Lecture 9 - Indexing arrays.ipynb

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   "source": [
    "import numpy as np"
   ]
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   "outputs": [],
   "source": [
    "arr = np.arange(0,11)"
   ]
  },
  {
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   "execution_count": 3,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
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     "execution_count": 3,
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    "arr"
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       "0"
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    "arr[0]"
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   "cell_type": "code",
   "execution_count": 5,
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    {
     "data": {
      "text/plain": [
       "8"
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     "execution_count": 5,
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   "source": [
    "arr[8]"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr[0:3] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100, 100, 100,   3,   4,   5,   6,   7,   8,   9,  10])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "arr"
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "slice_arr = arr[2:5]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
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    {
     "data": {
      "text/plain": [
       "array([100,   3,   4])"
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     "execution_count": 10,
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   "source": [
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   "execution_count": 11,
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   "source": [
    "slice_arr[:]=55"
   ]
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   "execution_count": 12,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100, 100,  55,  55,  55,   5,   6,   7,   8,   9,  10])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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    "arr"
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "arr_copy = arr.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100, 100,  55,  55,  55,   5,   6,   7,   8,   9,  10])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    "arr_copy"
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   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5, 10, 15],\n",
       "       [20, 25, 25],\n",
       "       [35, 40, 45]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "arr_2d = np.array(([5,10,15],[20,25,25],[35,40,45]))\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 19,
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   "source": [
    "arr_2d[0][1]"
   ]
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   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 25, 25]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d[:2][1:]"
   ]
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   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "array([[ 5, 10, 15],\n",
       "       [20, 25, 25],\n",
       "       [35, 40, 45]])"
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     },
     "execution_count": 21,
     "metadata": {},
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    "arr_2d"
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   "execution_count": 22,
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    {
     "data": {
      "text/plain": [
       "array([[10, 15],\n",
       "       [25, 25]])"
      ]
     },
     "execution_count": 22,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d[:2,1:]"
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  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr_2d2 = np.zeros((10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
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      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 26,
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    "arr_2d2"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "arr_length = arr_2d2.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
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    {
     "data": {
      "text/plain": [
       "10"
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     "execution_count": 29,
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    "arr_length"
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   "execution_count": 30,
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   "source": [
    "for i in range(arr_length):\n",
    "    arr_2d2[i] = i"
   ]
  },
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       "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.],\n",
       "       [ 3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.],\n",
       "       [ 4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.],\n",
       "       [ 5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.],\n",
       "       [ 6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.],\n",
       "       [ 7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.],\n",
       "       [ 8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.],\n",
       "       [ 9.,  9.,  9.,  9.,  9.,  9.,  9.,  9.,  9.,  9.]])"
      ]
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     "execution_count": 31,
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