{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# NumPy: een inleiding\n",
    "### K. Verbeeck, T. Van den Bossche, J. Maervoet\n",
    "Data Science (Theory) -- OGI03p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Werken met vectoren en matrices in standaard Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 3.2, 6, 5, 3.2, 6]\n",
      "[5, 3.2, 6, 5, 3.2, 6, 5, 3.2, 6]\n",
      "[[0, 1, 0], [1, 0, 1], [0, 1, 0]]\n"
     ]
    }
   ],
   "source": [
    "# lijsten (van lijsten) maar veel beperkingen\n",
    "\n",
    "vector = [5, 3.2, 6]\n",
    "\n",
    "print(vector + vector) # geen vector-optelling\n",
    "print(vector * 3) # geen vector-vermenigvuldiging\n",
    "\n",
    "matrix = [[0, 1, 0], [1, 0, 1], [0, 1, 0]]\n",
    "\n",
    "print(matrix) # geen pretty-print\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zUz6fT6+88sqg4+vq6uTz+QY8jh8/7uU0AQAGJHj55OfOndNNN92k733ve1q6dOllH9fS0qKUlJTw9tVXX+3F9AAAhngavOLiYhUXFw/5uLS0NF155ZXRnxAAwCxPgzdcs2fP1meffabrr79ejz32mObNm3fRsaFQSKFQKLzd09MjSbp3+g1K8CV6PleMrNf+3TzSU0AMLci8eaSngBjoc+clvR/15x1Vb1rJyMjQ5s2bVVNTo507dyo3N1fz589XfX39RY+pqqpSIBAIP7KysmI4YwDAWOFzzrmYfCOfT7t27dKSJUuGdNyiRYvk8/m0e/fuiF+PdIWXlZWl72gxV3gGcIVnC1d4NvS586rTXxQMBvu9n+PrGlVXeJEUFBToxIkTF/263+9XSkpKvwcAAF816oPX1NSkjIyMkZ4GAGCM8/RNK5988on++c9/hrdbW1vV3NysiRMnasqUKaqsrNSpU6f0wgsvSJI2btyoqVOnaubMmert7dX27dtVU1OjmpoaL6cJADDA0+A1Njb2e4dlRUWFJKmsrEzPP/+8Ojo61NbWFv56b2+v1q5dq1OnTmnChAmaOXOm9uzZo5KSEi+nCQAwIGZvWomVnp4eBQIB3rRiBG9asYU3rdhg9k0rAABEA8EDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACYQPACACQQPAGACwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACYQPACACQQPAGACwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACYQPACACQQPAGACwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACYQPACACQQPAGACwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJhA8AAAJngavKqqKt16661KTk5WWlqalixZopaWlksed/DgQeXl5SkpKUnTpk1TdXW1l9MEABjgafAOHjyoFStW6PDhw6qtrVVfX5+Kiop07ty5ix7T2tqqkpISzZ07V01NTVq/fr1WrVqlmpoaL6cKAIhzCV4++b59+/ptb9u2TWlpaTpy5IjuvPPOiMdUV1drypQp2rhxoyRpxowZamxs1IYNG7R06VIvpwsAiGMxfQ0vGAxKkiZOnHjRMQ0NDSoqKuq3b8GCBWpsbNT58+cHjA+FQurp6en3AADgq2IWPOecKioqdMcdd2jWrFkXHdfZ2an09PR++9LT09XX16fu7u4B46uqqhQIBMKPrKysqM8dADD2xSx4K1eu1NGjR/XHP/7xkmN9Pl+/bedcxP2SVFlZqWAwGH60t7dHZ8IAgLji6Wt4FzzyyCPavXu36uvrdc011ww6dvLkyers7Oy3r6urSwkJCZo0adKA8X6/X36/P6rzBQDEH0+v8JxzWrlypXbu3KnXX39dOTk5lzymsLBQtbW1/fbt379f+fn5SkxM9GqqAIA452nwVqxYoe3bt+vll19WcnKyOjs71dnZqf/+97/hMZWVlXrooYfC2+Xl5Tp58qQqKip07Ngxbd26VVu2bNHatWu9nCoAIM55GrxNmzYpGAzqO9/5jjIyMsKPHTt2hMd0dHSora0tvJ2Tk6O9e/eqrq5ON998s375y1/q6aef5iMJAICvxdPX8C682WQwzz///IB9d911l/72t795MCMAgFX8Lk0AgAkEDwBgAsEDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACYQPACACQQPAGACwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACYQPACACQQPAGACwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACYQPACACQQPAGACwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACYQPACACQQPAGACwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJjgafCqqqp06623Kjk5WWlpaVqyZIlaWloGPaaurk4+n2/A4/jx415OFQAQ5zwN3sGDB7VixQodPnxYtbW16uvrU1FRkc6dO3fJY1taWtTR0RF+XHvttV5OFQAQ5xK8fPJ9+/b12962bZvS0tJ05MgR3XnnnYMem5aWpiuvvPKS3yMUCikUCoW3g8GgJKlP5yU39DljbOk5+8VITwEx1OfOj/QUEAN9+vI8Oxfdv8Q9Dd5XXYjRxIkTLzl29uzZ+uyzz3T99dfrscce07x58yKOq6qq0hNPPDFg/1+19+tNFmPCVdNHegaIrfdHegKIodOnTysQCETt+Xwu2gm9COecFi9erDNnzuiNN9646LiWlhbV19crLy9PoVBIL774oqqrq1VXVxfxqvCrV3gff/yxsrOz1dbWFtV/UKNdT0+PsrKy1N7erpSUlJGeTsxYXLfFNUs2121xzdKXF0dTpkzRmTNnLusnfZcrZld4K1eu1NGjR/XXv/510HG5ubnKzc0NbxcWFqq9vV0bNmyIGDy/3y+/3z9gfyAQMPUvyAUpKSms2wiLa5ZsrtvimiXpiiui+zaTmHws4ZFHHtHu3bt14MABXXPNNUM+vqCgQCdOnPBgZgAAKzy9wnPO6ZFHHtGuXbtUV1ennJycYT1PU1OTMjIyojw7AIAlngZvxYoVevnll/WXv/xFycnJ6uzslPTljxsnTJggSaqsrNSpU6f0wgsvSJI2btyoqVOnaubMmert7dX27dtVU1Ojmpqay/qefr9fv/jFLyL+mDOesW4767a4Zsnmui2uWfJu3Z6+acXn80Xcv23bNi1fvlyStHz5cn3wwQeqq6uTJP3mN7/R5s2bderUKU2YMEEzZ85UZWWlSkpKvJomAMCAmL1LEwCAkcTv0gQAmEDwAAAmEDwAgAkEDwBgQlwE78yZMyotLVUgEFAgEFBpaak+/vjjQY9Zvnz5gFsQFRQUxGbCw/Tcc88pJydHSUlJysvLG/RXtElf3q0iLy9PSUlJmjZtmqqrq2M00+gZyprj5dZS9fX1WrRokTIzM+Xz+fTKK69c8pixfq6HuuZ4ONfDuX2aNPbP9UjeNi4ugvfAAw+oublZ+/bt0759+9Tc3KzS0tJLHrdw4cJ+tyDau3f0/sLpHTt2aPXq1Xr00UfV1NSkuXPnqri4WG1tbRHHt7a2qqSkRHPnzlVTU5PWr1+vVatWXfbnGUeDoa75grF+a6lz587ppptu0jPPPHNZ4+PhXA91zReM5XM9nNunxcO5HtHbxrkx7t1333WS3OHDh8P7GhoanCR3/Pjxix5XVlbmFi9eHIMZRsdtt93mysvL++277rrr3Lp16yKO/+lPf+quu+66fvt+9KMfuYKCAs/mGG1DXfOBAwecJHfmzJkYzC42JLldu3YNOiYezvX/upw1x+O57urqcpLcwYMHLzom3s61c5e37mid7zF/hdfQ0KBAIKDbb789vK+goECBQECHDh0a9Ni6ujqlpaVp+vTpevjhh9XV1eX1dIelt7dXR44cUVFRUb/9RUVFF11jQ0PDgPELFixQY2Ojzp8f/fcUG86aL5g9e7YyMjI0f/58HThwwMtpjgpj/Vx/HfF0ri/n9mnxeK6Hetu4r3O+x3zwOjs7lZaWNmB/Wlpa+FeZRVJcXKyXXnpJr7/+un7729/q7bff1t13393vVkOjRXd3tz7//HOlp6f325+enn7RNXZ2dkYc39fXp+7ubs/mGi3DWXNGRoY2b96smpoa7dy5U7m5uZo/f77q6+tjMeURM9bP9XDE27l2zqmiokJ33HGHZs2addFx8XauL3fd0TrfMb0B7FA8/vjjEW/s+r/efvttSZF/hZlz7qK/2kySli1bFv7zrFmzlJ+fr+zsbO3Zs0f33XffMGftra+u51JrjDQ+0v7RbChrHuqtpeJJPJzroYi3c325t0+T4utce3XbuIsZtcFbuXKl7r///kHHTJ06VUePHtWHH3444GsfffTRgP8TGkxGRoays7NH5W2IUlNTNW7cuAFXNl1dXRdd4+TJkyOOT0hI0KRJkzyba7QMZ82RFBQUaPv27dGe3qgy1s91tIzVc33h9mn19fWXvH1aPJ3roaw7kuGc71EbvNTUVKWmpl5yXGFhoYLBoN566y3ddtttkqQ333xTwWBQc+bMuezvd/r0abW3t4/K2xCNHz9eeXl5qq2t1b333hveX1tbq8WLF0c8prCwUK+++mq/ffv371d+fr4SExM9nW80DGfNkVi4tdRYP9fRMtbOtRvG7dPi4VwPZ92RDOt8f623vIwSCxcudDfeeKNraGhwDQ0N7oYbbnD33HNPvzG5ublu586dzjnnzp49637yk5+4Q4cOudbWVnfgwAFXWFjovvWtb7menp6RWMIl/elPf3KJiYluy5Yt7t1333WrV6923/zmN90HH3zgnHNu3bp1rrS0NDz+/fffd9/4xjfcmjVr3Lvvvuu2bNniEhMT3Z///OeRWsKQDXXNv/vd79yuXbvce++95/7xj3+4devWOUmupqZmpJYwLGfPnnVNTU2uqanJSXJPPfWUa2pqcidPnnTOxee5Huqa4+Fc//jHP3aBQMDV1dW5jo6O8OPTTz8Nj4nHcz2cdUfrfMdF8E6fPu0efPBBl5yc7JKTk92DDz444O2rkty2bducc859+umnrqioyF199dUuMTHRTZkyxZWVlbm2trbYT34Inn32WZedne3Gjx/vbrnlln5v4y0rK3N33XVXv/F1dXVu9uzZbvz48W7q1Klu06ZNMZ7x1zeUNf/617923/72t11SUpK76qqr3B133OH27NkzArP+ei68Bfurj7KyMudcfJ7roa45Hs51pPX+799TzsXnuR7OuqN1vrk9EADAhDH/sQQAAC4HwQMAmEDwAAAmEDwAgAkEDwBgAsEDAJhA8AAAJhA8AIAJBA8AYALBAwCYQPAAACb8H+7mADhTJFAjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wel integratie in bv matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "_, ax = plt.subplots()\n",
    "ax.imshow(matrix)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### De NumPy library\n",
    "* Performante ondersteuning voor grote multi-dimensionele arrays (matrices) en bijhorende operatoren uit de numerieke wiskunde\n",
    "* Centrale datastructuur: de n-dimensionale array _ndarray_ \n",
    "* __[Quickstart](https://numpy.org/doc/stable/user/quickstart.html)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Aanmaken van NumPy arrays en basiseigenschappen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 \n",
      " <class 'numpy.ndarray'>\n",
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# aanmaak met np.array\n",
    "a = np.array([1, 2, 3])\n",
    "print(a[0], '\\n', type(a))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 2.]\n",
      " [0. 0.]]\n",
      "\n",
      "ndim:  2\n",
      "\n",
      "shape:  (2, 2)\n",
      "\n",
      "dtype:  float64\n",
      "\n",
      "size:  4\n",
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]\n",
      " [15 16 17 18 19]\n",
      " [20 21 22 23 24]]\n"
     ]
    }
   ],
   "source": [
    "# basiseigenschappen\n",
    "matrix = np.array([[1, 2.0], [0, 0]])\n",
    "print(matrix)\n",
    "print('\\nndim: ', matrix.ndim)\n",
    "print('\\nshape: ', matrix.shape)\n",
    "print('\\ndtype: ', matrix.dtype) # NumPy heeft eigen datatypes\n",
    "print('\\nsize: ', matrix.size)\n",
    "\n",
    "# wijzigen van de shape\n",
    "matrix = np.array(range(25)).reshape(5,5)\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.   0.25 0.5  0.75 1.   1.25 1.5  1.75 2.   2.25 2.5  2.75]\n",
      "[0.   0.25 0.5  0.75 1.   1.25 1.5  1.75 2.   2.25 2.5  2.75]\n"
     ]
    }
   ],
   "source": [
    "# aanmaak met built-in functions\n",
    "\n",
    "print(np.arange(0, 3, 0.25)) # standard python kan geen ranges met kommagetallen genereren\n",
    "print(np.linspace(0, 2.75, 12)) # interval opgedeeld in gelijke delen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "[[1. 0.]\n",
      " [0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "print(np.zeros([3,3]))\n",
    "print(np.eye(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Rekenkundige bewerkingen op element-niveau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  3  5  7  9]\n",
      " [11 13 15 17 19]\n",
      " [21 23 25 27 29]\n",
      " [31 33 35 37 39]\n",
      " [41 43 45 47 49]]\n"
     ]
    }
   ],
   "source": [
    "# rekenkundige bewerkingen op element-niveau\n",
    "matrix = matrix * 2\n",
    "matrix = matrix + 1\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Indexing en slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "9\n",
      "[41 43 45 47 49]\n",
      "\n",
      " [[ 5  7  9]\n",
      " [15 17 19]]\n",
      "\n",
      " [[ 1  5  9]\n",
      " [21 25 29]\n",
      " [41 45 49]]\n",
      "\n",
      " [[41 43 45 47 49]\n",
      " [31 33 35 37 39]\n",
      " [21 23 25 27 29]\n",
      " [11 13 15 17 19]\n",
      " [ 1  3  5  7  9]]\n"
     ]
    }
   ],
   "source": [
    "# indexing en slicing - deel 1\n",
    "# slicing creëert views, geen kopieën\n",
    "print(matrix[1,0])\n",
    "print(matrix[0, -1])\n",
    "print(matrix[4]) # zelfde als [4,:]\n",
    "\n",
    "print('\\n', matrix[:2,-3:])\n",
    "print('\\n', matrix[::2,::2]) # step size 2\n",
    "print('\\n', matrix[::-1]) # negative step size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 10  9  9]\n"
     ]
    }
   ],
   "source": [
    "# indexing en slicing - deel 2\n",
    "# index arrays\n",
    "test = np.array([10, 9, 8, 7])\n",
    "print(test[ np.array([0, 0, 1, 1]) ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False False False False]\n",
      " [False False False False False]\n",
      " [False False False  True  True]\n",
      " [ True  True  True  True  True]\n",
      " [ True  True  True  True  True]]\n",
      "[[ 1  3  5  7  9]\n",
      " [11 13 15 17 19]\n",
      " [21 23 25  0  0]\n",
      " [ 0  0  0  0  0]\n",
      " [ 0  0  0  0  0]]\n"
     ]
    }
   ],
   "source": [
    "# indexing en slicing - deel 3\n",
    "# boolean \"mask\" index arrays\n",
    "\n",
    "masker = (matrix > 25)\n",
    "print(masker)\n",
    "\n",
    "matrix[masker] = 0\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Operatoren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2. 1. 1.]\n",
      " [1. 2. 1.]\n",
      " [1. 1. 2.]]\n",
      "[[5 5 5]\n",
      " [5 5 5]\n",
      " [5 5 5]]\n",
      "[[10.  5.  5.]\n",
      " [ 5. 10.  5.]\n",
      " [ 5.  5. 10.]]\n",
      "[[20. 20. 20.]\n",
      " [20. 20. 20.]\n",
      " [20. 20. 20.]]\n"
     ]
    }
   ],
   "source": [
    "# basisoperatoren\n",
    "\n",
    "d = np.ones(3) + np.eye(3) # som op element-niveau\n",
    "print(d)\n",
    "\n",
    "e = np.full((3,3), 5)\n",
    "print(e)\n",
    "print(d*e) # produkt op element-niveau (!)\n",
    "print(d@e) # dot-produkt (!) = d.dot(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4]\n",
      " [2 5]\n",
      " [3 6]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,3],[4,5,6]])\n",
    "print(a.T) # getransponeerde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.555555555555555\n",
      "[ 9.83333333 13.         11.83333333]\n",
      "[10.25 12.95 11.2 ]\n"
     ]
    }
   ],
   "source": [
    "# universal functions\n",
    "\n",
    "scores = np.array([[14.5, 7, 9], [12, 18, 11.5], [3, 14, 15]])\n",
    "print(np.mean(scores))\n",
    "print(np.mean(scores, axis = 0))\n",
    "print(np.average(scores, axis = 1, weights = [0.3, 0.2, 0.5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Zijn ndarrays lijsten?\n",
    "\n",
    "* Nee, ze zijn van het type _iterable_ net zoals lijsten\n",
    "* Het zijn eerder grote objecten die hun brondata zo efficiënt mogelijk beheren\n",
    "  * Sommige operaties (eenvoudige indexing en slicing) geven **views** terug en geen **copies**\n",
    "* Omdat ze _iterable_ zijn werken ze goed samen met libraries zoals _matplotlib_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Must-reads\n",
    "* __[Quickstart](https://numpy.org/doc/stable/user/quickstart.html)__\n",
    "* __[Linear algebra](https://numpy.org/doc/stable/reference/routines.linalg.html)__"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![image.png](attachment:image.png)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Diavoorstelling",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
