Add missing import

jakevdp · jakevdp · commit 0a12df40af0c · 2016-11-19T11:09:26.000-08:00
diff --git a/notebooks/02.09-Structured-Data-NumPy.ipynb b/notebooks/02.09-Structured-Data-NumPy.ipynb
@@ -30,8 +30,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy's *structured arrays* and *record arrays*, which provide efficient storage for compound, heterogeneous data.  While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas ``Dataframe``s, which we'll explore in [Chapter 3](03.00-Introduction-to-Pandas.ipynb).\n",
-    "\n",
+    "While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy's *structured arrays* and *record arrays*, which provide efficient storage for compound, heterogeneous data.  While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas ``Dataframe``s, which we'll explore in [Chapter 3](03.00-Introduction-to-Pandas.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Imagine that we have several categories of data on a number of people (say, name, age, and weight), and we'd like to store these values for use in a Python program.\n",
     "It would be possible to store these in three separate arrays:"
    ]
