Python_day4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a910942",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99a80395",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3,4])\n",
    "b = [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4842be9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n",
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0955427b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9ce1ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one directional array\n",
    "np.ones(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cef08097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d6d610d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22785012, 0.26746592],\n",
       "       [0.55187524, 0.10586904]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5ed7200c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 0, 2],\n",
       "       [0, 3, 1, 1],\n",
       "       [3, 3, 2, 4]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3-colums and 4 shape\n",
    "np.random.randint(0, 5, (3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d99825a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2524cd33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,\n",
       "        1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,\n",
       "        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,\n",
       "        3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,\n",
       "        4.4,  4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,\n",
       "        5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,  6.5,\n",
       "        6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,\n",
       "        7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,  8.7,\n",
       "        8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,  9.8,\n",
       "        9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
       "       11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. ,\n",
       "       12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1,\n",
       "       13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2,\n",
       "       14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3,\n",
       "       15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4,\n",
       "       16.5, 16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5,\n",
       "       17.6, 17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6,\n",
       "       18.7, 18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7,\n",
       "       19.8, 19.9])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,20,200, endpoint=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "45dcbb35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,\n",
       "        1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,\n",
       "        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,\n",
       "        3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,\n",
       "        4.4,  4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,\n",
       "        5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,  6.5,\n",
       "        6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,\n",
       "        7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,  8.7,\n",
       "        8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,  9.8,\n",
       "        9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
       "       11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. ,\n",
       "       12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1,\n",
       "       13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2,\n",
       "       14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3,\n",
       "       15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4,\n",
       "       16.5, 16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5,\n",
       "       17.6, 17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6,\n",
       "       18.7, 18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7,\n",
       "       19.8, 19.9])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,20,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b2d14607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2,3],[5,6,7]])\n",
    "a\n",
    "b = a.shape\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2a5a3b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "[0 1 5 3]\n",
      "[9 0 8]\n",
      "[0 1 5 3]\n",
      "[1 5]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[9, 8, 4, 0],\n",
       "       [0, 1, 5, 3],\n",
       "       [8, 8, 3, 1]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Array Attributes\n",
    "b = np.random.randint(0,10,[3,4])\n",
    "print(b[1,2])\n",
    "print(b[1])\n",
    "print(b[:,0])\n",
    "print(b[1,:])\n",
    "print(b[1, 1:3])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "defb9967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 7, 1, 5, 6, 1, 6, 2, 7, 8],\n",
       "       [1, 1, 0, 8, 7, 9, 8, 9, 1, 0],\n",
       "       [1, 1, 4, 7, 6, 8, 1, 7, 4, 9],\n",
       "       [8, 8, 6, 2, 5, 8, 6, 2, 5, 9],\n",
       "       [5, 3, 0, 8, 0, 7, 2, 4, 9, 9],\n",
       "       [6, 7, 3, 8, 2, 9, 5, 0, 3, 4],\n",
       "       [1, 0, 8, 9, 8, 3, 4, 4, 7, 1],\n",
       "       [4, 8, 2, 6, 3, 4, 5, 9, 2, 7],\n",
       "       [1, 6, 9, 2, 1, 0, 7, 1, 7, 9],\n",
       "       [8, 0, 3, 8, 9, 4, 0, 1, 4, 5]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.randint(0,10, [10,10])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d835cb1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 2, 5, 8, 6],\n",
       "       [0, 8, 0, 7, 2],\n",
       "       [3, 8, 2, 9, 5]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3:6, 2:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c4727005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 2, 5, 8, 6, 2, 5, 9],\n",
       "       [0, 8, 0, 7, 2, 4, 9, 9],\n",
       "       [3, 8, 2, 9, 5, 0, 3, 4]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3:6, 2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1f0e95d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.randint([2,3, 3])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5d593ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[9, 0, 0],\n",
       "        [9, 0, 0],\n",
       "        [5, 3, 3]],\n",
       "\n",
       "       [[2, 1, 9],\n",
       "        [8, 5, 6],\n",
       "        [0, 2, 3]]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.randint(0,10, [2,3, 3])\n",
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e5492278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 5, 6])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1, 1, :]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c0fa42e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 9,  0,  0],\n",
       "        [ 9,  0,  0],\n",
       "        [ 5,  3,  3]],\n",
       "\n",
       "       [[99, 99, 99],\n",
       "        [99, 99, 99],\n",
       "        [99, 99, 99]]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1]=99\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d029cf07",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (2,) into shape (0,3,3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [69]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a[ \u001b[38;5;241m2\u001b[39m: \u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m200\u001b[39m]\n\u001b[0;32m      2\u001b[0m a\n",
      "\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (2,) into shape (0,3,3)"
     ]
    }
   ],
   "source": [
    "a[2: 1] = [100, 200]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3ee65e4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3  5  6  5 16]\n",
      "[ 2  6  9  0 63]\n",
      "[20 30 30  0 70]\n",
      "[ 4  9  9  0 49]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([2,3,3,0,7])\n",
    "b = np.array([1,2,3,5,9])\n",
    "print(a+b)\n",
    "print(a*b)\n",
    "print(a*10)\n",
    "print(a**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "435f1460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.41421356, 1.73205081, 1.73205081, 0.        , 2.64575131])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8d1621bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8dbc5d1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Namratha Kaipa\\AppData\\Local\\Temp\\ipykernel_24760\\176755284.py:1: RuntimeWarning: divide by zero encountered in log\n",
      "  np.log(a)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.69314718, 1.09861229, 1.09861229,       -inf, 1.94591015])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ce4111b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.280350850198276"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "43b193ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True, False])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a<2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a29675b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a<10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0a53b88a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a<2).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "dc46c5be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 7 9 8]\n",
      " [0 6 4 5]\n",
      " [7 1 2 6]]\n",
      "[[ True False False False]\n",
      " [ True False False False]\n",
      " [False  True  True False]]\n"
     ]
    }
   ],
   "source": [
    "a= np.random.randint(0,10,(3,4))\n",
    "print(a)\n",
    "print(a<3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c895d3b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 1, 1, 1, 2, 2], dtype=int64),\n",
       " array([1, 2, 3, 1, 2, 3, 0, 3], dtype=int64))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(a>2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "17af4ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.where(a>2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ca6552af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.where(a>2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b2fc9e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Stacking splitting\n",
    "a = np.array([1,2,3,4,5,6])\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d1cf6fe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b=[4,5,6]\n",
    "print(a+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9f479edd",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Field elements must be 2- or 3-tuples, got '2'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [116]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a\u001b[38;5;241m=\u001b[39m \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[43m,\u001b[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[0;32m      2\u001b[0m b\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m6\u001b[39m],[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m])\n\u001b[0;32m      3\u001b[0m c\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m0\u001b[39m],[\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m3\u001b[39m])\n",
      "\u001b[1;31mTypeError\u001b[0m: Field elements must be 2- or 3-tuples, got '2'"
     ]
    }
   ],
   "source": [
    "a= np.array([1,2,3],[2,3,4])\n",
    "b= np.array([4,5,6],[2,3,4])\n",
    "c= np.array([8,9,0],[7,8,3])\n",
    "#np.concatenate((a,b,c), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b8f45b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## must work on this code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "22401ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 9, 3, 5, 3, 5, 8, 5, 8, 6, 4, 2, 8, 6],\n",
       "       [6, 6, 1, 0, 1, 3, 2, 5, 8, 2, 4, 4, 2, 6]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a= np.random.randint(0,10,[2,3])\n",
    "b= np.random.randint(0,10,[2,4])\n",
    "c= np.random.randint(0,10,[2,7])\n",
    "np.hstack((a,b,c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "eb3d0aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[5, 9, 3]]), array([[6, 6, 1]])]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(a,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "54c57d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "bcc2f1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.random.randint(0,10,[2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "dbf6fa33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[2, 6, 4, 1],\n",
       "        [2, 9, 5, 4],\n",
       "        [1, 9, 0, 1]],\n",
       "\n",
       "       [[0, 0, 6, 7],\n",
       "        [3, 8, 5, 5],\n",
       "        [4, 8, 1, 5]]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f124168d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1d3daf3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a866c4bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4ec94410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5da95847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[2, 6, 4, 1],\n",
       "        [2, 9, 5, 4],\n",
       "        [1, 9, 0, 1]],\n",
       "\n",
       "       [[0, 0, 6, 7],\n",
       "        [3, 8, 5, 5],\n",
       "        [4, 8, 1, 5]]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.absolute(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "006ca03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 3. , 5. , 4. ],\n",
       "       [2.5, 8.5, 5. , 4.5],\n",
       "       [2.5, 8.5, 0.5, 3. ]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f7cd18da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.66666667, 8.        , 3.        , 2.        ],\n",
       "       [2.33333333, 5.33333333, 4.        , 5.66666667]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ec4ccd1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a, axis=0).ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "2b152261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.25, 5.  , 2.75],\n",
       "       [3.25, 5.25, 4.5 ]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "6b600408",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7 7 1 1]\n",
      " [7 3 0 9]\n",
      " [8 0 3 2]\n",
      " [4 3 7 3]]\n",
      "[[1 1 7 7]\n",
      " [0 3 7 9]\n",
      " [0 2 3 8]\n",
      " [3 3 4 7]]\n",
      "[[4 0 0 1]\n",
      " [7 3 1 2]\n",
      " [7 3 3 3]\n",
      " [8 7 7 9]]\n"
     ]
    }
   ],
   "source": [
    "#Array Sorting\n",
    "a= np.random.randint(0,10,(4,4))\n",
    "b = np.sort(a)\n",
    "c = np.sort(a, axis=0)\n",
    "print(a)\n",
    "print(b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "36676bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 0 1 2]\n"
     ]
    }
   ],
   "source": [
    "sort_index = np.argsort(a[:, 0])\n",
    "print(sort_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "6e91027e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 3 7 3]\n",
      " [7 7 1 1]\n",
      " [7 3 0 9]\n",
      " [8 0 3 2]]\n"
     ]
    }
   ],
   "source": [
    "e = a[sort_index]\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6936ef3a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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