Lecture 13 - Array Input and Output.ipynb

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   "source": [
    "arr = np.arange(5)"
   ]
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       "array([0, 1, 2, 3, 4])"
      ]
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    "arr = np.arange(10)"
   ]
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   "execution_count": 6,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
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     "execution_count": 6,
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   "execution_count": 7,
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    {
     "data": {
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       "array([0, 1, 2, 3, 4])"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "np.load('myarray.npy')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
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   "source": [
    "arr1 = np.load('myarray.npy')"
   ]
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "source": [
    "arr2 = arr"
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   "execution_count": 10,
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    {
     "data": {
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       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
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     "execution_count": 10,
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    "arr2"
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   "cell_type": "code",
   "execution_count": 11,
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   "source": [
    "np.savez('ziparray.npz',x=arr1,y=arr2)"
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   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "archive_array = np.load('ziparray.npz')"
   ]
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   "cell_type": "code",
   "execution_count": 13,
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       "<numpy.lib.npyio.NpzFile at 0x1056bffd0>"
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     "execution_count": 13,
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    "archive_array"
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   "cell_type": "code",
   "execution_count": 14,
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    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   "source": [
    "archive_array['x']"
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   "cell_type": "code",
   "execution_count": 15,
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       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
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     "execution_count": 15,
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    "archive_array['y']"
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 16,
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   "source": [
    "arr = np.array([[1,2,3],[4,5,6]])\n",
    "arr"
   ]
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   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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   "source": [
    "np.savetxt('mytextarray.txt',arr,delimiter=',')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "arr = np.loadtxt('mytextarray.txt',delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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    "arr"
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