Numpy and Data Visualisation Basics 13_Aug Class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "59b5e32f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92bff4ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [1,2,3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e82ba8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b29abdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = [4,5,6,\"sudh\",34.565, True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "134ada78",
   "metadata": {},
   "outputs": [],
   "source": [
    "a1=np.array(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3423f703",
   "metadata": {},
   "outputs": [],
   "source": [
    "a2 = np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c00b687",
   "metadata": {},
   "outputs": [],
   "source": [
    "a3 = np.array([[[1,2,3],[4,5,6],[7,8,9]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7653a15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4', '5', '6', 'sudh', '34.565', 'True'], dtype='<U32')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4dbef3e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9241b98a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3],\n",
       "        [4, 5, 6],\n",
       "        [7, 8, 9]]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24cc7c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b17cfd2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "10da85d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36448123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "074b3b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c11554e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4e8fd2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a39d48bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f3b54249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 3)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "06f833d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d4e33001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(2,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7e54f407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[26, 35, 11, 37],\n",
       "       [19, 32,  4,  5],\n",
       "       [42, 35, 47, 37]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(2,50,(3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "66f694b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[43, 48, 13, 25],\n",
       "        [44,  2, 47,  9],\n",
       "        [11, 47,  8, 39]],\n",
       "\n",
       "       [[18, 31, 48, 15],\n",
       "        [11,  2, 28, 27],\n",
       "        [24, 20, 23, 19]]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(2,50,(2,3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ab2739d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.78437465, 0.9444482 , 0.09259575],\n",
       "       [0.34359999, 0.03304229, 0.63125583]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b818937d",
   "metadata": {},
   "outputs": [],
   "source": [
    "a4 = np.random.randn(4,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ccc8d0a7",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 16 into shape (3,4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [32]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43ma4\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 16 into shape (3,4)"
     ]
    }
   ],
   "source": [
    "a4.reshape(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "51c7a194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.56434446e+00, -1.66218683e+00,  1.33709386e+00,\n",
       "         1.15714983e+00, -3.49585081e-01,  9.12715001e-01,\n",
       "        -1.04015431e+00,  1.64382754e-01],\n",
       "       [ 1.92691928e+00, -2.91701238e-02,  1.33371072e+00,\n",
       "         2.42833375e-01, -9.75998443e-02, -3.02496026e+00,\n",
       "        -1.83777442e+00,  4.31005287e-05]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4.reshape(2,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2ff3aac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.56434446e+00, -1.66218683e+00],\n",
       "       [ 1.33709386e+00,  1.15714983e+00],\n",
       "       [-3.49585081e-01,  9.12715001e-01],\n",
       "       [-1.04015431e+00,  1.64382754e-01],\n",
       "       [ 1.92691928e+00, -2.91701238e-02],\n",
       "       [ 1.33371072e+00,  2.42833375e-01],\n",
       "       [-9.75998443e-02, -3.02496026e+00],\n",
       "       [-1.83777442e+00,  4.31005287e-05]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4.reshape(8,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ba7e8457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.56434446e+00],\n",
       "       [-1.66218683e+00],\n",
       "       [ 1.33709386e+00],\n",
       "       [ 1.15714983e+00],\n",
       "       [-3.49585081e-01],\n",
       "       [ 9.12715001e-01],\n",
       "       [-1.04015431e+00],\n",
       "       [ 1.64382754e-01],\n",
       "       [ 1.92691928e+00],\n",
       "       [-2.91701238e-02],\n",
       "       [ 1.33371072e+00],\n",
       "       [ 2.42833375e-01],\n",
       "       [-9.75998443e-02],\n",
       "       [-3.02496026e+00],\n",
       "       [-1.83777442e+00],\n",
       "       [ 4.31005287e-05]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4.reshape(16,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3d000a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.56434446e+00, -1.66218683e+00,  1.33709386e+00,\n",
       "         1.15714983e+00, -3.49585081e-01,  9.12715001e-01,\n",
       "        -1.04015431e+00,  1.64382754e-01],\n",
       "       [ 1.92691928e+00, -2.91701238e-02,  1.33371072e+00,\n",
       "         2.42833375e-01, -9.75998443e-02, -3.02496026e+00,\n",
       "        -1.83777442e+00,  4.31005287e-05]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4.reshape(2,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a009df39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.56434446e+00, -1.66218683e+00,  1.33709386e+00,\n",
       "         1.15714983e+00, -3.49585081e-01,  9.12715001e-01,\n",
       "        -1.04015431e+00,  1.64382754e-01],\n",
       "       [ 1.92691928e+00, -2.91701238e-02,  1.33371072e+00,\n",
       "         2.42833375e-01, -9.75998443e-02, -3.02496026e+00,\n",
       "        -1.83777442e+00,  4.31005287e-05]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4.reshape(2,-12232)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5a8badf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.56434446e+00, -1.66218683e+00,  1.33709386e+00,\n",
       "          1.15714983e+00],\n",
       "        [-3.49585081e-01,  9.12715001e-01, -1.04015431e+00,\n",
       "          1.64382754e-01]],\n",
       "\n",
       "       [[ 1.92691928e+00, -2.91701238e-02,  1.33371072e+00,\n",
       "          2.42833375e-01],\n",
       "        [-9.75998443e-02, -3.02496026e+00, -1.83777442e+00,\n",
       "          4.31005287e-05]]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4.reshape(2,2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "68035be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4', '5', '6', 'sudh', '34.565', 'True'], dtype='<U32')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8db8b69c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "958d4816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6', 'sudh', '34.565', 'True'], dtype='<U32')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[2:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "51acfaff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6', '34.565'], dtype='<U32')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[2:6:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b4de0157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['True', '34.565', 'sudh', '6', '5', '4'], dtype='<U32')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "17cdc736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7fc7dfb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[0]"
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  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5c507007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a689f4ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[:,[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5711c654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[[0,1],1:]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5db1a3fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 6])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[[0,1],[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d6a6ca6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a5 = np.random.randint(2,90,(6,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "04dd82f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 34, 55, 31, 79],\n",
       "       [19, 21,  7,  2, 24],\n",
       "       [66, 40, 44, 67,  8],\n",
       "       [88, 63, 23, 47, 52],\n",
       "       [38,  9, 25, 20, 49],\n",
       "       [61, 15, 77, 31, 52]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "739e2f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([55, 79, 66, 44, 67, 88, 63, 47, 52, 49, 61, 77, 52])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a5[a5>40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "939c5005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 34, 55, 31, 79],\n",
       "       [19, 21,  7,  2, 24],\n",
       "       [66, 40, 44, 67,  8],\n",
       "       [88, 63, 23, 47, 52],\n",
       "       [38,  9, 25, 20, 49],\n",
       "       [61, 15, 77, 31, 52]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b98726bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "a5[0,1] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "51790262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 15, 100,  55,  31,  79],\n",
       "       [ 19,  21,   7,   2,  24],\n",
       "       [ 66,  40,  44,  67,   8],\n",
       "       [ 88,  63,  23,  47,  52],\n",
       "       [ 38,   9,  25,  20,  49],\n",
       "       [ 61,  15,  77,  31,  52]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c3c6b921",
   "metadata": {},
   "outputs": [],
   "source": [
    "a6 = np.random.randint(0,3,(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0939f706",
   "metadata": {},
   "outputs": [],
   "source": [
    "a7 = np.random.randint(0,3,(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5a101f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 1],\n",
       "       [2, 1, 2],\n",
       "       [1, 1, 2]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cbd40ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [1, 2, 0],\n",
       "       [0, 0, 2]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "440ab498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [2, 2, 0],\n",
       "       [0, 0, 4]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6*a7 #element basis multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cec53afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 2],\n",
       "       [3, 2, 4],\n",
       "       [2, 2, 4]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6@a7 #correct way of matrix multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e7762a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 1],\n",
       "       [2, 1, 2],\n",
       "       [1, 1, 2]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0d7d265b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[102, 100, 101],\n",
       "       [102, 101, 102],\n",
       "       [101, 101, 102]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6+100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "78765efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 0, 2],\n",
       "       [4, 2, 4],\n",
       "       [2, 2, 4]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ef49053a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_8264\\1058679823.py:1: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  a6/0\n",
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_8264\\1058679823.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  a6/0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[inf, nan, inf],\n",
       "       [inf, inf, inf],\n",
       "       [inf, inf, inf]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6/0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "19ca7c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 1],\n",
       "       [2, 1, 2],\n",
       "       [1, 1, 2]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c1d86d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 0, 1],\n",
       "       [8, 1, 8],\n",
       "       [1, 1, 8]], dtype=int32)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "45db19dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "a8 = np.zeros((4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e8874f33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3388eca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "a9 = np.ones((4,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f173eed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "429dcb08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11., 11., 11., 11., 11.],\n",
       "       [11., 11., 11., 11., 11.],\n",
       "       [11., 11., 11., 11., 11.],\n",
       "       [11., 11., 11., 11., 11.]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a9+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "023f0897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 3., 4., 5., 6.],\n",
       "       [2., 3., 4., 5., 6.],\n",
       "       [2., 3., 4., 5., 6.],\n",
       "       [2., 3., 4., 5., 6.]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a9 + np.array([1,2,3,4,5]) #broadcasting coloumn wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b044723e",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,5) (4,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [81]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43ma9\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,5) (4,) "
     ]
    }
   ],
   "source": [
    "a9 + np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8eb8eae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2,3,4]]).T #transpose operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1148325a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3., 3.],\n",
       "       [4., 4., 4., 4., 4.],\n",
       "       [5., 5., 5., 5., 5.]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a9 + np.array([[1,2,3,4]]).T #rowise broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5ded5fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 2, 1],\n",
       "       [0, 1, 1],\n",
       "       [1, 2, 2]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a6.T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "951af9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 15, 100,  55,  31,  79],\n",
       "       [ 19,  21,   7,   2,  24],\n",
       "       [ 66,  40,  44,  67,   8],\n",
       "       [ 88,  63,  23,  47,  52],\n",
       "       [ 38,   9,  25,  20,  49],\n",
       "       [ 61,  15,  77,  31,  52]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a643c560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.87298335, 10.        ,  7.41619849,  5.56776436,  8.88819442],\n",
       "       [ 4.35889894,  4.58257569,  2.64575131,  1.41421356,  4.89897949],\n",
       "       [ 8.1240384 ,  6.32455532,  6.63324958,  8.18535277,  2.82842712],\n",
       "       [ 9.38083152,  7.93725393,  4.79583152,  6.8556546 ,  7.21110255],\n",
       "       [ 6.164414  ,  3.        ,  5.        ,  4.47213595,  7.        ],\n",
       "       [ 7.81024968,  3.87298335,  8.77496439,  5.56776436,  7.21110255]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(a5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "24316644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.26901737e+06, 2.68811714e+43, 7.69478527e+23, 2.90488497e+13,\n",
       "        2.03828107e+34],\n",
       "       [1.78482301e+08, 1.31881573e+09, 1.09663316e+03, 7.38905610e+00,\n",
       "        2.64891221e+10],\n",
       "       [4.60718663e+28, 2.35385267e+17, 1.28516001e+19, 1.25236317e+29,\n",
       "        2.98095799e+03],\n",
       "       [1.65163625e+38, 2.29378316e+27, 9.74480345e+09, 2.58131289e+20,\n",
       "        3.83100800e+22],\n",
       "       [3.18559318e+16, 8.10308393e+03, 7.20048993e+10, 4.85165195e+08,\n",
       "        1.90734657e+21],\n",
       "       [3.10429794e+26, 3.26901737e+06, 2.75851345e+33, 2.90488497e+13,\n",
       "        3.83100800e+22]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(a5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "09354aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.17609126, 2.        , 1.74036269, 1.49136169, 1.89762709],\n",
       "       [1.2787536 , 1.32221929, 0.84509804, 0.30103   , 1.38021124],\n",
       "       [1.81954394, 1.60205999, 1.64345268, 1.8260748 , 0.90308999],\n",
       "       [1.94448267, 1.79934055, 1.36172784, 1.67209786, 1.71600334],\n",
       "       [1.5797836 , 0.95424251, 1.39794001, 1.30103   , 1.69019608],\n",
       "       [1.78532984, 1.17609126, 1.88649073, 1.49136169, 1.71600334]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log10(a5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "bd0b0d0f",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [95]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2.5\u001b[39;49m\u001b[43m)\u001b[49m)\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "list(range(0,10,2.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "726ef0e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "07544c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 3.5, 6. , 8.5])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10,2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "bc58c71c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.        , 2.02040816, 2.04081633, 2.06122449, 2.08163265,\n",
       "       2.10204082, 2.12244898, 2.14285714, 2.16326531, 2.18367347,\n",
       "       2.20408163, 2.2244898 , 2.24489796, 2.26530612, 2.28571429,\n",
       "       2.30612245, 2.32653061, 2.34693878, 2.36734694, 2.3877551 ,\n",
       "       2.40816327, 2.42857143, 2.44897959, 2.46938776, 2.48979592,\n",
       "       2.51020408, 2.53061224, 2.55102041, 2.57142857, 2.59183673,\n",
       "       2.6122449 , 2.63265306, 2.65306122, 2.67346939, 2.69387755,\n",
       "       2.71428571, 2.73469388, 2.75510204, 2.7755102 , 2.79591837,\n",
       "       2.81632653, 2.83673469, 2.85714286, 2.87755102, 2.89795918,\n",
       "       2.91836735, 2.93877551, 2.95918367, 2.97959184, 3.        ])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(2,3,num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "58c75da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.        , 2.02040816, 2.04081633, 2.06122449, 2.08163265,\n",
       "        2.10204082, 2.12244898, 2.14285714, 2.16326531, 2.18367347,\n",
       "        2.20408163, 2.2244898 , 2.24489796, 2.26530612, 2.28571429,\n",
       "        2.30612245, 2.32653061, 2.34693878, 2.36734694, 2.3877551 ,\n",
       "        2.40816327, 2.42857143, 2.44897959, 2.46938776, 2.48979592,\n",
       "        2.51020408, 2.53061224, 2.55102041, 2.57142857, 2.59183673,\n",
       "        2.6122449 , 2.63265306, 2.65306122, 2.67346939, 2.69387755,\n",
       "        2.71428571, 2.73469388, 2.75510204, 2.7755102 , 2.79591837,\n",
       "        2.81632653, 2.83673469, 2.85714286, 2.87755102, 2.89795918,\n",
       "        2.91836735, 2.93877551, 2.95918367, 2.97959184, 3.        ]),\n",
       " 0.02040816326530612)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(2,3,num=50, retstep = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1f0d31e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  100.        ,   464.15888336,  2154.43469003, 10000.        ])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(2,4,num=4, base = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e50b99f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6effc10e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting cufflinks\n",
      "  Downloading cufflinks-0.17.3.tar.gz (81 kB)\n",
      "Requirement already satisfied: numpy>=1.9.2 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from cufflinks) (1.21.5)\n",
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      "Requirement already satisfied: plotly>=4.1.1 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from cufflinks) (5.6.0)\n",
      "Requirement already satisfied: six>=1.9.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from cufflinks) (1.16.0)\n",
      "Collecting colorlover>=0.2.1\n",
      "  Downloading colorlover-0.3.0-py3-none-any.whl (8.9 kB)\n",
      "Requirement already satisfied: setuptools>=34.4.1 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from cufflinks) (61.2.0)\n",
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      "Building wheels for collected packages: cufflinks\n",
      "  Building wheel for cufflinks (setup.py): started\n",
      "  Building wheel for cufflinks (setup.py): finished with status 'done'\n",
      "  Created wheel for cufflinks: filename=cufflinks-0.17.3-py3-none-any.whl size=68736 sha256=996f184455154c31722bf94a0fbeb5cd2e359daaad0b8633873797fab38a863f\n",
      "  Stored in directory: c:\\users\\hp\\appdata\\local\\pip\\cache\\wheels\\29\\b4\\f8\\2fd2206eeeba6ccad8167e4e8894b8c4ec27bf1342037fd136\n",
      "Successfully built cufflinks\n",
      "Installing collected packages: colorlover, cufflinks\n",
      "Successfully installed colorlover-0.3.0 cufflinks-0.17.3\n",
      "Collecting cufflinks\n",
      "  Using cached cufflinks-0.17.3.tar.gz (81 kB)\n",
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      "  Using cached colorlover-0.3.0-py3-none-any.whl (8.9 kB)\n",
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      "Requirement already satisfied: notebook>=4.4.1 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (6.4.8)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.11.3)\n",
      "Requirement already satisfied: argon2-cffi in c:\\users\\hp\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (21.3.0)\n",
      "Requirement already satisfied: nbconvert in c:\\users\\hp\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (6.4.4)\n",
      "Requirement already satisfied: Send2Trash>=1.8.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.8.0)\n",
      "Requirement already satisfied: terminado>=0.8.3 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.13.1)\n",
      "Requirement already satisfied: prometheus-client in c:\\users\\hp\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.13.1)\n",
      "Requirement already satisfied: pywinpty>=1.1.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from terminado>=0.8.3->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.0.2)\n",
      "Requirement already satisfied: argon2-cffi-bindings in c:\\users\\hp\\anaconda3\\lib\\site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (21.2.0)\n",
      "Requirement already satisfied: cffi>=1.0.1 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.15.0)\n",
      "Requirement already satisfied: pycparser in c:\\users\\hp\\anaconda3\\lib\\site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.21)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.0.1)\n",
      "Requirement already satisfied: bleach in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (4.1.0)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.5.0)\n",
      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.5.13)\n",
      "Requirement already satisfied: jupyterlab-pygments in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.1.2)\n",
      "Requirement already satisfied: testpath in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.5.0)\n",
      "Requirement already satisfied: defusedxml in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.7.1)\n",
      "Requirement already satisfied: beautifulsoup4 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (4.11.1)\n",
      "Requirement already satisfied: mistune<2,>=0.8.1 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.8.4)\n",
      "Requirement already satisfied: entrypoints>=0.2.2 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.4)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from beautifulsoup4->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.3.1)\n",
      "Requirement already satisfied: packaging in c:\\users\\hp\\anaconda3\\lib\\site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (21.3)\n",
      "Requirement already satisfied: webencodings in c:\\users\\hp\\anaconda3\\lib\\site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.5.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (3.0.4)\n",
      "Requirement already satisfied: executing in c:\\users\\hp\\anaconda3\\lib\\site-packages (from stack-data->ipython>=5.3.0->cufflinks) (0.8.3)\n",
      "Requirement already satisfied: pure-eval in c:\\users\\hp\\anaconda3\\lib\\site-packages (from stack-data->ipython>=5.3.0->cufflinks) (0.2.2)\n",
      "Requirement already satisfied: asttokens in c:\\users\\hp\\anaconda3\\lib\\site-packages (from stack-data->ipython>=5.3.0->cufflinks) (2.0.5)\n",
      "Building wheels for collected packages: cufflinks\n",
      "  Building wheel for cufflinks (setup.py): started\n",
      "  Building wheel for cufflinks (setup.py): finished with status 'done'\n",
      "  Created wheel for cufflinks: filename=cufflinks-0.17.3-py3-none-any.whl size=68736 sha256=672cb398c1120781d6bfb9a4ee39f1af251c077e399407f87efbf8c1e9b571bd\n",
      "  Stored in directory: c:\\users\\hp\\appdata\\local\\pip\\cache\\wheels\\29\\b4\\f8\\2fd2206eeeba6ccad8167e4e8894b8c4ec27bf1342037fd136\n",
      "Successfully built cufflinks\n",
      "Installing collected packages: colorlover, cufflinks\n",
      "Successfully installed colorlover-0.3.0 cufflinks-0.17.3\n"
     ]
    }
   ],
   "source": [
    "!pip install cufflinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "502d8da4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf #not an inbuild library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e0ac8928",
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "cf.go_offline()"
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  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "43f4b5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(100,3), columns = ['A','B','C'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "03d3f3d2",
   "metadata": {},
   "outputs": [
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       "      <th>B</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.962309</td>\n",
       "      <td>-0.182920</td>\n",
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       "      <td>1.394512</td>\n",
       "      <td>1.268747</td>\n",
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       "<p>100 rows × 3 columns</p>\n",
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       "           A         B         C\n",
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       "4   1.165987 -0.065448  0.325569\n",
       "..       ...       ...       ...\n",
       "95  1.084932  0.074361 -0.983207\n",
       "96  0.936178  0.665755 -2.821554\n",
       "97  0.399783  0.323632  0.268464\n",
       "98 -0.918428  1.394512  1.268747\n",
       "99 -0.277711 -1.063053  0.796503\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "95cfc5d7",
   "metadata": {},
   "outputs": [
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   "source": [
    "df.iplot(x ='A',y='B', mode = 'markers')"
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  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8917bbad",
   "metadata": {},
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   "source": [
    "df2 = sns.load_dataset('titanic')"
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  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "5368355f",
   "metadata": {},
   "outputs": [
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       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "a5efcd41",
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   "source": [
    "df2.iplot(kind = 'bar', x = 'sex', y = 'survived')"
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   "id": "754cf5fe",
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   "outputs": [],
   "source": []
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