15092022 - YZ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b16713b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f73797a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bir dizinin elemanlarını okumak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47be9f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([7, 5, 3, 9, 0, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0fe07c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f35cd964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 3, 9, 0, 2])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "344dac5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 3, 9])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71153052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4d73edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bir dizinin elemanlarını değiştirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f126e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ar = np.arange(1, 20)\n",
    "ar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4482d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  0  3  0  5  0  7  0  9  0 11  0 13  0 15  0 17  0 19]\n"
     ]
    }
   ],
   "source": [
    "rep1 = np.where(ar % 2 == 0, 0, ar)\n",
    "print(rep1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6b4f4b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30, 10, 10, 20, 20])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ar2 = np.array([10, 20, 30, 10, 10, 20, 20])\n",
    "ar2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9060e7ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[99 20 30 99 99 20 20]\n"
     ]
    }
   ],
   "source": [
    "rep2 = np.where(ar2 == 10, 99, ar2)\n",
    "print(rep2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0a61d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2 = np.arange(0, 100, 10)\n",
    "p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7b32604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33, 10, 20, 55, 40, 99, 60, 70, 80, 90])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.put(p2, [0, 3, 5], [33, 55, 99])\n",
    "p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bec64925",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dizilerdeki eksik değerler ile ilgilenme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9ef9a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10., nan, 20., 30., 60., nan, 90., inf])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([10, np.nan, 20, 30, 60, np.nan, 90, np.inf])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8b67e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, False, False,  True, False, False])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "781105a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 5], dtype=int64),)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(np.isnan(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fff17b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10., 99., 20., 30., 60., 99., 90., inf])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[np.isnan(a)] = 99\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54ecbfa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2., nan,  4.],\n",
       "       [nan,  6.,  7.,  8.],\n",
       "       [10., nan, 12., 13.],\n",
       "       [14., 15., 16., 17.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1,2,np.nan,4], [np.nan,6,7,8], [10,np.nan,12,13], [14,15,16,17]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e1287ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False,  True, False],\n",
       "       [ True, False, False, False],\n",
       "       [False,  True, False, False],\n",
       "       [False, False, False, False]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bcdfa386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2], dtype=int64), array([2, 0, 1], dtype=int64))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(np.isnan(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "48cc0756",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dizileri dikey ve yatay birleştirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "050f22f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros(20).reshape(2, -1)\n",
    "b = np.repeat(1, 20).reshape(2, -1)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "754f7c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dd8d88fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack([a, b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24d5d860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack([a, b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e1cf10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 40, 60])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = np.array([10,20,30,40,50,60])\n",
    "c2 = np.array([12,20,33,40,55,60])\n",
    "np.intersect1d(c1, c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3b7990ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 30, 50])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.setdiff1d(c1, c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a45f4fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = np.arange(0, 60)\n",
    "a1[np.where ((a1 > 20) & (a1 < 35))] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9ac30e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       40, 50])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[np.where(((a1 > 20) & (a1 < 35)) | (a1 % 10 == 0))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ada36c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[np.where(np.logical_and(a1 > 20, a1 < 35))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fb337605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
       "       27, 28, 29])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4 = np.arange(10, 30)\n",
    "a4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0e659791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13,\n",
       "       12, 11, 10])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8ebdb373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13,\n",
       "       12, 11])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a4[-1:0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b3f757f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13,\n",
       "       12, 11, 10])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.flip(a4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "64b3106e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  2,  8,  1],\n",
       "       [70, 50, 10, 67],\n",
       "       [45, 25, 75, 15],\n",
       "       [12,  9, 77,  4]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3 = np.array([[3,2,8,1], [70,50,10,67], [45,25,75,15], [12,9,77,4]])\n",
    "a3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "601c3c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12,  9, 77,  4],\n",
       "       [45, 25, 75, 15],\n",
       "       [70, 50, 10, 67],\n",
       "       [ 3,  2,  8,  1]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3[::-1,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5964edc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  8,  2,  3],\n",
       "       [67, 10, 50, 70],\n",
       "       [15, 75, 25, 45],\n",
       "       [ 4, 77,  9, 12]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3[:,::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "73c88665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4, 77,  9, 12],\n",
       "       [15, 75, 25, 45],\n",
       "       [67, 10, 50, 70],\n",
       "       [ 1,  8,  2,  3]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3[::-1,::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ecaad639",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sıralama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8abc7740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  5, 10, 12, 17, 22, 33, 92])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([10,5,2,22,12,92,17,33])\n",
    "np.sort(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9d2efa40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  2,  8,  1],\n",
       "       [70, 50, 10, 67],\n",
       "       [45, 25, 75, 15]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a3 = np.array([[3,2,8,1], [70,50,10,67], [45,25,75,15]])\n",
    "a3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d997a63b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  8],\n",
       "       [10, 50, 67, 70],\n",
       "       [15, 25, 45, 75]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "08912fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  8],\n",
       "       [10, 50, 67, 70],\n",
       "       [15, 25, 45, 75]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a3, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "06b9478e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  2,  8,  1],\n",
       "       [45, 25, 10, 15],\n",
       "       [70, 50, 75, 67]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a3, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8e12253b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([92, 33, 22, 17, 12, 10,  5,  2])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.sort(a)\n",
    "b = b[::-1]\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ebf0988b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([92, 33, 22, 17, 12, 10,  5,  2])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.sort(a)\n",
    "np.flip(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b74b1c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([92, 33, 22, 17, 12, 10,  5,  2])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1].sort()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "df286b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dosya Yükleme ve Kaydetme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "89c28f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 29, 88],\n",
       "       [ 1,  0,  8],\n",
       "       [33,  7, 99],\n",
       "       [39, 11, 98],\n",
       "       [22, 76, 87]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4 = np.loadtxt('data/sample.txt', dtype = np.integer)\n",
    "p4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c06d3e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Dogu', 'Turkey', 'Football'],\n",
       "       ['John', 'USA', 'Hockey'],\n",
       "       ['Paul', 'Canada', 'Basketball']], dtype='<U10')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p5 = np.genfromtxt('data/sample0.txt', dtype='str')\n",
    "p5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0b934660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dogu', 'Turkey', 'Football'], dtype='<U10')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p5[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eeb7f608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dogu', 'John', 'Paul'], dtype='<U10')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p5[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dbd5b37b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24., 88.],\n",
       "       [ 1.,  8.],\n",
       "       [33., 99.],\n",
       "       [39., 98.],\n",
       "       [22., 87.]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.loadtxt('data/sample.txt', usecols = (0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "47fd253f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10,  21,  32,  43,  54,  65,  76,  87,  98, 109, 120, 131, 142,\n",
       "       153, 164, 175, 186, 197])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p7 = np.arange(10, 200, 11)\n",
    "p7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "30c150e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('AIGroup8-1509.csv', p7, delimiter = ',', fmt='%i')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9383eaf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "p77 = np.savetxt('AIGroup8-1509-v2.csv', p7, delimiter = '\\n', fmt='%i')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "59e39ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10],\n",
       "       [ 11,  12,  13,  14,  15,  16,  17,  18,  19,  20,  21],\n",
       "       [ 22,  23,  24,  25,  26,  27,  28,  29,  30,  31,  32],\n",
       "       [ 33,  34,  35,  36,  37,  38,  39,  40,  41,  42,  43],\n",
       "       [ 44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54],\n",
       "       [ 55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65],\n",
       "       [ 66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76],\n",
       "       [ 77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87],\n",
       "       [ 88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98],\n",
       "       [ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109],\n",
       "       [110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p8 = np.arange(0,121).reshape(11,11)\n",
    "p8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9b054847",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('AIGroup8-1509.npy', p8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cb35e2db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10],\n",
       "       [ 11,  12,  13,  14,  15,  16,  17,  18,  19,  20,  21],\n",
       "       [ 22,  23,  24,  25,  26,  27,  28,  29,  30,  31,  32],\n",
       "       [ 33,  34,  35,  36,  37,  38,  39,  40,  41,  42,  43],\n",
       "       [ 44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54],\n",
       "       [ 55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65],\n",
       "       [ 66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76],\n",
       "       [ 77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87],\n",
       "       [ 88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98],\n",
       "       [ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109],\n",
       "       [110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p9 = np.load('AIGroup8-1509.npy')\n",
    "p9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e71cf165",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('numpyfile-1509', p8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "39aec449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10],\n",
       "       [ 11,  12,  13,  14,  15,  16,  17,  18,  19,  20,  21],\n",
       "       [ 22,  23,  24,  25,  26,  27,  28,  29,  30,  31,  32],\n",
       "       [ 33,  34,  35,  36,  37,  38,  39,  40,  41,  42,  43],\n",
       "       [ 44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54],\n",
       "       [ 55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65],\n",
       "       [ 66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76],\n",
       "       [ 77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87],\n",
       "       [ 88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98],\n",
       "       [ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109],\n",
       "       [110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p10 = np.load('numpyfile-1509.npy')\n",
    "p10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "87d6f2c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[     0,      1,      2, ...,    997,    998,    999],\n",
       "       [  1000,   1001,   1002, ...,   1997,   1998,   1999],\n",
       "       [  2000,   2001,   2002, ...,   2997,   2998,   2999],\n",
       "       ...,\n",
       "       [997000, 997001, 997002, ..., 997997, 997998, 997999],\n",
       "       [998000, 998001, 998002, ..., 998997, 998998, 998999],\n",
       "       [999000, 999001, 999002, ..., 999997, 999998, 999999]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p11 = np.arange(0, 1000000).reshape(1000, 1000)\n",
    "p11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "34188261",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed('test1509-AIGroup8.npz', p11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cbd49a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('test1509-AIGroup8N.npy', p11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0c67e0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename = 'npynpz.PNG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a7cf7ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Vektör işlemleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "949ebee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V3 = [4 6]\n"
     ]
    }
   ],
   "source": [
    "v1 = np.array([1,2])\n",
    "v2 = np.array([3,4])\n",
    "v3 = v1 + v2\n",
    "#v3 = np.add(v1, v2)\n",
    "print('V3 =', v3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7382aacd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20 42 72]\n"
     ]
    }
   ],
   "source": [
    "a1 = np.array([5,6,8])\n",
    "a2 = np.array([4,7,9])\n",
    "print(np.multiply(a1, a2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "78b29e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = np.array([1,2,3])\n",
    "a2 = np.array([4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "594108f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dot product -  32\n",
      "Dot product using np.dot -  32\n",
      "Dot product using np.inner -  32\n",
      "Dot product using np.multiply & sum -  32\n",
      "Dot product using np.matmul -  32\n",
      "Dot product using for loop -  32\n"
     ]
    }
   ],
   "source": [
    "dotp = a1@a2\n",
    "print(\"Dot product - \", dotp)\n",
    "\n",
    "dotp = np.dot(a1, a2)\n",
    "print(\"Dot product using np.dot - \", dotp)\n",
    "\n",
    "dotp = np.inner(a1, a2)\n",
    "print(\"Dot product using np.inner - \", dotp)\n",
    "\n",
    "dotp = sum(np.multiply(a1, a2))\n",
    "print(\"Dot product using np.multiply & sum - \", dotp)\n",
    "\n",
    "dotp = np.matmul(a1, a2)\n",
    "print(\"Dot product using np.matmul - \", dotp)\n",
    "\n",
    "dotp = 0\n",
    "for i in range(len(a1)):\n",
    "    dotp = dotp + a1[i]*a2[i]\n",
    "print(\"Dot product using for loop - \", dotp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a75b054e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.539392014169456\n"
     ]
    }
   ],
   "source": [
    "v3 = np.array([1,2,3,4,5,6])\n",
    "length = np.sqrt(np.dot(v3, v3))\n",
    "print(length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "64824920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.539392014169456\n"
     ]
    }
   ],
   "source": [
    "length = np.sqrt(sum(np.multiply(v3, v3)))\n",
    "print(length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d69bf56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.605551275463989, array([0.5547002 , 0.83205029]))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = [2,3]\n",
    "length_v1 = np.sqrt(np.dot(v1, v1))\n",
    "norm_v1 = v1 / length_v1\n",
    "length_v1, norm_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4ac29049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5547002 , 0.83205029])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_v1 = v1 / np.linalg.norm(v1)\n",
    "norm_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "79a11a9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Matrisler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "50e2de54",
   "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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b5ea2690",
   "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.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "cf355f49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[356 389  74 108 217 456 348 425 340 249]\n",
      " [335  69 276  46 453 465 459   8 267 119]\n",
      " [447 211 199 170 453  86 377 245 397 299]\n",
      " [367 123 477 344 229 208 316 309  31 454]\n",
      " [  5 463 391  57 457 164 191 387  42 103]\n",
      " [140 269 474 417 227  92 281  86 371 125]\n",
      " [283 214 143 370 374 128  91 417 471 112]\n",
      " [475 191 481 254 497 483 402 339 166 354]\n",
      " [450  55 219 274 158 228 210 150 201  14]\n",
      " [267  80 409   3 316 108 253 420 284 300]]\n"
     ]
    }
   ],
   "source": [
    "mat1 = np.random.randint(0, 500, (10,10))\n",
    "print(mat1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9a3b566c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([356, 389, 456, 348, 425, 340, 335, 453, 465, 459, 447, 453, 377,\n",
       "       397, 367, 477, 344, 316, 309, 454, 463, 391, 457, 387, 474, 417,\n",
       "       371, 370, 374, 417, 471, 475, 481, 497, 483, 402, 339, 354, 450,\n",
       "       409, 316, 420])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat1[mat1>300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1e82f13a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I = np.eye(9)\n",
    "I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bd6399eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "D = np.diag([1,2,3,4,5,6,7,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d06f87f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 4, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 5, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 6, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 7, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 8]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "859b76e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower traingular matrix - \n",
      " [[-0.69924563  0.          0.          0.          0.        ]\n",
      " [ 1.92101809 -1.24077012  0.          0.          0.        ]\n",
      " [-0.35969148 -0.73371946 -0.13898368  0.          0.        ]\n",
      " [ 0.78132189  0.22269854 -1.02349397 -0.88796126  0.        ]\n",
      " [ 3.26744778 -1.00788843  0.01316044  0.1798867  -1.36439753]]\n",
      "\n",
      "\n",
      "Upper triangular matrix - \n",
      " [[-0.69924563  1.19726417  0.66301405  0.70132996 -1.28712046]\n",
      " [ 0.         -1.24077012  0.95188763 -0.93778723 -0.22275235]\n",
      " [ 0.          0.         -0.13898368  0.33476026 -0.25213777]\n",
      " [ 0.          0.          0.         -0.88796126 -1.26138189]\n",
      " [ 0.          0.          0.          0.         -1.36439753]]\n"
     ]
    }
   ],
   "source": [
    "M = np.random.randn(5,5)\n",
    "U = np.triu(M)\n",
    "L = np.tril(M)\n",
    "\n",
    "print(\"Lower traingular matrix - \\n\", L)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Upper triangular matrix - \\n\", U)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cc6aa18a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2., nan,  4.],\n",
       "       [nan,  6.,  7.,  8.],\n",
       "       [10., nan, 12., 13.],\n",
       "       [14., 15., 16., 17.]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8f939be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., nan,  4., nan,  6.,  7.,  8., 10., nan, 12., 13., 14.,\n",
       "       15., 16., 17.])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9b888582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., nan,  4., nan,  6.,  7.,  8., 10., nan, 12., 13., 14.,\n",
       "       15., 16., 17.])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ff27337e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Matrix(M) ==> \n",
      " [[ 1  2  3]\n",
      " [ 4 -3  6]\n",
      " [ 7  8  0]]\n",
      "\n",
      "Transpose of M ==> \n",
      " [[ 1  4  7]\n",
      " [ 2 -3  8]\n",
      " [ 3  6  0]]\n",
      "\n",
      "Transpose of M ==> \n",
      " [[ 1  4  7]\n",
      " [ 2 -3  8]\n",
      " [ 3  6  0]]\n"
     ]
    }
   ],
   "source": [
    "M = np.array([[1,2,3], [4, -3, 6], [7, 8, 0]])\n",
    "\n",
    "print(\"\\n Matrix(M) ==> \\n\", M)\n",
    "\n",
    "print(\"\\nTranspose of M ==> \\n\", np.transpose(M))\n",
    "\n",
    "#OR\n",
    "\n",
    "print(\"\\nTranspose of M ==> \\n\", M.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3c999ce9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determinant of M ==>  195.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Determinant of M ==> \", np.linalg.det(M))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "19eb9624",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inverse of M ==> \n",
      " [[-0.24615385  0.12307692  0.10769231]\n",
      " [ 0.21538462 -0.10769231  0.03076923]\n",
      " [ 0.27179487  0.03076923 -0.05641026]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Inverse of M ==> \\n\", np.linalg.inv(M))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "30f8a45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M1 = np.array([[1,2], [4,5]])\n",
    "M1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "aaf59780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 57,  78],\n",
       "       [156, 213]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M1@M1@M1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ac1b61ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15417, 21060],\n",
       "       [42120, 57537]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.matrix_power(M1, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8626f57e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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