numpy_start.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.15.1'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.__version__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "2\n",
      "[1, 2, 3, 5]\n"
     ]
    }
   ],
   "source": [
    "x = True\n",
    "y = x\n",
    "x = False\n",
    "print(y)\n",
    "\n",
    "x = 2\n",
    "y = x\n",
    "x = 3\n",
    "print(y)\n",
    "\n",
    "x = [1,2,3]\n",
    "y = x\n",
    "x.append(5)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"img/diffintcpy.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = list(range(10))\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2 = [str(c) for c in L]\n",
    "L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import array\n",
    "L = list(range(10))\n",
    "A = array.array('i', L)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Массивы numpy однородны, если типы не совпадают - производится повышающее приведение."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.14, 4.  , 2.  , 3.  ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([3.14, 4, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4.], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3, 4], dtype='float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[range(2, 5), range(4, 7), range(6, 9)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4],\n",
       "       [4, 5, 6],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [range(i, i+3) for i in [2, 4, 6]]\n",
    "print(a)\n",
    "np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,5), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.14, 3.14, 3.14, 3.14, 3.14],\n",
       "       [3.14, 3.14, 3.14, 3.14, 3.14],\n",
       "       [3.14, 3.14, 3.14, 3.14, 3.14]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((3,5), 3.14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,20,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.38303978, 0.93564049, 0.93533302],\n",
       "       [0.95822581, 0.74247345, 0.30606027],\n",
       "       [0.55273187, 0.79360422, 0.92599448]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.64205479,  0.11843933,  0.58173108],\n",
       "       [-1.57575729,  0.78710593, -0.9015265 ],\n",
       "       [-0.05366371, -1.16295209, -0.63821982]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#создаем массив 3х3 нормально распределенных случайных значения с медианой 0 и стандартным отклонением 1\n",
    "np.random.normal(0, 1, (3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 9, 6],\n",
       "       [3, 1, 9],\n",
       "       [5, 4, 8]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, (3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1.])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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