diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index a893b2ba1a189..efe0b22f3b046 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1786,40 +1786,50 @@ def to_json(self, path_or_buf=None, orient=None, date_format=None,
                             index=index)
 
     def to_hdf(self, path_or_buf, key, **kwargs):
-        """Write the contained data to an HDF5 file using HDFStore.
+        """
+        Write the contained data to an HDF5 file using HDFStore.
+
+        Hierarchical Data Format (HDF) is self-describing, allowing an
+        application to interpret the structure and contents of a file with
+        no outside information. One HDF file can hold a mix of related objects
+        which can be accessed as a group or as individual objects.
+
+        In order to add another DataFrame or Series to an existing HDF file
+        please use append mode and a different a key.
+
+        For more information see the :ref:`user guide <io.html#io-hdf5>`.
 
         Parameters
         ----------
-        path_or_buf : the path (string) or HDFStore object
-        key : string
-            identifier for the group in the store
-        mode : optional, {'a', 'w', 'r+'}, default 'a'
-
-          ``'w'``
-              Write; a new file is created (an existing file with the same
-              name would be deleted).
-          ``'a'``
-              Append; an existing file is opened for reading and writing,
-              and if the file does not exist it is created.
-          ``'r+'``
-              It is similar to ``'a'``, but the file must already exist.
-        format : 'fixed(f)|table(t)', default is 'fixed'
-            fixed(f) : Fixed format
-                       Fast writing/reading. Not-appendable, nor searchable
-            table(t) : Table format
-                       Write as a PyTables Table structure which may perform
-                       worse but allow more flexible operations like searching
-                       / selecting subsets of the data
-        append : boolean, default False
-            For Table formats, append the input data to the existing
-        data_columns :  list of columns, or True, default None
+        path_or_buf : str or pandas.HDFStore
+            File path or HDFStore object.
+        key : str
+            Identifier for the group in the store.
+        mode : {'a', 'w', 'r+'}, default 'a'
+            Mode to open file:
+            
+            - 'w': write, a new file is created (an existing file with
+            the same name would be deleted).
+            - 'a': append, an existing file is opened for reading and
+            writing, and if the file does not exist it is created.
+            - 'r+': similar to 'a', but the file must already exist.
+        format : {'fixed', 'table'}, default 'fixed'
+            Possible values:
+            
+            - 'fixed': Fixed format. Fast writing/reading. Not-appendable,
+            nor searchable.
+            - 'table': Table format. Write as a PyTables Table structure
+            which may perform worse but allow more flexible operations
+            like searching / selecting subsets of the data.
+        append : bool, default False
+            For Table formats, append the input data to the existing.
+        data_columns :  list of columns or True, optional
             List of columns to create as indexed data columns for on-disk
             queries, or True to use all columns. By default only the axes
             of the object are indexed. See `here
             <http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns>`__.
-
             Applicable only to format='table'.
-        complevel : int, 0-9, default None
+        complevel : {0-9}, optional
             Specifies a compression level for data.
             A value of 0 disables compression.
         complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
@@ -1831,11 +1841,49 @@ def to_hdf(self, path_or_buf, key, **kwargs):
             Specifying a compression library which is not available issues
             a ValueError.
         fletcher32 : bool, default False
-            If applying compression use the fletcher32 checksum
-        dropna : boolean, default False.
+            If applying compression use the fletcher32 checksum.
+        dropna : bool, default False
             If true, ALL nan rows will not be written to store.
-        """
 
+        See Also
+        --------
+        DataFrame.read_hdf : Read from HDF file.
+        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+        DataFrame.to_sql : Write to a sql table.
+        DataFrame.to_feather : Write out feather-format for DataFrames.
+        DataFrame.to_csv : Write out to a csv file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
+        ...                   index=['a', 'b', 'c'])
+        >>> df.to_hdf('data.h5', key='df', mode='w')
+
+        We can add another object to the same file:
+
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.to_hdf('data.h5', key='s')
+
+        Reading from HDF file:
+
+        >>> pd.read_hdf('data.h5', 'df')
+        A  B
+        a  1  4
+        b  2  5
+        c  3  6
+        >>> pd.read_hdf('data.h5', 's')
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+
+        Deleting file with data:
+
+        >>> import os
+        >>> os.remove('data.h5')
+
+        """
         from pandas.io import pytables
         return pytables.to_hdf(path_or_buf, key, self, **kwargs)