BUG: Group-by numeric type-coercion with datetime

doc/source/whatsnew/v0.20.0.txt

@@ -886,3 +886,4 @@ Bug Fixes
 - Bug in ``pd.melt()`` where passing a tuple value for ``value_vars`` caused a ``TypeError`` (:issue:`15348`)
 - Bug in ``.eval()`` which caused multiline evals to fail with local variables not on the first line (:issue:`15342`)
 - Bug in ``pd.read_msgpack`` which did not allow to load dataframe with an index of type ``CategoricalIndex`` (:issue:`15487`)
+- Bug in ``groupby.apply()`` coercing ``object`` series to numeric types, when not all values were numeric (:issue:`15680`)

pandas/core/groupby.py

@@ -10,6 +10,7 @@
     zip, range, lzip,
     callable, map
 )
+
 from pandas import compat
 from pandas.compat.numpy import function as nv
 from pandas.compat.numpy import _np_version_under1p8
@@ -3424,6 +3425,7 @@ def _decide_output_index(self, output, labels):
 
     def _wrap_applied_output(self, keys, values, not_indexed_same=False):
         from pandas.core.index import _all_indexes_same
+        from pandas.tools.util import to_numeric
 
         if len(keys) == 0:
             return DataFrame(index=keys)
@@ -3566,7 +3568,8 @@ def first_non_None_value(values):
                 # as we are stacking can easily have object dtypes here
                 so = self._selected_obj
                 if (so.ndim == 2 and so.dtypes.apply(is_datetimelike).any()):
-                    result = result._convert(numeric=True)
+                    result = result.apply(
+                        lambda x: to_numeric(x, errors='ignore'))
                     date_cols = self._selected_obj.select_dtypes(
                         include=['datetime', 'timedelta']).columns
                     date_cols = date_cols.intersection(result.columns)

pandas/tests/groupby/test_groupby.py

@@ -4314,6 +4314,16 @@ def test_cummin_cummax(self):
         expected = pd.Series([1, 2, 1], name='b')
         tm.assert_series_equal(result, expected)
 
+    def test_numeric_coercion(self):
+        # GH 14423
+        df = pd.DataFrame({'Number': [1, 2],
+                           'Date': ["2017-03-02"] * 2,
+                           'Str': ["foo", "inf"]})
+        expected = df.groupby(['Number']).apply(lambda x: x.iloc[0])
+        df.Date = pd.to_datetime(df.Date)
+        result = df.groupby(['Number']).apply(lambda x: x.iloc[0])
+        tm.assert_series_equal(result['Str'], expected['Str'])
+
 
 def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
     tups = lmap(tuple, df[keys].values)