3장_넘파이_배열_프로그래밍_(3.3).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a28e93a",
   "metadata": {},
   "source": [
    "# 배열의 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cce564d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.arange(1, 10001)\n",
    "y = np.arange(10001, 20001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "584f67f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10002, 10004, 10006, 10008, 10010, 10012, 10014, 10016, 10018,\n",
       "       10020])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.zeros_like(x)\n",
    "for i in range(10000):\n",
    "    z[i] = x[i] + y[i]\n",
    "\n",
    "z[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f50d7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10002, 10004, 10006, 10008, 10010, 10012, 10014, 10016, 10018,\n",
       "       10020])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = x + y\n",
    "z[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c632a1bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "b = np.array([4, 2, 2, 4])\n",
    "c = np.array([1, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71d50cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(a == c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2d72506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(5)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08162a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  2.71828183,  7.3890561 , 20.08553692, 54.59815003])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32cedccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.69314718, 1.09861229, 1.38629436, 1.60943791])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(a + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12399bd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00a1037e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0, 100, 200, 300, 400, 500, 600, 700, 800, 900])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0d823192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(12).reshape(3, 4) # 생성 후 바로 변환\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "85b140a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,  100,  200,  300],\n",
       "       [ 400,  500,  600,  700],\n",
       "       [ 800,  900, 1000, 1100]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9125c70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3213b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.ones_like(x)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "58d9c822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f18b8bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c4bf333a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [1, 2, 3],\n",
       "       [2, 3, 4],\n",
       "       [3, 4, 5],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.vstack([range(7)[i:i+3] for i in range(5)])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "255929dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.arange(5)[:, np.newaxis]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "19cad15d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "67e549cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x. shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "344524b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 2,  3,  4],\n",
       "       [ 4,  5,  6],\n",
       "       [ 6,  7,  8],\n",
       "       [ 8,  9, 10]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b1fa4bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3, 4])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "366d5318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "376434fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1, 3, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cc417f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "662fab45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3a01f2e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1, 2, 3, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8bd9db68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.75"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a5e7541d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "30a48458",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_17084\\2477046962.py:1: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  a = np.zeros((100, 100), dtype=np.int)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((100, 100), dtype=np.int)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7f871908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.any(a != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f5587910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(a == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4fc42edb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [2, 2]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1, 1], [2, 2]])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d6b31a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c7c7a9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(axis= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8dc28a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array(range(30)).reshape(5,6)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3d3ebd3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "435"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1d72fba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15,  51,  87, 123, 159])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "df32ec7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 11, 17, 23, 29])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f5f2dacb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12., 13., 14., 15., 16., 17.])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1b7e5a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f4712a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  3,  5,  7],\n",
       "       [ 1, 12, 11,  9],\n",
       "       [ 2, 15,  1, 14]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[4,  3,  5,  7],\n",
    "              [1, 12, 11,  9],\n",
    "              [2, 15,  1, 14]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "797e22ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  4,  5,  7],\n",
       "       [ 1,  9, 11, 12],\n",
       "       [ 1,  2, 14, 15]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7e7812bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  3,  1,  7],\n",
       "       [ 2, 12,  5,  9],\n",
       "       [ 4, 15, 11, 14]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, axis=0) # 각 행 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2f973a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "a.sort(axis=1) # 각 열 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "52b90c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[  1,    2,    3,    4],\n",
    "       [ 46,   99,  100,   71],\n",
    "       [ 81,   59,   90,  100]])\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "09e12fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng_rank = a.argsort(axis = 1)[1]\n",
    "eng_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "65b06f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = a[:,eng_rank]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c7b2a6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   4,   2,   3],\n",
       "       [ 46,  71,  99, 100],\n",
       "       [ 81, 100,  59,  90]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "78d33f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[  1,    2,    3,    4],\n",
    "       [ 46,   99,  100,   71],\n",
    "       [ 81,   59,   90,  100]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a885bb64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_rank = a.argsort()[2]\n",
    "math_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b382c13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   2,   3,   4],\n",
       "       [ 46,  99, 100,  71],\n",
       "       [ 81,  59,  90, 100]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = a[:,[1, 0, 2, 3]]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c023b28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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