Lecture 11 - Universal Array Functions.ipynb

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   "source": [
    "import numpy as np"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 5,
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     "output_type": "execute_result"
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   "source": [
    "arr = np.arange(11)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Universal Functions can be called on every member of the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  1.        ,  1.41421356,  1.73205081,  2.        ,\n",
       "        2.23606798,  2.44948974,  2.64575131,  2.82842712,  3.        ,\n",
       "        3.16227766])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Such as sqrt()\n",
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e+00,   2.71828183e+00,   7.38905610e+00,\n",
       "         2.00855369e+01,   5.45981500e+01,   1.48413159e+02,\n",
       "         4.03428793e+02,   1.09663316e+03,   2.98095799e+03,\n",
       "         8.10308393e+03,   2.20264658e+04])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exponential\n",
    "np.exp(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.60093632,  0.83260771,  0.90446449,  0.23807202, -0.18070298,\n",
       "        1.03920362, -0.20443971, -0.92539182,  0.33565368, -0.08755375])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create an array of a normal distribution of random numbers\n",
    "A = np.random.randn(10)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.93530995, -0.45896965,  1.08379422,  0.27158349, -1.0529907 ,\n",
       "        0.65791426, -1.15007728,  1.52236757, -0.93136591,  0.01241913])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "# create another for further use\n",
    "B = np.random.randn(10)\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.53624627,  0.37363805,  1.98825871,  0.50965551, -1.23369368,\n",
       "        1.69711788, -1.35451699,  0.59697575, -0.59571224, -0.07513462])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Binary functions: Add\n",
    "np.add(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.93530995,  0.83260771,  1.08379422,  0.27158349, -0.18070298,\n",
       "        1.03920362, -0.20443971,  1.52236757,  0.33565368,  0.01241913])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the min/max  between two arrays, returns the max value for each index between two arrays\n",
    "np.maximum(A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the Docs: http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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