data-processing/numpy-file-input-and-output-with-arrays.ipynb

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   "source": [
    "# NumPy File Input and Output with Arrays\n",
    "- np.save\n",
    "- np.load\n",
    "- np.savez\n",
    "\n",
    "`np.save` and `np.load` are the two workhorse functions for efficiently saving and loading array data on disk. Arrays are saved by default in an uncompressed raw binary format with file extension **.npy**."
   ]
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.arange(10)\n",
    "np.save('some_array', arr)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "np.load('some_array.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!rm -f some_array.npy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You save multiple arrays in a zip archive using `np.savez` and passing the arrays as keyword arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.savez('array_archive.npz', a=arr, b=arr)\n",
    "arch = np.load('array_archive.npz')\n",
    "arch['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!rm -f array_archive.npz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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