BUG: don't assume series is length > 0

doc/source/whatsnew/v0.23.0.txt

@@ -589,3 +589,4 @@ Other
 ^^^^^
 
 - Improved error message when attempting to use a Python keyword as an identifier in a ``numexpr`` backed query (:issue:`18221`)
+- Bug in `Series.memory_usage` which assumes series will always have more than one element (:issue:`19368`)

pandas/_libs/lib.pyx

@@ -59,8 +59,14 @@ def memory_usage_of_objects(ndarray[object, ndim=1] arr):
     cdef Py_ssize_t i, n
     cdef int64_t s = 0
 
+    # The problem here is that... 
+    # A SparseArray of size 1 that has fill_value = the only value
+    # will cause this
+
+    # n = 1
+    #   
     n = len(arr)
-    for i from 0 <= i < n:
+    for i in range(n):
         s += arr[i].__sizeof__()
     return s
 
@@ -131,10 +137,10 @@ def fast_unique_multiple(list arrays):
         dict table = {}
         object val, stub = 0
 
-    for i from 0 <= i < k:
+    for i in range(k):
         buf = arrays[i]
         n = len(buf)
-        for j from 0 <= j < n:
+        for j in range(n):
             val = buf[j]
             if val not in table:
                 table[val] = stub
@@ -158,10 +164,10 @@ def fast_unique_multiple_list(list lists):
         dict table = {}
         object val, stub = 0
 
-    for i from 0 <= i < k:
+    for i in range(k):
         buf = lists[i]
         n = len(buf)
-        for j from 0 <= j < n:
+        for j in range(n):
             val = buf[j]
             if val not in table:
                 table[val] = stub
@@ -200,7 +206,7 @@ def fast_unique_multiple_list_gen(object gen, bint sort=True):
 
     for buf in gen:
         n = len(buf)
-        for j from 0 <= j < n:
+        for j in range(n): 
             val = buf[j]
             if val not in table:
                 table[val] = stub
@@ -830,15 +836,15 @@ def count_level_2d(ndarray[uint8_t, ndim=2, cast=True] mask,
     if axis == 0:
         counts = np.zeros((max_bin, k), dtype='i8')
         with nogil:
-            for i from 0 <= i < n:
-                for j from 0 <= j < k:
+            for i in range(n):
+                for j in range(n):
                     counts[labels[i], j] += mask[i, j]
 
     else:  # axis == 1
         counts = np.zeros((n, max_bin), dtype='i8')
         with nogil:
-            for i from 0 <= i < n:
-                for j from 0 <= j < k:
+            for i in range(n):
+                for j in range(k):
                     counts[i, labels[j]] += mask[i, j]
 
     return counts

pandas/core/base.py

@@ -1069,12 +1069,18 @@ def memory_usage(self, deep=False):
         --------
         numpy.ndarray.nbytes
         """
-        if hasattr(self.values, 'memory_usage'):
-            return self.values.memory_usage(deep=deep)
+        # 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(values, 'memory_usage'):
+            return values.memory_usage(deep=deep)
 
-        v = self.values.nbytes
+        v = values.nbytes
         if deep and is_object_dtype(self) and not PYPY:
-            v += lib.memory_usage_of_objects(self.values)
+            v += lib.memory_usage_of_objects(values)
         return v
 
     def factorize(self, sort=False, na_sentinel=-1):

pandas/tests/sparse/series/test_series.py

@@ -23,6 +23,8 @@
 from pandas.core.sparse.api import SparseSeries
 from pandas.tests.series.test_api import SharedWithSparse
 
+from itertools import product
+
 
 def _test_data1():
     # nan-based
@@ -971,6 +973,17 @@ def test_combine_first(self):
         tm.assert_sp_series_equal(result, result2)
         tm.assert_sp_series_equal(result, expected)
 
+    @pytest.mark.parametrize('deep,fill_values', [([True, False],
+                                                   [0, 1, np.nan, None])])
+    def test_memory_usage_deep(self, deep, fill_values):
+        for deep, fill_value in product(deep, fill_values):
+            sparse_series = SparseSeries(fill_values, fill_value=fill_value)
+            dense_series = Series(fill_values)
+            sparse_usage = sparse_series.memory_usage(deep=deep)
+            dense_usage = dense_series.memory_usage(deep=deep)
+
+            assert sparse_usage < dense_usage
+
 
 class TestSparseHandlingMultiIndexes(object):