diff --git a/notebooks_v1/03.08-Aggregation-and-Grouping.ipynb b/notebooks_v1/03.08-Aggregation-and-Grouping.ipynb
index be00723d1..c168ea207 100644
--- a/notebooks_v1/03.08-Aggregation-and-Grouping.ipynb
+++ b/notebooks_v1/03.08-Aggregation-and-Grouping.ipynb
@@ -32,7 +32,10 @@
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    "source": [
     "An essential piece of analysis of large data is efficient summarization: computing aggregations like ``sum()``, ``mean()``, ``median()``, ``min()``, and ``max()``, in which a single number gives insight into the nature of a potentially large dataset.\n",
@@ -49,9 +52,7 @@
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-   "metadata": {
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    "source": [
     "import numpy as np\n",
@@ -88,7 +89,10 @@
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@@ -254,13 +277,16 @@
    "cell_type": "code",
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     {
      "data": {
       "text/plain": [
-       "2.8119254917081569"
+       "np.float64(2.811925491708157)"
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@@ -276,13 +302,16 @@
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@@ -305,13 +334,29 @@
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@@ -374,7 +419,10 @@
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@@ -440,13 +491,29 @@
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@@ -633,13 +700,29 @@
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@@ -715,13 +798,16 @@
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     {
      "data": {
       "text/plain": [
-       "<pandas.core.groupby.DataFrameGroupBy object at 0x117272160>"
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001F8F1CDE650>"
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@@ -834,13 +936,16 @@
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@@ -856,13 +961,16 @@
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      "data": {
       "text/plain": [
-       "<pandas.core.groupby.SeriesGroupBy object at 0x117272da0>"
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@@ -886,7 +994,10 @@
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@@ -935,7 +1046,10 @@
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@@ -979,15 +1093,31 @@
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@@ -1156,13 +1286,13 @@
        "Transit Timing Variations      2012.5  2013.25  2014.0  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "planets.groupby('method')['year'].describe().unstack()"
+    "planets.groupby('method')['year'].describe()"
    ]
   },
   {
@@ -1193,7 +1323,10 @@
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@@ -1289,7 +1422,10 @@
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@@ -1383,7 +1519,10 @@
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@@ -1455,7 +1594,10 @@
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@@ -1733,7 +1878,10 @@
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@@ -2638,7 +2804,7 @@
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@@ -2652,9 +2818,9 @@
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