diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index 1eedb9e2a8274..089e35e8e93b2 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -1306,33 +1306,40 @@ def assert_frame_equal(left, right, check_dtype=True,
                        check_categorical=True,
                        check_like=False,
                        obj='DataFrame'):
-    """Check that left and right DataFrame are equal.
+    """
+    Check that left and right DataFrame are equal.
+
+    This function is intended to compare two DataFrames and output any
+    differences. Is is mostly intended for use in unit tests.
+    Additional parameters allow varying the strictness of the
+    equality checks performed.
 
     Parameters
     ----------
     left : DataFrame
+        First DataFrame to compare.
     right : DataFrame
+        Second DataFrame to compare.
     check_dtype : bool, default True
         Whether to check the DataFrame dtype is identical.
-    check_index_type : bool / string {'equiv'}, default False
+    check_index_type : {'equiv'} or bool, default 'equiv'
         Whether to check the Index class, dtype and inferred_type
         are identical.
-    check_column_type : bool / string {'equiv'}, default False
+    check_column_type : {'equiv'} or bool, default 'equiv'
         Whether to check the columns class, dtype and inferred_type
         are identical.
-    check_frame_type : bool, default False
+    check_frame_type : bool, default True
         Whether to check the DataFrame class is identical.
     check_less_precise : bool or int, default False
         Specify comparison precision. Only used when check_exact is False.
         5 digits (False) or 3 digits (True) after decimal points are compared.
-        If int, then specify the digits to compare
+        If int, then specify the digits to compare.
     check_names : bool, default True
         Whether to check that the `names` attribute for both the `index`
         and `column` attributes of the DataFrame is identical, i.e.
 
         * left.index.names == right.index.names
         * left.columns.names == right.columns.names
-
     by_blocks : bool, default False
         Specify how to compare internal data. If False, compare by columns.
         If True, compare by blocks.
@@ -1345,10 +1352,39 @@ def assert_frame_equal(left, right, check_dtype=True,
     check_like : bool, default False
         If True, ignore the order of index & columns.
         Note: index labels must match their respective rows
-        (same as in columns) - same labels must be with the same data
+        (same as in columns) - same labels must be with the same data.
     obj : str, default 'DataFrame'
         Specify object name being compared, internally used to show appropriate
-        assertion message
+        assertion message.
+
+    See Also
+    --------
+    assert_series_equal : Equivalent method for asserting Series equality.
+    DataFrame.equals : Check DataFrame equality.
+
+    Examples
+    --------
+    This example shows comparing two DataFrames that are equal
+    but with columns of differing dtypes.
+
+    >>> from pandas.util.testing import assert_frame_equal
+    >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
+    >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
+
+    df1 equals itself.
+    >>> assert_frame_equal(df1, df1)
+
+    df1 differs from df2 as column 'b' is of a different type.
+    >>> assert_frame_equal(df1, df2)
+    Traceback (most recent call last):
+    AssertionError: Attributes are different
+
+    Attribute "dtype" are different
+    [left]:  int64
+    [right]: float64
+
+    Ignore differing dtypes in columns with check_dtype.
+    >>> assert_frame_equal(df1, df2, check_dtype=False)
     """
 
     # instance validation