diff --git a/extra_gradient_descent_comparison.ipynb b/extra_gradient_descent_comparison.ipynb
index 053ffd2..ee5ed6e 100644
--- a/extra_gradient_descent_comparison.ipynb
+++ b/extra_gradient_descent_comparison.ipynb
@@ -20,7 +20,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from __future__ import print_function, division, unicode_literals\n",
     "import numpy as np\n",
     "\n",
     "%matplotlib nbagg\n",
diff --git a/math_linear_algebra.ipynb b/math_linear_algebra.ipynb
index 0718eff..e281e26 100644
--- a/math_linear_algebra.ipynb
+++ b/math_linear_algebra.ipynb
@@ -11,22 +11,6 @@
     "*Machine Learning relies heavily on Linear Algebra, so it is essential to understand what vectors and matrices are, what operations you can perform with them, and how they can be useful.*"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we start, let's ensure that this notebook works well in both Python 2 and 3:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __future__ import division, print_function, unicode_literals"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/tools_matplotlib.ipynb b/tools_matplotlib.ipynb
index e2aba24..992f0cd 100644
--- a/tools_matplotlib.ipynb
+++ b/tools_matplotlib.ipynb
@@ -26,24 +26,6 @@
     "# Plotting your first graph"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First let's make sure that this notebook works well in both python 2 and 3:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from __future__ import division, print_function, unicode_literals"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -53,10 +35,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib"
@@ -71,10 +51,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -91,9 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -115,7 +91,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -134,9 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot([-3, -2, 5, 0], [1, 6, 4, 3])\n",
@@ -154,9 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -177,9 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot(x, y)\n",
@@ -207,9 +176,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100], [0, 0, 100, 100, 0, 100, 130, 100, 0])\n",
@@ -228,9 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot([0, 100, 100, 0, 0, 100, 50, 0, 100], [0, 0, 100, 100, 0, 100, 130, 100, 0], \"g--\")\n",
@@ -250,9 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\", [0, 100, 50, 0, 100], [0, 100, 130, 100, 0], \"g--\")\n",
@@ -270,9 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot([0, 100, 100, 0, 0], [0, 0, 100, 100, 0], \"r-\")\n",
@@ -292,9 +253,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.linspace(-1.4, 1.4, 30)\n",
@@ -313,7 +272,6 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -338,7 +296,6 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -360,7 +317,6 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -394,9 +350,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.subplot(2, 2, 1)  # 2 rows, 2 columns, 1st subplot = top left\n",
@@ -418,9 +372,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.subplot2grid((3,3), (0, 0), rowspan=2, colspan=2)\n",
@@ -453,7 +405,6 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -494,9 +445,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import this"
@@ -513,7 +462,6 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -556,9 +504,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.linspace(-1.5, 1.5, 30)\n",
@@ -588,9 +534,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot(x, x**2, px, py, \"ro\")\n",
@@ -611,7 +555,6 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -637,9 +580,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with plt.xkcd():\n",
@@ -665,9 +606,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.linspace(-1.4, 1.4, 50)\n",
@@ -690,7 +629,6 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -739,9 +677,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.linspace(-2, 2, 100)\n",
@@ -784,9 +720,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "radius = 1\n",
@@ -811,7 +745,6 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -840,9 +773,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.contourf(X, Y, Z, cmap=matplotlib.cm.coolwarm)\n",
@@ -867,9 +798,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import rand\n",
@@ -888,9 +817,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x, y, scale = rand(3, 100)\n",
@@ -910,7 +837,6 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -938,9 +864,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
@@ -971,9 +895,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = [1, 1.1, 1.8, 2, 2.1, 3.2, 3, 3, 3, 3]\n",
@@ -992,7 +914,6 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -1028,9 +949,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.image as mpimg\n",
@@ -1049,9 +968,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.imshow(img)\n",
@@ -1068,9 +985,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.imshow(img)\n",
@@ -1088,9 +1003,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "img = np.arange(100*100).reshape(100, 100)\n",
@@ -1110,7 +1023,6 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -1130,7 +1042,6 @@
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -1155,7 +1066,6 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -1198,9 +1108,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.linspace(-1, 1, 100)\n",
@@ -1234,9 +1142,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "Writer = animation.writers['ffmpeg']\n",
@@ -1255,21 +1161,21 @@
  ],
  "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": "3.7.4"
   },
   "toc": {
    "toc_cell": true,
@@ -1280,5 +1186,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/tools_numpy.ipynb b/tools_numpy.ipynb
index ed2d81b..06b00f2 100644
--- a/tools_numpy.ipynb
+++ b/tools_numpy.ipynb
@@ -8,19 +8,7 @@
     "\n",
     "*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 make sure that this notebook works both in python 2 and 3:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from __future__ import division, print_function, unicode_literals"
+    "# Creating arrays"
    ]
   },
   {
@@ -58,9 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.zeros(5)"
@@ -76,9 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.zeros((3,4))"
@@ -103,9 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = np.zeros((3,4))\n",
@@ -115,9 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a.shape"
@@ -126,9 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a.ndim  # equal to len(a.shape)"
@@ -137,9 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a.size"
@@ -156,9 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.zeros((2,3,4))"
@@ -175,9 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "type(np.zeros((3,4)))"
@@ -196,9 +168,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.ones((3,4))"
@@ -215,9 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.full((3,4), np.pi)"
@@ -235,7 +203,6 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -254,9 +221,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.array([[1,2,3,4], [10, 20, 30, 40]])"
@@ -274,7 +239,6 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -292,9 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.arange(1.0, 5.0)"
@@ -310,9 +272,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.arange(1, 5, 0.5)"
@@ -328,9 +288,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(np.arange(0, 5/3, 1/3)) # depending on floating point errors, the max value is 4/3 or 5/3.\n",
@@ -349,9 +307,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(np.linspace(0, 5/3, 6))"
@@ -369,9 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.random.rand(3,4)"
@@ -387,9 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.random.randn(3,4)"
@@ -406,7 +358,6 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -418,9 +369,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.hist(np.random.rand(100000), normed=True, bins=100, histtype=\"step\", color=\"blue\", label=\"rand\")\n",
@@ -444,9 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def my_function(z, y, x):\n",
@@ -487,7 +434,6 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -499,9 +445,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c = np.arange(1.0, 5.0)\n",
@@ -518,9 +462,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "d = np.arange(1, 5, dtype=np.complex64)\n",
@@ -540,9 +482,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "e = np.arange(1, 5, dtype=np.complex64)\n",
@@ -561,7 +501,6 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -580,9 +519,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "if (hasattr(f.data, \"tobytes\")):\n",
@@ -612,9 +549,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "g = np.arange(24)\n",
@@ -625,9 +560,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "g.shape = (6, 4)\n",
@@ -639,7 +572,6 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -661,7 +593,6 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -681,9 +612,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "g2[1, 2] = 999\n",
@@ -700,9 +629,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "g"
@@ -719,9 +646,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "g.ravel()"
@@ -739,7 +664,6 @@
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -784,9 +708,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "h = np.arange(5).reshape(1, 1, 5)\n",
@@ -803,9 +725,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "h + [10, 20, 30, 40, 50]  # same as: h + [[[10, 20, 30, 40, 50]]]"
@@ -822,9 +742,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "k = np.arange(6).reshape(2, 3)\n",
@@ -841,9 +759,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "k + [[100], [200]]  # same as: k + [[100, 100, 100], [200, 200, 200]]"
@@ -859,9 +775,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "k + [100, 200, 300]  # after rule 1: [[100, 200, 300]], and after rule 2: [[100, 200, 300], [100, 200, 300]]"
@@ -877,9 +791,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "k + 1000  # same as: k + [[1000, 1000, 1000], [1000, 1000, 1000]]"
@@ -896,9 +808,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "try:\n",
@@ -926,9 +836,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "k1 = np.arange(0, 5, dtype=np.uint8)\n",
@@ -938,9 +846,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "k2 = k1 + np.array([5, 6, 7, 8, 9], dtype=np.int8)\n",
@@ -957,9 +863,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "k3 = k1 + 1.5\n",
@@ -983,9 +887,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m = np.array([20, -5, 30, 40])\n",
@@ -1002,9 +904,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m < 25  # equivalent to m < [25, 25, 25, 25]"
@@ -1020,9 +920,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m[m < 25]"
@@ -1048,9 +946,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
@@ -1070,9 +966,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for func in (a.min, a.max, a.sum, a.prod, a.std, a.var):\n",
@@ -1089,9 +983,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c=np.arange(24).reshape(2,3,4)\n",
@@ -1101,9 +993,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c.sum(axis=0)  # sum across matrices"
@@ -1112,9 +1002,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c.sum(axis=1)  # sum across rows"
@@ -1130,9 +1018,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c.sum(axis=(0,2))  # sum across matrices and columns"
@@ -1141,9 +1027,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "0+1+2+3 + 12+13+14+15, 4+5+6+7 + 16+17+18+19, 8+9+10+11 + 20+21+22+23"
@@ -1160,9 +1044,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
@@ -1179,9 +1061,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(\"Original ndarray\")\n",
@@ -1202,9 +1082,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = np.array([1, -2, 3, 4])\n",
@@ -1215,9 +1093,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.greater(a, b)  # equivalent to a > b"
@@ -1226,9 +1102,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.maximum(a, b)"
@@ -1237,9 +1111,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.copysign(a, b)"
@@ -1257,9 +1129,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = np.array([1, 5, 3, 19, 13, 7, 3])\n",
@@ -1269,9 +1139,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[2:5]"
@@ -1280,9 +1148,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[2:-1]"
@@ -1291,9 +1157,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[:2]"
@@ -1302,9 +1166,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[2::2]"
@@ -1313,9 +1175,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[::-1]"
@@ -1331,9 +1191,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[3]=999\n",
@@ -1350,9 +1208,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[2:5] = [997, 998, 999]\n",
@@ -1370,9 +1226,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[2:5] = -1\n",
@@ -1390,7 +1244,6 @@
    "cell_type": "code",
    "execution_count": 74,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -1411,9 +1264,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "try:\n",
@@ -1432,9 +1283,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a_slice = a[2:6]\n",
@@ -1445,9 +1294,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[3] = 2000\n",
@@ -1464,9 +1311,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "another_slice = a[2:6].copy()\n",
@@ -1477,9 +1322,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a[3] = 4000\n",
@@ -1497,9 +1340,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b = np.arange(48).reshape(4, 12)\n",
@@ -1509,9 +1350,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b[1, 2]  # row 1, col 2"
@@ -1520,9 +1359,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b[1, :]  # row 1, all columns"
@@ -1531,9 +1368,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b[:, 1]  # all rows, column 1"
@@ -1550,7 +1385,6 @@
    "cell_type": "code",
    "execution_count": 84,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -1561,9 +1395,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b[1:2, :]"
@@ -1588,7 +1420,6 @@
    "cell_type": "code",
    "execution_count": 86,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -1599,9 +1430,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b[:, (-1, 2, -1)]  # all rows, columns -1 (last), 2 and -1 (again, and in this order)"
@@ -1617,9 +1446,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "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)"
@@ -1636,9 +1463,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c = b.reshape(4,2,6)\n",
@@ -1648,9 +1473,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c[2, 1, 4]  # matrix 2, row 1, col 4"
@@ -1659,9 +1482,7 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c[2, :, 3]  # matrix 2, all rows, col 3"
@@ -1677,9 +1498,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c[2, 1]  # Return matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
@@ -1696,9 +1515,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c[2, ...]  #  matrix 2, all rows, all columns.  This is equivalent to c[2, :, :]"
@@ -1707,9 +1524,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c[2, 1, ...]  # matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
@@ -1718,9 +1533,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c[2, ..., 3]  # matrix 2, all rows, column 3.  This is equivalent to c[2, :, 3]"
@@ -1730,7 +1543,6 @@
    "cell_type": "code",
    "execution_count": 96,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -1749,9 +1561,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b = np.arange(48).reshape(4, 12)\n",
@@ -1761,9 +1571,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "rows_on = np.array([True, False, True, False])\n",
@@ -1773,9 +1581,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cols_on = np.array([False, True, False] * 4)\n",
@@ -1793,9 +1599,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b[np.ix_(rows_on, cols_on)]"
@@ -1804,9 +1608,7 @@
   {
    "cell_type": "code",
    "execution_count": 101,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.ix_(rows_on, cols_on)"
@@ -1822,9 +1624,7 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b[b % 3 == 1]"
@@ -1841,9 +1641,7 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "c = np.arange(24).reshape(2, 3, 4)  # A 3D array (composed of two 3x4 matrices)\n",
@@ -1853,9 +1651,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for m in c:\n",
@@ -1866,9 +1662,7 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for i in range(len(c)):  # Note that len(c) == c.shape[0]\n",
@@ -1886,9 +1680,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for i in c.flat:\n",
@@ -1906,9 +1698,7 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q1 = np.full((3,4), 1.0)\n",
@@ -1918,9 +1708,7 @@
   {
    "cell_type": "code",
    "execution_count": 108,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q2 = np.full((4,4), 2.0)\n",
@@ -1930,9 +1718,7 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q3 = np.full((3,4), 3.0)\n",
@@ -1950,9 +1736,7 @@
   {
    "cell_type": "code",
    "execution_count": 110,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q4 = np.vstack((q1, q2, q3))\n",
@@ -1962,9 +1746,7 @@
   {
    "cell_type": "code",
    "execution_count": 111,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q4.shape"
@@ -1983,9 +1765,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q5 = np.hstack((q1, q3))\n",
@@ -1995,9 +1775,7 @@
   {
    "cell_type": "code",
    "execution_count": 113,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q5.shape"
@@ -2013,9 +1791,7 @@
   {
    "cell_type": "code",
    "execution_count": 114,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "try:\n",
@@ -2035,9 +1811,7 @@
   {
    "cell_type": "code",
    "execution_count": 115,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q7 = np.concatenate((q1, q2, q3), axis=0)  # Equivalent to vstack\n",
@@ -2047,9 +1821,7 @@
   {
    "cell_type": "code",
    "execution_count": 116,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q7.shape"
@@ -2073,9 +1845,7 @@
   {
    "cell_type": "code",
    "execution_count": 117,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q8 = np.stack((q1, q3))\n",
@@ -2085,9 +1855,7 @@
   {
    "cell_type": "code",
    "execution_count": 118,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q8.shape"
@@ -2106,9 +1874,7 @@
   {
    "cell_type": "code",
    "execution_count": 119,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "r = np.arange(24).reshape(6,4)\n",
@@ -2125,9 +1891,7 @@
   {
    "cell_type": "code",
    "execution_count": 120,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "r1, r2, r3 = np.vsplit(r, 3)\n",
@@ -2137,9 +1901,7 @@
   {
    "cell_type": "code",
    "execution_count": 121,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "r2"
@@ -2148,9 +1910,7 @@
   {
    "cell_type": "code",
    "execution_count": 122,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "r3"
@@ -2166,9 +1926,7 @@
   {
    "cell_type": "code",
    "execution_count": 123,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "r4, r5 = np.hsplit(r, 2)\n",
@@ -2178,9 +1936,7 @@
   {
    "cell_type": "code",
    "execution_count": 124,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "r5"
@@ -2199,9 +1955,7 @@
   {
    "cell_type": "code",
    "execution_count": 125,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "t = np.arange(24).reshape(4,2,3)\n",
@@ -2218,9 +1972,7 @@
   {
    "cell_type": "code",
    "execution_count": 126,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "t1 = t.transpose((1,2,0))\n",
@@ -2230,9 +1982,7 @@
   {
    "cell_type": "code",
    "execution_count": 127,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "t1.shape"
@@ -2248,9 +1998,7 @@
   {
    "cell_type": "code",
    "execution_count": 128,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "t2 = t.transpose()  # equivalent to t.transpose((2, 1, 0))\n",
@@ -2260,9 +2008,7 @@
   {
    "cell_type": "code",
    "execution_count": 129,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "t2.shape"
@@ -2278,9 +2024,7 @@
   {
    "cell_type": "code",
    "execution_count": 130,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "t3 = t.swapaxes(0,1)  # equivalent to t.transpose((1, 0, 2))\n",
@@ -2290,9 +2034,7 @@
   {
    "cell_type": "code",
    "execution_count": 131,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "t3.shape"
@@ -2312,9 +2054,7 @@
   {
    "cell_type": "code",
    "execution_count": 132,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m1 = np.arange(10).reshape(2,5)\n",
@@ -2324,9 +2064,7 @@
   {
    "cell_type": "code",
    "execution_count": 133,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m1.T"
@@ -2343,7 +2081,6 @@
    "cell_type": "code",
    "execution_count": 134,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -2356,7 +2093,6 @@
    "cell_type": "code",
    "execution_count": 135,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -2374,9 +2110,7 @@
   {
    "cell_type": "code",
    "execution_count": 136,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m2r = m2.reshape(1,5)\n",
@@ -2386,9 +2120,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m2r.T"
@@ -2405,9 +2137,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n1 = np.arange(10).reshape(2, 5)\n",
@@ -2417,9 +2147,7 @@
   {
    "cell_type": "code",
    "execution_count": 139,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n2 = np.arange(15).reshape(5,3)\n",
@@ -2429,9 +2157,7 @@
   {
    "cell_type": "code",
    "execution_count": 140,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n1.dot(n2)"
@@ -2455,9 +2181,7 @@
   {
    "cell_type": "code",
    "execution_count": 141,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy.linalg as linalg\n",
@@ -2469,9 +2193,7 @@
   {
    "cell_type": "code",
    "execution_count": 142,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "linalg.inv(m3)"
@@ -2487,9 +2209,7 @@
   {
    "cell_type": "code",
    "execution_count": 143,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "linalg.pinv(m3)"
@@ -2506,9 +2226,7 @@
   {
    "cell_type": "code",
    "execution_count": 144,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m3.dot(linalg.inv(m3))"
@@ -2524,9 +2242,7 @@
   {
    "cell_type": "code",
    "execution_count": 145,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.eye(3)"
@@ -2543,9 +2259,7 @@
   {
    "cell_type": "code",
    "execution_count": 146,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q, r = linalg.qr(m3)\n",
@@ -2555,9 +2269,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "r"
@@ -2566,9 +2278,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q.dot(r)  # q.r equals m3"
@@ -2585,9 +2295,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "linalg.det(m3)  # Computes the matrix determinant"
@@ -2604,9 +2312,7 @@
   {
    "cell_type": "code",
    "execution_count": 150,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "eigenvalues, eigenvectors = linalg.eig(m3)\n",
@@ -2616,9 +2322,7 @@
   {
    "cell_type": "code",
    "execution_count": 151,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "eigenvectors # v"
@@ -2627,9 +2331,7 @@
   {
    "cell_type": "code",
    "execution_count": 152,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m3.dot(eigenvectors) - eigenvalues * eigenvectors  # m3.v - λ*v = 0"
@@ -2646,9 +2348,7 @@
   {
    "cell_type": "code",
    "execution_count": 153,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "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",
@@ -2658,9 +2358,7 @@
   {
    "cell_type": "code",
    "execution_count": 154,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "U, S_diag, V = linalg.svd(m4)\n",
@@ -2670,9 +2368,7 @@
   {
    "cell_type": "code",
    "execution_count": 155,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "S_diag"
@@ -2688,9 +2384,7 @@
   {
    "cell_type": "code",
    "execution_count": 156,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "S = np.zeros((4, 5))\n",
@@ -2701,9 +2395,7 @@
   {
    "cell_type": "code",
    "execution_count": 157,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "V"
@@ -2712,9 +2404,7 @@
   {
    "cell_type": "code",
    "execution_count": 158,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "U.dot(S).dot(V) # U.Σ.V == m4"
@@ -2730,9 +2420,7 @@
   {
    "cell_type": "code",
    "execution_count": 159,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.diag(m3)  # the values in the diagonal of m3 (top left to bottom right)"
@@ -2741,9 +2429,7 @@
   {
    "cell_type": "code",
    "execution_count": 160,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.trace(m3)  # equivalent to np.diag(m3).sum()"
@@ -2769,9 +2455,7 @@
   {
    "cell_type": "code",
    "execution_count": 161,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "coeffs  = np.array([[2, 6], [5, 3]])\n",
@@ -2790,9 +2474,7 @@
   {
    "cell_type": "code",
    "execution_count": 162,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "coeffs.dot(solution), depvars  # yep, it's the same"
@@ -2809,7 +2491,6 @@
    "cell_type": "code",
    "execution_count": 163,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -2830,9 +2511,7 @@
   {
    "cell_type": "code",
    "execution_count": 164,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import math\n",
@@ -2852,9 +2531,7 @@
   {
    "cell_type": "code",
    "execution_count": 165,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x_coords = np.arange(0, 1024)  # [0, 1, 2, ..., 1023]\n",
@@ -2866,9 +2543,7 @@
   {
    "cell_type": "code",
    "execution_count": 166,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "Y"
@@ -2886,9 +2561,7 @@
   {
    "cell_type": "code",
    "execution_count": 167,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = np.sin(X*Y/40.5)"
@@ -2904,9 +2577,7 @@
   {
    "cell_type": "code",
    "execution_count": 168,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2931,7 +2602,6 @@
    "cell_type": "code",
    "execution_count": 169,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -2943,9 +2613,7 @@
   {
    "cell_type": "code",
    "execution_count": 170,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.save(\"my_array\", a)"
@@ -2961,9 +2629,7 @@
   {
    "cell_type": "code",
    "execution_count": 171,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with open(\"my_array.npy\", \"rb\") as f:\n",
@@ -2982,9 +2648,7 @@
   {
    "cell_type": "code",
    "execution_count": 172,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a_loaded = np.load(\"my_array.npy\")\n",
@@ -3002,9 +2666,7 @@
   {
    "cell_type": "code",
    "execution_count": 173,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.savetxt(\"my_array.csv\", a)"
@@ -3020,9 +2682,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with open(\"my_array.csv\", \"rt\") as f:\n",
@@ -3057,9 +2717,7 @@
   {
    "cell_type": "code",
    "execution_count": 176,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a_loaded = np.loadtxt(\"my_array.csv\", delimiter=\",\")\n",
@@ -3077,9 +2735,7 @@
   {
    "cell_type": "code",
    "execution_count": 177,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b = np.arange(24, dtype=np.uint8).reshape(2, 3, 4)\n",
@@ -3107,9 +2763,7 @@
   {
    "cell_type": "code",
    "execution_count": 179,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with open(\"my_arrays.npz\", \"rb\") as f:\n",
@@ -3128,9 +2782,7 @@
   {
    "cell_type": "code",
    "execution_count": 180,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "my_arrays = np.load(\"my_arrays.npz\")\n",
@@ -3147,9 +2799,7 @@
   {
    "cell_type": "code",
    "execution_count": 181,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "my_arrays.keys()"
@@ -3158,9 +2808,7 @@
   {
    "cell_type": "code",
    "execution_count": 182,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "my_arrays[\"my_a\"]"
@@ -3177,21 +2825,21 @@
  ],
  "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": "3.7.4"
   },
   "toc": {
    "toc_cell": false,
@@ -3209,5 +2857,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/tools_pandas.ipynb b/tools_pandas.ipynb
index 6580f20..8c11356 100644
--- a/tools_pandas.ipynb
+++ b/tools_pandas.ipynb
@@ -16,24 +16,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Setup\n",
-    "First, let's make sure this notebook works well in both python 2 and 3:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __future__ import division, print_function, unicode_literals"
+    "# Setup"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's import `pandas`. People usually import it as `pd`:"
+    "First, let's import `pandas`. People usually import it as `pd`:"
    ]
   },
   {
@@ -2817,7 +2807,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.7.4"
   },
   "toc": {
    "toc_cell": false,