ID column names.*id.*sample-id"
+        ):
+            merge(md1, md2)
+
+    def test_merge_mismatched_md_column_type_designations(self):
+        index1 = pd.Index(['sample1', 'sample2', 'sample3'], name='id')
+        data1 = [['a', 'd', 'h'],
+                 ['b', 'e', 'i'],
+                 ['c', 'f', 'j']]
+        md1 = qiime2.Metadata(
+            pd.DataFrame(
+                data1,
+                index=index1,
+                dtype=object,
+                columns=['col1', 'col2', 'col3']
+            )
+        )
+        index2 = pd.Index(['sample4', 'sample5', 'sample6'], name='id')
+        data2 = [['k', 'n', 40.0],
+                 ['l', 'o', 41.0],
+                 ['m', 'p', 42.0]]
+        md2 = qiime2.Metadata(
+            pd.DataFrame(
+                data2,
+                index=index2,
+                columns=['col1', 'col2', 'col3']
+            )
+        )
+        with self.assertRaisesRegex(
+            ValueError,
+            "Metadata files contain the shared column 'col3' with different "
+            "type designations. In 'metadata1', the column 'col3' is of type "
+            r"\'CategoricalMetadataColumn\', and in 'metadata2', it is of "
+            r"type \'NumericMetadataColumn\'. These type designations must "
+            "match."
+        ):
+            merge(md1, md2)