diff --git a/index.ipynb b/index.ipynb
index a9cb620..1f27ceb 100644
--- a/index.ipynb
+++ b/index.ipynb
@@ -7,31 +7,32 @@
     "# Machine Learning Notebooks\n",
     "\n",
     "Welcome to the Machine Learning Notebooks.\n",
-    "**This work is in progress.**\n",
     "\n",
     "## Tools\n",
     "* [NumPy](tools_numpy.ipynb)\n",
-    "* [Matplotlib](tools_matplotlib.ipynb)"
+    "* [Matplotlib](tools_matplotlib.ipynb)\n",
+    "\n",
+    "**This work is in progress, more notebooks are coming soon...**"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,
diff --git a/tools_matplotlib.ipynb b/tools_matplotlib.ipynb
index 5538fb5..bf5d64c 100644
--- a/tools_matplotlib.ipynb
+++ b/tools_matplotlib.ipynb
@@ -20,7 +20,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we need to import the `matplotlib` library."
+    "First let's make sure that this notebook works well in both python 2 and 3:"
    ]
   },
   {
@@ -30,6 +30,26 @@
     "collapsed": true
    },
    "outputs": [],
+   "source": [
+    "from __future__ import division\n",
+    "from __future__ import print_function\n",
+    "from __future__ import unicode_literals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to import the `matplotlib` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "import matplotlib"
    ]
@@ -43,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -62,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -85,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -105,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -125,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -148,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -178,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -199,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -221,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -241,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -263,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -283,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -308,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -330,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -365,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -389,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -423,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -465,7 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -483,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -527,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -559,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -581,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -608,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -636,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -660,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -710,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -755,7 +775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -781,7 +801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -811,7 +831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -838,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -859,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -880,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -909,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
@@ -942,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -962,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -999,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -1008,7 +1028,7 @@
     "import matplotlib.image as mpimg\n",
     "\n",
     "img = mpimg.imread('my_square_function.png')\n",
-    "print img.shape, img.dtype"
+    "print(img.shape, img.dtype)"
    ]
   },
   {
@@ -1020,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
@@ -1039,7 +1059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -1059,14 +1079,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "img = np.arange(100*100).reshape(100, 100)\n",
-    "print img\n",
+    "print(img)\n",
     "plt.imshow(img)\n",
     "plt.show()"
    ]
@@ -1080,7 +1100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1100,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1125,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1146,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "collapsed": true
    },
@@ -1169,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
@@ -1205,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
@@ -1227,21 +1247,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,
diff --git a/tools_numpy.ipynb b/tools_numpy.ipynb
index 515d02f..5788905 100644
--- a/tools_numpy.ipynb
+++ b/tools_numpy.ipynb
@@ -8,7 +8,7 @@
     "*NumPy is the fundamental library for scientific computing with Python. NumPy is centered around a powerful N-dimensional array object, and it also contains useful linear algebra, Fourier transform, and random number functions.*\n",
     "\n",
     "## Creating arrays\n",
-    "First let's import `numpy`:"
+    "First let's make sure that this notebook works both in python 2 and 3:"
    ]
   },
   {
@@ -18,6 +18,26 @@
     "collapsed": true
    },
    "outputs": [],
+   "source": [
+    "from __future__ import division\n",
+    "from __future__ import print_function\n",
+    "from __future__ import unicode_literals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's import `numpy`. Most people import it as `np`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np"
    ]
@@ -38,13 +58,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.zeros(5)"
+    "print(np.zeros(5))"
    ]
   },
   {
@@ -56,13 +76,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.zeros((3,4))"
+    "print(np.zeros((3,4)))"
    ]
   },
   {
@@ -83,17 +103,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a = np.zeros((3,4))\n",
-    "print a\n",
-    "print \"Shape:\", a.shape\n",
-    "print \"Rank:\", a.ndim\n",
-    "print \"Size:\", a.size"
+    "print(a)\n",
+    "print(\"Shape:\", a.shape)\n",
+    "print(\"Rank:\", a.ndim)\n",
+    "print(\"Size:\", a.size)"
    ]
   },
   {
@@ -106,13 +126,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.zeros((2,3,4))"
+    "print(np.zeros((2,3,4)))"
    ]
   },
   {
@@ -125,13 +145,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print type(np.zeros((3,4)))"
+    "print(type(np.zeros((3,4))))"
    ]
   },
   {
@@ -146,13 +166,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.ones((3,4))"
+    "print(np.ones((3,4)))"
    ]
   },
   {
@@ -165,13 +185,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.full((3,4), np.pi)"
+    "print(np.full((3,4), np.pi))"
    ]
   },
   {
@@ -184,14 +204,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "print np.empty((2,3))"
+    "print(np.empty((2,3)))"
    ]
   },
   {
@@ -204,15 +224,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "b = np.array([[1,2,3,4], [10, 20, 30, 40]])\n",
-    "print type(b)\n",
-    "print b"
+    "print(type(b))\n",
+    "print(b)"
    ]
   },
   {
@@ -225,14 +245,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "print np.arange(1, 5)"
+    "print(np.arange(1, 5))"
    ]
   },
   {
@@ -244,13 +264,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.arange(1.0, 5.0)"
+    "print(np.arange(1.0, 5.0))"
    ]
   },
   {
@@ -262,13 +282,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.arange(1, 5, 0.5)"
+    "print(np.arange(1, 5, 0.5))"
    ]
   },
   {
@@ -280,15 +300,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.arange(0, 5/3.0, 1/3.0) # depending on floating point errors, the max value is 4/3.0 or 5/3.0.\n",
-    "print np.arange(0, 5/3.0, 0.333333333)\n",
-    "print np.arange(0, 5/3.0, 0.333333334)\n"
+    "print(np.arange(0, 5/3, 1/3)) # depending on floating point errors, the max value is 4/3 or 5/3.\n",
+    "print(np.arange(0, 5/3, 0.333333333))\n",
+    "print(np.arange(0, 5/3, 0.333333334))\n"
    ]
   },
   {
@@ -301,13 +321,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.linspace(0, 5/3.0, 6)"
+    "print(np.linspace(0, 5/3, 6))"
    ]
   },
   {
@@ -321,13 +341,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.random.rand(3,4)"
+    "print(np.random.rand(3,4))"
    ]
   },
   {
@@ -339,13 +359,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.random.randn(3,4)"
+    "print(np.random.randn(3,4))"
    ]
   },
   {
@@ -357,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -386,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -395,7 +415,7 @@
     "def my_function(z, y, x):\n",
     "    return x * y + z\n",
     "\n",
-    "print np.fromfunction(my_function, (3, 2, 10))"
+    "print(np.fromfunction(my_function, (3, 2, 10)))"
    ]
   },
   {
@@ -428,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -436,10 +456,10 @@
    "outputs": [],
    "source": [
     "c = np.arange(1, 5)\n",
-    "print c.dtype, c\n",
+    "print(c.dtype, c)\n",
     "\n",
     "c = np.arange(1.0, 5.0)\n",
-    "print c.dtype, c"
+    "print(c.dtype, c)"
    ]
   },
   {
@@ -451,21 +471,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "d = np.arange(1, 5, dtype=np.complex64)\n",
-    "print d.dtype, d"
+    "print(d.dtype, d)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Available data types include `int8`, `int16`, `int32`, `int64`, `uint8`/`16`/`32`/`64`, `float16`/`32`/`64` and `complex64`/`128`. Check out [the documentation](http://docs.scipy.org/doc/numpy-1.10.1/user/basics.types.html) for the full list.\n",
+    "Available data types include `int8`, `int16`, `int32`, `int64`, `uint8`|`16`|`32`|`64`, `float16`|`32`|`64` and `complex64`|`128`. Check out [the documentation](http://docs.scipy.org/doc/numpy-1.10.1/user/basics.types.html) for the full list.\n",
     "\n",
     "### `itemsize`\n",
     "The `itemsize` attribute returns the size (in bytes) of each item:"
@@ -473,14 +493,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "e = np.arange(1, 5, dtype=np.complex64)\n",
-    "print e.itemsize"
+    "print(e.itemsize)"
    ]
   },
   {
@@ -493,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -501,10 +521,10 @@
    "outputs": [],
    "source": [
     "f = np.array([[1,2],[1000, 2000]], dtype=np.int32)\n",
-    "print \"Array:\"\n",
-    "print f\n",
-    "print \"Raw data:\"\n",
-    "print [ord(item) for item in f.data]"
+    "print(\"Array:\")\n",
+    "print(f)\n",
+    "print(\"Raw data:\")\n",
+    "print(f.data.hex())"
    ]
   },
   {
@@ -523,19 +543,6 @@
     "Changing the shape of an `ndarray` is as simple as setting its `shape` attribute. However, the array's size must remain the same."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "g = np.arange(24)\n",
-    "print g\n",
-    "print \"Rank:\", g.ndim"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 25,
@@ -544,14 +551,27 @@
    },
    "outputs": [],
    "source": [
-    "g.shape = (6, 4)\n",
-    "print g\n",
-    "print \"Rank:\", g.ndim"
+    "g = np.arange(24)\n",
+    "print(g)\n",
+    "print(\"Rank:\", g.ndim)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "g.shape = (6, 4)\n",
+    "print(g)\n",
+    "print(\"Rank:\", g.ndim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -559,8 +579,8 @@
    "outputs": [],
    "source": [
     "g.shape = (2, 3, 4)\n",
-    "print g\n",
-    "print \"Rank:\", g.ndim"
+    "print(g)\n",
+    "print(\"Rank:\", g.ndim)"
    ]
   },
   {
@@ -573,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -581,8 +601,8 @@
    "outputs": [],
    "source": [
     "g2 = g.reshape(4,6)\n",
-    "print g2\n",
-    "print \"Rank:\", g2.ndim"
+    "print(g2)\n",
+    "print(\"Rank:\", g2.ndim)"
    ]
   },
   {
@@ -594,14 +614,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "g2[1, 2] = 999\n",
-    "print g2"
+    "print(g2)"
    ]
   },
   {
@@ -613,13 +633,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print g"
+    "print(g)"
    ]
   },
   {
@@ -632,14 +652,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "g3 = g.ravel()\n",
-    "print g3"
+    "print(g3)"
    ]
   },
   {
@@ -647,12 +667,12 @@
    "metadata": {},
    "source": [
     "## Arithmetic operations\n",
-    "All the usual arithmetic operators (`+`, `-`, `*`, `/`, `**`, etc.) can be used with `ndarray`s. They apply *elementwise*:"
+    "All the usual arithmetic operators (`+`, `-`, `*`, `/`, `//`, `**`, etc.) can be used with `ndarray`s. They apply *elementwise*:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -661,12 +681,13 @@
    "source": [
     "a = np.array([14, 23, 32, 41])\n",
     "b = np.array([5,  4,  3,  2])\n",
-    "print \"a + b  =\", a + b\n",
-    "print \"a - b  =\", a - b\n",
-    "print \"a * b  =\", a * b\n",
-    "print \"a / b  =\", a / b\n",
-    "print \"a % b  =\", a % b\n",
-    "print \"a ** b =\", a ** b"
+    "print(\"a + b  =\", a + b)\n",
+    "print(\"a - b  =\", a - b)\n",
+    "print(\"a * b  =\", a * b)\n",
+    "print(\"a / b  =\", a / b)\n",
+    "print(\"a // b  =\", a // b)\n",
+    "print(\"a % b  =\", a % b)\n",
+    "print(\"a ** b =\", a ** b)"
    ]
   },
   {
@@ -697,14 +718,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "h = np.arange(5).reshape(1, 1, 5)\n",
-    "print h"
+    "print(h)"
    ]
   },
   {
@@ -716,13 +737,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print h + [10, 20, 30, 40, 50]  # same as: h + [[[10, 20, 30, 40, 50]]]"
+    "print(h + [10, 20, 30, 40, 50])  # same as: h + [[[10, 20, 30, 40, 50]]]"
    ]
   },
   {
@@ -735,14 +756,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "k = np.arange(6).reshape(2, 3)\n",
-    "print k"
+    "print(k)"
    ]
   },
   {
@@ -754,13 +775,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print k + [[100], [200]]  # same as: k + [[100, 100, 100], [200, 200, 200]]"
+    "print(k + [[100], [200]])  # same as: k + [[100, 100, 100], [200, 200, 200]]"
    ]
   },
   {
@@ -772,13 +793,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print k + [100, 200, 300]  # after rule 1: [[100, 200, 300]], and after rule 2: [[100, 200, 300], [100, 200, 300]]"
+    "print(k + [100, 200, 300])  # after rule 1: [[100, 200, 300]], and after rule 2: [[100, 200, 300], [100, 200, 300]]"
    ]
   },
   {
@@ -790,13 +811,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print k + 1000  # same as: k + [[1000, 1000, 1000], [1000, 1000, 1000]]"
+    "print(k + 1000)  # same as: k + [[1000, 1000, 1000], [1000, 1000, 1000]]"
    ]
   },
   {
@@ -809,16 +830,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "try:\n",
-    "    print k + [33, 44]\n",
-    "except ValueError, e:\n",
-    "    print e"
+    "    print(k + [33, 44])\n",
+    "except ValueError as e:\n",
+    "    print(e)"
    ]
   },
   {
@@ -837,18 +858,6 @@
     "When trying to combine arrays with different `dtype`s, NumPy will *upcast* to a type capable of handling all possible values (regardless of what the *actual* values are)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "k1 = np.arange(0, 5, dtype=np.uint8)\n",
-    "print k1.dtype, k1"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 40,
@@ -856,9 +865,21 @@
     "collapsed": false
    },
    "outputs": [],
+   "source": [
+    "k1 = np.arange(0, 5, dtype=np.uint8)\n",
+    "print(k1.dtype, k1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "k2 = k1 + np.array([5, 6, 7, 8, 9], dtype=np.int8)\n",
-    "print k2.dtype, k2"
+    "print(k2.dtype, k2)"
    ]
   },
   {
@@ -870,14 +891,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "k3 = k1 + 1.5\n",
-    "print k3.dtype, k3"
+    "print(k3.dtype, k3)"
    ]
   },
   {
@@ -896,14 +917,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "m = np.array([20, -5, 30, 40])\n",
-    "print m < [15, 16, 35, 36]"
+    "print(m < [15, 16, 35, 36])"
    ]
   },
   {
@@ -915,13 +936,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print m < 25  # equivalent to m < [25, 25, 25, 25]"
+    "print(m < 25)  # equivalent to m < [25, 25, 25, 25]"
    ]
   },
   {
@@ -933,13 +954,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print m[m < 25]"
+    "print(m[m < 25])"
    ]
   },
   {
@@ -961,15 +982,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
-    "print a\n",
-    "print \"mean =\", a.mean()"
+    "print(a)\n",
+    "print(\"mean =\", a.mean())"
    ]
   },
   {
@@ -983,14 +1004,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "for func in (a.min, a.max, a.sum, a.prod, a.std, a.var):\n",
-    "    print func.__name__, \"=\", func()"
+    "    print(func.__name__, \"=\", func())"
    ]
   },
   {
@@ -1000,18 +1021,6 @@
     "These functions accept an optional argument `axis` which lets you ask for the operation to be performed on elements along the given axis. For example:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "c=np.arange(24).reshape(2,3,4)\n",
-    "print c"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 48,
@@ -1020,7 +1029,8 @@
    },
    "outputs": [],
    "source": [
-    "print c.sum(axis=0)  # sum across matrices"
+    "c=np.arange(24).reshape(2,3,4)\n",
+    "print(c)"
    ]
   },
   {
@@ -1031,7 +1041,18 @@
    },
    "outputs": [],
    "source": [
-    "print c.sum(axis=1)  # sum across rows"
+    "print(c.sum(axis=0))  # sum across matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(c.sum(axis=1))  # sum across rows"
    ]
   },
   {
@@ -1043,14 +1064,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print c.sum(axis=(0,2))  # sum across matrices and columns\n",
-    "print 0+1+2+3 + 12+13+14+15, 4+5+6+7 + 16+17+18+19, 8+9+10+11 + 20+21+22+23"
+    "print(c.sum(axis=(0,2)))  # sum across matrices and columns\n",
+    "print(0+1+2+3 + 12+13+14+15, 4+5+6+7 + 16+17+18+19, 8+9+10+11 + 20+21+22+23)"
    ]
   },
   {
@@ -1063,14 +1084,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
-    "print np.square(a)"
+    "print(np.square(a))"
    ]
   },
   {
@@ -1082,17 +1103,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print \"Original ndarray\"\n",
-    "print a\n",
+    "print(\"Original ndarray\")\n",
+    "print(a)\n",
     "for func in (np.abs, np.sqrt, np.exp, np.log, np.sign, np.ceil, np.modf, np.isnan, np.cos):\n",
-    "    print \"\\n\", func.__name__\n",
-    "    print func(a)"
+    "    print(\"\\n\", func.__name__)\n",
+    "    print(func(a))"
    ]
   },
   {
@@ -1105,7 +1126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false
    },
@@ -1113,18 +1134,7 @@
    "source": [
     "a = np.array([1, -2, 3, 4])\n",
     "b = np.array([2, 8, -1, 7])\n",
-    "print np.add(a, b)  # equivalent to a + b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "print np.greater(a, b)  # equivalent to a > b"
+    "print(np.add(a, b))  # equivalent to a + b"
    ]
   },
   {
@@ -1135,7 +1145,7 @@
    },
    "outputs": [],
    "source": [
-    "print np.maximum(a, b)"
+    "print(np.greater(a, b))  # equivalent to a > b"
    ]
   },
   {
@@ -1146,7 +1156,18 @@
    },
    "outputs": [],
    "source": [
-    "print np.copysign(a, b)"
+    "print(np.maximum(a, b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(np.copysign(a, b))"
    ]
   },
   {
@@ -1158,18 +1179,6 @@
     "One-dimensional NumPy arrays can be accessed more or less like regular python arrays:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "a = np.array([1, 5, 3, 19, 13, 7, 3])\n",
-    "print a[3]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 58,
@@ -1178,7 +1187,8 @@
    },
    "outputs": [],
    "source": [
-    "print a[2:5]"
+    "a = np.array([1, 5, 3, 19, 13, 7, 3])\n",
+    "print(a[3])"
    ]
   },
   {
@@ -1189,7 +1199,7 @@
    },
    "outputs": [],
    "source": [
-    "print a[2:-1]"
+    "print(a[2:5])"
    ]
   },
   {
@@ -1200,7 +1210,7 @@
    },
    "outputs": [],
    "source": [
-    "print a[:2]"
+    "print(a[2:-1])"
    ]
   },
   {
@@ -1211,7 +1221,7 @@
    },
    "outputs": [],
    "source": [
-    "print a[2::2]"
+    "print(a[:2])"
    ]
   },
   {
@@ -1222,7 +1232,18 @@
    },
    "outputs": [],
    "source": [
-    "print a[::-1]"
+    "print(a[2::2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(a[::-1])"
    ]
   },
   {
@@ -1234,14 +1255,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a[3]=999\n",
-    "print a"
+    "print(a)"
    ]
   },
   {
@@ -1253,14 +1274,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a[2:5] = [997, 998, 999]\n",
-    "print a"
+    "print(a)"
    ]
   },
   {
@@ -1273,14 +1294,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a[2:5] = -1\n",
-    "print a"
+    "print(a)"
    ]
   },
   {
@@ -1292,7 +1313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1301,8 +1322,8 @@
    "source": [
     "try:\n",
     "    a[2:5] = [1,2,3,4,5,6]  # too long\n",
-    "except ValueError, e:\n",
-    "    print e"
+    "except ValueError as e:\n",
+    "    print(e)"
    ]
   },
   {
@@ -1314,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "collapsed": false
    },
@@ -1322,8 +1343,8 @@
    "source": [
     "try:\n",
     "    del a[2:5]\n",
-    "except ValueError, e:\n",
-    "    print e"
+    "except ValueError as e:\n",
+    "    print(e)"
    ]
   },
   {
@@ -1335,7 +1356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "collapsed": false
    },
@@ -1343,19 +1364,19 @@
    "source": [
     "a_slice = a[2:6]\n",
     "a_slice[1] = 1000\n",
-    "print a  # the original array was modified!"
+    "print(a)  # the original array was modified!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a[3] = 2000\n",
-    "print a_slice  # similarly, modifying the original array modifies the slice!"
+    "print(a_slice)  # similarly, modifying the original array modifies the slice!"
    ]
   },
   {
@@ -1367,7 +1388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {
     "collapsed": false
    },
@@ -1375,19 +1396,19 @@
    "source": [
     "another_slice = a[2:6].copy()\n",
     "another_slice[1] = 3000\n",
-    "print a  # the original array is untouched"
+    "print(a)  # the original array is untouched"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a[3] = 4000\n",
-    "print another_slice  # similary, modifying the original array does not affect the slice copy"
+    "print(another_slice)  # similary, modifying the original array does not affect the slice copy"
    ]
   },
   {
@@ -1398,18 +1419,6 @@
     "Multi-dimensional arrays can be accessed in a similar way by providing an index or slice for each axis, separated by commas:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "b = np.arange(48).reshape(4, 12)\n",
-    "print b"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 73,
@@ -1418,7 +1427,8 @@
    },
    "outputs": [],
    "source": [
-    "print b[1, 2]  # row 1, col 2"
+    "b = np.arange(48).reshape(4, 12)\n",
+    "print(b)"
    ]
   },
   {
@@ -1429,7 +1439,7 @@
    },
    "outputs": [],
    "source": [
-    "print b[1, :]  # row 1, all columns"
+    "print(b[1, 2])  # row 1, col 2"
    ]
   },
   {
@@ -1440,7 +1450,18 @@
    },
    "outputs": [],
    "source": [
-    "print b[:, 1]  # all rows, column 1"
+    "print(b[1, :])  # row 1, all columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(b[:, 1])  # all rows, column 1"
    ]
   },
   {
@@ -1452,15 +1473,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "print b[1, :]\n",
-    "print b[1:2, :]"
+    "print(b[1, :])\n",
+    "print(b[1:2, :])"
    ]
   },
   {
@@ -1480,25 +1501,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "print b[(0,2), 2:5]  # rows 0 and 2, columns 2 to 4 (5-1)"
+    "print(b[(0,2), 2:5])  # rows 0 and 2, columns 2 to 4 (5-1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print b[:, (-1, 2, -1)]  # all rows, columns -1 (last), 2 and -1 (again, and in this order)"
+    "print(b[:, (-1, 2, -1)])  # all rows, columns -1 (last), 2 and -1 (again, and in this order)"
    ]
   },
   {
@@ -1510,13 +1531,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print b[(-1, 2, -1, 2), (5, 9, 1, 9)]  # returns a 1D array with b[-1, 5], b[2, 9], b[-1, 1] and b[2, 9] (again)"
+    "print(b[(-1, 2, -1, 2), (5, 9, 1, 9)])  # returns a 1D array with b[-1, 5], b[2, 9], b[-1, 1] and b[2, 9] (again)"
    ]
   },
   {
@@ -1527,18 +1548,6 @@
     "Everything works just as well with higher dimensional arrays, but it's useful to look at a few examples:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "c = b.reshape(4,2,6)\n",
-    "print c"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 81,
@@ -1547,7 +1556,8 @@
    },
    "outputs": [],
    "source": [
-    "print c[2, 1, 4]  # matrix 2, row 1, col 4"
+    "c = b.reshape(4,2,6)\n",
+    "print(c)"
    ]
   },
   {
@@ -1558,7 +1568,18 @@
    },
    "outputs": [],
    "source": [
-    "print c[2, :, 3]  # matrix 2, all rows, col 3"
+    "print(c[2, 1, 4])  # matrix 2, row 1, col 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(c[2, :, 3])  # matrix 2, all rows, col 3"
    ]
   },
   {
@@ -1570,13 +1591,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print c[2, 1]  # Return matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
+    "print(c[2, 1])  # Return matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
    ]
   },
   {
@@ -1587,17 +1608,6 @@
     "You may also write an ellipsis (`...`) to ask that all non-specified axes be entirely included."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "print c[2, ...]  #  matrix 2, all rows, all columns.  This is equivalent to c[2, :, :]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 85,
@@ -1606,7 +1616,7 @@
    },
    "outputs": [],
    "source": [
-    "print c[2, 1, ...]  # matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
+    "print(c[2, ...])  #  matrix 2, all rows, all columns.  This is equivalent to c[2, :, :]"
    ]
   },
   {
@@ -1617,19 +1627,30 @@
    },
    "outputs": [],
    "source": [
-    "print c[2, ..., 3]  # matrix 2, all rows, column 3.  This is equivalent to c[2, :, 3]"
+    "print(c[2, 1, ...])  # matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 87,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(c[2, ..., 3])  # matrix 2, all rows, column 3.  This is equivalent to c[2, :, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
    "source": [
-    "print c[..., 3]  # all matrices, all rows, column 3.  This is equivalent to c[:, :, 3]"
+    "print(c[..., 3])  # all matrices, all rows, column 3.  This is equivalent to c[:, :, 3]"
    ]
   },
   {
@@ -1640,18 +1661,6 @@
     "You can also provide an `ndarray` of boolean values on one axis to specify the indices that you want to access."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "b = np.arange(48).reshape(4, 12)\n",
-    "print b"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 89,
@@ -1660,8 +1669,8 @@
    },
    "outputs": [],
    "source": [
-    "rows_on = np.array([True, False, True, False])\n",
-    "print b[rows_on, :]  # Rows 0 and 2, all columns. Equivalent to b[(0, 2), :]"
+    "b = np.arange(48).reshape(4, 12)\n",
+    "print(b)"
    ]
   },
   {
@@ -1671,9 +1680,21 @@
     "collapsed": false
    },
    "outputs": [],
+   "source": [
+    "rows_on = np.array([True, False, True, False])\n",
+    "print(b[rows_on, :])  # Rows 0 and 2, all columns. Equivalent to b[(0, 2), :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "cols_on = np.array([False, True, False] * 4)\n",
-    "print b[:, cols_on]  # All rows, columns 1, 4, 7 and 10"
+    "print(b[:, cols_on])  # All rows, columns 1, 4, 7 and 10"
    ]
   },
   {
@@ -1684,17 +1705,6 @@
     "You cannot use boolean indexing this way on multiple axes, but you can work around this by using the `ix_` function:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "print b[np.ix_(rows_on, cols_on)]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 92,
@@ -1703,7 +1713,18 @@
    },
    "outputs": [],
    "source": [
-    "print np.ix_(rows_on, cols_on)"
+    "print(b[np.ix_(rows_on, cols_on)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(np.ix_(rows_on, cols_on))"
    ]
   },
   {
@@ -1715,13 +1736,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 94,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print b[b % 3 == 1]"
+    "print(b[b % 3 == 1])"
    ]
   },
   {
@@ -1732,18 +1753,6 @@
     "Iterating over `ndarray`s is very similar to iterating over regular python arrays. Note that iterating over multidimensional arrays is done with respect to the first axis."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "c = np.arange(24).reshape(2, 3, 4)  # A 3D array (composed of two 3x4 matrices)\n",
-    "print c"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 95,
@@ -1752,9 +1761,8 @@
    },
    "outputs": [],
    "source": [
-    "for m in c:\n",
-    "    print \"Item:\"\n",
-    "    print m"
+    "c = np.arange(24).reshape(2, 3, 4)  # A 3D array (composed of two 3x4 matrices)\n",
+    "print(c)"
    ]
   },
   {
@@ -1764,10 +1772,23 @@
     "collapsed": false
    },
    "outputs": [],
+   "source": [
+    "for m in c:\n",
+    "    print(\"Item:\")\n",
+    "    print(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "for i in range(len(c)):  # Note that len(c) == c.shape[0]\n",
-    "    print \"Item:\"\n",
-    "    print c[i]"
+    "    print(\"Item:\")\n",
+    "    print(c[i])"
    ]
   },
   {
@@ -1779,14 +1800,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "for i in c.flat:\n",
-    "    print \"Item:\", i"
+    "    print(\"Item:\", i)"
    ]
   },
   {
@@ -1799,7 +1820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {
     "collapsed": false
    },
@@ -1808,12 +1829,12 @@
     "q1 = np.full((3,4), 1.0)\n",
     "q2 = np.full((4,4), 2.0)\n",
     "q3 = np.full((3,4), 3.0)\n",
-    "print \"q1\"\n",
-    "print q1\n",
-    "print \"q2\"\n",
-    "print q2\n",
-    "print \"q3\"\n",
-    "print q3"
+    "print(\"q1\")\n",
+    "print(q1)\n",
+    "print(\"q2\")\n",
+    "print(q2)\n",
+    "print(\"q3\")\n",
+    "print(q3)"
    ]
   },
   {
@@ -1826,15 +1847,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 100,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "q4 = np.vstack((q1, q2, q3))\n",
-    "print q4\n",
-    "print \"shape =\", q4.shape"
+    "print(q4)\n",
+    "print(\"shape =\", q4.shape)"
    ]
   },
   {
@@ -1849,15 +1870,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 101,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "q5 = np.hstack((q1, q3))\n",
-    "print q5\n",
-    "print \"shape =\", q5.shape"
+    "print(q5)\n",
+    "print(\"shape =\", q5.shape)"
    ]
   },
   {
@@ -1869,7 +1890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {
     "collapsed": false
    },
@@ -1877,8 +1898,8 @@
    "source": [
     "try:\n",
     "    q5 = np.hstack((q1, q2, q3))\n",
-    "except ValueError, e:\n",
-    "    print e"
+    "except ValueError as e:\n",
+    "    print(e)"
    ]
   },
   {
@@ -1891,15 +1912,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "q7 = np.concatenate((q1, q2, q3), axis=0)  # Equivalent to vstack\n",
-    "print q7\n",
-    "print \"shape =\", q7.shape"
+    "print(q7)\n",
+    "print(\"shape =\", q7.shape)"
    ]
   },
   {
@@ -1919,15 +1940,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "q8 = np.stack((q1, q3))\n",
-    "print q8\n",
-    "print \"shape =\", q8.shape"
+    "print(q8)\n",
+    "print(\"shape =\", q8.shape)"
    ]
   },
   {
@@ -1942,14 +1963,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 105,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "r = np.arange(24).reshape(6,4)\n",
-    "print r"
+    "print(r)"
    ]
   },
   {
@@ -1961,19 +1982,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 106,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "r1, r2, r3 = np.vsplit(r, 3)\n",
-    "print \"r1\"\n",
-    "print r1\n",
-    "print \"r2\"\n",
-    "print r2\n",
-    "print \"r3\"\n",
-    "print r3"
+    "print(\"r1\")\n",
+    "print(r1)\n",
+    "print(\"r2\")\n",
+    "print(r2)\n",
+    "print(\"r3\")\n",
+    "print(r3)"
    ]
   },
   {
@@ -1985,17 +2006,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 107,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "r4, r5 = np.hsplit(r, 2)\n",
-    "print \"r4\"\n",
-    "print r4\n",
-    "print \"r5\"\n",
-    "print r5"
+    "print(\"r4\")\n",
+    "print(r4)\n",
+    "print(\"r5\")\n",
+    "print(r5)"
    ]
   },
   {
@@ -2010,14 +2031,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 108,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "t = np.arange(24).reshape(4,2,3)\n",
-    "print t"
+    "print(t)"
    ]
   },
   {
@@ -2029,15 +2050,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 109,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "t1 = t.transpose((1,2,0))\n",
-    "print t1\n",
-    "print \"shape =\", t1.shape"
+    "print(t1)\n",
+    "print(\"shape =\", t1.shape)"
    ]
   },
   {
@@ -2049,15 +2070,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 110,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "t2 = t.transpose()  # equivalent to t.transpose((2, 1, 0))\n",
-    "print t2\n",
-    "print \"shape =\", t2.shape"
+    "print(t2)\n",
+    "print(\"shape =\", t2.shape)"
    ]
   },
   {
@@ -2069,15 +2090,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 111,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "t3 = t.swapaxes(0,1)  # equivalent to t.transpose((1, 0, 2))\n",
-    "print t3\n",
-    "print \"shape =\", t3.shape"
+    "print(t3)\n",
+    "print(\"shape =\", t3.shape)"
    ]
   },
   {
@@ -2091,18 +2112,6 @@
     "The `T` attribute is equivalent to calling `transpose()` when the rank is ≥2:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "m1 = np.arange(10).reshape(2,5)\n",
-    "print m1"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 112,
@@ -2111,7 +2120,19 @@
    },
    "outputs": [],
    "source": [
-    "print m1.T"
+    "m1 = np.arange(10).reshape(2,5)\n",
+    "print(m1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(m1.T)"
    ]
   },
   {
@@ -2121,19 +2142,6 @@
     "The `T` attribute has no effect on rank 0 (empty) or rank 1 arrays:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "m2 = np.arange(5)\n",
-    "print m2"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 114,
@@ -2143,7 +2151,20 @@
    },
    "outputs": [],
    "source": [
-    "print m2.T"
+    "m2 = np.arange(5)\n",
+    "print(m2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "print(m2.T)"
    ]
   },
   {
@@ -2153,18 +2174,6 @@
     "We can get the desired transposition by first reshaping the 1D array to a single-row matrix (2D):"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "m2r = m2.reshape(1,5)\n",
-    "print m2r"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 116,
@@ -2173,7 +2182,19 @@
    },
    "outputs": [],
    "source": [
-    "print m2r.T"
+    "m2r = m2.reshape(1,5)\n",
+    "print(m2r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(m2r.T)"
    ]
   },
   {
@@ -2184,18 +2205,6 @@
     "Let's create two matrices and execute a matrix dot product using the `dot` method."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "n1 = np.arange(10).reshape(2, 5)\n",
-    "print n1"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 118,
@@ -2204,8 +2213,8 @@
    },
    "outputs": [],
    "source": [
-    "n2 = np.arange(15).reshape(5,3)\n",
-    "print n2"
+    "n1 = np.arange(10).reshape(2, 5)\n",
+    "print(n1)"
    ]
   },
   {
@@ -2216,7 +2225,19 @@
    },
    "outputs": [],
    "source": [
-    "print n1.dot(n2)"
+    "n2 = np.arange(15).reshape(5,3)\n",
+    "print(n2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(n1.dot(n2))"
    ]
   },
   {
@@ -2236,7 +2257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 121,
    "metadata": {
     "collapsed": false
    },
@@ -2245,18 +2266,18 @@
     "import numpy.linalg as linalg\n",
     "\n",
     "m3 = np.array([[1,2,3],[5,7,11],[21,29,31]])\n",
-    "print m3"
+    "print(m3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 122,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print linalg.inv(m3)"
+    "print(linalg.inv(m3))"
    ]
   },
   {
@@ -2268,13 +2289,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 123,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print linalg.pinv(m3)"
+    "print(linalg.pinv(m3))"
    ]
   },
   {
@@ -2287,13 +2308,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 124,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print m3.dot(linalg.inv(m3))"
+    "print(m3.dot(linalg.inv(m3)))"
    ]
   },
   {
@@ -2305,13 +2326,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 125,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print np.eye(3)"
+    "print(np.eye(3))"
    ]
   },
   {
@@ -2324,19 +2345,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 126,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "q, r = linalg.qr(m3)\n",
-    "print \"q\"\n",
-    "print q\n",
-    "print \"r\"\n",
-    "print r\n",
-    "print \"q.r (equal to m3)\"\n",
-    "print q.dot(r)"
+    "print(\"q\")\n",
+    "print(q)\n",
+    "print(\"r\")\n",
+    "print(r)\n",
+    "print(\"q.r (equal to m3)\")\n",
+    "print(q.dot(r))"
    ]
   },
   {
@@ -2349,13 +2370,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 127,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print linalg.det(m3)  # Computes the matrix determinant"
+    "print(linalg.det(m3))  # Computes the matrix determinant"
    ]
   },
   {
@@ -2368,19 +2389,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 128,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "eigenvalues, eigenvectors = linalg.eig(m3)\n",
-    "print \"Eigen values (λ)\"\n",
-    "print eigenvalues\n",
-    "print \"Eigen vectors (v)\"\n",
-    "print eigenvectors\n",
-    "print \"m3.v - λ*v (equals 0)\"\n",
-    "print m3.dot(eigenvectors) - eigenvalues * eigenvectors"
+    "print(\"Eigen values (λ)\")\n",
+    "print(eigenvalues)\n",
+    "print(\"Eigen vectors (v)\")\n",
+    "print(eigenvectors)\n",
+    "print(\"m3.v - λ*v (equals 0)\")\n",
+    "print(m3.dot(eigenvectors) - eigenvalues * eigenvectors)"
    ]
   },
   {
@@ -2393,19 +2414,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 129,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "m4 = np.array([[1,0,0,0,2], [0,0,3,0,0], [0,0,0,0,0], [0,2,0,0,0]])\n",
-    "print m4"
+    "print(m4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 130,
    "metadata": {
     "collapsed": false
    },
@@ -2414,14 +2435,14 @@
     "U, S_diag, V = linalg.svd(m4)\n",
     "S = np.zeros((4, 5))\n",
     "S[np.diag_indices(4)] = S_diag\n",
-    "print \"U\"\n",
-    "print U\n",
-    "print \"Σ\"\n",
-    "print S\n",
-    "print \"V\"\n",
-    "print V\n",
-    "print \"U.Σ.V (equals m4)\"\n",
-    "print U.dot(S).dot(V) # equal to m4"
+    "print(\"U\")\n",
+    "print(U)\n",
+    "print(\"Σ\")\n",
+    "print(S)\n",
+    "print(\"V\")\n",
+    "print(V)\n",
+    "print(\"U.Σ.V (equals m4)\")\n",
+    "print(U.dot(S).dot(V)) # equal to m4"
    ]
   },
   {
@@ -2431,17 +2452,6 @@
     "### Diagonal and trace"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "print np.diag(m3)  # the values in the diagonal of m3 (top left to bottom right)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 131,
@@ -2450,7 +2460,18 @@
    },
    "outputs": [],
    "source": [
-    "print np.trace(m3)  # equivalent to np.diag(m3).sum()"
+    "print(np.diag(m3))  # the values in the diagonal of m3 (top left to bottom right)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(np.trace(m3))  # equivalent to np.diag(m3).sum()"
    ]
   },
   {
@@ -2472,7 +2493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 133,
    "metadata": {
     "collapsed": false
    },
@@ -2481,7 +2502,7 @@
     "coeffs  = np.array([[2, 6], [5, 3]])\n",
     "depvars = np.array([6, -9])\n",
     "solution = linalg.solve(coeffs, depvars)\n",
-    "print solution"
+    "print(solution)"
    ]
   },
   {
@@ -2493,14 +2514,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 134,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "print coeffs.dot(solution)\n",
-    "print depvars"
+    "print(coeffs.dot(solution))\n",
+    "print(depvars)"
    ]
   },
   {
@@ -2512,14 +2533,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 135,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "print np.allclose(coeffs.dot(solution), depvars)"
+    "print(np.allclose(coeffs.dot(solution), depvars))"
    ]
   },
   {
@@ -2529,12 +2550,12 @@
     "## Vectorization\n",
     "Instead of executing operations on individual array items, one at a time, your code is much more efficient if you try to stick to array operations. This is called *vectorization*. This way, you can benefit from NumPy's many optimizations.\n",
     "\n",
-    "For example, let's say we want to generate a 768x1024 array based on the formula $sin(xy/40)$. A **bad** option would be to do the math in python using nested loops:"
+    "For example, let's say we want to generate a 768x1024 array based on the formula $sin(xy/40.5)$. A **bad** option would be to do the math in python using nested loops:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 136,
    "metadata": {
     "collapsed": false
    },
@@ -2542,8 +2563,8 @@
    "source": [
     "import math\n",
     "data = np.empty((768, 1024))\n",
-    "for y in xrange(768):\n",
-    "    for x in xrange(1024):\n",
+    "for y in range(768):\n",
+    "    for x in range(1024):\n",
     "        data[y, x] = math.sin(x*y/40.5)  # BAD! Very inefficient."
    ]
   },
@@ -2556,7 +2577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 137,
    "metadata": {
     "collapsed": false
    },
@@ -2565,11 +2586,11 @@
     "x_coords = np.arange(0, 1024)  # [0, 1, 2, ..., 1023]\n",
     "y_coords = np.arange(0, 768)   # [0, 1, 2, ..., 767]\n",
     "X, Y = np.meshgrid(x_coords, y_coords)\n",
-    "print \"X\"\n",
-    "print X\n",
+    "print(\"X\")\n",
+    "print(X)\n",
     "print\n",
-    "print \"Y\"\n",
-    "print Y"
+    "print(\"Y\")\n",
+    "print(Y)"
    ]
   },
   {
@@ -2583,7 +2604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 138,
    "metadata": {
     "collapsed": false
    },
@@ -2601,7 +2622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 139,
    "metadata": {
     "collapsed": false
    },
@@ -2627,7 +2648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 140,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -2635,12 +2656,12 @@
    "outputs": [],
    "source": [
     "a = np.random.rand(2,3)\n",
-    "print a"
+    "print(a)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 141,
    "metadata": {
     "collapsed": false
    },
@@ -2658,7 +2679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 142,
    "metadata": {
     "collapsed": false
    },
@@ -2666,7 +2687,7 @@
    "source": [
     "with open(\"my_array.npy\", \"rb\") as f:\n",
     "    content = f.read()\n",
-    "    print repr(content)"
+    "    print(repr(content))"
    ]
   },
   {
@@ -2678,14 +2699,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 143,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a_loaded = np.load(\"my_array.npy\")\n",
-    "print a_loaded"
+    "print(a_loaded)"
    ]
   },
   {
@@ -2698,7 +2719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 144,
    "metadata": {
     "collapsed": false
    },
@@ -2716,7 +2737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 145,
    "metadata": {
     "collapsed": false
    },
@@ -2724,7 +2745,7 @@
    "source": [
     "with open(\"my_array.csv\", \"rt\") as f:\n",
     "    content = f.read()\n",
-    "    print content"
+    "    print(content)"
    ]
   },
   {
@@ -2736,7 +2757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 146,
    "metadata": {
     "collapsed": true
    },
@@ -2754,14 +2775,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 147,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "a_loaded = np.loadtxt(\"my_array.csv\", delimiter=\",\")\n",
-    "print a_loaded"
+    "print(a_loaded)"
    ]
   },
   {
@@ -2774,19 +2795,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 148,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "b = np.arange(24, dtype=np.uint8).reshape(2, 3, 4)\n",
-    "print b"
+    "print(b)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 149,
    "metadata": {
     "collapsed": true
    },
@@ -2804,7 +2825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 150,
    "metadata": {
     "collapsed": false
    },
@@ -2812,7 +2833,7 @@
    "source": [
     "with open(\"my_arrays.npz\", \"rb\") as f:\n",
     "    content = f.read()\n",
-    "    print repr(content)[:200], \"[...]\""
+    "    print(repr(content)[:200], \"[...]\")"
    ]
   },
   {
@@ -2824,7 +2845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 151,
    "metadata": {
     "collapsed": false
    },
@@ -2840,17 +2861,6 @@
     "This is a dict-like object which loads the arrays lazily:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "print my_arrays.keys()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 152,
@@ -2859,7 +2869,18 @@
    },
    "outputs": [],
    "source": [
-    "print my_arrays[\"my_a\"]"
+    "print(my_arrays.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(my_arrays[\"my_a\"])"
    ]
   },
   {
@@ -2873,21 +2894,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,