6. Copies and Views.ipynb

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   "source": [
    "When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### No Copy at All\n",
    "Simple assignments make no copy of array objects or of their data."
   ]
  },
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   "execution_count": 2,
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       "True"
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     "execution_count": 2,
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     "output_type": "execute_result"
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   ],
   "source": [
    "a = np.arange(12)\n",
    "b = a            # no new object is created    \n",
    "b is a           # a and b are two names for the same ndarray object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1],\n",
       "        [ 2,  3]],\n",
       "\n",
       "       [[ 4,  5],\n",
       "        [ 6,  7]],\n",
       "\n",
       "       [[ 8,  9],\n",
       "        [10, 11]]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape=3,2,2        # changes the shape of a\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.reshape(4,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### View or Shallow Copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1],\n",
       "        [ 2,  3]],\n",
       "\n",
       "       [[ 4,  5],\n",
       "        [ 6,  7]],\n",
       "\n",
       "       [[ 8,  9],\n",
       "        [10, 11]]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = a.view()\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c is a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(3)\n",
    "y = np.arange(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.base is x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "c.base is a # c is a view of the data owned by a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1],\n",
       "        [ 2,  3]],\n",
       "\n",
       "       [[ 4,  5],\n",
       "        [ 6,  7]],\n",
       "\n",
       "       [[ 8,  9],\n",
       "        [10, 11]]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1],\n",
       "        [256, 256]],\n",
       "\n",
       "       [[  4,   5],\n",
       "        [  6,   7]],\n",
       "\n",
       "       [[  8,   9],\n",
       "        [ 10,  11]]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0,1] = 256   # a's data changes\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1],\n",
       "        [256, 256]],\n",
       "\n",
       "       [[  4,   5],\n",
       "        [  6,   7]],\n",
       "\n",
       "       [[  8,   9],\n",
       "        [ 10,  11]]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "c"
   ]
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  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1],\n",
       "        [256, 256]],\n",
       "\n",
       "       [[  4,   5],\n",
       "        [  6,   7]],\n",
       "\n",
       "       [[  8,   9],\n",
       "        [ 10,  11]]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1],\n",
       "        [256, 256]],\n",
       "\n",
       "       [[  4,   5],\n",
       "        [  6,   7]],\n",
       "\n",
       "       [[  8,   9],\n",
       "        [ 10,  11]]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[100, 100]],\n",
       "\n",
       "       [[100, 100]],\n",
       "\n",
       "       [[100, 100]]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = a[ : , 1:3]               # spaces added for clarity; could also be written \"s = a[:,1:3]\"\n",
    "s[:] = 100                    # s[:] is a view of s. Note the difference between s=10 and s[:]=10\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deep Copy\n",
    "The copy method makes a complete copy of the array and its data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = a.copy() # a new array object with new data is created\n",
    "d is a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.base is a                           # d doesn't share anything with a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[9999, 9999],\n",
       "        [ 256,  256]],\n",
       "\n",
       "       [[   4,    5],\n",
       "        [   6,    7]],\n",
       "\n",
       "       [[   8,    9],\n",
       "        [  10,   11]]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0,0] = 9999\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1],\n",
       "        [256, 256]],\n",
       "\n",
       "       [[  4,   5],\n",
       "        [  6,   7]],\n",
       "\n",
       "       [[  8,   9],\n",
       "        [ 10,  11]]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions and Methods Overview\n",
    "Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list.\n",
    "\n",
    "#### Array Creation\n",
    "arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r, zeros, zeros_like\n",
    "#### Conversions\n",
    "ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat\n",
    "#### Manipulations\n",
    "array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack\n",
    "#### Questions\n",
    "all, any, nonzero, where\n",
    "#### Ordering\n",
    "argmax, argmin, argsort, max, min, ptp, searchsorted, sort\n",
    "#### Operations\n",
    "choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum\n",
    "#### Basic Statistics\n",
    "cov, mean, std, var\n",
    "#### Basic Linear Algebra\n",
    "cross, dot, outer, linalg.svd, vdot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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