{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "942f3aba",
   "metadata": {},
   "source": [
    "# Arithmetic\n",
    "\n",
    "Arrays allow you to perform stack operations on data without having to use `for` loops. This is called *vectorisation* in NumPy. For all arithmetic operations between arrays of the same size, the operation is applied element by element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc82112f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.04778788, -0.03971093, -1.71110236],\n",
       "       [ 1.18746556,  0.5643496 ,  0.2859378 ],\n",
       "       [-1.28686573,  0.40332209, -1.35913635],\n",
       "       [-0.20140587,  1.60498449,  0.23844523],\n",
       "       [-0.74730775,  0.11601887, -0.34109057],\n",
       "       [-0.08702842, -1.1238352 ,  1.21659397],\n",
       "       [ 0.54382846,  0.12373199, -0.02012923]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "data = np.random.randn(7, 3)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d3fdcf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-20.92580985, -25.18198603,  -0.58441857],\n",
       "       [  0.84212969,   1.77195127,   3.49726411],\n",
       "       [ -0.77708185,   2.47940793,  -0.73576135],\n",
       "       [ -4.96509854,   0.62305898,   4.19383522],\n",
       "       [ -1.3381368 ,   8.61928749,  -2.93177261],\n",
       "       [-11.49049937,  -0.88981018,   0.82196692],\n",
       "       [  1.83881514,   8.0819842 , -49.67899596]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 / data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b409fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.28368106e-03, 1.57695769e-03, 2.92787128e+00],\n",
       "       [1.41007445e+00, 3.18490476e-01, 8.17604243e-02],\n",
       "       [1.65602341e+00, 1.62668712e-01, 1.84725163e+00],\n",
       "       [4.05643252e-02, 2.57597521e+00, 5.68561274e-02],\n",
       "       [5.58468871e-01, 1.34603783e-02, 1.16342779e-01],\n",
       "       [7.57394581e-03, 1.26300555e+00, 1.48010090e+00],\n",
       "       [2.95749391e-01, 1.53096057e-02, 4.05185952e-04]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data**2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb8388b",
   "metadata": {},
   "source": [
    "Comparison of two arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "248171de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False, False],\n",
       "       [ True,  True, False],\n",
       "       [False,  True, False],\n",
       "       [ True,  True,  True],\n",
       "       [False, False,  True],\n",
       "       [ True, False,  True],\n",
       "       [ True,  True, False]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = np.random.randn(7, 3)\n",
    "data > data2"
   ]
  }
 ],
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