Updates the documentation for pandas.DataFrame.to_sparse.

Author: Gioia Ballin

pandas/core/frame.py

@@ -1418,16 +1418,41 @@ def from_csv(cls, path, header=0, sep=',', index_col=0, parse_dates=True,
 
     def to_sparse(self, fill_value=None, kind='block'):
         """
-        Convert to SparseDataFrame
+        Convert to SparseDataFrame.
+
+        Implement the sparse version of the DataFrame meaning that any data matching
+        a specific value it's omitted in the representation. The sparse DataFrame takes
+        less memory on disk when pickled and in the Python interpreter.
 
         Parameters
         ----------
         fill_value : float, default NaN
+            The specific value that should be omitted in the representation.
         kind : {'block', 'integer'}
+            The kind of the SparseIndex tracking where data has been omitted.
+            The block kind is recommended since it’s more memory efficient:
+            it tracks just the locations and sizes of the blocks of data that
+            are not equal to the fill value while the integer kind keeps an
+            array with all those locations.
 
         Returns
         -------
         y : SparseDataFrame
+
+        See Also
+        --------
+        pandas.DataFrame.to_dense: converts the DataFrame back to the its dense form
+
+        Examples
+        --------
+
+        Compressing on the zero value.
+
+        >>> df = pd.DataFrame(np.random.randn(1000, 4))
+        >>> df.iloc[:995] = 0.
+        >>> sdf = df.to_sparse(fill_value=0.)
+        >>> sdf.density
+        0.005
         """
         from pandas.core.sparse.frame import SparseDataFrame
         return SparseDataFrame(self._series, index=self.index,