diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index a66d00fff9714..d6b16e6cb4ad8 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -12,8 +12,8 @@
 # pylint: disable=E1101,E1103
 # pylint: disable=W0212,W0231,W0703,W0622
 
-import functools
 import collections
+import functools
 import itertools
 import sys
 import types
@@ -5873,35 +5873,81 @@ def _get_agg_axis(self, axis_num):
 
     def mode(self, axis=0, numeric_only=False):
         """
-        Gets the mode(s) of each element along the axis selected. Adds a row
-        for each mode per label, fills in gaps with nan.
+        Get the mode(s) of each element along the axis selected. The mode of
+        a set of values is the value or set of values that appear most often.
 
-        Note that there could be multiple values returned for the selected
-        axis (when more than one item share the maximum frequency), which is
-        the reason why a dataframe is returned. If you want to impute missing
-        values with the mode in a dataframe ``df``, you can just do this:
-        ``df.fillna(df.mode().iloc[0])``
+        Adds a row for each mode per label, filling gaps with NaN.
 
         Parameters
         ----------
         axis : {0 or 'index', 1 or 'columns'}, default 0
-            * 0 or 'index' : get mode of each column
-            * 1 or 'columns' : get mode of each row
+            The axis to iterate over while searching for the mode.
+            To find the mode for each column, use ``axis=0``.
+            To find the mode for each row, use ``axis=1``.
         numeric_only : boolean, default False
-            if True, only apply to numeric columns
+            If True, only apply to numeric dimensions.
 
         Returns
         -------
         modes : DataFrame (sorted)
+            A DataFrame containing the modes
+            If ``axis=0``, there will be one column per column in the original
+            DataFrame, with as many rows as there are modes.
+            If ``axis=1``, there will be one row per row in the original
+            DataFrame, with as many columns as there are modes.
+
+        Notes
+        -----
+        There may be multiple values returned for the selected
+        axis when more than one item share the maximum frequency, which is
+        the reason why a DataFrame is returned.
+
+        See Also
+        --------
+        Series.mode : Return the highest frequency value in a Series.
+        Series.value_counts : Return the counts of values in a Series.
 
         Examples
         --------
-        >>> df = pd.DataFrame({'A': [1, 2, 1, 2, 1, 2, 3]})
-        >>> df.mode()
-           A
-        0  1
-        1  2
-        """
+
+        ``mode`` returns a DataFrame with multiple rows if there is more than
+        one mode. Missing entries are imputed with NaN.
+
+        >>> grades = pd.DataFrame({
+        ...     'Science': [80, 70, 80, 75, 80, 75, 85, 90, 80, 70],
+        ...     'Math': [70, 70, 75, 75, 80, 80, 85, 85, 90, 90]
+        ... })
+        >>> grades.mode()
+           Science  Math
+        0     80.0    70
+        1      NaN    75
+        2      NaN    80
+        3      NaN    85
+        4      NaN    90
+        >>> grades.apply(lambda x: x.value_counts())
+            Science  Math
+        70        2     2
+        75        2     2
+        80        4     2
+        85        1     2
+        90        1     2
+
+        Use ``axis=1`` to apply mode over columns (get the mode of each row).
+
+        >>> student_grades = pd.DataFrame.from_dict({
+        ...     'Alice': [80, 85, 90, 85, 95],
+        ...     'Bob': [70, 80, 80, 75, 90]
+        ... }, 'index')
+        >>> student_grades
+                0   1   2   3   4
+        Alice  80  85  90  85  95
+        Bob    70  80  80  75  90
+        >>> student_grades.mode(axis=1)
+                0
+        Alice  85
+        Bob    80
+        """
+        
         data = self if not numeric_only else self._get_numeric_data()
 
         def f(s):