diff --git a/fireant/slicer/metrics.py b/fireant/slicer/metrics.py
index 5c1dd30f..ed0ca104 100644
--- a/fireant/slicer/metrics.py
+++ b/fireant/slicer/metrics.py
@@ -61,9 +61,3 @@ def __repr__(self):
     def share(self):
         self._share = True
         return self
-
-    @property
-    @immutable
-    def share(self):
-        self._share = True
-        return self
diff --git a/fireant/slicer/queries/finders.py b/fireant/slicer/queries/finders.py
index e7bae090..39c9b261 100644
--- a/fireant/slicer/queries/finders.py
+++ b/fireant/slicer/queries/finders.py
@@ -112,7 +112,7 @@ def find_share_dimensions(dimensions, operations):
 
     dimension_map = {dimension.key: dimension
                      for dimension in dimensions}
-    
+
     return [dimension_map[dimension.key]
             for dimension in share_operations_over_dimensions]
 
diff --git a/fireant/slicer/totals.py b/fireant/slicer/totals.py
index 5160f685..5c7c11d7 100644
--- a/fireant/slicer/totals.py
+++ b/fireant/slicer/totals.py
@@ -70,12 +70,12 @@ def _scrub_totals_for_multilevel_index_df(data_frame, dimensions):
                                    index=data_frame.index)
 
     """
-    If a row in the data frame is for totals for one index level, all of the subsequent index levels will also use a 
+    If a row in the data frame is for totals for one index level, all of the subsequent index levels will also use a
     totals marker. In order to avoid filtering the wrong rows, a new data frame is created similar to `is_totals_marker`
-    except a cell is only set to True if that value is a totals marker for the corresponding index level, the leaves of 
+    except a cell is only set to True if that value is a totals marker for the corresponding index level, the leaves of
     the dimension value tree.
-    
-    This is acheived by rolling an XOR function across each index level with the previous level.
+
+    This is achieved by rolling an XOR function across each index level with the previous level.
     """
     first_column = is_total_marker.columns[0]
     is_totals_marker_leaf = pd.DataFrame(is_total_marker[first_column])