intor_to_numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy\n",
    "==="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use lists to calculate:\n",
    "$\\vec{a}.\\vec{b}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.323200000000007\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "n = 100000000\n",
    "a = [0.1 for i in range(n)]\n",
    "b = [0.1 for i in range(n)]\n",
    "\n",
    "c = 0\n",
    "\n",
    "start = time.clock()\n",
    "\n",
    "for i in range(n):\n",
    "    c += a[i]*b[i]\n",
    "\n",
    "end = time.clock()\n",
    "speed_of_python = end-start\n",
    "print(speed_of_python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "arrays\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1D 2D and more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2],[3,4]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n",
      "(4, 4)\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4])\n",
    "print(a.shape)\n",
    "a = np.array([[1,2,1,1],[3,4,1,1],[5,6,1,1], [5,6,1,1]])\n",
    "print(a.shape)\n",
    "print(a.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing and slicing, : and ::, copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 1 1]\n",
      "1\n",
      "[2 4 6 6]\n",
      "[3 4 1 1]\n",
      "[[1 0 1 1]\n",
      " [3 0 1 1]\n",
      " [5 0 1 1]\n",
      " [5 0 1 1]]\n",
      "[[3 0 1 1]\n",
      " [5 0 1 1]]\n",
      "[[-1  0 -1  1]\n",
      " [ 3  0  1  1]\n",
      " [-1  0 -1  1]\n",
      " [ 5  0  1  1]]\n",
      "[[-1  0 -1  1]\n",
      " [ 3  0  1  1]\n",
      " [-1  0 -1  1]\n",
      " [ 5  0  1  1]]\n",
      "[[-1  0 -1  1]\n",
      " [ 3  0  1  1]\n",
      " [-1  0 -1  1]\n",
      " [ 5  0  1  1]]\n"
     ]
    }
   ],
   "source": [
    "print(a[1])\n",
    "print(a[1,2])\n",
    "print(a[:,1])\n",
    "print(a[1,:])\n",
    "\n",
    "a[:,1] = 0\n",
    "print(a)\n",
    "print(a[1:3,:])\n",
    "a[::2,::2]=-1\n",
    "print(a)\n",
    "b = a.copy()\n",
    "print(b)\n",
    "b[:,:]=0\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "flatten, ravel, reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0, -1,  1,  3,  0,  1,  1, -1,  0, -1,  1,  5,  0,  1,  1])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.flatten()\n",
    "a.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
      "[[[ 0  1]\n",
      "  [ 2  3]\n",
      "  [ 4  5]\n",
      "  [ 6  7]]\n",
      "\n",
      " [[ 8  9]\n",
      "  [10 11]\n",
      "  [12 13]\n",
      "  [14 15]]\n",
      "\n",
      " [[16 17]\n",
      "  [18 19]\n",
      "  [20 21]\n",
      "  [22 23]]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array(range(24))\n",
    "print(a)\n",
    "a = a.reshape((3,4,2))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " np.newaxis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 2, 1)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,:,:, np.newaxis].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "zeros, ones, zeros_like, ones_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "[[[0 0]\n",
      "  [0 0]\n",
      "  [0 0]\n",
      "  [0 0]]\n",
      "\n",
      " [[0 0]\n",
      "  [0 0]\n",
      "  [0 0]\n",
      "  [0 0]]\n",
      "\n",
      " [[0 0]\n",
      "  [0 0]\n",
      "  [0 0]\n",
      "  [0 0]]]\n",
      "[[[1 1]\n",
      "  [1 1]\n",
      "  [1 1]\n",
      "  [1 1]]\n",
      "\n",
      " [[1 1]\n",
      "  [1 1]\n",
      "  [1 1]\n",
      "  [1 1]]\n",
      "\n",
      " [[1 1]\n",
      "  [1 1]\n",
      "  [1 1]\n",
      "  [1 1]]]\n"
     ]
    }
   ],
   "source": [
    "print(np.zeros((3,3)))\n",
    "print(np.ones((3,3)))\n",
    "print(np.zeros_like(a))\n",
    "print(np.ones_like(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.ones(10)\n",
    "b = np.ones(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3.0 * a "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0984109999999987\n"
     ]
    }
   ],
   "source": [
    "\n",
    "a = np.ones(n)*0.1\n",
    "b = np.ones(n)*0.1\n",
    "\n",
    "start = time.clock()\n",
    "\n",
    "np.dot(a,b)\n",
    "end = time.clock()\n",
    "speed_of_numpy = end-start\n",
    "print(speed_of_numpy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155.70617105811556"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speed_of_python/speed_of_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "       ..., \n",
       "       [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1., ...,  1.,  1.,  1.]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((100,100))\n",
    "b = np.ones((100,100))\n",
    "\n",
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 100.,  100.,  100., ...,  100.,  100.,  100.],\n",
       "       [ 100.,  100.,  100., ...,  100.,  100.,  100.],\n",
       "       [ 100.,  100.,  100., ...,  100.,  100.,  100.],\n",
       "       ..., \n",
       "       [ 100.,  100.,  100., ...,  100.,  100.,  100.],\n",
       "       [ 100.,  100.,  100., ...,  100.,  100.,  100.],\n",
       "       [ 100.,  100.,  100., ...,  100.,  100.,  100.]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dot(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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