2-numpy-tutorial.ipynb

{
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  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy Module\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "<p>NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Credits\n",
    "[tutorialspoint](https://www.tutorialspoint.com/numpy/index.htm)"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy array Objects\n",
    "creating a multi-dimensional array object,\n",
    "\n",
    "![arrays.png](attachment:arrays.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11, 12, 13, 14], [22, 23, 24, 25], [33, 34, 35, 36]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "array1=[[11,12,13,14],\n",
    "        [22,23,24,25],\n",
    "       [33,34,35,36]]\n",
    "\n",
    "array1_new=np.array(array1)\n",
    "\n",
    "print(array1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11 12 13 14]\n",
      " [22 23 24 25]\n",
      " [33 34 35 36]]\n"
     ]
    }
   ],
   "source": [
    "print(array1_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "x=[1,2,3,4]\n",
    "\n",
    "array2=np.array(x)\n",
    "#array2=np.asarray(x)\n",
    "print(array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "myList=[[1, 2, 3],\n",
    "        [4, 5, 6]]\n",
    "\n",
    "array3 = np.array(myList,dtype=np.int)  \n",
    "print(array3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>shape and ndim</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "array1=[[11,12,13,14],\n",
    "        [22,23,24,25],\n",
    "       [33,34,35,36]]\n",
    "\n",
    "array1_new=np.array(array1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "print(array1_new.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "x=[1,2,3,4]\n",
    "array2=np.array(x)\n",
    "print(array2.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 4)\n"
     ]
    }
   ],
   "source": [
    "print(array1_new.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>np.arrange() and np.resize()</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11 12 13 14]\n",
      " [22 23 24 25]\n",
      " [33 34 35 36]]\n"
     ]
    }
   ],
   "source": [
    "print(array1_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 4)\n"
     ]
    }
   ],
   "source": [
    "print(array1_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11 12 13]\n",
      " [14 22 23]\n",
      " [24 25 33]\n",
      " [34 35 36]]\n"
     ]
    }
   ],
   "source": [
    "array2=array1_new.reshape(4,3)\n",
    "print(array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11 12]\n",
      " [13 14]\n",
      " [22 23]\n",
      " [24 25]\n",
      " [33 34]\n",
      " [35 36]]\n"
     ]
    }
   ],
   "source": [
    "array2=array1_new.reshape(6,2)\n",
    "print(array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11]\n",
      " [12]\n",
      " [13]\n",
      " [14]\n",
      " [22]\n",
      " [23]\n",
      " [24]\n",
      " [25]\n",
      " [33]\n",
      " [34]\n",
      " [35]\n",
      " [36]]\n"
     ]
    }
   ],
   "source": [
    "array2=array1_new.reshape(12,1)\n",
    "print(array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11 12 13 14 22 23 24 25 33 34 35 36]]\n"
     ]
    }
   ],
   "source": [
    "array2=array1_new.reshape(1,12)\n",
    "print(array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-10  -8  -6  -4  -2   0   2   4   6   8]\n"
     ]
    }
   ],
   "source": [
    "array=np.arange(-10,10,2)\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Array Creation Routines</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1065353216 1073741824]\n",
      " [1077936128 1082130432]\n",
      " [1084227584 1086324736]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "x = np.empty((3,2), dtype=np.int) \n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It creates an uninitialized array of specified shape and dtype. The elements in an array show random values as they are not initialized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# array of five zeros. Default dtype is float \n",
    "x = np.zeros(5) \n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros((5,3),dtype=np.float) \n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0]\n",
      " [0 0 0]\n",
      " [0 0 0]\n",
      " [0 0 0]\n",
      " [0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros((5,3),dtype=np.int) \n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1]\n",
      " [1 1 1]\n",
      " [1 1 1]\n",
      " [1 1 1]\n",
      " [1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "x = np.ones((5,3),dtype=np.int) \n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This routine are useful for converting Python sequence into ndarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-10  -8  -6  -4  -2   0   2   4   6   8]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(-10,10,2) \n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns an ndarray object containing evenly spaced values within a given range. The format of the function is as follows −\n",
    "\n",
    "```numpy.arange(start, stop, step, dtype)```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Indexing & Slicing</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11 60 99 41 45]\n",
      " [49 67 95 71 32]\n",
      " [23  4 66 14 43]\n",
      " [69 91 45 38 70]\n",
      " [81 84 68 47 45]\n",
      " [51 12 52 39 14]\n",
      " [22 84 55 82 97]\n",
      " [31 11 60 82 12]\n",
      " [ 6 63 36 33 18]\n",
      " [36 43 42 15 37]]\n"
     ]
    }
   ],
   "source": [
    "array=np.random.randint(0,100,(10,5))\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array1: [[52 19 16]\n",
      " [92 76 88]\n",
      " [53 49 49]\n",
      " [ 6 60 61]]\n",
      "array2: [[79 87 18]\n",
      " [78 37 22]\n",
      " [92 37  5]\n",
      " [38 21 37]]\n",
      "array3: [[131 106  34]\n",
      " [170 113 110]\n",
      " [145  86  54]\n",
      " [ 44  81  98]]\n"
     ]
    }
   ],
   "source": [
    "array1=np.random.randint(0,100,(4,3))\n",
    "array2=np.random.randint(0,100,(4,3))\n",
    "\n",
    "array3=array1+array2\n",
    "print('array1:',array1)\n",
    "print('array2:',array2)\n",
    "print('array3:',array3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array1: [[73 88 57]\n",
      " [10 93 12]\n",
      " [34  0 36]\n",
      " [58  1  9]]\n",
      "array2: [[ 63  78  47]\n",
      " [  0  83   2]\n",
      " [ 24 -10  26]\n",
      " [ 48  -9  -1]]\n"
     ]
    }
   ],
   "source": [
    "array1=np.random.randint(0,100,(4,3))\n",
    "\n",
    "array2=array1/10\n",
    "\n",
    "print('array1:',array1)\n",
    "print('array2:',array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array1: [[24 67  5]\n",
      " [17 56 99]\n",
      " [94 62 26]\n",
      " [21 84 66]]\n",
      "array2: [[89 93 22 61 68]\n",
      " [26 95 10 24  2]\n",
      " [ 0 75 23 96 60]]\n",
      "array4: [[ 3878  8972  1313  3552  2066]\n",
      " [ 2969 14326  3211 11885  7208]\n",
      " [ 9978 16582  3286  9718  8076]\n",
      " [ 4053 14883  2820  9633  5556]]\n"
     ]
    }
   ],
   "source": [
    "array1=np.random.randint(0,100,(4,3))\n",
    "array2=np.random.randint(0,100,(3,5))\n",
    "\n",
    "print('array1:',array1)\n",
    "print('array2:',array2)\n",
    "\n",
    "array4=np.matmul(array1,array2)\n",
    "\n",
    "print('array4:',array4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[88  4  5 74 29  8 26]\n",
      " [78 85 11 53  4 99 32]\n",
      " [32 50 83 95  9 49 73]\n",
      " [20  1  6 26 12  9 97]\n",
      " [37 38 14  7 55 81 46]]\n"
     ]
    }
   ],
   "source": [
    "array=np.random.randint(0,100,(5,7))\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99\n"
     ]
    }
   ],
   "source": [
    "print(np.max(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[88 85 83 95 55 99 97]\n"
     ]
    }
   ],
   "source": [
    "print(np.max(array,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 2 4 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(array,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[88 99 95 97 81]\n"
     ]
    }
   ],
   "source": [
    "print(np.max(array,axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 5 3 6 5]\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(array,axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98\n"
     ]
    }
   ],
   "source": [
    "print(np.max(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.22\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2711\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69\n"
     ]
    }
   ],
   "source": [
    "print(array[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[69  4]\n",
      " [92 27]\n",
      " [70 83]]\n"
     ]
    }
   ],
   "source": [
    "print(array[0:3,2:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.16\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(np.min(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96\n"
     ]
    }
   ],
   "source": [
    "print(np.max(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27  7 33  1  6 20 76 27  0 27]\n"
     ]
    }
   ],
   "source": [
    "print(np.min(array,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[62.  43.4 69.  34.2 63.8 62.2 83.6 52.6 33.6 57.2]\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(array,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.16\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[94 95 84 11 82 80  3 47 46 74]\n",
      " [91 64 20 76 91 32 93 78 13 72]\n",
      " [68 98 39 60 29 63 70 51 55 55]\n",
      " [ 3 58 42 17 93 20 49 37 30 78]\n",
      " [66 35 31 92 48 30  1 59 22 72]]\n"
     ]
    }
   ],
   "source": [
    "array=np.random.randint(0,100,(5,10))\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[94 95 84 11]\n",
      " [91 64 20 76]]\n"
     ]
    }
   ],
   "source": [
    "print(array[0:2,0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[94 95 84 11]\n",
      " [91 64 20 76]\n",
      " [68 98 39 60]\n",
      " [ 3 58 42 17]\n",
      " [66 35 31 92]]\n"
     ]
    }
   ],
   "source": [
    "print(array[:,0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[82 91 29 93 48]\n"
     ]
    }
   ],
   "source": [
    "print(array[:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[94 95 84 11 82 80  3 47 46 74]\n",
      " [91 64 20 76 91 32 93 78 13 72]]\n"
     ]
    }
   ],
   "source": [
    "print(array[:2,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.3\n"
     ]
    }
   ],
   "source": [
    "avg=np.mean(array[:2,:])\n",
    "print(avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1 - Numpy and Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dataset=pd.read_csv(r'C:\\Users\\Migara Liyanage\\Desktop\\Advaced Python Workshop\\DAY 02\\codes\\student-marks.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Name  Maths  Physics  Chemistry\n",
      "0       John     45       22         98\n",
      "1    William     44       23          2\n",
      "2      Henry     22       89         35\n",
      "3      Chris     88       55         23\n",
      "4       Jack     45       89         45\n",
      "5   Stephane     64       45         48\n",
      "6     Harald     12       12         12\n",
      "7     Hearmy     78       94         65\n",
      "8     Sinsel     98       95         23\n",
      "9     George     45       65         89\n",
      "10     James     64       32         26\n",
      "11      Jade     23       78         47\n",
      "12     Tommy     45       98         14\n",
      "13     Yonus     78       45         44\n",
      "14     Randy     95       12         84\n",
      "15       Rex     31       95         98\n",
      "16    Tucsan     77       32         56\n",
      "17     Amell     87       78         34\n",
      "18       Vin     65       45         78\n",
      "19    Hector     32       12         78\n"
     ]
    }
   ],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['John' 45 22 98]\n",
      " ['William' 44 23 2]\n",
      " ['Henry' 22 89 35]\n",
      " ['Chris' 88 55 23]\n",
      " ['Jack' 45 89 45]\n",
      " ['Stephane' 64 45 48]\n",
      " ['Harald' 12 12 12]\n",
      " ['Hearmy' 78 94 65]\n",
      " ['Sinsel' 98 95 23]\n",
      " ['George' 45 65 89]\n",
      " ['James' 64 32 26]\n",
      " ['Jade' 23 78 47]\n",
      " ['Tommy' 45 98 14]\n",
      " ['Yonus' 78 45 44]\n",
      " ['Randy' 95 12 84]\n",
      " ['Rex' 31 95 98]\n",
      " ['Tucsan' 77 32 56]\n",
      " ['Amell' 87 78 34]\n",
      " ['Vin' 65 45 78]\n",
      " ['Hector' 32 12 78]]\n"
     ]
    }
   ],
   "source": [
    "dataset=pd.read_csv(r'C:\\Users\\Migara Liyanage\\Desktop\\Advaced Python Workshop\\DAY 02\\codes\\student-marks.csv').values\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[55.0 23.0 48.666666666666664 55.333333333333336 59.666666666666664\n",
      " 52.333333333333336 12.0 79.0 72.0 66.33333333333333 40.666666666666664\n",
      " 49.333333333333336 52.333333333333336 55.666666666666664\n",
      " 63.666666666666664 74.66666666666667 55.0 66.33333333333333\n",
      " 62.666666666666664 40.666666666666664]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "avg=np.mean(dataset[:,1:],axis=1)\n",
    "print(avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findGrade(mark):\n",
    "    if(mark>=0 and mark<35):\n",
    "        grade='F'\n",
    "    elif(mark>=35 and mark<45):\n",
    "        grade='S'\n",
    "    elif(mark>=45 and mark<65):\n",
    "        grade='C'\n",
    "    elif(mark>=65 and mark<75):\n",
    "        grade='B'\n",
    "    elif(mark>=75 and mark<=100):\n",
    "        grade='A'\n",
    "    return grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C' 'S' 'F' 'A' 'C' 'C' 'F' 'A' 'A' 'C' 'C' 'F' 'C' 'A' 'A' 'F' 'A' 'A'\n",
      " 'B' 'F']\n",
      "[['C']\n",
      " ['S']\n",
      " ['F']\n",
      " ['A']\n",
      " ['C']\n",
      " ['C']\n",
      " ['F']\n",
      " ['A']\n",
      " ['A']\n",
      " ['C']\n",
      " ['C']\n",
      " ['F']\n",
      " ['C']\n",
      " ['A']\n",
      " ['A']\n",
      " ['F']\n",
      " ['A']\n",
      " ['A']\n",
      " ['B']\n",
      " ['F']]\n"
     ]
    }
   ],
   "source": [
    "mathsGrades=np.array([findGrade(i) for i in dataset[:,1]])\n",
    "\n",
    "print(mathsGrades)\n",
    "mathsGrades=mathsGrades.reshape(-1,1)\n",
    "print(mathsGrades)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['F']\n",
      " ['F']\n",
      " ['A']\n",
      " ['C']\n",
      " ['A']\n",
      " ['C']\n",
      " ['F']\n",
      " ['A']\n",
      " ['A']\n",
      " ['B']\n",
      " ['F']\n",
      " ['A']\n",
      " ['A']\n",
      " ['C']\n",
      " ['F']\n",
      " ['A']\n",
      " ['F']\n",
      " ['A']\n",
      " ['C']\n",
      " ['F']]\n"
     ]
    }
   ],
   "source": [
    "phyGrades=np.array([findGrade(i) for i in dataset[:,2]])\n",
    "phyGrades=phyGrades.reshape(-1,1)\n",
    "print(phyGrades)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['A']\n",
      " ['F']\n",
      " ['S']\n",
      " ['F']\n",
      " ['C']\n",
      " ['C']\n",
      " ['F']\n",
      " ['B']\n",
      " ['F']\n",
      " ['A']\n",
      " ['F']\n",
      " ['C']\n",
      " ['F']\n",
      " ['S']\n",
      " ['A']\n",
      " ['A']\n",
      " ['C']\n",
      " ['F']\n",
      " ['A']\n",
      " ['A']]\n"
     ]
    }
   ],
   "source": [
    "chemGrades=np.array([findGrade(i) for i in dataset[:,3]])\n",
    "chemGrades=chemGrades.reshape(-1,1)\n",
    "print(chemGrades)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[55.0]\n",
      " [23.0]\n",
      " [48.666666666666664]\n",
      " [55.333333333333336]\n",
      " [59.666666666666664]\n",
      " [52.333333333333336]\n",
      " [12.0]\n",
      " [79.0]\n",
      " [72.0]\n",
      " [66.33333333333333]\n",
      " [40.666666666666664]\n",
      " [49.333333333333336]\n",
      " [52.333333333333336]\n",
      " [55.666666666666664]\n",
      " [63.666666666666664]\n",
      " [74.66666666666667]\n",
      " [55.0]\n",
      " [66.33333333333333]\n",
      " [62.666666666666664]\n",
      " [40.666666666666664]]\n"
     ]
    }
   ],
   "source": [
    "avg=avg.reshape(-1,1).reshape(-1,1)\n",
    "print(avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['John' 45 22 98]\n",
      " ['William' 44 23 2]\n",
      " ['Henry' 22 89 35]\n",
      " ['Chris' 88 55 23]\n",
      " ['Jack' 45 89 45]\n",
      " ['Stephane' 64 45 48]\n",
      " ['Harald' 12 12 12]\n",
      " ['Hearmy' 78 94 65]\n",
      " ['Sinsel' 98 95 23]\n",
      " ['George' 45 65 89]\n",
      " ['James' 64 32 26]\n",
      " ['Jade' 23 78 47]\n",
      " ['Tommy' 45 98 14]\n",
      " ['Yonus' 78 45 44]\n",
      " ['Randy' 95 12 84]\n",
      " ['Rex' 31 95 98]\n",
      " ['Tucsan' 77 32 56]\n",
      " ['Amell' 87 78 34]\n",
      " ['Vin' 65 45 78]\n",
      " ['Hector' 32 12 78]]\n"
     ]
    }
   ],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['John' 45 22 98 'C']\n",
      " ['William' 44 23 2 'S']\n",
      " ['Henry' 22 89 35 'F']\n",
      " ['Chris' 88 55 23 'A']\n",
      " ['Jack' 45 89 45 'C']\n",
      " ['Stephane' 64 45 48 'C']\n",
      " ['Harald' 12 12 12 'F']\n",
      " ['Hearmy' 78 94 65 'A']\n",
      " ['Sinsel' 98 95 23 'A']\n",
      " ['George' 45 65 89 'C']\n",
      " ['James' 64 32 26 'C']\n",
      " ['Jade' 23 78 47 'F']\n",
      " ['Tommy' 45 98 14 'C']\n",
      " ['Yonus' 78 45 44 'A']\n",
      " ['Randy' 95 12 84 'A']\n",
      " ['Rex' 31 95 98 'F']\n",
      " ['Tucsan' 77 32 56 'A']\n",
      " ['Amell' 87 78 34 'A']\n",
      " ['Vin' 65 45 78 'B']\n",
      " ['Hector' 32 12 78 'F']]\n"
     ]
    }
   ],
   "source": [
    "dataset=np.append(dataset,mathsGrades,axis=1)\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['John' 45 22 98 'C' 'F']\n",
      " ['William' 44 23 2 'S' 'F']\n",
      " ['Henry' 22 89 35 'F' 'A']\n",
      " ['Chris' 88 55 23 'A' 'C']\n",
      " ['Jack' 45 89 45 'C' 'A']\n",
      " ['Stephane' 64 45 48 'C' 'C']\n",
      " ['Harald' 12 12 12 'F' 'F']\n",
      " ['Hearmy' 78 94 65 'A' 'A']\n",
      " ['Sinsel' 98 95 23 'A' 'A']\n",
      " ['George' 45 65 89 'C' 'B']\n",
      " ['James' 64 32 26 'C' 'F']\n",
      " ['Jade' 23 78 47 'F' 'A']\n",
      " ['Tommy' 45 98 14 'C' 'A']\n",
      " ['Yonus' 78 45 44 'A' 'C']\n",
      " ['Randy' 95 12 84 'A' 'F']\n",
      " ['Rex' 31 95 98 'F' 'A']\n",
      " ['Tucsan' 77 32 56 'A' 'F']\n",
      " ['Amell' 87 78 34 'A' 'A']\n",
      " ['Vin' 65 45 78 'B' 'C']\n",
      " ['Hector' 32 12 78 'F' 'F']]\n"
     ]
    }
   ],
   "source": [
    "dataset=np.append(dataset,phyGrades,axis=1)\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['John' 45 22 98 'C' 'F' 'A']\n",
      " ['William' 44 23 2 'S' 'F' 'F']\n",
      " ['Henry' 22 89 35 'F' 'A' 'S']\n",
      " ['Chris' 88 55 23 'A' 'C' 'F']\n",
      " ['Jack' 45 89 45 'C' 'A' 'C']\n",
      " ['Stephane' 64 45 48 'C' 'C' 'C']\n",
      " ['Harald' 12 12 12 'F' 'F' 'F']\n",
      " ['Hearmy' 78 94 65 'A' 'A' 'B']\n",
      " ['Sinsel' 98 95 23 'A' 'A' 'F']\n",
      " ['George' 45 65 89 'C' 'B' 'A']\n",
      " ['James' 64 32 26 'C' 'F' 'F']\n",
      " ['Jade' 23 78 47 'F' 'A' 'C']\n",
      " ['Tommy' 45 98 14 'C' 'A' 'F']\n",
      " ['Yonus' 78 45 44 'A' 'C' 'S']\n",
      " ['Randy' 95 12 84 'A' 'F' 'A']\n",
      " ['Rex' 31 95 98 'F' 'A' 'A']\n",
      " ['Tucsan' 77 32 56 'A' 'F' 'C']\n",
      " ['Amell' 87 78 34 'A' 'A' 'F']\n",
      " ['Vin' 65 45 78 'B' 'C' 'A']\n",
      " ['Hector' 32 12 78 'F' 'F' 'A']]\n"
     ]
    }
   ],
   "source": [
    "dataset=np.append(dataset,chemGrades,axis=1)\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['John' 45 22 98 'C' 'F' 'A' 55.0]\n",
      " ['William' 44 23 2 'S' 'F' 'F' 23.0]\n",
      " ['Henry' 22 89 35 'F' 'A' 'S' 48.666666666666664]\n",
      " ['Chris' 88 55 23 'A' 'C' 'F' 55.333333333333336]\n",
      " ['Jack' 45 89 45 'C' 'A' 'C' 59.666666666666664]\n",
      " ['Stephane' 64 45 48 'C' 'C' 'C' 52.333333333333336]\n",
      " ['Harald' 12 12 12 'F' 'F' 'F' 12.0]\n",
      " ['Hearmy' 78 94 65 'A' 'A' 'B' 79.0]\n",
      " ['Sinsel' 98 95 23 'A' 'A' 'F' 72.0]\n",
      " ['George' 45 65 89 'C' 'B' 'A' 66.33333333333333]\n",
      " ['James' 64 32 26 'C' 'F' 'F' 40.666666666666664]\n",
      " ['Jade' 23 78 47 'F' 'A' 'C' 49.333333333333336]\n",
      " ['Tommy' 45 98 14 'C' 'A' 'F' 52.333333333333336]\n",
      " ['Yonus' 78 45 44 'A' 'C' 'S' 55.666666666666664]\n",
      " ['Randy' 95 12 84 'A' 'F' 'A' 63.666666666666664]\n",
      " ['Rex' 31 95 98 'F' 'A' 'A' 74.66666666666667]\n",
      " ['Tucsan' 77 32 56 'A' 'F' 'C' 55.0]\n",
      " ['Amell' 87 78 34 'A' 'A' 'F' 66.33333333333333]\n",
      " ['Vin' 65 45 78 'B' 'C' 'A' 62.666666666666664]\n",
      " ['Hector' 32 12 78 'F' 'F' 'A' 40.666666666666664]]\n"
     ]
    }
   ],
   "source": [
    "dataset=np.append(dataset,avg,axis=1)\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('new-dataset',dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Mismatch between array dtype ('object') and format specifier ('%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36msavetxt\u001b[1;34m(fname, X, fmt, delimiter, newline, header, footer, comments, encoding)\u001b[0m\n\u001b[0;32m   1446\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1447\u001b[1;33m                     \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mformat\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnewline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1448\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: must be real number, not str",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-dc8a4a60beab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavetxt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"dataset-new.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\",\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36msavetxt\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36msavetxt\u001b[1;34m(fname, X, fmt, delimiter, newline, header, footer, comments, encoding)\u001b[0m\n\u001b[0;32m   1449\u001b[0m                     raise TypeError(\"Mismatch between array dtype ('%s') and \"\n\u001b[0;32m   1450\u001b[0m                                     \u001b[1;34m\"format specifier ('%s')\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1451\u001b[1;33m                                     % (str(X.dtype), format))\n\u001b[0m\u001b[0;32m   1452\u001b[0m                 \u001b[0mfh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1453\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Mismatch between array dtype ('object') and format specifier ('%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e')"
     ]
    }
   ],
   "source": [
    "np.savetxt(\"dataset-new.csv\", dataset, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0   1   2   3  4  5  6        7\n",
      "0       John  45  22  98  C  F  A       55\n",
      "1    William  44  23   2  S  F  F       23\n",
      "2      Henry  22  89  35  F  A  S  48.6667\n",
      "3      Chris  88  55  23  A  C  F  55.3333\n",
      "4       Jack  45  89  45  C  A  C  59.6667\n",
      "5   Stephane  64  45  48  C  C  C  52.3333\n",
      "6     Harald  12  12  12  F  F  F       12\n",
      "7     Hearmy  78  94  65  A  A  B       79\n",
      "8     Sinsel  98  95  23  A  A  F       72\n",
      "9     George  45  65  89  C  B  A  66.3333\n",
      "10     James  64  32  26  C  F  F  40.6667\n",
      "11      Jade  23  78  47  F  A  C  49.3333\n",
      "12     Tommy  45  98  14  C  A  F  52.3333\n",
      "13     Yonus  78  45  44  A  C  S  55.6667\n",
      "14     Randy  95  12  84  A  F  A  63.6667\n",
      "15       Rex  31  95  98  F  A  A  74.6667\n",
      "16    Tucsan  77  32  56  A  F  C       55\n",
      "17     Amell  87  78  34  A  A  F  66.3333\n",
      "18       Vin  65  45  78  B  C  A  62.6667\n",
      "19    Hector  32  12  78  F  F  A  40.6667\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame(data=dataset)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        name maths phy chem m-grade p-grade c-grade      avg\n",
      "0       John    45  22   98       C       F       A       55\n",
      "1    William    44  23    2       S       F       F       23\n",
      "2      Henry    22  89   35       F       A       S  48.6667\n",
      "3      Chris    88  55   23       A       C       F  55.3333\n",
      "4       Jack    45  89   45       C       A       C  59.6667\n",
      "5   Stephane    64  45   48       C       C       C  52.3333\n",
      "6     Harald    12  12   12       F       F       F       12\n",
      "7     Hearmy    78  94   65       A       A       B       79\n",
      "8     Sinsel    98  95   23       A       A       F       72\n",
      "9     George    45  65   89       C       B       A  66.3333\n",
      "10     James    64  32   26       C       F       F  40.6667\n",
      "11      Jade    23  78   47       F       A       C  49.3333\n",
      "12     Tommy    45  98   14       C       A       F  52.3333\n",
      "13     Yonus    78  45   44       A       C       S  55.6667\n",
      "14     Randy    95  12   84       A       F       A  63.6667\n",
      "15       Rex    31  95   98       F       A       A  74.6667\n",
      "16    Tucsan    77  32   56       A       F       C       55\n",
      "17     Amell    87  78   34       A       A       F  66.3333\n",
      "18       Vin    65  45   78       B       C       A  62.6667\n",
      "19    Hector    32  12   78       F       F       A  40.6667\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame(data=dataset,columns=[\"name\",\"maths\",\"phy\",\"chem\",\"m-grade\",\"p-grade\",\"c-grade\",\"avg\"])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('new-dataset.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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