BUG:reorder type check/conversion so wide_to_long handles str arg for stubnames. GH22468 (pandas-dev#22490)

Author: csmcallister

doc/source/whatsnew/v0.24.0.txt

@@ -802,6 +802,7 @@ Reshaping
 - Bug in :meth:`DataFrame.replace` raises ``RecursionError`` when replacing empty lists (:issue:`22083`)
 - Bug in :meth:`Series.replace` and meth:`DataFrame.replace` when dict is used as the ``to_replace`` value and one key in the dict is is another key's value, the results were inconsistent between using integer key and using string key (:issue:`20656`)
 - Bug in :meth:`DataFrame.drop_duplicates` for empty ``DataFrame`` which incorrectly raises an error (:issue:`20516`)
+- Bug in :func:`pandas.wide_to_long` when a string is passed to the stubnames argument and a column name is a substring of that stubname (:issue:`22468`)
 
 Build Changes
 ^^^^^^^^^^^^^

pandas/core/reshape/melt.py

@@ -409,14 +409,14 @@ def melt_stub(df, stub, i, j, value_vars, sep):
 
         return newdf.set_index(i + [j])
 
-    if any(col in stubnames for col in df.columns):
-        raise ValueError("stubname can't be identical to a column name")
-
     if not is_list_like(stubnames):
         stubnames = [stubnames]
     else:
         stubnames = list(stubnames)
 
+    if any(col in stubnames for col in df.columns):
+        raise ValueError("stubname can't be identical to a column name")
+
     if not is_list_like(i):
         i = [i]
     else:

pandas/tests/reshape/test_melt.py

@@ -640,3 +640,24 @@ def test_float_suffix(self):
         result = wide_to_long(df, ['result', 'treatment'],
                               i='A', j='colname', suffix='[0-9.]+', sep='_')
         tm.assert_frame_equal(result, expected)
+
+    def test_col_substring_of_stubname(self):
+        # GH22468
+        # Don't raise ValueError when a column name is a substring
+        # of a stubname that's been passed as a string
+        wide_data = {'node_id': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},
+                     'A': {0: 0.80, 1: 0.0, 2: 0.25, 3: 1.0, 4: 0.81},
+                     'PA0': {0: 0.74, 1: 0.56, 2: 0.56, 3: 0.98, 4: 0.6},
+                     'PA1': {0: 0.77, 1: 0.64, 2: 0.52, 3: 0.98, 4: 0.67},
+                     'PA3': {0: 0.34, 1: 0.70, 2: 0.52, 3: 0.98, 4: 0.67}
+                     }
+        wide_df = pd.DataFrame.from_dict(wide_data)
+        expected = pd.wide_to_long(wide_df,
+                                   stubnames=['PA'],
+                                   i=['node_id', 'A'],
+                                   j='time')
+        result = pd.wide_to_long(wide_df,
+                                 stubnames='PA',
+                                 i=['node_id', 'A'],
+                                 j='time')
+        tm.assert_frame_equal(result, expected)