Lecture 13 - Array Input and Output.ipynb

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   "source": [
    "import numpy as np"
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   "source": [
    "arr = np.arange(5)"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
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    "arr"
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   "source": [
    "# to save simply call np.save, give it a name and specify which array\n",
    "# notice the file my_array.npy now resides in the directory\n",
    "np.save('my_array', arr)"
   ]
  },
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   "cell_type": "code",
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       "array([0, 1, 2, 3, 4])"
      ]
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     "execution_count": 7,
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   "source": [
    "# we load the array using np.load and specifying the file name\n",
    "np.load('my_array.npy')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can save multiple arrays\n",
    "arr1 = np.load('my_array.npy')\n",
    "arr2 = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we use the np.savez() method to save multiple files, give it a name, specify the arrays to be saved\n",
    "np.savez('ziparray.npz', x=arr1, y=arr2)\n",
    "# notice the new file in the root directory: ziparray.npz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we load arrays using the load method specifying the name of the file\n",
    "mult_arrays = np.load('ziparray.npz')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# and we can iterate through the arrays using index notation\n",
    "mult_arrays['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
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     "execution_count": 12,
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   "source": [
    "mult_arrays['y']"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# we can save text files too\n",
    "# in the example, we create a matrix\n",
    "arr = np.array([[1,2,3], [4,5,6]])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we use the np.savetxt() method to save, giving it a name, specifying the array and delimiter\n",
    "np.savetxt('mytext_array.txt', arr, delimiter=',')\n",
    "# again, note the file now in the root directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now to load a text file\n",
    "arr = np.loadtxt('mytext_array.txt', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "arr"
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  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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