diff --git a/openmc/arithmetic.py b/openmc/arithmetic.py
index 6a781cb5a2..fe002f1e31 100644
--- a/openmc/arithmetic.py
+++ b/openmc/arithmetic.py
@@ -237,9 +237,6 @@ class CrossFilter(object):
         left / right filters
     num_bins : Integral
         The number of filter bins (always 1 if aggregate_filter is defined)
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        crossfilter's bins.
 
     """
 
@@ -250,7 +247,6 @@ class CrossFilter(object):
         self._type = '({0} {1} {2})'.format(left_type, binary_op, right_type)
 
         self._bins = {}
-        self._stride = None
 
         self._left_filter = None
         self._right_filter = None
@@ -314,10 +310,6 @@ class CrossFilter(object):
         else:
             return 0
 
-    @property
-    def stride(self):
-        return self._stride
-
     @type.setter
     def type(self, filter_type):
         if filter_type not in _FILTER_TYPES:
@@ -347,10 +339,6 @@ class CrossFilter(object):
         cv.check_value('binary_op', binary_op, _TALLY_ARITHMETIC_OPS)
         self._binary_op = binary_op
 
-    @stride.setter
-    def stride(self, stride):
-        self._stride = stride
-
     def get_bin_index(self, filter_bin):
         """Returns the index in the CrossFilter for some bin.
 
@@ -611,9 +599,6 @@ class AggregateFilter(object):
         The filter bins included in the aggregation
     num_bins : Integral
         The number of filter bins (always 1 if aggregate_filter is defined)
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        aggregatefilter's bins.
 
     """
 
@@ -622,7 +607,6 @@ class AggregateFilter(object):
         self._type = '{0}({1})'.format(aggregate_op,
                                        aggregate_filter.short_name.lower())
         self._bins = None
-        self._stride = None
 
         self._aggregate_filter = None
         self._aggregate_op = None
@@ -684,10 +668,6 @@ class AggregateFilter(object):
     def num_bins(self):
         return len(self.bins) if self.aggregate_filter else 0
 
-    @property
-    def stride(self):
-        return self._stride
-
     @type.setter
     def type(self, filter_type):
         if filter_type not in _FILTER_TYPES:
@@ -714,10 +694,6 @@ class AggregateFilter(object):
         cv.check_value('aggregate_op', aggregate_op, _TALLY_AGGREGATE_OPS)
         self._aggregate_op = aggregate_op
 
-    @stride.setter
-    def stride(self, stride):
-        self._stride = stride
-
     def get_bin_index(self, filter_bin):
         """Returns the index in the AggregateFilter for some bin.
 
@@ -753,7 +729,7 @@ class AggregateFilter(object):
         else:
             return self.bins.index(filter_bin)
 
-    def get_pandas_dataframe(self, data_size, summary=None, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, summary=None, **kwargs):
         """Builds a Pandas DataFrame for the AggregateFilter's bins.
 
         This method constructs a Pandas DataFrame object for the AggregateFilter
@@ -762,8 +738,10 @@ class AggregateFilter(object):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
         summary : None or Summary
             An optional Summary object to be used to construct columns for
             distribcell tally filters (default is None). NOTE: This parameter
@@ -793,7 +771,7 @@ class AggregateFilter(object):
             filter_bins[i] = bin
 
         # Repeat and tile bins as needed for DataFrame
-        filter_bins = np.repeat(filter_bins, self.stride)
+        filter_bins = np.repeat(filter_bins, stride)
         tile_factor = data_size / len(filter_bins)
         filter_bins = np.tile(filter_bins, tile_factor)
 
diff --git a/openmc/filter.py b/openmc/filter.py
index 2b329ef0e0..fc133168d2 100644
--- a/openmc/filter.py
+++ b/openmc/filter.py
@@ -86,9 +86,6 @@ class Filter(IDManagerMixin):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -99,7 +96,6 @@ class Filter(IDManagerMixin):
         self.bins = bins
         self.id = filter_id
         self._num_bins = 0
-        self._stride = None
 
     def __eq__(self, other):
         if type(self) is not type(other):
@@ -194,10 +190,6 @@ class Filter(IDManagerMixin):
     def num_bins(self):
         return self._num_bins
 
-    @property
-    def stride(self):
-        return self._stride
-
     @bins.setter
     def bins(self, bins):
         # Format the bins as a 1D numpy array.
@@ -214,16 +206,6 @@ class Filter(IDManagerMixin):
         cv.check_greater_than('filter num_bins', num_bins, 0, equality=True)
         self._num_bins = num_bins
 
-    @stride.setter
-    def stride(self, stride):
-        cv.check_type('filter stride', stride, Integral)
-        if stride < 0:
-            msg = 'Unable to set stride "{0}" for a "{1}" since it ' \
-                  'is a negative value'.format(stride, type(self))
-            raise ValueError(msg)
-
-        self._stride = stride
-
     def check_bins(self, bins):
         """Make sure given bins are valid for this filter.
 
@@ -270,11 +252,7 @@ class Filter(IDManagerMixin):
             Whether the filter can be merged
 
         """
-
-        if type(self) is not type(other):
-            return False
-
-        return True
+        return type(self) is type(other)
 
     def merge(self, other):
         """Merge this filter with another.
@@ -402,7 +380,7 @@ class Filter(IDManagerMixin):
         # Return a 1-tuple of the bin.
         return (self.bins[bin_index],)
 
-    def get_pandas_dataframe(self, data_size, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, **kwargs):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -411,13 +389,15 @@ class Filter(IDManagerMixin):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Keyword arguments
         -----------------
         paths : bool
-            Only used for DistirbcellFilter.  If True (default), expand
+            Only used for DistribcellFilter.  If True (default), expand
             distribcell indices into multi-index columns describing the path
             to that distribcell through the CSG tree.  NOTE: This option assumes
             that all distribcell paths are of the same length and do not have
@@ -431,11 +411,6 @@ class Filter(IDManagerMixin):
             the total number of bins in the corresponding tally, with the filter
             bin appropriately tiled to map to the corresponding tally bins.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -444,7 +419,7 @@ class Filter(IDManagerMixin):
         # Initialize Pandas DataFrame
         df = pd.DataFrame()
 
-        filter_bins = np.repeat(self.bins, self.stride)
+        filter_bins = np.repeat(self.bins, stride)
         tile_factor = data_size // len(filter_bins)
         filter_bins = np.tile(filter_bins, tile_factor)
         df = pd.concat([df, pd.DataFrame(
@@ -502,9 +477,6 @@ class UniverseFilter(WithIDFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
     @property
@@ -535,9 +507,6 @@ class MaterialFilter(WithIDFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
     @property
@@ -568,15 +537,12 @@ class CellFilter(WithIDFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
     @property
     def bins(self):
         return self._bins
-      
+
     @bins.setter
     def bins(self, bins):
         self._smart_set_bins(bins, openmc.Cell)
@@ -601,15 +567,12 @@ class CellFromFilter(WithIDFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
     @property
     def bins(self):
         return self._bins
-      
+
     @bins.setter
     def bins(self, bins):
         self._smart_set_bins(bins, openmc.Cell)
@@ -634,9 +597,6 @@ class CellbornFilter(WithIDFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
     @property
@@ -668,9 +628,6 @@ class SurfaceFilter(Filter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
     @property
@@ -696,7 +653,7 @@ class SurfaceFilter(Filter):
     @num_bins.setter
     def num_bins(self, num_bins): pass
 
-    def get_pandas_dataframe(self, data_size, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, **kwargs):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -705,8 +662,10 @@ class SurfaceFilter(Filter):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Returns
         -------
@@ -717,11 +676,6 @@ class SurfaceFilter(Filter):
             the corresponding tally, with the filter bin appropriately tiled to
             map to the corresponding tally bins.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -730,7 +684,7 @@ class SurfaceFilter(Filter):
         # Initialize Pandas DataFrame
         df = pd.DataFrame()
 
-        filter_bins = np.repeat(self.bins, self.stride)
+        filter_bins = np.repeat(self.bins, stride)
         tile_factor = data_size // len(filter_bins)
         filter_bins = np.tile(filter_bins, tile_factor)
         filter_bins = [_CURRENT_NAMES[x] for x in filter_bins]
@@ -760,9 +714,6 @@ class MeshFilter(Filter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -854,7 +805,7 @@ class MeshFilter(Filter):
             y = bin_index - (x * ny)
             return (x, y)
 
-    def get_pandas_dataframe(self, data_size, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, **kwargs):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -863,8 +814,10 @@ class MeshFilter(Filter):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Returns
         -------
@@ -875,11 +828,6 @@ class MeshFilter(Filter):
             corresponding tally, with the filter bin appropriately tiled to map
             to the corresponding tally bins.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -906,7 +854,7 @@ class MeshFilter(Filter):
 
         # Generate multi-index sub-column for x-axis
         filter_bins = np.arange(1, nx + 1)
-        repeat_factor = ny * nz * self.stride
+        repeat_factor = ny * nz * stride
         filter_bins = np.repeat(filter_bins, repeat_factor)
         tile_factor = data_size // len(filter_bins)
         filter_bins = np.tile(filter_bins, tile_factor)
@@ -914,7 +862,7 @@ class MeshFilter(Filter):
 
         # Generate multi-index sub-column for y-axis
         filter_bins = np.arange(1, ny + 1)
-        repeat_factor = nz * self.stride
+        repeat_factor = nz * stride
         filter_bins = np.repeat(filter_bins, repeat_factor)
         tile_factor = data_size // len(filter_bins)
         filter_bins = np.tile(filter_bins, tile_factor)
@@ -922,7 +870,7 @@ class MeshFilter(Filter):
 
         # Generate multi-index sub-column for z-axis
         filter_bins = np.arange(1, nz + 1)
-        repeat_factor = self.stride
+        repeat_factor = stride
         filter_bins = np.repeat(filter_bins, repeat_factor)
         tile_factor = data_size // len(filter_bins)
         filter_bins = np.tile(filter_bins, tile_factor)
@@ -952,9 +900,6 @@ class RealFilter(Filter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -1063,9 +1008,6 @@ class EnergyFilter(RealFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -1100,7 +1042,7 @@ class EnergyFilter(RealFilter):
                       'increasing'.format(bins, type(self))
                 raise ValueError(msg)
 
-    def get_pandas_dataframe(self, data_size, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, **kwargs):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -1109,8 +1051,10 @@ class EnergyFilter(RealFilter):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Returns
         -------
@@ -1121,11 +1065,6 @@ class EnergyFilter(RealFilter):
             corresponding tally, with the filter bin appropriately tiled to map
             to the corresponding tally bins.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -1136,8 +1075,8 @@ class EnergyFilter(RealFilter):
 
         # Extract the lower and upper energy bounds, then repeat and tile
         # them as necessary to account for other filters.
-        lo_bins = np.repeat(self.bins[:-1], self.stride)
-        hi_bins = np.repeat(self.bins[1:], self.stride)
+        lo_bins = np.repeat(self.bins[:-1], stride)
+        hi_bins = np.repeat(self.bins[1:], stride)
         tile_factor = data_size // len(lo_bins)
         lo_bins = np.tile(lo_bins, tile_factor)
         hi_bins = np.tile(hi_bins, tile_factor)
@@ -1167,9 +1106,6 @@ class EnergyoutFilter(EnergyFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -1229,9 +1165,6 @@ class DistribcellFilter(Filter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
     paths : list of str
         The paths traversed through the CSG tree to reach each distribcell
         instance (for 'distribcell' filters only)
@@ -1296,7 +1229,7 @@ class DistribcellFilter(Filter):
         # the Cell in the Geometry (consecutive integers starting at 0).
         return filter_bin
 
-    def get_pandas_dataframe(self, data_size, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, **kwargs):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -1305,8 +1238,10 @@ class DistribcellFilter(Filter):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Keyword arguments
         -----------------
@@ -1331,11 +1266,6 @@ class DistribcellFilter(Filter):
             of bins in the corresponding tally, with the filter bin
             appropriately tiled to map to the corresponding tally bins.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -1418,7 +1348,7 @@ class DistribcellFilter(Filter):
 
                 # Tile the Multi-index columns
                 for level_key, level_bins in level_dict.items():
-                    level_bins = np.repeat(level_bins, self.stride)
+                    level_bins = np.repeat(level_bins, stride)
                     tile_factor = data_size // len(level_bins)
                     level_bins = np.tile(level_bins, tile_factor)
                     level_dict[level_key] = level_bins
@@ -1434,7 +1364,7 @@ class DistribcellFilter(Filter):
         # NOTE: This is performed regardless of whether the user
         # requests Summary geometric information
         filter_bins = np.arange(self.num_bins)
-        filter_bins = np.repeat(filter_bins, self.stride)
+        filter_bins = np.repeat(filter_bins, stride)
         tile_factor = data_size // len(filter_bins)
         filter_bins = np.tile(filter_bins, tile_factor)
         df = pd.DataFrame({self.short_name.lower() : filter_bins})
@@ -1474,9 +1404,6 @@ class MuFilter(RealFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -1504,7 +1431,7 @@ class MuFilter(RealFilter):
                       'increasing'.format(bins, type(self))
                 raise ValueError(msg)
 
-    def get_pandas_dataframe(self, data_size, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, **kwargs):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -1513,8 +1440,10 @@ class MuFilter(RealFilter):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Returns
         -------
@@ -1525,11 +1454,6 @@ class MuFilter(RealFilter):
             corresponding tally, with the filter bin appropriately tiled to map
             to the corresponding tally bins.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -1540,8 +1464,8 @@ class MuFilter(RealFilter):
 
         # Extract the lower and upper energy bounds, then repeat and tile
         # them as necessary to account for other filters.
-        lo_bins = np.repeat(self.bins[:-1], self.stride)
-        hi_bins = np.repeat(self.bins[1:], self.stride)
+        lo_bins = np.repeat(self.bins[:-1], stride)
+        hi_bins = np.repeat(self.bins[1:], stride)
         tile_factor = data_size // len(lo_bins)
         lo_bins = np.tile(lo_bins, tile_factor)
         hi_bins = np.tile(hi_bins, tile_factor)
@@ -1579,9 +1503,6 @@ class PolarFilter(RealFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -1609,7 +1530,7 @@ class PolarFilter(RealFilter):
                       'increasing'.format(bins, type(self))
                 raise ValueError(msg)
 
-    def get_pandas_dataframe(self, data_size, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, **kwargs):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -1618,8 +1539,10 @@ class PolarFilter(RealFilter):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Returns
         -------
@@ -1630,11 +1553,6 @@ class PolarFilter(RealFilter):
             corresponding tally, with the filter bin appropriately tiled to map
             to the corresponding tally bins.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -1645,8 +1563,8 @@ class PolarFilter(RealFilter):
 
         # Extract the lower and upper angle bounds, then repeat and tile
         # them as necessary to account for other filters.
-        lo_bins = np.repeat(self.bins[:-1], self.stride)
-        hi_bins = np.repeat(self.bins[1:], self.stride)
+        lo_bins = np.repeat(self.bins[:-1], stride)
+        hi_bins = np.repeat(self.bins[1:], stride)
         tile_factor = data_size // len(lo_bins)
         lo_bins = np.tile(lo_bins, tile_factor)
         hi_bins = np.tile(hi_bins, tile_factor)
@@ -1684,9 +1602,6 @@ class AzimuthalFilter(RealFilter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -1714,7 +1629,7 @@ class AzimuthalFilter(RealFilter):
                       'increasing'.format(bins, type(self))
                 raise ValueError(msg)
 
-    def get_pandas_dataframe(self, data_size, paths=True):
+    def get_pandas_dataframe(self, data_size, stride, paths=True):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -1723,8 +1638,10 @@ class AzimuthalFilter(RealFilter):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Returns
         -------
@@ -1735,11 +1652,6 @@ class AzimuthalFilter(RealFilter):
             corresponding tally, with the filter bin appropriately tiled to map
             to the corresponding tally bins.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -1750,8 +1662,8 @@ class AzimuthalFilter(RealFilter):
 
         # Extract the lower and upper angle bounds, then repeat and tile
         # them as necessary to account for other filters.
-        lo_bins = np.repeat(self.bins[:-1], self.stride)
-        hi_bins = np.repeat(self.bins[1:], self.stride)
+        lo_bins = np.repeat(self.bins[:-1], stride)
+        hi_bins = np.repeat(self.bins[1:], stride)
         tile_factor = data_size // len(lo_bins)
         lo_bins = np.tile(lo_bins, tile_factor)
         hi_bins = np.tile(hi_bins, tile_factor)
@@ -1785,9 +1697,6 @@ class DelayedGroupFilter(Filter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
     @property
@@ -1840,9 +1749,6 @@ class EnergyFunctionFilter(Filter):
         Unique identifier for the filter
     num_bins : Integral
         The number of filter bins (always 1 for this filter)
-    stride : Integral
-        The number of filter, nuclide and score bins within each of this
-        filter's bins.
 
     """
 
@@ -1850,7 +1756,6 @@ class EnergyFunctionFilter(Filter):
         self.energy = energy
         self.y = y
         self.id = filter_id
-        self._stride = None
 
     def __eq__(self, other):
         if type(self) is not type(other):
@@ -2006,7 +1911,7 @@ class EnergyFunctionFilter(Filter):
         """This function is invalid for EnergyFunctionFilters."""
         raise RuntimeError('EnergyFunctionFilters have no get_bin() method')
 
-    def get_pandas_dataframe(self, data_size, **kwargs):
+    def get_pandas_dataframe(self, data_size, stride, **kwargs):
         """Builds a Pandas DataFrame for the Filter's bins.
 
         This method constructs a Pandas DataFrame object for the filter with
@@ -2015,8 +1920,10 @@ class EnergyFunctionFilter(Filter):
 
         Parameters
         ----------
-        data_size : Integral
+        data_size : int
             The total number of bins in the tally corresponding to this filter
+        stride : int
+            Stride in memory for the filter
 
         Returns
         -------
@@ -2027,11 +1934,6 @@ class EnergyFunctionFilter(Filter):
             EnergyFunctionFilters. The number of rows in the DataFrame is the
             same as the total number of bins in the corresponding tally.
 
-        Raises
-        ------
-        ImportError
-            When Pandas is not installed
-
         See also
         --------
         Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
@@ -2052,7 +1954,7 @@ class EnergyFunctionFilter(Filter):
         # hex characters) of the digest are probably sufficient.
         out = out[:14]
 
-        filter_bins = np.repeat(out, self.stride)
+        filter_bins = np.repeat(out, stride)
         tile_factor = data_size // len(filter_bins)
         filter_bins = np.tile(filter_bins, tile_factor)
         df = pd.concat([df, pd.DataFrame(
diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py
index 192f484a5e..6a74ee2313 100644
--- a/openmc/mgxs/mgxs.py
+++ b/openmc/mgxs/mgxs.py
@@ -2751,7 +2751,6 @@ class TransportXS(MGXS):
             # Switch EnergyoutFilter to EnergyFilter.
             old_filt = self.tallies['scatter-1'].filters[-1]
             new_filt = openmc.EnergyFilter(old_filt.bins)
-            new_filt.stride = old_filt.stride
             self.tallies['scatter-1'].filters[-1] = new_filt
 
             self._rxn_rate_tally = \
@@ -2771,7 +2770,6 @@ class TransportXS(MGXS):
             # Switch EnergyoutFilter to EnergyFilter.
             old_filt = self.tallies['scatter-1'].filters[-1]
             new_filt = openmc.EnergyFilter(old_filt.bins)
-            new_filt.stride = old_filt.stride
             self.tallies['scatter-1'].filters[-1] = new_filt
 
             # Compute total cross section
diff --git a/openmc/statepoint.py b/openmc/statepoint.py
index 16d5b5d848..4656e08560 100644
--- a/openmc/statepoint.py
+++ b/openmc/statepoint.py
@@ -415,9 +415,6 @@ class StatePoint(object):
 
                         tally.scores.append(score)
 
-                    # Compute and set the filter strides
-                    tally._update_filter_strides()
-
                     # Add Tally to the global dictionary of all Tallies
                     tally.sparse = self.sparse
                     self._tallies[tally_id] = tally
diff --git a/openmc/tallies.py b/openmc/tallies.py
index e049d69837..9f8f7e505e 100644
--- a/openmc/tallies.py
+++ b/openmc/tallies.py
@@ -78,6 +78,8 @@ class Tally(IDManagerMixin):
     shape : 3-tuple of int
         The shape of the tally data array ordered as the number of filter bins,
         nuclide bins and score bins
+    filter_strides : list of int
+        Stride in memory for each filter
     num_realizations : int
         Total number of realizations
     with_summary : bool
@@ -818,10 +820,6 @@ class Tally(IDManagerMixin):
         # Differentiate Tally with a new auto-generated Tally ID
         merged_tally.id = None
 
-        # If the two tallies are equal, simply return copy
-        if self == other:
-            return merged_tally
-
         # Create deep copy of other tally to use for array concatenation
         other_copy = copy.deepcopy(other)
 
@@ -877,9 +875,6 @@ class Tally(IDManagerMixin):
         else:
             self._derived = True
 
-        # Update filter strides in merged tally
-        merged_tally._update_filter_strides()
-
         # Concatenate sum arrays if present in both tallies
         if self.sum is not None and other_copy.sum is not None:
             self_sum = self.get_reshaped_data(value='sum')
@@ -1538,11 +1533,10 @@ class Tally(IDManagerMixin):
 
         # Build DataFrame columns for filters if user requested them
         if filters:
-
             # Append each Filter's DataFrame to the overall DataFrame
-            for self_filter in self.filters:
-                filter_df = self_filter.get_pandas_dataframe(
-                    data_size, paths=paths)
+            for f, stride in zip(self.filters, self.filter_strides):
+                filter_df = f.get_pandas_dataframe(
+                    data_size, stride, paths=paths)
                 df = pd.concat([df, filter_df], axis=1)
 
         # Include DataFrame column for nuclides if user requested it
@@ -1867,20 +1861,16 @@ class Tally(IDManagerMixin):
                 new_score = cross_score(self_score, other_score, binary_op)
                 new_tally.scores.append(new_score)
 
-        # Update the new tally's filter strides
-        new_tally._update_filter_strides()
-
         return new_tally
 
-    def _update_filter_strides(self):
-        """Update each filter's stride based on the tally's nuclides and scores
-        for derived tallies created by tally arithmetic.
-        """
-
+    @property
+    def filter_strides(self):
+        all_strides = []
         stride = self.num_nuclides * self.num_scores
         for self_filter in reversed(self.filters):
-            self_filter.stride = stride
+            all_strides.append(stride)
             stride *= self_filter.num_bins
+        return all_strides[::-1]
 
     def _align_tally_data(self, other, filter_product, nuclide_product,
                           score_product):
@@ -2019,10 +2009,6 @@ class Tally(IDManagerMixin):
                 if other_index != i:
                     other._swap_scores(score, other.scores[i])
 
-        # Update the tallies' filter strides
-        other._update_filter_strides()
-        self._update_filter_strides()
-
         data = {}
         data['self'] = {}
         data['other'] = {}
@@ -2107,9 +2093,6 @@ class Tally(IDManagerMixin):
         self.filters[filter1_index] = filter2
         self.filters[filter2_index] = filter1
 
-        # Update the tally's filter strides
-        self._update_filter_strides()
-
         # Realign the data
         for i, (bin1, bin2) in enumerate(product(filter1_bins, filter2_bins)):
             filter_bins = [(bin1,), (bin2,)]
@@ -2861,9 +2844,6 @@ class Tally(IDManagerMixin):
                 find_filter.bins = np.unique(find_filter.bins[bin_indices])
                 find_filter.num_bins = num_bins
 
-        # Update the new tally's filter strides
-        new_tally._update_filter_strides()
-
         # If original tally was sparse, sparsify the sliced tally
         new_tally.sparse = self.sparse
         return new_tally
@@ -3010,9 +2990,6 @@ class Tally(IDManagerMixin):
         else:
             tally_sum._scores = copy.deepcopy(self.scores)
 
-        # Update the tally sum's filter strides
-        tally_sum._update_filter_strides()
-
         # Reshape condensed data arrays with one dimension for all filters
         mean = np.reshape(mean, tally_sum.shape)
         std_dev = np.reshape(std_dev, tally_sum.shape)
@@ -3170,9 +3147,6 @@ class Tally(IDManagerMixin):
         else:
             tally_avg._scores = copy.deepcopy(self.scores)
 
-        # Update the tally avg's filter strides
-        tally_avg._update_filter_strides()
-
         # Reshape condensed data arrays with one dimension for all filters
         mean = np.reshape(mean, tally_avg.shape)
         std_dev = np.reshape(std_dev, tally_avg.shape)
@@ -3246,9 +3220,6 @@ class Tally(IDManagerMixin):
             new_tally._std_dev = np.zeros(new_tally.shape, dtype=np.float64)
             new_tally._std_dev[diag_indices, :, :] = self.std_dev
 
-        # Update the new tally's filter strides
-        new_tally._update_filter_strides()
-
         # If original tally was sparse, sparsify the diagonalized tally
         new_tally.sparse = self.sparse
         return new_tally