diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index f649cce985474..b25781f87872a 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2302,8 +2302,7 @@ def first(self, numeric_only: bool = False, min_count: int = -1):
         Parameters
         ----------
         numeric_only : bool, default False
-            Include only float, int, boolean columns. If None, will attempt to use
-            everything, then use only numeric data.
+            Include only float, int, boolean columns.
         min_count : int, default -1
             The required number of valid values to perform the operation. If fewer
             than ``min_count`` non-NA values are present the result will be NA.
@@ -2323,8 +2322,20 @@ def first(self, numeric_only: bool = False, min_count: int = -1):
 
         Examples
         --------
-        >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3]))
+        >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3],
+        ...                        D=['3/11/2000', '3/12/2000', '3/13/2000']))
+        >>> df['D'] = pd.to_datetime(df['D'])
         >>> df.groupby("A").first()
+             B  C          D
+        A
+        1  5.0  1 2000-03-11
+        3  6.0  3 2000-03-13
+        >>> df.groupby("A").first(min_count=2)
+            B    C          D
+        A
+        1 NaN  1.0 2000-03-11
+        3 NaN  NaN        NaT
+        >>> df.groupby("A").first(numeric_only=True)
              B  C
         A
         1  5.0  1