Lecture07(2D_Array01).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Одномерные массивы в Python"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7, -5,  9,  1,  0, -2,  4,  3,  6])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "l=[7,-5,9,1,0,-2,4,3,6]\n",
    "m=np.array(l)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Основы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4], dtype='int32')\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3. 4.]\n"
     ]
    }
   ],
   "source": [
    "b = np.array([1,2,3,4], dtype='float')\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-4 -3 -2 -1  0  1  2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array(range(-4,3,1))\n",
    "print(b)\n",
    "type(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Методы массивов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  даёт  размерность а\n",
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  число элементов\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Type\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Size\n",
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get total size\n",
    "a.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить количество элементов\n",
    "a.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Генерация различных типов массивов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-4 -3 -2 -1  0  1  2]\n"
     ]
    }
   ],
   "source": [
    "b = np.array(range(-4,3,1))\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.  6.  9. 14. 21. 30. 41. 54. 69. 86.]\n"
     ]
    }
   ],
   "source": [
    "x=5.0\n",
    "l=[x+i**2  for i in range(10)]\n",
    "a=np.array(l)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  3  5  7  9 11 13 15 17 19]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1.,  3.,  5.,  7.,  9., 11., 13., 15., 17., 19.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=np.arange(1,20,2)\n",
    "print(d)\n",
    "d1=np.array(d,float)\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.     1.792  2.583  3.375  4.167  4.958  5.75   6.542  7.333  8.125\n",
      "  8.917  9.708 10.5   11.292 12.083 12.875 13.667 14.458 15.25  16.042\n",
      " 16.833 17.625 18.417 19.208 20.   ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  2,  3,  4,  4,  5,  6,  7,  8,  8,  9, 10, 11, 12, 12, 13,\n",
       "       14, 15, 16, 16, 17, 18, 19, 20])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=np.linspace(1,20,25)\n",
    "print(np.around(d,3))\n",
    "d1=np.array(d,int)\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[67.994 62.04  10.524 57.107 21.795 18.467 41.098 60.342  0.107 33.239\n",
      " 53.376 14.255 80.666 18.514 73.479 49.111 98.703 33.524 13.511  7.248\n",
      "  2.43  50.64  21.761 64.033 14.189 88.911 49.582  1.767 14.571 71.947\n",
      " 99.049 43.549 16.622 31.837 31.725 68.693 83.98  52.939 12.945  8.095\n",
      " 41.24  31.027 55.718  2.559 88.042  4.271 71.29  97.84  12.232 63.931\n",
      " 89.442 74.85  53.228 62.799 12.749 67.778  0.92  38.064 21.518  8.425\n",
      " 14.836 73.804 14.362 46.237 52.067 19.808 75.822 64.604 83.882 50.767\n",
      " 16.719 76.639 49.365 87.231 84.522  8.624 20.844 68.135 95.982 41.969\n",
      " 13.404 79.654 78.517 31.145 94.43  66.623 85.147 35.117 94.59  41.738\n",
      "  7.718  6.108 72.891 70.311 18.155 83.649 31.22  24.954 22.955 86.591]\n"
     ]
    }
   ],
   "source": [
    "d=np.random.rand(100)*100\n",
    "print(np.around(d,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([53, 30, 68, 14, 99, 56, 64, 58, 17, 37,  7, 87, 84, 72, 53, 53, 88,\n",
       "       63,  2, 52, 50,  0,  9, 75, 21, 97, 69, 25, 61, 11, 82, 85,  5, 29,\n",
       "       19, 39, 24, 76, 83, 27, 12, 87, 81, 22,  3, 93, 50, 28, 61, 78, 60,\n",
       "        3, 43, 51, 77, 42,  3, 14, 57, 34, 97, 13, 23, 96, 64, 96, 69, 72,\n",
       "       55, 52,  2, 93,  2,  0, 79, 77, 85, 59, 12, 15, 60, 30,  1, 98, 80,\n",
       "       24, 45,  9, 44, 92, 76, 30,  5, 70, 13, 36, 17, 80, 10, 43])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "items = numpy.random.randint(100, size=100)\n",
    "print(len(items))\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 [72 59 68 98 87]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([72, 59, 68, 98, 87, 39, 10, 31, 12, 67, 19,  4, 62, 40, 22, 13,  6,\n",
       "       55,  5, 73, 14, 70, 69, 65, 76, 75, 64, 46, 60, 41, 42, 92, 17, 74,\n",
       "       90, 16, 53, 95, 21, 66, 11, 35, 48, 82, 28, 91, 18, 71, 57, 89, 78,\n",
       "       51,  8,  0, 81, 94,  1, 56, 24, 88, 77, 20,  7, 93, 86, 97, 83, 84,\n",
       "       26, 52, 30, 47, 79,  3, 25, 99, 23, 80, 45, 44, 50,  2, 61, 37, 27,\n",
       "       34, 29, 96, 36, 38, 15,  9, 58, 54, 43, 63, 85, 32, 33, 49])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "items = numpy.random.choice(100, 100, replace=False)\n",
    "print(len(items), items[:5])  # 100 \n",
    "items\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = a.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Математика с  одномерными массивами\n",
    "[математические функции в numpy ](https://docs.scipy.org/doc/numpy/reference/routines.math.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0,  1,  2])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 1. , 1.5, 2. ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 2, 4, 4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([1,0,1,0])\n",
    "print(a)\n",
    "a[0:4] + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  4.,  9., 16.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=np.array([1.,2,3,4])\n",
    "d ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.54030231, -0.41614684, -0.9899925 , -0.65364362])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the sin\n",
    "np.cos(a)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Статистика массива"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = np.array([1,2,3,4,5,6,7,8,9])\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(stats, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(stats, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Чтение/запись  данных из файла методами NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1  13  21  11 196  75   4   3  34]\n"
     ]
    }
   ],
   "source": [
    "filedata = np.genfromtxt('data1.txt', delimiter=',')\n",
    "filedata = filedata.astype('int32')\n",
    "print(filedata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Запись данных в файл"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.  13.  21.  11. 196.  75.   4.   3.  34.]\n"
     ]
    }
   ],
   "source": [
    "np.savetxt('test22.txt', filedata, fmt='%1.4e')   # use exponential notation\n",
    "filedata1 = np.genfromtxt('test22.txt', delimiter=',')\n",
    "filedata1 = filedata1.astype('float')\n",
    "print(filedata1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Запись и чтение массива в формате .npy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.random.randint(10,size=(1,10))\n",
    "np.save('arr', a)\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "((filedata > 2) & (filedata < 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(filedata < 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Извлекаем элементы <12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filedata[filedata < 12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Извлекаем чётные элементы  массива"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "filedata[filedata%2==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Фильтрация массива по индексам  элеметов массива "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.linspace(1.5,8,12)\n",
    "print(np.around(a,2)) \n",
    "np.around(a[0:4:1],2) #Извлекаем элементы a[0],a[1],a[2],a[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.around(a,2)) \n",
    "np.around(a[0:12:2],2) #Извлекаем элементы с чётными индексами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.around(a,2)) \n",
    "np.around(a[3:12:3],2) #Извлекаем элементы с индексами кратными 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Доступ / изменение определенных элементов массива"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a[0]=100\n",
    "a[3]=50\n",
    "np.around(a,2)"
   ]
  }
 ],
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