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   "source": [
    "# N-dimensional array in Python\n",
    "\n",
    "An array is a list or collection of homogenous elements, i.e., same type of items. An N-dimensional array is a collection of such arrays, and in simplest terms can be described as an array of arrays.\n",
    "\n",
    "A two dimensional array, also called a matrix (plural: matrices), is very common and most of us would be familiar with it. An array of matrices can be visualized as a 3 dimensional array. An array can be defined using the '.array' method of the numpy module. A range of functions such as dtype, shape, size, etc., are available to find out about various attributes of the array.\n",
    "\n",
    "For more details, please refer to the documentation https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html of the ndarray method.\n",
    "\n",
    "Looking at an example would help us understand in better detail.\n",
    "\n",
    "### Exercise 1\n",
    "\n",
    "* Create a three dimensional array, named tdarray consisting of 3 matrices = ${[[1,2,3],[a,b,c]],[[4,5,6],[d,e,f]],[[7,8,9],[g,h,i]]}$\n",
    "* Access the element 'h' using indices and store it into a variable called 'target'. Print target out.\n",
    "* Print the data type of the array using the '.dtype' method, and the shape of the array using the '.shape' method."
   ]
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   "execution_count": 2,
   "metadata": {
    "tags": [
     "s1",
     "ce",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h \n",
      "Datatype of the array is:  <U1 \n",
      "Shape of the array is:  (3, 2, 3)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "tags": [
     "s1",
     "l1",
     "hint"
    ]
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   "outputs": [],
   "source": []
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   "cell_type": "code",
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    "tags": [
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   "outputs": [],
   "source": [
    "tdarray = np.array([[['1','2','3'],['a','b','c']],[['4','5','6'],['d','e','f']],[['7','8','9'],['g','h','i']]])\n",
    "target = tdarray[2][1][1]\n",
    "print(target,\"\\nDatatype of the array is: \",tdarray.dtype,\"\\nShape of the array is: \",tdarray.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "s1",
     "hid",
     "l1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "ref_tmp_var = False\n",
    "\n",
    "try:\n",
    "    tdarray_ = np.array([[['1','2','3'],['a','b','c']],[['4','5','6'],['d','e','f']],[['7','8','9'],['g','h','i']]])\n",
    "\n",
    "    if target == 'h' and tdarray.dtype == tdarray_.dtype and tdarray.shape == tdarray_.shape:\n",
    "        ref_assert_var = True\n",
    "        ref_tmp_var = True\n",
    "    else:\n",
    "        ref_assert_var = False\n",
    "        print('Please follow the instructions given and use the same variables provided in the instructions.')\n",
    "except Exception:\n",
    "    print('Please follow the instructions given and use the same variables provided in the instructions.')\n",
    "\n",
    "assert ref_tmp_var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "l2",
     "content",
     "s2"
    ]
   },
   "source": [
    "It is not possible to physically represent a n-dimensional array (where n>3). However, it is simple to code them. Let us try.\n",
    "\n",
    "### Exercise 2\n",
    "\n",
    "* Create a 4-dimensional array, named fdarray, with elements 1,2,3,4...so on and with a shape 4,3,2,2.\n",
    "* Print out the shape of the array to verify your answer. Also access the element '10' using appropriate indices, assign it to a variable target2 and print out target2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "l2",
     "ce",
     "s2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the array is:  (4, 3, 2, 2) \n",
      " 10\n"
     ]
    }
   ],
   "source": [
    "# Manual creation - Bad example\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "tags": [
     "l2",
     "s2",
     "hint"
    ]
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "tags": [
     "l2",
     "s2",
     "ans"
    ]
   },
   "outputs": [],
   "source": [
    "fdarray = np.array([[[[1,2],[3,4]],[[5,6],[7,8]],[[9,10],[11,12]]],[[[13,14],[15,16]],[[17,18],[19,20]],[[21,22],[23,24]]],[[[25,26],[27,28]],[[29,30],[31,32]],[[33,34],[35,36]]],[[[37,38],[39,40]],[[41,42],[43,44]],[[45,46],[47,48]]]])\n",
    "target2 = fdarray[0][2][0][1]\n",
    "print(\"Shape of the array is: \",fdarray.shape,\"\\n\",target2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "l2",
     "hid",
     "s2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "ref_tmp_var = False\n",
    "\n",
    "try:\n",
    "    fdarray_ = np.array([[[[1,2],[3,4]],[[5,6],[7,8]],[[9,10],[11,12]]],[[[13,14],[15,16]],[[17,18],[19,20]],[[21,22],[23,24]]],[[[25,26],[27,28]],[[29,30],[31,32]],[[33,34],[35,36]]],[[[37,38],[39,40]],[[41,42],[43,44]],[[45,46],[47,48]]]])\n",
    "\n",
    "    if fdarray.shape == fdarray_.shape and target2 == int('10'):\n",
    "        ref_assert_var = True\n",
    "        ref_tmp_var = True\n",
    "    else:\n",
    "        ref_assert_var = False\n",
    "        print('Please follow the instructions given and use the same variables provided in the instructions.')\n",
    "\n",
    "except Exception:\n",
    "    print('Please follow the instructions given and use the same variables provided in the instructions.')\n",
    "\n",
    "assert ref_tmp_var"
   ]
  }
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