Numpy_Exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Import the numpy package under the name as np  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Print the numpy version and the configuration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.19.0\n",
      "blas_mkl_info:\n",
      "  NOT AVAILABLE\n",
      "blis_info:\n",
      "  NOT AVAILABLE\n",
      "openblas_info:\n",
      "    library_dirs = ['D:\\\\a\\\\1\\\\s\\\\numpy\\\\build\\\\openblas_info']\n",
      "    libraries = ['openblas_info']\n",
      "    language = f77\n",
      "    define_macros = [('HAVE_CBLAS', None)]\n",
      "blas_opt_info:\n",
      "    library_dirs = ['D:\\\\a\\\\1\\\\s\\\\numpy\\\\build\\\\openblas_info']\n",
      "    libraries = ['openblas_info']\n",
      "    language = f77\n",
      "    define_macros = [('HAVE_CBLAS', None)]\n",
      "lapack_mkl_info:\n",
      "  NOT AVAILABLE\n",
      "openblas_lapack_info:\n",
      "    library_dirs = ['D:\\\\a\\\\1\\\\s\\\\numpy\\\\build\\\\openblas_lapack_info']\n",
      "    libraries = ['openblas_lapack_info']\n",
      "    language = f77\n",
      "    define_macros = [('HAVE_CBLAS', None)]\n",
      "lapack_opt_info:\n",
      "    library_dirs = ['D:\\\\a\\\\1\\\\s\\\\numpy\\\\build\\\\openblas_lapack_info']\n",
      "    libraries = ['openblas_lapack_info']\n",
      "    language = f77\n",
      "    define_macros = [('HAVE_CBLAS', None)]\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(np.__version__)\n",
    "print(np.show_config())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Create a null vector of size 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "arr = np.zeros(10)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.  How to find the memory size of any array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 Bytes\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1,2,3,4,5,6,7])\n",
    "np.size(arr)\n",
    "print(\"%d Bytes\" % (arr.size * arr.itemsize))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.  How to get the documentation of the numpy add function from the command line? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.add?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.  Create a null vector of size 10 but the fifth value which is 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "arr = np.zeros(10)\n",
    "arr[4] = 1\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.  Create a vector with values ranging from 10 to 49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n",
      " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48]\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(10,49)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.  Reverse a vector (first element becomes last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 11 12 13 14 15 16 17 18 19]\n",
      "[19 18 17 16 15 14 13 12 11 10]\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(10,20)\n",
    "print(arr)\n",
    "arr = arr[::-1]\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9.  Create a 3x3 matrix with values ranging from 0 to 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(0,9).reshape(3,3)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0, 1, 4], dtype=int64),)\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1,2,0,0,4,0])\n",
    "result = np.nonzero(arr)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. Create a 3x3 identity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.eye(3,3)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12. Create a 3x3x3 array with random values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr \n",
      " [[[0.86802543 0.99845998 0.81394011]\n",
      "  [0.14609417 0.91013668 0.13687813]\n",
      "  [0.85212832 0.75761954 0.56316711]]\n",
      "\n",
      " [[0.39569077 0.49555394 0.44939597]\n",
      "  [0.7231844  0.32235793 0.60986589]\n",
      "  [0.99317192 0.78557317 0.57182198]]\n",
      "\n",
      " [[0.49408251 0.39542347 0.23184135]\n",
      "  [0.62056534 0.91582285 0.22025325]\n",
      "  [0.97671431 0.46111843 0.10747123]]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.random.random((3,3,3))\n",
    "print(\"arr \\n\", arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 13. Create a 10x10 array with random values and find the minimum and maximum values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.32101574 0.43733864 0.3520021  0.59750053 0.28467858 0.84317779\n",
      "  0.68056234 0.3628329  0.41168193 0.83271655]\n",
      " [0.77733174 0.52071834 0.24469275 0.37450806 0.80346205 0.47191166\n",
      "  0.92929745 0.91089337 0.55924738 0.37097278]\n",
      " [0.01920834 0.33063263 0.96874537 0.4881529  0.27692451 0.45402008\n",
      "  0.3923428  0.90685919 0.25316619 0.47501366]\n",
      " [0.65087415 0.35400711 0.04894093 0.74928693 0.4342096  0.25596424\n",
      "  0.27924738 0.60530905 0.41174078 0.90440232]\n",
      " [0.62313476 0.73642806 0.18503962 0.58892673 0.57622583 0.14650758\n",
      "  0.73043602 0.31009678 0.13942942 0.62627251]\n",
      " [0.00913218 0.33357421 0.56128067 0.8317885  0.44347084 0.35045153\n",
      "  0.47526188 0.01217602 0.93106164 0.15059829]\n",
      " [0.46257472 0.96269019 0.04862317 0.04450411 0.78892722 0.23751735\n",
      "  0.91670165 0.00181405 0.06248457 0.58955577]\n",
      " [0.24138235 0.5852633  0.34308327 0.66271306 0.06671709 0.85765354\n",
      "  0.65823609 0.27599299 0.85913815 0.44784619]\n",
      " [0.1920773  0.49922334 0.53522783 0.62972955 0.08888857 0.08844234\n",
      "  0.05598568 0.8653051  0.58046224 0.72329252]\n",
      " [0.31181949 0.54430685 0.72964453 0.3501437  0.9358502  0.08478075\n",
      "  0.718777   0.38824127 0.89324296 0.10998853]]\n",
      "0.0018140485598401312\n"
     ]
    }
   ],
   "source": [
    "arr = np.random.random((10,10))\n",
    "print(arr)\n",
    "print(np.min(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 14. Create a random vector of size 30 and find the mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.5\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(30)\n",
    "print(np.mean(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 15. Create a 2d array with 1 on the border and 0 inside"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.ones((5,5),dtype='int')\n",
    "arr[1:-1,1:-1] = 0\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 16. How to add a border (filled with 0's) around an existing array?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0]\n",
      " [0 1 1 1 1 1 0]\n",
      " [0 1 1 1 1 1 0]\n",
      " [0 1 1 1 1 1 0]\n",
      " [0 1 1 1 1 1 0]\n",
      " [0 1 1 1 1 1 0]\n",
      " [0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.ones((5,5),dtype='int')\n",
    "arr = np.pad(arr, pad_width=1, mode='constant', constant_values=0)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 17. What is the result of the following expression?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "0 * np.nan\n",
    "np.nan == np.nan\n",
    "np.inf > np.nan\n",
    "np.nan - np.nan\n",
    "np.nan in set([np.nan])\n",
    "0.3 == 3 * 0.1\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0]\n",
      " [1 0 0 0 0]\n",
      " [0 2 0 0 0]\n",
      " [0 0 3 0 0]\n",
      " [0 0 0 4 0]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.diag([1,2,3,4],k=-1)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.zeros((8,8), dtype = 'int')\n",
    "arr[0::2,1::2] = 1\n",
    "arr[1::2,0::2] = 1\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 5, 4)\n"
     ]
    }
   ],
   "source": [
    "print (np.unravel_index(100, (6,7,8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}