num.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in d:\\coading\\python\\venv\\lib\\site-packages (2.1.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "#1D Array\n",
    "arr1=np.array([1,2,3,4,5])\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4, 5]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2=np.array([1,2,3,4,5])\n",
    "arr2.reshape(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4 5]\n",
      " [6 7 8 9 0]]\n"
     ]
    }
   ],
   "source": [
    "arr2=np.array([[1,2,3,4,5],[6,7,8,9,0]])\n",
    "print(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [2],\n",
       "       [4],\n",
       "       [6],\n",
       "       [8]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10,2).reshape(5,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "(2, 3)\n",
      "2\n",
      "6\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "a=np.array([[1,2,3],[4,5,6]])\n",
    "print(a)\n",
    "print(a.shape)\n",
    "print(a.ndim) #Number of Dimension\n",
    "print(a.size)\n",
    "print(a.dtype)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11 22 33 44 55]\n",
      "[ -9 -18 -27 -36 -45]\n",
      "[ 10  40  90 160 250]\n",
      "[0.1 0.1 0.1 0.1 0.1]\n"
     ]
    }
   ],
   "source": [
    "#Numpy Vectorized Operation\n",
    "a1=np.array([1,2,3,4,5])\n",
    "a2=np.array([10,20,30,40,50])\n",
    "print(a1+a2)\n",
    "print(a1-a2)\n",
    "print(a1*a2)\n",
    "print(a1/a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         1.41421356 1.73205081 2.         2.23606798]\n",
      "[  2.71828183   7.3890561   20.08553692  54.59815003 148.4131591 ]\n",
      "[ 0.84147098  0.90929743  0.14112001 -0.7568025  -0.95892427]\n",
      "[0.         0.69314718 1.09861229 1.38629436 1.60943791]\n"
     ]
    }
   ],
   "source": [
    "#Universal Function\n",
    "a=np.array([1,2,3,4,5])\n",
    "print(np.sqrt(a))\n",
    "print(np.exp(a))\n",
    "print(np.sin(a))\n",
    "print(np.log(a))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "arr=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(12)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[2][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.41421356 -0.70710678  0.          0.70710678  1.41421356]\n"
     ]
    }
   ],
   "source": [
    "data=np.array([1,2,3,4,5])\n",
    "mean=np.mean(data)\n",
    "standard_deviation=np.std(data)\n",
    "normalizes_data=(data-mean)/standard_deviation\n",
    "print(normalizes_data)"
   ]
  }
 ],
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