20-Numpy Operations.ipynb

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   "source": [
    "# NumPy Operations"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Arithmetic\n",
    "\n",
    "You can easily perform array with array arithmetic, or scalar with array arithmetic. Let's see some examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy as np\n",
    "arr = np.arange(0,10)"
   ]
  },
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
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     "execution_count": 2,
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   "source": [
    "arr + arr"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "arr * arr"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
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     "execution_count": 4,
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     "output_type": "execute_result"
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   "source": [
    "arr - arr"
   ]
  },
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   "execution_count": 5,
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     "text": [
      "/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ nan,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.])"
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     "execution_count": 5,
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   "source": [
    "# Warning on division by zero, but not an error!\n",
    "# Just replaced with nan\n",
    "arr/arr"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([        inf,  1.        ,  0.5       ,  0.33333333,  0.25      ,\n",
       "        0.2       ,  0.16666667,  0.14285714,  0.125     ,  0.11111111])"
      ]
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     "execution_count": 6,
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   "source": [
    "# Also warning, but not an error instead infinity\n",
    "1/arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   8,  27,  64, 125, 216, 343, 512, 729])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr**3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universal Array Functions\n",
    "\n",
    "Numpy comes with many [universal array functions](http://docs.scipy.org/doc/numpy/reference/ufuncs.html), which are essentially just mathematical operations you can use to perform the operation across the array. Let's show some common ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  1.        ,  1.41421356,  1.73205081,  2.        ,\n",
       "        2.23606798,  2.44948974,  2.64575131,  2.82842712,  3.        ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Taking Square Roots\n",
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calcualting exponential (e^)\n",
    "np.exp(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(arr) #same as arr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.84147098,  0.90929743,  0.14112001, -0.7568025 ,\n",
       "       -0.95892427, -0.2794155 ,  0.6569866 ,  0.98935825,  0.41211849])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(arr)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 16,
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     "name": "stderr",
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      "/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([       -inf,  0.        ,  0.69314718,  1.09861229,  1.38629436,\n",
       "        1.60943791,  1.79175947,  1.94591015,  2.07944154,  2.19722458])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!\n",
    "\n",
    "That's all we need to know for now!"
   ]
  }
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