Numpy Revision.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy\n",
    " It provides a high-performance multidimensional array object, and tools for working with these arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Numpy Array </h3>\n",
    "It is a grid of values, all of the same type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=np.array([2,3,4,5]) #create 1-D numpy array\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape  #shape of array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dtype   #type of data stored in array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(A)   #numpy array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Creating Arrays </b> using different numpy functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4, 5],\n",
       "       [9, 2, 4, 7],\n",
       "       [4, 1, 7, 3]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using python lists\n",
    "A1=[2,3,4,5]\n",
    "A2=[9,2,4,7]\n",
    "A3=[4,1,7,3]\n",
    "B=np.array([A1,A2,A3])\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array of zeros: \n",
      " [[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]]\n",
      "Array of ones: \n",
      " [[1.+0.j 1.+0.j 1.+0.j]\n",
      " [1.+0.j 1.+0.j 1.+0.j]\n",
      " [1.+0.j 1.+0.j 1.+0.j]]\n"
     ]
    }
   ],
   "source": [
    "# creating arrays of zeros and ones\n",
    "#array of zeros by defining size and data type\n",
    "A4=np.zeros([3,4] , dtype=int)\n",
    "print(\"Array of zeros: \\n\",A4)\n",
    "\n",
    "# array of ones \n",
    "A5=np.ones([3,3],dtype=complex)\n",
    "print(\"Array of ones: \\n\",A5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20 25 30 35 40 45 50 55]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 5.        ,  5.55555556,  6.11111111,  6.66666667,  7.22222222,\n",
       "        7.77777778,  8.33333333,  8.88888889,  9.44444444, 10.        ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating array by defining the range and difference\n",
    "A6=np.arange(20,60,5)\n",
    "print(A6)\n",
    "\n",
    "# creating array by defining the range and size \n",
    "A7=np.linspace(5,10,10)\n",
    "A7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.47164991, 0.0586086 , 0.42527427],\n",
       "       [0.99410296, 0.8201669 , 0.50252526],\n",
       "       [0.17323542, 0.87778119, 0.54379496]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating array by using random numbers\n",
    "A8=np.random.rand(3,3)   #array having any random numbers from value 0 to 1\n",
    "A8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Reshaping </b> arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4, 5],\n",
       "       [9, 2, 4, 7],\n",
       "       [4, 1, 7, 3]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.shape  #shape of array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4],\n",
       "       [5, 9, 2],\n",
       "       [4, 7, 4],\n",
       "       [1, 7, 3]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reshaping\n",
    "B=B.reshape(4,3)\n",
    "B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Changing </b> datatype of any array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2. 3. 4.]\n",
      " [5. 9. 2.]\n",
      " [4. 7. 4.]\n",
      " [1. 7. 3.]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B=np.array(B,dtype=float)\n",
    "print(B)\n",
    "B.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5. 9.]\n",
      " [4. 7.]\n",
      " [1. 7.]]\n"
     ]
    }
   ],
   "source": [
    "print(B[1:,:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 7., 1.],\n",
       "       [4., 7., 4.],\n",
       "       [2., 9., 5.],\n",
       "       [4., 3., 2.]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.flip(B)  #reverse array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Identity matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 1]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3,dtype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Transpose of matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 5., 4., 1.],\n",
       "       [3., 9., 7., 7.],\n",
       "       [4., 2., 4., 3.]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Basic Formula Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean \n",
    "A=[20,34,12,65,67,39]\n",
    "np.mean(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.710303393882636"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard devaiation\n",
    "np.std(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.5"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# median\n",
    "np.median(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Matrix Sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  8,  7],\n",
       "       [12,  8, 12],\n",
       "       [11,  5, 11]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=np.array([[2,3,4],[3,5,7],[9,4,6]])\n",
    "B=np.array([[12,5,3],[9,3,5],[2,1,5]])\n",
    "A+B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Matrix Multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 59,  23,  41],\n",
       "       [ 95,  37,  69],\n",
       "       [156,  63,  77]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 15, 12],\n",
       "       [27, 15, 35],\n",
       "       [18,  4, 30]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A*(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can easily do other mathetical operations very easily using this"
   ]
  }
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