diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb
index 5207ac4f3..897af8e3f 100644
--- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb
@@ -24,7 +24,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Many Monte Carlo-based neutron particle transport codes, including OpenMC, use continuous energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. An example of U-235's fission continuous energy cross section along with a 16-group cross section computed for a light water reactor spectrum is displayed below."
+    "Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. An example of U-235's continuous-energy fission cross section along with a 16-group cross section computed for a light water reactor spectrum is displayed below."
    ]
   },
   {
@@ -79,7 +79,7 @@
     "### Spatial and Energy Discretization\n",
     "The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n",
     "\n",
-    "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, or an unstructured mesh such as the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**."
+    "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**."
    ]
   },
   {
@@ -518,7 +518,7 @@
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
       "      Version:        0.7.0\n",
       "      Git SHA1:       c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n",
-      "      Date/Time:      2015-11-30 21:26:04\n",
+      "      Date/Time:      2015-12-02 09:11:05\n",
       "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
@@ -605,19 +605,19 @@
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
       " Total time for initialization     =  4.1700E-01 seconds\n",
-      "   Reading cross sections          =  9.7000E-02 seconds\n",
-      " Total time in simulation          =  1.4656E+01 seconds\n",
-      "   Time in transport only          =  1.4643E+01 seconds\n",
-      "   Time in inactive batches        =  1.7940E+00 seconds\n",
-      "   Time in active batches          =  1.2862E+01 seconds\n",
+      "   Reading cross sections          =  8.9000E-02 seconds\n",
+      " Total time in simulation          =  1.4728E+01 seconds\n",
+      "   Time in transport only          =  1.4712E+01 seconds\n",
+      "   Time in inactive batches        =  1.7890E+00 seconds\n",
+      "   Time in active batches          =  1.2939E+01 seconds\n",
       "   Time synchronizing fission bank =  5.0000E-03 seconds\n",
-      "     Sampling source sites         =  4.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
-      "   Time accumulating tallies       =  0.0000E+00 seconds\n",
-      " Total time for finalization       =  0.0000E+00 seconds\n",
-      " Total time elapsed                =  1.5082E+01 seconds\n",
-      " Calculation Rate (inactive)       =  13935.3 neutrons/second\n",
-      " Calculation Rate (active)         =  7774.84 neutrons/second\n",
+      "     Sampling source sites         =  3.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  1.0000E-03 seconds\n",
+      " Total time for finalization       =  1.0000E-03 seconds\n",
+      " Total time elapsed                =  1.5155E+01 seconds\n",
+      " Calculation Rate (inactive)       =  13974.3 neutrons/second\n",
+      " Calculation Rate (active)         =  7728.57 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -776,13 +776,6 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1254: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
index 99610944b..6194b154a 100644
--- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
@@ -10,7 +10,7 @@
     "* Calculation of cross sections on a **nuclide-by-nuclide basis**\n",
     "* The use of **[tally precision triggers](https://mit-crpg.github.io/openmc/usersguide/input.html#trigger-element)** with multi-group cross sections\n",
     "* Built-in features for **energy condensation** in downstream data processing\n",
-    "* The use of **[PyNE](http://pyne.io/) to plot** continuous energy vs. multi-group cross sections\n",
+    "* The use of **[PyNE](http://pyne.io/) to plot** continuous-energy vs. multi-group cross sections\n",
     "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n",
     "\n",
     "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, this Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data. We recommend using [Pandas](http://pandas.pydata.org/) >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases of [Pandas](http://pandas.pydata.org/)."
@@ -448,7 +448,7 @@
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
       "      Version:        0.7.0\n",
       "      Git SHA1:       c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n",
-      "      Date/Time:      2015-11-30 20:39:59\n",
+      "      Date/Time:      2015-12-02 09:13:42\n",
       "      MPI Processes:  3\n",
       "\n",
       " ===========================================================================\n",
@@ -568,20 +568,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  6.7400E-01 seconds\n",
-      "   Reading cross sections          =  1.4300E-01 seconds\n",
-      " Total time in simulation          =  1.3404E+02 seconds\n",
-      "   Time in transport only          =  1.1927E+02 seconds\n",
-      "   Time in inactive batches        =  7.6750E+00 seconds\n",
-      "   Time in active batches          =  1.2636E+02 seconds\n",
-      "   Time synchronizing fission bank =  1.4700E+01 seconds\n",
-      "     Sampling source sites         =  6.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  5.0000E-03 seconds\n",
-      "   Time accumulating tallies       =  4.0000E-03 seconds\n",
-      " Total time for finalization       =  1.5000E-02 seconds\n",
-      " Total time elapsed                =  1.3475E+02 seconds\n",
-      " Calculation Rate (inactive)       =  13029.3 neutrons/second\n",
-      " Calculation Rate (active)         =  3165.53 neutrons/second\n",
+      " Total time for initialization     =  7.5700E-01 seconds\n",
+      "   Reading cross sections          =  1.5800E-01 seconds\n",
+      " Total time in simulation          =  1.4921E+02 seconds\n",
+      "   Time in transport only          =  1.4336E+02 seconds\n",
+      "   Time in inactive batches        =  8.6210E+00 seconds\n",
+      "   Time in active batches          =  1.4059E+02 seconds\n",
+      "   Time synchronizing fission bank =  5.6060E+00 seconds\n",
+      "     Sampling source sites         =  1.4000E-02 seconds\n",
+      "     SEND/RECV source sites        =  4.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  6.0000E-03 seconds\n",
+      " Total time for finalization       =  1.3000E-02 seconds\n",
+      " Total time elapsed                =  1.5002E+02 seconds\n",
+      " Calculation Rate (inactive)       =  11599.6 neutrons/second\n",
+      " Calculation Rate (active)         =  2845.11 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -801,14 +801,6 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1254: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1272: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1197,172 +1189,170 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[  NORMAL ]  Ray tracing for track segmentation...\n",
-      "[  NORMAL ]  Dumping tracks to file...\n",
+      "[  NORMAL ]  Importing ray tracing data from file...\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
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+      "[  NORMAL ]  Iteration 15:\tk_eff = 0.488317\tres = 1.458E-02\n",
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      ]
     }
    ],
@@ -1396,8 +1386,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.223729\n",
-      "openmoc keff = 1.219880\n",
-      "bias [pcm]: -384.9\n"
+      "openmoc keff = 1.219868\n",
+      "bias [pcm]: -386.1\n"
      ]
     }
    ],
@@ -1470,240 +1460,239 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[  NORMAL ]  Ray tracing for track segmentation...\n",
-      "[  NORMAL ]  Dumping tracks to file...\n",
+      "[  NORMAL ]  Importing ray tracing data from file...\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:\tk_eff = 0.495594\tres = 0.000E+00\n",
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+      "[  NORMAL ]  Iteration 230:\tk_eff = 1.222447\tres = 1.016E-05\n"
      ]
     }
    ],
@@ -1730,8 +1719,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.223729\n",
-      "openmoc keff = 1.222448\n",
-      "bias [pcm]: -128.1\n"
+      "openmoc keff = 1.222447\n",
+      "bias [pcm]: -128.2\n"
      ]
     }
    ],
@@ -1772,7 +1761,7 @@
    "source": [
     "It is often insightful to generate visual depictions of multi-group cross sections. There are many different types of plots which may be useful for multi-group cross section visualization, only a few of which will be shown here for enrichment and inspiration.\n",
     "\n",
-    "One particularly useful visualization is a comparison of the continuous energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the open source [PyNE](http://pyne.io/) library to parse continuous energy multi-group cross sections from the cross section data library provided with OpenMC. First, we instantiate a `pyne.ace.Library` object for U-235 as follows."
+    "One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the open source [PyNE](http://pyne.io/) library to parse continuous-energy cross sections from the cross section data library provided with OpenMC. First, we instantiate a `pyne.ace.Library` object for U-235 as follows."
    ]
   },
   {
@@ -1783,14 +1772,14 @@
    },
    "outputs": [],
    "source": [
-    "# Instantiate a PyNE ACE continuous energy cross sections library\n",
+    "# Instantiate a PyNE ACE continuous-energy cross sections library\n",
     "pyne_lib = pyne.ace.Library('../../../../data/nndc/293.6K/U_235_293.6K.ace')\n",
     "pyne_lib.read('92235.71c')\n",
     "\n",
     "# Extract the U-235 data from the library\n",
     "u235 = pyne_lib.tables['92235.71c']\n",
     "\n",
-    "# Extract the continuous energy U-235 fission cross section data\n",
+    "# Extract the continuous-energy U-235 fission cross section data\n",
     "fission = u235.reactions[18]"
    ]
   },
@@ -1798,7 +1787,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we use [`matplotlib`](http://matplotlib.org/) and [`seaborn`](http://stanford.edu/~mwaskom/software/seaborn/) to plot the continuous energy and multi-group cross sections on a single plot."
+    "Now, we use [`matplotlib`](http://matplotlib.org/) and [`seaborn`](http://stanford.edu/~mwaskom/software/seaborn/) to plot the continuous-energy and multi-group cross sections on a single plot."
    ]
   },
   {
@@ -1822,7 +1811,7 @@
      "data": {
       "image/png": 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NiQo+A2zbVhpS7/HHs7juuuDB588+s3PyyQURB6bT7T6mu1ai9ZoTfG5s5bMg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\nRETE6eQvWE4Yr8iVHuYAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f60360154d0>"
+       "<matplotlib.figure.Figure at 0x7f7e2c03a610>"
       ]
      },
      "metadata": {},
@@ -1830,7 +1819,7 @@
     }
    ],
    "source": [
-    "# Create a loglog plot of the U-235 continuous energy fission cross section \n",
+    "# Create a loglog plot of the U-235 continuous-energy fission cross section \n",
     "plt.loglog(u235.energy, fission.sigma, color='b', linewidth=1)\n",
     "\n",
     "# Extract energy group bounds and MGXS values to plot\n",
@@ -1905,7 +1894,7 @@
      "data": {
       "image/png": 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wbSf2Z9YJjmvrdqU/RRMR2wFHAttLmlH2/sw6wXFtVVD2jE5LAt8CJkh6ocx9mXWK49qq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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6035ea2c90>"
+       "<matplotlib.figure.Figure at 0x7f7e2c0e0810>"
       ]
      },
      "metadata": {},
diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
index c3f4280de..930203660 100644
--- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
@@ -543,7 +543,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we must specify to the `Library` which types of cross sections to compute. In particular, the following are the multi-group cross section `MGXS` subclasses are mapped to string codes accepted by the `Library` class:\n",
+    "Now, we must specify to the `Library` which types of cross sections to compute. In particular, the following are the multi-group cross section `MGXS` subclasses that are mapped to string codes accepted by the `Library` class:\n",
     "\n",
     "* `TotalXS` (`\"total\"`)\n",
     "* `TransportXS` (`\"transport\"`)\n",
@@ -580,7 +580,7 @@
    "source": [
     "Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material,\"` `\"cell,\"` and `\"universe\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells in the fuel assembly geometry.\n",
     "\n",
-    "**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property. In our case, we wish to compute multi-group cross sectoins in each and every cell since they will be needed in our downstream OpenMOC calculation on the identical combinatorial geometry mesh."
+    "**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property. In our case, we wish to compute multi-group cross sections in each and every cell since they will be needed in our downstream OpenMOC calculation on the identical combinatorial geometry mesh."
    ]
   },
   {
diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py
index 96fb6e07e..635c822e3 100644
--- a/openmc/mgxs/mgxs.py
+++ b/openmc/mgxs/mgxs.py
@@ -1225,28 +1225,29 @@ class MGXS(object):
             df = df.drop('score', axis=1)
 
         # Override energy groups bounds with indices
-        groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
-        groups = np.repeat(groups, self.num_nuclides)
+        all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
+        all_groups = np.repeat(all_groups, self.num_nuclides)
         if 'energy [MeV]' in df and 'energyout [MeV]' in df:
             df.rename(columns={'energy [MeV]': 'group in'}, inplace=True)
-            in_groups = np.tile(groups, self.num_subdomains)
+            in_groups = np.tile(all_groups, self.num_subdomains)
             in_groups = np.repeat(in_groups, self.num_groups)
             df['group in'] = in_groups
 
             df.rename(columns={'energyout [MeV]': 'group out'}, inplace=True)
-            out_groups = np.tile(groups, self.num_subdomains * self.num_groups)
+            out_groups = \
+                np.tile(all_groups, self.num_subdomains * self.num_groups)
             df['group out'] = out_groups
             columns = ['group in', 'group out']
 
         elif 'energyout [MeV]' in df:
             df.rename(columns={'energyout [MeV]': 'group out'}, inplace=True)
-            in_groups = np.tile(groups, self.num_subdomains)
+            in_groups = np.tile(all_groups, self.num_subdomains)
             df['group out'] = in_groups
             columns = ['group out']
 
         elif 'energy [MeV]' in df:
             df.rename(columns={'energy [MeV]': 'group in'}, inplace=True)
-            in_groups = np.tile(groups, self.num_subdomains)
+            in_groups = np.tile(all_groups, self.num_subdomains)
             df['group in'] = in_groups
             columns = ['group in']