ML day 1.ipynb

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     "text": [
      "[[10]\n",
      " [50]\n",
      " [30]]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a=np.array([[10],[50],[30]])\n",
    "print(a)\n",
    "print(type(a))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5d66075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 1)\n"
     ]
    }
   ],
   "source": [
    "print(a.shape)\n",
    "# yeh bata raha ki number of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f365926c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "print(a.ndim)# it shows the dimensions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95613ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 6 4 7 8]\n",
      "[[1 2]\n",
      " [6 4]\n",
      " [7 8]]\n"
     ]
    }
   ],
   "source": [
    "AB=np.array([1,2,6,4,7,8])\n",
    "print(AB)\n",
    "DC=AB.reshape([3,2])# we are reshaping it , and remember valid row and column pair\n",
    "print(DC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7103f80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "a=np.arange(24)\n",
    "print(a)\n",
    "print(a.ndim)#we are  giving dimensions to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be3da5ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2]\n",
      "  [ 3  4  5]\n",
      "  [ 6  7  8]\n",
      "  [ 9 10 11]]\n",
      "\n",
      " [[12 13 14]\n",
      "  [15 16 17]\n",
      "  [18 19 20]\n",
      "  [21 22 23]]]\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "b=a.reshape(2,4,3)\n",
    "print(b)\n",
    "print(b.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0afe2aab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0.]\n",
      " [0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "p=np.zeros((2,2))#ones array can also be printed in place of zeros\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea77b9c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3. 4. 5.]\n"
     ]
    }
   ],
   "source": [
    "p=np.array([1,2,3,4,5], dtype=np.float32)# hamne array ko float values mai convert kiya hai\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4c18a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "#creating an indentity matrix\n",
    "a=np.eye(8)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f34fa33f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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