NumPy_Assignment 14.03.24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b956e8b-c012-4b7d-9fe1-be68fe668606",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Import the numpy package under the name as np & print the numpy version</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "668e8221",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c171220-e212-4057-ad4c-e809ba8961fd",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a null vector of size 10</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "107c7c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_vektor = np.full((10), np.nan)\n",
    "null_vektor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1e69582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_vektor.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2891b252-a1db-4ab4-a164-47c9cce9a271",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a null vector of size 10 but the fifth value which is 1</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "41bafa0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_vektor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "51e58682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, nan,  1., nan, nan, nan, nan, nan])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_vektor[4] = 1\n",
    "null_vektor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491e5318-b216-48d8-8f35-82cd66594408",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a vector with values ranging from 10 to 50</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "36253df9",
   "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, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
       "       44, 45, 46, 47, 48, 49, 50])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector = np.arange(10,51)\n",
    "vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7818669-03a5-4f61-b08f-b85b8dd94372",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Reverse a vector (first element becomes last)</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "027a4256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34,\n",
       "       33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17,\n",
       "       16, 15, 14, 13, 12, 11, 10])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reverse_vector = vector[::-1]\n",
    "reverse_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f4a94e4-596a-4c85-8356-7eb86d6cacbe",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a 3x3 matrix with values ranging from 0 to 9</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bc73f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = np.arange(9).reshape(3, 3)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df95fd1-f316-4976-849b-756f927f382c",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Find indices of non­zero elements from [1,2,0,0,4,0]</b><font color='black'>\n",
    "**Extra research is required**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95edf755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 4], dtype=int64),)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([1,2,0,0,4,0])\n",
    "nonzero = np.nonzero(arr1)\n",
    "nonzero"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc45ac3-9297-4ea0-9fd8-8534c46935e4",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a 3x3 identity matrix</b><font color='black'>\n",
    "\"Identity matrix\" is a special type of square matrix. It is a square matrix where all elements on its main diagonal are 1, and all other elements are 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb3b3ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kare_matris = np.eye(3)\n",
    "kare_matris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa19488a-0613-48f3-8604-4499afe134e8",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a 3x3x3 array with random values [1,2,0,0,4,0]</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1fecd76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 0, 0],\n",
       "        [1, 1, 0],\n",
       "        [1, 0, 2]],\n",
       "\n",
       "       [[4, 1, 2],\n",
       "        [0, 0, 0],\n",
       "        [0, 1, 2]],\n",
       "\n",
       "       [[2, 1, 1],\n",
       "        [0, 1, 1],\n",
       "        [0, 1, 4]]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.random.choice([1,2,0,0,4,0], size= (3,3,3))\n",
    "arr2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "456bd772-149a-410a-9c86-04f1b19c6ae1",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a 10x10 array with random values and find the minimum and maximum values</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ec01322d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54, 46, 77, 46,  2,  5,  5, 88, 85,  1],\n",
       "       [14, 87, 60, 42, 98, 75, 22, 96, 29, 36],\n",
       "       [74, 76,  7, 69, 19, 35, 13, 40, 27, 99],\n",
       "       [41, 22, 16, 40, 64, 77, 54, 40, 73, 10],\n",
       "       [45, 38, 23, 79, 46, 76, 39, 49,  0, 27],\n",
       "       [29, 70, 17, 77, 48, 29, 28, 55, 18, 30],\n",
       "       [69, 82, 53, 61, 63, 78, 94, 55,  5, 14],\n",
       "       [92, 90, 60, 29, 91, 38, 31, 86, 19,  9],\n",
       "       [70, 57, 24, 32, 18, 59, 87, 35, 78, 95],\n",
       "       [98, 53, 91,  4, 11, 83,  0, 74,  7, 32]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = np.random.randint(100, size = (10, 10))\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7b39f8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8482d511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cbf0c5f-e2dd-4418-b923-a52bbda70b7e",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a random vector of size 30 and find the mean value</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "625f82b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25, 17, 14, 98, 91, 72, 47, 94, 31, 27, 52, 34, 70,  8, 16, 42, 11,\n",
       "       14, 10, 12, 84,  2, 84, 54, 31, 18, 90, 34, 25, 58])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector2 = np.random.randint(1, 100, size=30)\n",
    "vector2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4e89783f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.166666666666664"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(vector2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcdb4e96",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>How can I create an array with dimensions (6, 7, 8) using arange, and find its last element?</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "844deeaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1,   2,   3,   4,   5,   6,   7],\n",
       "        [  8,   9,  10,  11,  12,  13,  14,  15],\n",
       "        [ 16,  17,  18,  19,  20,  21,  22,  23],\n",
       "        [ 24,  25,  26,  27,  28,  29,  30,  31],\n",
       "        [ 32,  33,  34,  35,  36,  37,  38,  39],\n",
       "        [ 40,  41,  42,  43,  44,  45,  46,  47],\n",
       "        [ 48,  49,  50,  51,  52,  53,  54,  55]],\n",
       "\n",
       "       [[ 56,  57,  58,  59,  60,  61,  62,  63],\n",
       "        [ 64,  65,  66,  67,  68,  69,  70,  71],\n",
       "        [ 72,  73,  74,  75,  76,  77,  78,  79],\n",
       "        [ 80,  81,  82,  83,  84,  85,  86,  87],\n",
       "        [ 88,  89,  90,  91,  92,  93,  94,  95],\n",
       "        [ 96,  97,  98,  99, 100, 101, 102, 103],\n",
       "        [104, 105, 106, 107, 108, 109, 110, 111]],\n",
       "\n",
       "       [[112, 113, 114, 115, 116, 117, 118, 119],\n",
       "        [120, 121, 122, 123, 124, 125, 126, 127],\n",
       "        [128, 129, 130, 131, 132, 133, 134, 135],\n",
       "        [136, 137, 138, 139, 140, 141, 142, 143],\n",
       "        [144, 145, 146, 147, 148, 149, 150, 151],\n",
       "        [152, 153, 154, 155, 156, 157, 158, 159],\n",
       "        [160, 161, 162, 163, 164, 165, 166, 167]],\n",
       "\n",
       "       [[168, 169, 170, 171, 172, 173, 174, 175],\n",
       "        [176, 177, 178, 179, 180, 181, 182, 183],\n",
       "        [184, 185, 186, 187, 188, 189, 190, 191],\n",
       "        [192, 193, 194, 195, 196, 197, 198, 199],\n",
       "        [200, 201, 202, 203, 204, 205, 206, 207],\n",
       "        [208, 209, 210, 211, 212, 213, 214, 215],\n",
       "        [216, 217, 218, 219, 220, 221, 222, 223]],\n",
       "\n",
       "       [[224, 225, 226, 227, 228, 229, 230, 231],\n",
       "        [232, 233, 234, 235, 236, 237, 238, 239],\n",
       "        [240, 241, 242, 243, 244, 245, 246, 247],\n",
       "        [248, 249, 250, 251, 252, 253, 254, 255],\n",
       "        [256, 257, 258, 259, 260, 261, 262, 263],\n",
       "        [264, 265, 266, 267, 268, 269, 270, 271],\n",
       "        [272, 273, 274, 275, 276, 277, 278, 279]],\n",
       "\n",
       "       [[280, 281, 282, 283, 284, 285, 286, 287],\n",
       "        [288, 289, 290, 291, 292, 293, 294, 295],\n",
       "        [296, 297, 298, 299, 300, 301, 302, 303],\n",
       "        [304, 305, 306, 307, 308, 309, 310, 311],\n",
       "        [312, 313, 314, 315, 316, 317, 318, 319],\n",
       "        [320, 321, 322, 323, 324, 325, 326, 327],\n",
       "        [328, 329, 330, 331, 332, 333, 334, 335]]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4 = np.arange(0,336).reshape(6,7,8)\n",
    "arr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93bc1aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "335"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4[-1][-1][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[864,   8, 354,  97, 815, 674, 502, 309],\n",
       "        [844, 311, 757, 781, 994, 953, 161,  32],\n",
       "        [606, 782, 360, 729, 655, 506,  28, 883],\n",
       "        [321, 539, 167, 868, 206, 909, 554, 739],\n",
       "        [809, 963, 264,  94, 668, 290, 379, 223],\n",
       "        [472, 326, 101,  80, 322, 999,  70, 886],\n",
       "        [725, 194, 439, 352, 556, 209, 538, 320]],\n",
       "\n",
       "       [[863, 376, 227, 643,   8,  28, 161, 932],\n",
       "        [298,  80, 595, 416, 838, 811, 960, 331],\n",
       "        [184, 240, 714, 730, 432, 402,  44, 695],\n",
       "        [930, 598, 974, 216, 514, 346, 116, 213],\n",
       "        [917, 134, 670, 992, 319, 967,  68, 889],\n",
       "        [432, 255, 916, 248, 952, 964, 679, 641],\n",
       "        [682, 331, 541, 379, 894, 126, 400, 101]],\n",
       "\n",
       "       [[134, 218, 198, 509, 395, 985,  86, 847],\n",
       "        [ 34,  49, 242, 894, 100, 419, 755, 362],\n",
       "        [479, 737, 508, 515, 110, 432,  58, 719],\n",
       "        [175, 463, 474, 476, 153, 486, 433, 455],\n",
       "        [921, 205, 620, 713, 722, 120, 655, 196],\n",
       "        [854, 870, 923, 522, 692,   3, 190, 715],\n",
       "        [ 24, 816, 268, 917, 222, 151, 438, 407]],\n",
       "\n",
       "       [[580, 664, 484, 786, 564, 605,  24, 651],\n",
       "        [597, 763, 803, 761, 913, 868, 695, 771],\n",
       "        [489, 276, 569, 983, 476, 219,  81,  55],\n",
       "        [673, 516, 777, 157, 960, 415, 683, 828],\n",
       "        [249, 256, 792, 570, 492, 195, 747, 602],\n",
       "        [888, 909,  83, 512, 274, 186, 456, 816],\n",
       "        [718, 988,  77, 115, 203, 638, 734,  85]],\n",
       "\n",
       "       [[894, 162, 845, 698, 874, 856,  73, 152],\n",
       "        [250, 715, 825, 995, 348, 982, 684, 559],\n",
       "        [225, 391, 961, 321, 216, 901, 235, 220],\n",
       "        [612,  13, 516, 856,  10, 321, 700, 800],\n",
       "        [353, 694, 971, 701, 320, 862,  94, 388],\n",
       "        [942,   7, 683, 996, 678, 156, 942, 584],\n",
       "        [441, 801, 577, 183, 775, 551, 571, 182]],\n",
       "\n",
       "       [[594, 578, 562, 787, 534,  10,  23, 324],\n",
       "        [927, 839, 890, 788, 263, 710, 925, 764],\n",
       "        [ 20, 169, 174, 932,  93, 891, 449, 213],\n",
       "        [ 26, 856, 707,  55, 115, 915, 289, 790],\n",
       "        [455, 357,  31, 456, 887, 290,  65, 807],\n",
       "        [ 46, 476, 746, 189, 333,  97, 777,  29],\n",
       "        [621, 811, 987, 529, 499, 843, 228,  72]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1, 1000, (6,7,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc74cce-1589-434f-929e-d4f809eac34d",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a 5x5 matrix with row values ranging from 0 to 5</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a38a09b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix2 = np.tile(np.arange(0, 5), (5,1))\n",
    "matrix2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4707a57c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix2 = np.full((5,5), range(0,5))\n",
    "matrix2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbea287c-b2ef-4c94-b4bc-e047ee69abd1",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a vector of size 12 with values ranging from 0 to 1 (excluding 0 and 1)</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b6caf3c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.08333333, 0.16666667, 0.25      , 0.33333333,\n",
       "       0.41666667, 0.5       , 0.58333333, 0.66666667, 0.75      ,\n",
       "       0.83333333, 0.91666667])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector3 = np.linspace(0,1,12, endpoint=False) #?????? EMİN DEĞİLİM\n",
    "vector3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6bcd6f0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector3.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc7d714-45a5-4bc7-a52e-37abe31bfb1f",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a random vector of size 10 and sort it</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c0a59fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27, 31, 42, 35, 38, 41, 24, 19, 32, 25])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector4 = np.random.randint(1,50,10)\n",
    "vector4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c9296fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 24, 25, 27, 31, 32, 35, 38, 41, 42])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(vector4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bab07386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector4.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d58bab8-cab1-4318-9405-6e54a17ac346",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Consider two random array A and B, check if they are equal</b><font color='black'>\n",
    "**Extra research is required**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07d21f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.random.rand(3, 3)\n",
    "B = np.random.rand(3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "631e072a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal(A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add060d3-6cca-47eb-8a48-2b9f33227130",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Generate a random vector of size 10 and replace the maximum value with 0</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6edc3993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33, 15, 44, 33, 43, 26, 40,  7, 16, 49])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector5 = np.random.randint(1,50,10)\n",
    "vector5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0cfa585c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33, 15, 44, 33, 43, 26, 40,  7, 16,  0])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector5[vector5.argmax()] = 0\n",
    "vector5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d99138b-91e6-4d19-9195-1f384a68063b",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>How to find the most frequent value in an array?</b><font color='black'>\n",
    "**Extra research is required**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19a6efab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 2, 4, 4, 5, 5, 6, 6, 7, 5, 4, 5, 6, 3, 3, 7, 2, 7, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5 = np.random.randint(2,8,20)\n",
    "arr5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "247c0e84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(arr5).argmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "643742e8",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a 2d array with 1 on the border and 0 inside</b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "affc3c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1],\n",
       "       [1, 0, 0, 0, 1],\n",
       "       [1, 0, 0, 0, 1],\n",
       "       [1, 0, 0, 0, 1],\n",
       "       [1, 1, 1, 1, 1]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_2d = np.ones((5,5), dtype = int)\n",
    "array_2d[1:-1, 1:-1] = 0\n",
    "array_2d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "823dbb42",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a 5x5 matrix with values 1,2,3,4 just below the diagonal</b><font color='black'>\n",
    "**Extra research may be required**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "e65c7127",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "7b368d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix3 = np.zeros((5, 5), dtype=int)\n",
    "matrix3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "5c5557a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix3[0][0] = 1\n",
    "matrix3[1][1] = 2\n",
    "matrix3[2][2] = 3\n",
    "matrix3[3][3] = 4\n",
    "matrix3[4][4] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "f7aec6a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0],\n",
       "       [0, 0, 0, 4, 0],\n",
       "       [0, 0, 0, 0, 5]])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix3  #istenenden farklı ama böyle daha güzel oldu :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b54bac",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a 8x8 matrix and fill it with a checkerboard pattern</b><font color='black'>"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "2ce4419c",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d51e25f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix4 = np.zeros((8, 8),  dtype=int)  \n",
    "matrix4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4eecab07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix5 = np.ones((8, 8),  dtype=int)   #1. method\n",
    "for i in range(8):\n",
    "    for j in range(8):\n",
    "        if (i +j) % 2 == 0:\n",
    "            matrix5[i, j] = 0\n",
    "matrix5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3cfbd636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix5 = np.zeros((8,8), dtype = int)  #2. method\n",
    "matrix5[1::2, ::2] = 1\n",
    "matrix5[::2, 1::2] = 1\n",
    "matrix5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50007555",
   "metadata": {},
   "source": [
    "## <font color='green'> <b>Create a checkerboard 8x8 matrix using the tile function</b><font color='black'>\n",
    "**Extra research is required**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0adfdd50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix6 = np.array([0, 1] * 4)\n",
    "matrix7 = np.array([1, 0] * 4)                 #1. method\n",
    "\n",
    "checkerboard = np.tile(np.vstack((matrix6, matrix7)), (4,1))\n",
    "checkerboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "636b958a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix6 = np.array([[0,1], [1,0]])        #2. method\n",
    "\n",
    "checkerboard = np.tile(matrix6, (4,4))\n",
    "\n",
    "checkerboard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed06e2e9",
   "metadata": {},
   "source": [
    "# THANK YOU"
   ]
  }
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