Define memory_usage on SparseArray

Author: hexgnu

pandas/core/base.py

@@ -1070,10 +1070,10 @@ def memory_usage(self, deep=False):
         numpy.ndarray.nbytes
         """
         # Use sparse values if they exist for memory consumption
-        if hasattr(self.values, 'sp_values'):
-            values = self.values.sp_values
-        else:
-            values = self.values
+        # if hasattr(self.values, 'sp_values'):
+        #    values = self.values.sp_values
+        # else:
+        values = self.values
 
         if hasattr(values, 'memory_usage'):
             return values.memory_usage(deep=deep)

pandas/core/sparse/array.py

@@ -11,7 +11,7 @@
 from pandas.core.base import PandasObject
 
 from pandas import compat
-from pandas.compat import range
+from pandas.compat import range, PYPY
 from pandas.compat.numpy import function as nv
 
 from pandas.core.dtypes.generic import ABCSparseSeries
@@ -30,6 +30,7 @@
 from pandas.core.dtypes.missing import isna, notna, na_value_for_dtype
 
 import pandas._libs.sparse as splib
+import pandas._libs.lib as lib
 from pandas._libs.sparse import SparseIndex, BlockIndex, IntIndex
 from pandas._libs import index as libindex
 import pandas.core.algorithms as algos
@@ -238,6 +239,41 @@ def kind(self):
         elif isinstance(self.sp_index, IntIndex):
             return 'integer'
 
+    def memory_usage(self, deep=False):
+        """Memory usage of SparseArray
+
+        Parameters
+        ----------
+        deep : bool
+            Introspect the data deeply, interrogate
+            `object` dtypes for system-level memory consumption
+
+        Returns
+        -------
+        scalar bytes of memory consumed
+
+        Notes
+        -----
+        Memory usage does not include memory of empty cells filled by
+        fill_value. And it does not include memory consumed by 
+        elements that are not components of the array if deep=False
+
+        See also
+        --------
+        Series.memory_usage
+        """
+
+        values = self.sp_values
+        if hasattr(values, 'memory_usage'):
+            return values.memory_usage(deep=deep)
+
+        v = values.nbytes
+
+        if deep and is_object_dtype(self) and not PYPY:
+            v += lib.memory_usage_of_objects(values)
+
+        return v
+
     def __array_wrap__(self, out_arr, context=None):
         """
         NumPy calls this method when ufunc is applied