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5.2_notebook_quizz_numpy2d.ipynb

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   "source": [
    "<h3> Get to Know a numpy Array </h3>"
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   "source": [
    "You will use the numpy array <code> A</code> for the following "
   ]
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   "source": [
    "import numpy as np\n",
    "A=np.array([[11,12],[21,22],[31,32]])\n"
   ]
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   "source": [
    "1) type using the function type "
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     "data": {
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       "numpy.ndarray"
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    "type(A)"
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    "2) the shape of the array "
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       "(3, 2)"
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    "A.shape"
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   "source": [
    "3) the type of data in the array "
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     "data": {
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       "dtype('int64')"
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    "A.dtype"
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   "source": [
    "4) Find the second row of the numpy array <code>A</code>:"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 22])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "A[1]"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "you will use the following numpy arrays for the next questions "
   ]
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "A=np.array([[11,12],[21,22]])\n",
    "B=np.array([[1, 0],[0,1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) multiply array <code> A </code> and <code>B</code>"
   ]
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   "metadata": {},
   "source": []
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  0],\n",
       "       [ 0, 22]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A*B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)  plot the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 12],\n",
       "       [21, 22]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A,B)\n",
    "np.dot(B,A)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<small>Copyright &copy; 2018 IBM Cognitive Class. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/).</small>"
   ]
  }
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