PY0101EN-5.2_notebook_quizz_numpy2d.ipynb

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   "source": [
    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
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   "cell_type": "markdown",
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   "source": [
    "<h3> Get to Know a numpy Array </h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "You will use the numpy array <code> A</code> for the following \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "A=np.array([[11,12],[21,22],[31,32]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) type using the function type \n"
   ]
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    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
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   ],
   "source": [
    "type(A)"
   ]
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   "source": [
    "2) the shape of the array \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "A.shape"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) the type of data in the array \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 4,
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   "source": [
    "A.dtype"
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   "cell_type": "markdown",
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   "source": [
    "4) Find the second row of the numpy array <code>A</code>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 22])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Two kinds of Multiplying  </h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you will use the following numpy arrays for the next questions \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  0],\n",
       "       [ 0, 22]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A*B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)  Perform matrix multiplication on array <code> A</code> and <code> B</code> (order will not matter in this case) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 12],\n",
       "       [21, 22]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A,B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "<h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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