Lecture08(Array2D_Lec2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Двумерные  массивы, матрицы  в Python"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Графическое представление 2-х мерного  массива или матрицы         ![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Создания специальных типов 2-мерных массивов в numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "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": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.zeros((4,4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1=np.ones((4,4))\n",
    "a1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Создаём матрицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]])"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy.matlib\n",
    "m=np.matlib.zeros((3, 3))\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 1., 1.]])"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy.matlib\n",
    "m1=np.matlib.ones((3, 3))\n",
    "m1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Математика с матрицами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[2., 2., 2.],\n",
       "        [2., 2., 2.],\n",
       "        [2., 2., 2.]])"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2=d*2\n",
    "b2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2[0,:].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[6., 6., 6.],\n",
       "        [6., 6., 6.],\n",
       "        [6., 6., 6.]])"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.84147098, 0.84147098, 0.84147098],\n",
       "        [0.84147098, 0.84147098, 0.84147098],\n",
       "        [0.84147098, 0.84147098, 0.84147098]])"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Type\n",
    "d.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Size\n",
    "d.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get total size\n",
    "d\n",
    ".nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить количество элементов\n",
    "a.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обработка массивов и матриц с помощью циклов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13., 18., 13., 12.],\n",
       "       [ 4.,  5., 17., 18.],\n",
       "       [ 9., 11.,  9., 15.],\n",
       "       [19., 12., 13., 18.]])"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        a[i,j]=np.random.randint(20)\n",
    "a\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Находим сумму элементов >10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179.0"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=a[a>10].sum()\n",
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206.0"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45., 46., 52., 63.])"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([56., 44., 44., 62.])"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[3., 3., 3.],\n",
       "        [3., 3., 3.],\n",
       "        [3., 3., 3.]])"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Запись матрицы   в файл методами Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[13., 18., 13., 12.],\n",
       "        [ 4.,  5., 17., 18.],\n",
       "        [ 9., 11.,  9., 15.],\n",
       "        [19., 12., 13., 18.]])"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd=np.mat(a)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('d:\\\\test022.txt', dd, fmt='%1.4e')   # use exponential notation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Чтение/запись  данных из файла методами NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12., 13., 14., 15.],\n",
       "       [16., 17., 18., 19.],\n",
       "       [20., 21., 22., 23.]])"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd= np.genfromtxt('d:\\\\test023.txt', delimiter='')\n",
    "fd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12., 13., 14., 15., 16.],\n",
       "       [17., 18., 19., 20., 21.],\n",
       "       [33., 44., 55., 66., 77.]])"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd= np.genfromtxt('d:\\\\Книга1.csv', delimiter=';')\n",
    "fd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Запись и чтение матрицы/массива в формате .npy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13. 18. 13. 12.]\n",
      " [ 4.  5. 17. 18.]\n",
      " [ 9. 11.  9. 15.]\n",
      " [19. 12. 13. 18.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.save('arr', dd)\n",
    "a2 = np.load('arr.npy')\n",
    "print(a2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Фильтрация массива  по значениям элеметов массива с помощью масок "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[False, False, False, False],\n",
       "        [ True,  True, False, False],\n",
       "        [ True,  True,  True, False],\n",
       "        [False, False, False, False]])"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((dd > 2) & (dd < 12))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ True,  True,  True, False],\n",
       "        [False, False,  True,  True],\n",
       "        [False, False, False,  True],\n",
       "        [ True, False,  True,  True]])"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd > 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Извлекаем элементы <12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 4.,  5.,  9., 11.,  9.]])"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd[dd < 12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Извлекаем чётные элементы  матрицы/массива"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[18., 12.,  4., 18., 12., 18.]])"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd[dd%2==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Фильтрация массива по индексам  элеметов маптрицы/массива "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[13., 18., 13., 12.],\n",
       "        [ 4.,  5., 17., 18.]])"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd[0:2:1,:] #Извлекаем строки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[13., 18.],\n",
       "        [ 4.,  5.],\n",
       "        [ 9., 11.],\n",
       "        [19., 12.]])"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd[:,0:2:1] #Извлекаем столбцы"
   ]
  }
 ],
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