assig-Copy1.1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6 7 8 9]\n",
      "[9 8 7 6 5 4 3 2 1]\n"
     ]
    }
   ],
   "source": [
    "#reverse array\n",
    "x=np.arange(1,10)\n",
    "print(x)\n",
    "y=x[::-1]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3*3 matrix 0-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0,9).reshape(3,3)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "[[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]]\n"
     ]
    }
   ],
   "source": [
    "#2d array with 1 on the border and 0 inside\n",
    "n = input()\n",
    "a = np.ones((int(n),int(n)),dtype=np.int64)\n",
    "a[1:-1,1:-1]=0\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.96511458 0.4659734  0.28349425]\n",
      " [0.59851573 0.68608783 0.16558568]\n",
      " [0.75381641 0.09300748 0.3896926 ]\n",
      " [0.23215354 0.16140283 0.45846296]\n",
      " [0.27010203 0.53995674 0.40073385]]\n",
      "\n",
      "[[0.39036763 0.80366769]\n",
      " [0.45233223 0.63749524]\n",
      " [0.98729332 0.85304157]]\n",
      "\n",
      "[[0.86741627 1.31451961]\n",
      " [0.70746245 1.05963695]\n",
      " [0.72107671 0.99753371]\n",
      " [0.61627035 0.6805558 ]\n",
      " [0.74532079 0.90313476]]\n"
     ]
    }
   ],
   "source": [
    "#Multiply a 5x3 matrix by a 3x2 matrix (real matrix product)\n",
    "a = np.random.random((5,3))\n",
    "print(a)\n",
    "print('')\n",
    "b = np.random.random((3,2))\n",
    "print(b)\n",
    "print('')\n",
    "c = np.dot(a,b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.65468427 2.97791305 2.8249959  2.60705813]\n",
      "\n",
      "[2. 2. 2. 2.]\n",
      "\n",
      "[2. 2. 2. 2.]\n",
      "\n",
      "[2. 2. 2. 2.]\n",
      "\n",
      "[2 2 2 2]\n",
      "\n",
      "[2. 2. 2. 2.]\n"
     ]
    }
   ],
   "source": [
    "#Extract the integer part of a random array using 5 differentmethods\n",
    "a = np.random.uniform(2,3,4)\n",
    "print(a)\n",
    "print('')\n",
    "print (a - a%1)\n",
    "print('')\n",
    "print (np.floor(a))\n",
    "print('')\n",
    "print (np.ceil(a)-1)\n",
    "print('')\n",
    "print (a.astype(int))\n",
    "print('')\n",
    "print (np.trunc(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 0 1 0]\n",
      "\n",
      "[1 0 1 0 1 0]\n",
      "\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#Python program to check two random arrays are equal or not.\n",
    "x = np.random.randint(0,2,6)\n",
    "print(x)\n",
    "print('')\n",
    "y = np.random.randint(0,2,6)\n",
    "print(y)\n",
    "print('')\n",
    "arr_equal = np.allclose(x, y)\n",
    "print(arr_equal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "4.496533769285221\n"
     ]
    }
   ],
   "source": [
    "#Python program to find the nearest value from a given value in anarray.\n",
    "import numpy as np\n",
    "x = np.random.uniform(1, 12, 5)\n",
    "key = int(input())\n",
    "n = x.flat[np.abs(x - key).argmin()]\n",
    "print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10]\n",
      "\n",
      "[10]\n"
     ]
    }
   ],
   "source": [
    "#largest value of an array\n",
    "x = np.arange(11)\n",
    "print(x)\n",
    "print('')\n",
    "np.random.shuffle(x)\n",
    "n = 1\n",
    "print (x[np.argsort(x)[-n:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "3.935146415755968\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "#the closest value (to a given scalar) in an array\n",
    "x = np.arange(10)\n",
    "print(x)\n",
    "a = np.random.uniform(0,10)\n",
    "print(a)\n",
    "index = (np.abs(x-a)).argmin()\n",
    "print(x[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13  7  7  7  2  3 16 16  2 11]\n",
      "\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "# the most frequent value in an array\n",
    "a = np.random.randint(0,20,10)\n",
    "print(a)\n",
    "print('')\n",
    "print(np.bincount(a).argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4837 4654 4527 5150 4960 5026 4447 4651 4835 4907 4848 4856 4933 4742\n",
      "  4886 4847 4845 4952 5085 4821]\n",
      " [4739 4910 4643 5248 4839 4791 4754 4902 4790 5161 5182 4652 4910 5225\n",
      "  5096 5256 5052 4763 4474 4903]\n",
      " [4756 5268 5074 5022 4860 4810 4947 4740 4860 4667 4837 4847 4882 5217\n",
      "  4950 4918 4586 4985 4987 5037]\n",
      " [4832 4398 4644 5131 4343 5128 4798 5101 4982 4929 4883 4884 5009 4831\n",
      "  4738 5173 4480 4715 4710 4939]\n",
      " [4776 5096 4511 4956 4764 4885 5051 4707 4984 4785 5141 4716 4806 4625\n",
      "  5146 4707 5075 4791 4888 4693]\n",
      " [4924 4940 4834 4897 5253 5086 5631 4607 4874 4992 4964 4578 4680 4899\n",
      "  4975 4808 5031 5091 4300 4586]\n",
      " [5153 4655 4804 4959 4682 4994 4938 4893 5131 5047 4902 5007 5059 5071\n",
      "  5279 5197 4997 5251 4967 4817]\n",
      " [5330 4845 5090 4887 4775 4853 5207 4817 5328 5017 4690 4893 4929 5152\n",
      "  5324 4701 5068 5073 5079 4684]\n",
      " [4615 5076 4792 5073 5064 4784 5052 5091 5070 5196 5030 4962 4718 4862\n",
      "  4859 4588 4997 4683 5193 5164]\n",
      " [4665 4643 5250 5106 4930 4802 4806 4804 4919 5146 4964 4582 4856 4993\n",
      "  4922 4629 5130 5099 4819 4831]]\n",
      "\n",
      "[[4837 4654 4527 5150 4960 5026 4447 4651 4835 4907 4848 4856 4933 4742\n",
      "  4886 4847 4845 4952 5085 4821]\n",
      " [4739 4910 4643 5248 4839 4791 4754 4902 4790 5161 5182 4652 4910 5225\n",
      "  5096 5256 5052 4763 4474 4903]\n",
      " [4756 5268 5074 5022 4860 4810 4947 4740 4860 4667 4837 4847 4882 5217\n",
      "  4950 4918 4586 4985 4987 5037]\n",
      " [4832 4398 4644 5131 4343 5128 4798 5101 4982 4929 4883 4884 5009 4831\n",
      "  4738 5173 4480 4715 4710 4939]\n",
      " [4776 5096 4511 4956 4764 4885 5051 4707 4984 4785 5141 4716 4806 4625\n",
      "  5146 4707 5075 4791 4888 4693]\n",
      " [4924 4940 4834 4897 5253 5086 5631 4607 4874 4992 4964 4578 4680 4899\n",
      "  4975 4808 5031 5091 4300 4586]\n",
      " [5153 4655 4804 4959 4682 4994 4938 4893 5131 5047 4902 5007 5059 5071\n",
      "  5279 5197 4997 5251 4967 4817]\n",
      " [5330 4845 5090 4887 4775 4853 5207 4817 5328 5017 4690 4893 4929 5152\n",
      "  5324 4701 5068 5073 5079 4684]\n",
      " [4615 5076 4792 5073 5064 4784 5052 5091 5070 5196 5030 4962 4718 4862\n",
      "  4859 4588 4997 4683 5193 5164]\n",
      " [4665 4643 5250 5106 4930 4802 4806 4804 4919 5146 4964 4582 4856 4993\n",
      "  4922 4629 5130 5099 4819 4831]]\n"
     ]
    }
   ],
   "source": [
    "# four dimensions array, how to get sum over thelast two axis at once\n",
    "a = np.random.randint(0,50,(10,20,10,20))\n",
    "sum = a.sum(axis=(-2,-1))\n",
    "print(sum)\n",
    "print('')\n",
    "sum = a.reshape(a.shape[:-2] + (-1,)).sum(axis=-1)\n",
    "print(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[31  1 29 20 24 18 16 41 46 44]\n",
      " [ 6 46 11 37 43 41 16  3  5 46]\n",
      " [42 44 47 36 10 31  5 14 38 19]\n",
      " [49 13 17 12 27  2  9  9  1  6]\n",
      " [14  5 36 12 21 37 31 16  9 42]\n",
      " [33  7 18  6 36 10  0 18 22 18]\n",
      " [44 23 44 27 31 48  7 28 24 29]\n",
      " [ 0 13 14  1 13 32 12 40 18 45]\n",
      " [47  2 44 20 31 47 28 47  0 35]\n",
      " [ 8 26 37 41 41 46 22 25 11 13]]\n",
      "[[31  1 29 20 24 18 16 41 46 44]\n",
      " [47  2 44 20 31 47 28 47  0 35]\n",
      " [14  5 36 12 21 37 31 16  9 42]\n",
      " [33  7 18  6 36 10  0 18 22 18]\n",
      " [49 13 17 12 27  2  9  9  1  6]\n",
      " [ 0 13 14  1 13 32 12 40 18 45]\n",
      " [44 23 44 27 31 48  7 28 24 29]\n",
      " [ 8 26 37 41 41 46 22 25 11 13]\n",
      " [42 44 47 36 10 31  5 14 38 19]\n",
      " [ 6 46 11 37 43 41 16  3  5 46]]\n"
     ]
    }
   ],
   "source": [
    "#sort an array by the nth column\n",
    "a = np.random.randint(0,50,(10,10))\n",
    "print(a)\n",
    "print(a[a[:,1].argsort()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.38110364 0.95840035 0.69394174 0.35009556 0.20754897\n",
      "  0.83731882 0.70426155 0.83907353 0.49631457]\n",
      " [0.38110364 0.         0.64539376 0.38472776 0.21976874 0.44581291\n",
      "  0.52316695 0.36386231 0.49889005 0.18040939]\n",
      " [0.95840035 0.64539376 0.         0.26678752 0.85376307 0.89487595\n",
      "  0.12310697 0.28527163 0.16699691 0.70985713]\n",
      " [0.69394174 0.38472776 0.26678752 0.         0.59978562 0.64781825\n",
      "  0.14405463 0.08084742 0.15966144 0.47789886]\n",
      " [0.35009556 0.21976874 0.85376307 0.59978562 0.         0.50594483\n",
      "  0.73356986 0.5686499  0.69857211 0.18794415]\n",
      " [0.20754897 0.44581291 0.89487595 0.64781825 0.50594483 0.\n",
      "  0.78206472 0.68147806 0.8055732  0.60587071]\n",
      " [0.83731882 0.52316695 0.12310697 0.14405463 0.73356986 0.78206472\n",
      "  0.         0.16772386 0.08844455 0.59608   ]\n",
      " [0.70426155 0.36386231 0.28527163 0.08084742 0.5686499  0.68147806\n",
      "  0.16772386 0.         0.13646245 0.42848411]\n",
      " [0.83907353 0.49889005 0.16699691 0.15966144 0.69857211 0.8055732\n",
      "  0.08844455 0.13646245 0.         0.54656934]\n",
      " [0.49631457 0.18040939 0.70985713 0.47789886 0.18794415 0.60587071\n",
      "  0.59608    0.42848411 0.54656934 0.        ]]\n",
      "\n",
      "\n",
      "[[0.         0.76171527 0.83484271 0.34573762 0.23243135 0.53477748\n",
      "  0.42634497 0.48574276 0.91337989 0.80283676]\n",
      " [0.76171527 0.         0.45483411 0.77084816 0.84792983 0.39680377\n",
      "  0.79034713 0.50489587 0.55919562 0.40746628]\n",
      " [0.83484271 0.45483411 0.         0.64315206 0.79095453 0.75306708\n",
      "  0.61336963 0.36016589 0.10887657 0.04959424]\n",
      " [0.34573762 0.77084816 0.64315206 0.         0.16847055 0.72091704\n",
      "  0.08072421 0.29994498 0.68968398 0.62887421]\n",
      " [0.23243135 0.84792983 0.79095453 0.16847055 0.         0.71470621\n",
      "  0.24029905 0.4336697  0.84673466 0.77101812]\n",
      " [0.53477748 0.39680377 0.75306708 0.72091704 0.71470621 0.\n",
      "  0.77696091 0.58832772 0.86114434 0.70420992]\n",
      " [0.42634497 0.79034713 0.61336963 0.08072421 0.24029905 0.77696091\n",
      "  0.         0.29572149 0.64861973 0.60499667]\n",
      " [0.48574276 0.50489587 0.36016589 0.29994498 0.4336697  0.58832772\n",
      "  0.29572149 0.         0.42908348 0.3375366 ]\n",
      " [0.91337989 0.55919562 0.10887657 0.68968398 0.84673466 0.86114434\n",
      "  0.64861973 0.42908348 0.         0.15844726]\n",
      " [0.80283676 0.40746628 0.04959424 0.62887421 0.77101812 0.70420992\n",
      "  0.60499667 0.3375366  0.15844726 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# random vector with shape (100,2) representingcoordinates, find point by point distances\n",
    "a = np.random.random((10,2))\n",
    "x,y = np.atleast_2d(a[:,0], a[:,1])\n",
    "b = np.sqrt( (x-x.T)**2 + (y-y.T)**2)\n",
    "print(b)\n",
    "print('')\n",
    "print('')\n",
    "import scipy\n",
    "import scipy.spatial\n",
    "a = np.random.random((10,2))\n",
    "b = scipy.spatial.distance.cdist(a,a)\n",
    "print(b)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.47717369 0.47959102 0.48905694 0.17168448 0.704967   0.83612305\n",
      " 0.87329476 0.10810613 0.82828771 0.87013079 0.73308426 0.20178965\n",
      " 0.72051163 0.27922553 0.56274012]\n",
      "\n",
      "[ 0.47717369  0.47959102  0.48905694  0.17168448  0.704967    0.83612305\n",
      " -1.          0.10810613  0.82828771  0.87013079  0.73308426  0.20178965\n",
      "  0.72051163  0.27922553  0.56274012]\n"
     ]
    }
   ],
   "source": [
    "#Python program to create random vector of size 15 and replacethe maximum value by -1\n",
    "x=np.random.random(15)\n",
    "print (x)\n",
    "print('')\n",
    "x[x.argmax()]=-1\n",
    "print (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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