BUG: Parse two date columns broken in read_csv with multiple headers

Fix for GH15376

In `io/parsers/_try_convert_dates()` when selecting columns based on a
column index from a set of columns with multi-level names, the column
`name` was converted to a string.  This appears to be a bug since the
`name` was a tuple before the conversion.  This causes problems
downstream when threre is an attempt to use this name to lookup a
column, and that lookup fails becuase the desired column is keyed from
the tuple, not its string representation.

Author: stephenrauch

doc/source/whatsnew/v0.20.0.txt

@@ -574,6 +574,7 @@ Bug Fixes
 
 
 
+- Bug in ``.read_csv()`` which caused ``parse_dates={'datetime': [0, 1]}`` to fail with multiline headers (:issue:`15376`)
 
 
 - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`)

pandas/io/parsers.py

@@ -2856,7 +2856,7 @@ def _try_convert_dates(parser, colspec, data_dict, columns):
         if c in colset:
             colnames.append(c)
         elif isinstance(c, int) and c not in columns:
-            colnames.append(str(columns[c]))
+            colnames.append(columns[c])
         else:
             colnames.append(c)
 

pandas/tests/io/test_date_converters.py

@@ -148,3 +148,21 @@ def test_parse_date_column_with_empty_string(self):
                          [621, ' ']]
         expected = DataFrame(expected_data, columns=['case', 'opdate'])
         assert_frame_equal(result, expected)
+
+    def test_parse_date_time_multi_level_column_name(self):
+        data = """\
+D,T,A,B
+date, time,a,b
+2001-01-05, 09:00:00, 0.0, 10.
+2001-01-06, 00:00:00, 1.0, 11.
+"""
+        datecols = {'date_time': [0, 1]}
+        result = read_csv(StringIO(data), sep=',', header=[0, 1],
+                          parse_dates=datecols,
+                          date_parser=conv.parse_date_time)
+
+        expected_data = [[datetime(2001, 1, 5, 9, 0, 0), 0., 10.],
+                         [datetime(2001, 1, 6, 0, 0, 0), 1., 11.]]
+        expected = DataFrame(expected_data,
+                             columns=['date_time', ('A', 'a'), ('B', 'b')])
+        assert_frame_equal(result, expected)