tools_numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**도구 - 넘파이(NumPy)**\n",
    "\n",
    "*넘파이(NumPy)는 파이썬의 과학 컴퓨팅을 위한 기본 라이브러리입니다. 넘파이의 핵심은 강력한 N-차원 배열 객체입니다. 또한 선형 대수, 푸리에(Fourier) 변환, 유사 난수 생성과 같은 유용한 함수들도 제공합니다.\"\n",
    "\n",
    "# 배열 생성\n",
    "\n",
    "먼저 파이썬 2와 3에 모두 호환되도록 필요한 모듈을 임포트합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, unicode_literals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`numpy`를 임포트해 보죠. 대부분의 사람들이 `np`로 알리아싱하여 임포트합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.zeros`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`zeros` 함수는 0으로 채워진 배열을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2D 배열(즉, 행렬)을 만들려면 원하는 행과 열의 크기를 튜플로 전달합니다. 예를 들어 다음은 $3 \\times 4$ 크기의 행렬입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 용어\n",
    "\n",
    "* 넘파이에서 각 차원을 **축**(axis) 이라고 합니다\n",
    "* 축의 개수를 **랭크**(rank) 라고 합니다.\n",
    "    * 예를 들어, 위의 $3 \\times 4$ 행렬은 랭크 2인 배열입니다(즉 2차원입니다).\n",
    "    * 첫 번째 축의 길이는 3이고 두 번째 축의 길이는 4입니다.\n",
    "* 배열의 축 길이를 배열의 **크기**(shape)라고 합니다.\n",
    "    * 예를 들어, 위 행렬의 크기는 `(3, 4)`입니다.\n",
    "    * 랭크는 크기의 길이와 같습니다.\n",
    "* 배열의 **사이즈**(size)는 전체 원소의 개수입니다. 축의 길이를 모두 곱해서 구할 수 있습니다(가령, $3 \\times 4=12$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((3,4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim  # len(a.shape)와 같습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N-차원 배열\n",
    "임의의 랭크 수를 가진 N-차원 배열을 만들 수 있습니다. 예를 들어, 다음은 크기가 `(2,3,4)`인 3D 배열(랭크=3)입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 배열 타입\n",
    "넘파이 배열의 타입은 `ndarray`입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.zeros((3,4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.ones`\n",
    "`ndarray`를 만들 수 있는 넘파이 함수가 많습니다.\n",
    "\n",
    "다음은 1로 채워진 $3 \\times 4$ 크기의 행렬입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.full`\n",
    "주어진 값으로 지정된 크기의 배열을 초기화합니다. 다음은 `π`로 채워진 $3 \\times 4$ 크기의 행렬입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.14159265, 3.14159265, 3.14159265, 3.14159265],\n",
       "       [3.14159265, 3.14159265, 3.14159265, 3.14159265],\n",
       "       [3.14159265, 3.14159265, 3.14159265, 3.14159265]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((3,4), np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.empty`\n",
    "초기화되지 않은 $2 \\times 3$ 크기의 배열을 만듭니다(배열의 내용은 예측이 불가능하며 메모리 상황에 따라 달라집니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.16318369e-028, 5.30276956e+180, 5.05117710e-038],\n",
       "       [4.57045791e-071, 4.46470455e-086, 3.36009604e-143]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## np.array\n",
    "`array` 함수는 파이썬 리스트를 사용하여 `ndarray`를 초기화합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [10, 20, 30, 40]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2,3,4], [10, 20, 30, 40]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.arange`\n",
    "파이썬의 기본 `range` 함수와 비슷한 넘파이 `range` 함수를 사용하여 `ndarray`를 만들 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "부동 소수도 가능합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1.0, 5.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬의 기본 `range` 함수처럼 건너 뛰는 정도를 지정할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1, 5, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "부동 소수를 사용하면 원소의 개수가 일정하지 않을 수 있습니다. 예를 들면 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n",
      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n",
      "[0.         0.33333333 0.66666667 1.         1.33333334]\n"
     ]
    }
   ],
   "source": [
    "print(np.arange(0, 5/3, 1/3)) # 부동 소수 오차 때문에, 최댓값은 4/3 또는 5/3이 됩니다.\n",
    "print(np.arange(0, 5/3, 0.333333333))\n",
    "print(np.arange(0, 5/3, 0.333333334))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.linspace`\n",
    "이런 이유로 부동 소수를 사용할 땐 `arange` 대신에 `linspace` 함수를 사용하는 것이 좋습니다. `linspace` 함수는 지정된 개수만큼 두 값 사이를 나눈 배열을 반환합니다(`arange`와는 다르게 최댓값이 **포함**됩니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n"
     ]
    }
   ],
   "source": [
    "print(np.linspace(0, 5/3, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.rand`와 `np.randn`\n",
    "넘파이의 `random` 모듈에는 `ndarray`를 랜덤한 값으로 초기화할 수 있는 함수들이 많이 있습니다.\n",
    "예를 들어, 다음은 (균등 분포인) 0과 1사이의 랜덤한 부동 소수로 $3 \\times 4$ 행렬을 초기화합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.26272018, 0.93881917, 0.93207489, 0.01658648],\n",
       "       [0.02431461, 0.62299001, 0.71867234, 0.85509338],\n",
       "       [0.27360841, 0.95553865, 0.41186574, 0.30810366]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음은 평균이 0이고 분산이 1인 일변량 [정규 분포](https://ko.wikipedia.org/wiki/%EC%A0%95%EA%B7%9C_%EB%B6%84%ED%8F%AC)(가우시안 분포)에서 샘플링한 랜덤한 부동 소수를 담은 $3 \\times 4$ 행렬입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.32054258, -1.93692499,  0.21501349,  1.11438429],\n",
       "       [ 0.70528145, -0.09409769, -1.02990618,  0.43173201],\n",
       "       [-1.03069971, -0.09908788,  1.16040291, -0.08943257]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 분포의 모양을 알려면 맷플롯립을 사용해 그려보는 것이 좋습니다(더 자세한 것은 [맷플롯립 튜토리얼](tools_matplotlib.ipynb)을 참고하세요):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.rand(100000), normed=True, bins=100, histtype=\"step\", color=\"blue\", label=\"rand\")\n",
    "plt.hist(np.random.randn(100000), normed=True, bins=100, histtype=\"step\", color=\"red\", label=\"randn\")\n",
    "plt.axis([-2.5, 2.5, 0, 1.1])\n",
    "plt.legend(loc = \"upper left\")\n",
    "plt.title(\"Random distributions\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## np.fromfunction\n",
    "함수를 사용하여 `ndarray`를 초기화할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
       "        [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]],\n",
       "\n",
       "       [[ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.],\n",
       "        [ 2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_function(z, y, x):\n",
    "    return x * y + z\n",
    "\n",
    "np.fromfunction(my_function, (3, 2, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "넘파이는 먼저 크기가 `(3, 2, 10)`인 세 개의 `ndarray`(차원마다 하나씩)를 만듭니다. 각 배열은 축을 따라 좌표 값과 같은 값을 가집니다. 예를 들어, `z` 축에 있는 배열의 모든 원소는 z-축의 값과 같습니다:\n",
    "\n",
    "    [[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
    "      [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]\n",
    "    \n",
    "     [[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
    "      [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]\n",
    "    \n",
    "     [[ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]\n",
    "      [ 2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]]]\n",
    "\n",
    "위의 식 `x * y + z`에서 x, y, z는 사실 `ndarray`입니다(배열의 산술 연산에 대해서는 아래에서 설명합니다). 중요한 점은 함수 `my_function`이 원소마다 호출되는 것이 아니고 딱 **한 번** 호출된다는 점입니다. 그래서 매우 효율적으로 초기화할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 배열 데이터\n",
    "## `dtype`\n",
    "넘파이의 `ndarray`는 모든 원소가 동일한 타입(보통 숫자)을 가지기 때문에 효율적입니다. `dtype` 속성으로 쉽게 데이터 타입을 확인할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64 [1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "c = np.arange(1, 5)\n",
    "print(c.dtype, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 [1. 2. 3. 4.]\n"
     ]
    }
   ],
   "source": [
    "c = np.arange(1.0, 5.0)\n",
    "print(c.dtype, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "넘파이가 데이터 타입을 결정하도록 내버려 두는 대신 `dtype` 매개변수를 사용해서 배열을 만들 때 명시적으로 지정할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complex64 [1.+0.j 2.+0.j 3.+0.j 4.+0.j]\n"
     ]
    }
   ],
   "source": [
    "d = np.arange(1, 5, dtype=np.complex64)\n",
    "print(d.dtype, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "가능한 데이터 타입은 `int8`, `int16`, `int32`, `int64`, `uint8`|`16`|`32`|`64`, `float16`|`32`|`64`, `complex64`|`128`가 있습니다. 전체 리스트는 [온라인 문서](http://docs.scipy.org/doc/numpy/user/basics.types.html)를 참고하세요.\n",
    "\n",
    "## `itemsize`\n",
    "`itemsize` 속성은 각 아이템의 크기(바이트)를 반환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = np.arange(1, 5, dtype=np.complex64)\n",
    "e.itemsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `data` 버퍼\n",
    "배열의 데이터는 1차원 바이트 버퍼로 메모리에 저장됩니다. `data` 속성을 사용해 참조할 수 있습니다(사용할 일은 거의 없겠지만요)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<memory at 0x7f37d88f73a8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = np.array([[1,2],[1000, 2000]], dtype=np.int32)\n",
    "f.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬 2에서는 `f.data`가 버퍼이고 파이썬 3에서는 memoryview입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\xe8\\x03\\x00\\x00\\xd0\\x07\\x00\\x00'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if (hasattr(f.data, \"tobytes\")):\n",
    "    data_bytes = f.data.tobytes() # python 3\n",
    "else:\n",
    "    data_bytes = memoryview(f.data).tobytes() # python 2\n",
    "\n",
    "data_bytes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여러 개의 `ndarray`가 데이터 버퍼를 공유할 수 있습니다. 하나를 수정하면 다른 것도 바뀝니다. 잠시 후에 예를 살펴 보겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 배열 크기 변경\n",
    "\n",
    "## 자신을 변경\n",
    "\n",
    "`ndarray`의 `shape` 속성을 지정하면 간단히 크기를 바꿀 수 있습니다. 배열의 원소 개수는 동일하게 유지됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
      "랭크: 1\n"
     ]
    }
   ],
   "source": [
    "g = np.arange(24)\n",
    "print(g)\n",
    "print(\"랭크:\", g.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n",
      "랭크: 2\n"
     ]
    }
   ],
   "source": [
    "g.shape = (6, 4)\n",
    "print(g)\n",
    "print(\"랭크:\", g.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n",
      "랭크: 3\n"
     ]
    }
   ],
   "source": [
    "g.shape = (2, 3, 4)\n",
    "print(g)\n",
    "print(\"랭크:\", g.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `reshape`\n",
    "\n",
    "`reshape` 함수는 동일한 데이터를 가리키는 새로운 `ndarray` 객체를 반환합니다. 한 배열을 수정하면 다른 것도 함께 바뀝니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 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",
      "랭크: 2\n"
     ]
    }
   ],
   "source": [
    "g2 = g.reshape(4,6)\n",
    "print(g2)\n",
    "print(\"랭크:\", g2.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "행 1, 열 2의 원소를 999로 설정합니다(인덱싱 방식은 아래를 참고하세요)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   2,   3,   4,   5],\n",
       "       [  6,   7, 999,   9,  10,  11],\n",
       "       [ 12,  13,  14,  15,  16,  17],\n",
       "       [ 18,  19,  20,  21,  22,  23]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g2[1, 2] = 999\n",
    "g2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이에 상응하는 `g`의 원소도 수정됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1,   2,   3],\n",
       "        [  4,   5,   6,   7],\n",
       "        [999,   9,  10,  11]],\n",
       "\n",
       "       [[ 12,  13,  14,  15],\n",
       "        [ 16,  17,  18,  19],\n",
       "        [ 20,  21,  22,  23]]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `ravel`\n",
    "\n",
    "마지막으로 `ravel` 함수는 동일한 데이터를 가리키는 새로운 1차원 `ndarray`를 반환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7, 999,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 산술 연산\n",
    "\n",
    "일반적인 산술 연산자(`+`, `-`, `*`, `/`, `//`, `**` 등)는 모두 `ndarray`와 사용할 수 있습니다. 이 연산자는 원소별로 적용됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + b  = [19 27 35 43]\n",
      "a - b  = [ 9 19 29 39]\n",
      "a * b  = [70 92 96 82]\n",
      "a / b  = [ 2.8         5.75       10.66666667 20.5       ]\n",
      "a // b  = [ 2  5 10 20]\n",
      "a % b  = [4 3 2 1]\n",
      "a ** b = [537824 279841  32768   1681]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([14, 23, 32, 41])\n",
    "b = np.array([5,  4,  3,  2])\n",
    "print(\"a + b  =\", a + b)\n",
    "print(\"a - b  =\", a - b)\n",
    "print(\"a * b  =\", a * b)\n",
    "print(\"a / b  =\", a / b)\n",
    "print(\"a // b  =\", a // b)\n",
    "print(\"a % b  =\", a % b)\n",
    "print(\"a ** b =\", a ** b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기 곱셈은 행렬 곱셈이 아닙니다. 행렬 연산은 아래에서 설명합니다.\n",
    "\n",
    "배열의 크기는 같아야 합니다. 그렇지 않으면 넘파이가 브로드캐스팅 규칙을 적용합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 브로드캐스팅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "일반적으로 넘파이는 동일한 크기의 배열을 기대합니다. 그렇지 않은 상황에는 브로드캐시틍 규칙을 적용합니다:\n",
    "\n",
    "## 규칙 1\n",
    "\n",
    "배열의 랭크가 동일하지 않으면 랭크가 맞을 때까지 랭크가 작은 배열 앞에 1을 추가합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 1, 2, 3, 4]]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = np.arange(5).reshape(1, 1, 5)\n",
    "h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기에 `(1,1,5)` 크기의 3D 배열에 `(5,)` 크기의 1D 배열을 더해 보죠. 브로드캐스팅의 규칙 1이 적용됩니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[10, 21, 32, 43, 54]]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h + [10, 20, 30, 40, 50]  # 다음과 동일합니다: h + [[[10, 20, 30, 40, 50]]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 규칙 2\n",
    "\n",
    "특정 차원이 1인 배열은 그 차원에서 크기가 가장 큰 배열의 크기에 맞춰 동작합니다. 배열의 원소가 차원을 따라 반복됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = np.arange(6).reshape(2, 3)\n",
    "k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`(2,3)` 크기의 2D `ndarray`에 `(2,1)` 크기의 2D 배열을 더해 보죠. 넘파이는 브로드캐스팅 규칙 2를 적용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 101, 102],\n",
       "       [203, 204, 205]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + [[100], [200]]  # 다음과 같습니다: k + [[100, 100, 100], [200, 200, 200]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "규칙 1과 2를 합치면 다음과 같이 동작합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 201, 302],\n",
       "       [103, 204, 305]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + [100, 200, 300]  # 규칙 1 적용: [[100, 200, 300]], 규칙 2 적용: [[100, 200, 300], [100, 200, 300]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또 매우 간단히 다음 처럼 해도 됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1000, 1001, 1002],\n",
       "       [1003, 1004, 1005]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + 1000  # 다음과 같습니다: k + [[1000, 1000, 1000], [1000, 1000, 1000]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 규칙 3\n",
    "\n",
    "규칙 1 & 2을 적용했을 때 모든 배열의 크기가 맞아야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operands could not be broadcast together with shapes (2,3) (2,) \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    k + [33, 44]\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "브로드캐스팅 규칙은 산술 연산 뿐만 아니라 넘파이 연산에서 많이 사용됩니다. 아래에서 더 보도록 하죠. 브로드캐스팅에 관한 더 자세한 정보는 [온라인 문서](https://docs.scipy.org/doc/numpy-dev/user/basics.broadcasting.html)를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 업캐스팅\n",
    "\n",
    "`dtype`이 다른 배열을 합칠 때 넘파이는 (실제 값에 상관없이) 모든 값을 다룰 수 있는 타입으로 업캐스팅합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8 [0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "k1 = np.arange(0, 5, dtype=np.uint8)\n",
    "print(k1.dtype, k1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 [ 5  7  9 11 13]\n"
     ]
    }
   ],
   "source": [
    "k2 = k1 + np.array([5, 6, 7, 8, 9], dtype=np.int8)\n",
    "print(k2.dtype, k2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모든 `int8`과 `uint8` 값(-128에서 255까지)을 표현하기 위해 `int16`이 필요합니다. 이 코드에서는 `uint8`이면 충분하지만 업캐스팅되었습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 [1.5 2.5 3.5 4.5 5.5]\n"
     ]
    }
   ],
   "source": [
    "k3 = k1 + 1.5\n",
    "print(k3.dtype, k3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 조건 연산자"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "조건 연산자도 원소별로 적용됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, False])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.array([20, -5, 30, 40])\n",
    "m < [15, 16, 35, 36]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "브로드캐스팅을 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m < 25  # m < [25, 25, 25, 25] 와 동일"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "불리언 인덱싱과 함께 사용하면 아주 유용합니다(아래에서 설명하겠습니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, -5])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[m < 25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 수학 함수와 통계 함수"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ndarray`에서 사용할 수 있는 수학 함수와 통계 함수가 많습니다.\n",
    "\n",
    "## `ndarray` 메서드\n",
    "\n",
    "일부 함수는 `ndarray` 메서드로 제공됩니다. 예를 들면:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.5  3.1  7. ]\n",
      " [10.  11.  12. ]]\n",
      "평균 = 6.766666666666667\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
    "print(a)\n",
    "print(\"평균 =\", a.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 명령은 크기에 상관없이 `ndarray`에 있는 모든 원소의 평균을 계산합니다.\n",
    "\n",
    "다음은 유용한 `ndarray` 메서드입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min = -2.5\n",
      "max = 12.0\n",
      "sum = 40.6\n",
      "prod = -71610.0\n",
      "std = 5.084835843520964\n",
      "var = 25.855555555555554\n"
     ]
    }
   ],
   "source": [
    "for func in (a.min, a.max, a.sum, a.prod, a.std, a.var):\n",
    "    print(func.__name__, \"=\", func())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 함수들은 선택적으로 매개변수 `axis`를 사용합니다. 지정된 축을 따라 원소에 연산을 적용하는데 사용합니다. 예를 들면:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=np.arange(24).reshape(2,3,4)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 14, 16, 18],\n",
       "       [20, 22, 24, 26],\n",
       "       [28, 30, 32, 34]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=0)  # 첫 번째 축을 따라 더함, 결과는 3x4 배열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 15, 18, 21],\n",
       "       [48, 51, 54, 57]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=1)  # 두 번째 축을 따라 더함, 결과는 2x4 배열"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여러 축에 대해서 더할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  92, 124])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=(0,2))  # 첫 번째 축과 세 번째 축을 따라 더함, 결과는 (3,) 배열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 92, 124)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0+1+2+3 + 12+13+14+15, 4+5+6+7 + 16+17+18+19, 8+9+10+11 + 20+21+22+23"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 일반 함수\n",
    "\n",
    "넘파이는 일반 함수(universal function) 또는 **ufunc**라고 부르는 원소별 함수를 제공합니다. 예를 들면 `square` 함수는 원본 `ndarray`를 복사하여 각 원소를 제곱한 새로운 `ndarray` 객체를 반환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.25,   9.61,  49.  ],\n",
       "       [100.  , 121.  , 144.  ]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
    "np.square(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음은 유용한 단항 일반 함수들입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "원본 ndarray\n",
      "[[-2.5  3.1  7. ]\n",
      " [10.  11.  12. ]]\n",
      "\n",
      " absolute\n",
      "[[ 2.5  3.1  7. ]\n",
      " [10.  11.  12. ]]\n",
      "\n",
      " sqrt\n",
      "[[       nan 1.76068169 2.64575131]\n",
      " [3.16227766 3.31662479 3.46410162]]\n",
      "\n",
      " exp\n",
      "[[8.20849986e-02 2.21979513e+01 1.09663316e+03]\n",
      " [2.20264658e+04 5.98741417e+04 1.62754791e+05]]\n",
      "\n",
      " log\n",
      "[[       nan 1.13140211 1.94591015]\n",
      " [2.30258509 2.39789527 2.48490665]]\n",
      "\n",
      " sign\n",
      "[[-1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "\n",
      " ceil\n",
      "[[-2.  4.  7.]\n",
      " [10. 11. 12.]]\n",
      "\n",
      " modf\n",
      "(array([[-0.5,  0.1,  0. ],\n",
      "       [ 0. ,  0. ,  0. ]]), array([[-2.,  3.,  7.],\n",
      "       [10., 11., 12.]]))\n",
      "\n",
      " isnan\n",
      "[[False False False]\n",
      " [False False False]]\n",
      "\n",
      " cos\n",
      "[[-0.80114362 -0.99913515  0.75390225]\n",
      " [-0.83907153  0.0044257   0.84385396]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \"\"\"\n",
      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in log\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "print(\"원본 ndarray\")\n",
    "print(a)\n",
    "for func in (np.abs, np.sqrt, np.exp, np.log, np.sign, np.ceil, np.modf, np.isnan, np.cos):\n",
    "    print(\"\\n\", func.__name__)\n",
    "    print(func(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 이항 일반 함수\n",
    "\n",
    "두 개의 `ndarray`에 원소별로 적용되는 이항 함수도 많습니다. 두 배열이 동일한 크기가 아니면 브로드캐스팅 규칙이 적용됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  6,  2, 11])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, -2, 3, 4])\n",
    "b = np.array([2, 8, -1, 7])\n",
    "np.add(a, b)  # a + b 와 동일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.greater(a, b)  # a > b 와 동일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 8, 3, 7])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.maximum(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., -3.,  4.])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.copysign(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 배열 인덱싱\n",
    "\n",
    "## 1차원 배열\n",
    "\n",
    "1차원 넘파이 배열은 보통의 파이썬 배열과 비슷하게 사용할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 5, 3, 19, 13, 7, 3])\n",
    "a[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 19, 13])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 19, 13,  7])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 13,  3])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 13, 19,  3,  5,  1])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "물론 원소를 수정할 수 있죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   5,   3, 999,  13,   7,   3])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3]=999\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "슬라이싱을 사용해 `ndarray`를 수정할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   5, 997, 998, 999,   7,   3])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5] = [997, 998, 999]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 보통의 파이썬 배열과 차이점\n",
    "\n",
    "보통의 파이썬 배열과 대조적으로 `ndarray` 슬라이싱에 하나의 값을 할당하면 슬라이싱 전체에 복사됩니다. 위에서 언급한 브로드캐스팅 덕택입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  5, -1, -1, -1,  7,  3])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5] = -1\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또한 이런 식으로 `ndarray` 크기를 늘리거나 줄일 수 없습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot copy sequence with size 6 to array axis with dimension 3\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    a[2:5] = [1,2,3,4,5,6]  # 너무 길어요\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "원소를 삭제할 수도 없습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot delete array elements\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    del a[2:5]\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "중요한 점은 `ndarray`의 슬라이싱은 같은 데이터 버퍼를 바라보는 뷰(view)입니다. 슬라이싱된 객체를 수정하면 실제 원본 `ndarray`가 수정됩니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    5,   -1, 1000,   -1,    7,    3])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_slice = a[2:6]\n",
    "a_slice[1] = 1000\n",
    "a  # 원본 배열이 수정됩니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -1, 2000,   -1,    7])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3] = 2000\n",
    "a_slice  # 비슷하게 원본 배열을 수정하면 슬라이싱 객체에도 반영됩니다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터를 복사하려면 `copy` 메서드를 사용해야 합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    5,   -1, 2000,   -1,    7,    3])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "another_slice = a[2:6].copy()\n",
    "another_slice[1] = 3000\n",
    "a  # 원본 배열이 수정되지 않습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -1, 3000,   -1,    7])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3] = 4000\n",
    "another_slice  # 마찬가지로 원본 배열을 수정해도 복사된 배열은 바뀌지 않습니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 다차원 배열\n",
    "\n",
    "다차원 배열은 비슷한 방식으로 각 축을 따라 인덱싱 또는 슬라이싱해서 사용합니다. 콤마로 구분합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
       "       [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(48).reshape(4, 12)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, 2]  # 행 1, 열 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, :]  # 행 1, 모든 열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 13, 25, 37])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:, 1]  # 모든 행, 열 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**주의**: 다음 두 표현에는 미묘한 차이가 있습니다: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1:2, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "첫 번째 표현식은 `(12,)` 크기인 1D 배열로 행이 하나입니다. 두 번째는 `(1, 12)` 크기인 2D 배열로 같은 행을 반환합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 팬시 인덱싱(Fancy indexing)\n",
    "\n",
    "관심 대상의 인덱스 리스트를 지정할 수도 있습니다. 이를 팬시 인덱싱이라고 부릅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3,  4],\n",
       "       [26, 27, 28]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[(0,2), 2:5]  # 행 0과 2, 열 2에서 4(5-1)까지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  2, 11],\n",
       "       [23, 14, 23],\n",
       "       [35, 26, 35],\n",
       "       [47, 38, 47]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:, (-1, 2, -1)]  # 모든 행, 열 -1 (마지막), 2와 -1 (다시 반대 방향으로)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여러 개의 인덱스 리스트를 지정하면 인덱스에 맞는 값이 포함된 1D `ndarray`를 반환됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41, 33, 37, 33])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[(-1, 2, -1, 2), (5, 9, 1, 9)]  # returns a 1D array with b[-1, 5], b[2, 9], b[-1, 1] and b[2, 9] (again)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 고차원\n",
    "\n",
    "고차원에서도 동일한 방식이 적용됩니다. 몇 가지 예를 살펴 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3,  4,  5],\n",
       "        [ 6,  7,  8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15, 16, 17],\n",
       "        [18, 19, 20, 21, 22, 23]],\n",
       "\n",
       "       [[24, 25, 26, 27, 28, 29],\n",
       "        [30, 31, 32, 33, 34, 35]],\n",
       "\n",
       "       [[36, 37, 38, 39, 40, 41],\n",
       "        [42, 43, 44, 45, 46, 47]]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = b.reshape(4,2,6)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1, 4]  # 행렬 2, 행 1, 열 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27, 33])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, :, 3]  # 행렬 2, 모든 행, 열 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어떤 축에 대한 인덱스를 지정하지 않으면 이 축의 모든 원소가 반환됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 31, 32, 33, 34, 35])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1]  # 행렬 2, 행 1, 모든 열이 반환됩니다. c[2, 1, :]와 동일합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 생략 부호 (`...`)\n",
    "\n",
    "생략 부호(`...`)를 쓰면 모든 지정하지 않은 축의 원소를 포함합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, ...]  #  행렬 2, 모든 행, 모든 열. c[2, :, :]와 동일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 31, 32, 33, 34, 35])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1, ...]  # 행렬 2, 행 1, 모든 열. c[2, 1, :]와 동일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27, 33])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, ..., 3]  # 행렬 2, 모든 행, 열 3. c[2, :, 3]와 동일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  9],\n",
       "       [15, 21],\n",
       "       [27, 33],\n",
       "       [39, 45]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[..., 3]  # 모든 행렬, 모든 행, 열 3. c[:, :, 3]와 동일"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 불리언 인덱싱\n",
    "\n",
    "불리언 값을 가진 `ndarray`를 사용해 축의 인덱스를 지정할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
       "       [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(48).reshape(4, 12)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_on = np.array([True, False, True, False])\n",
    "b[rows_on, :]  # 행 0과 2, 모든 열. b[(0, 2), :]와 동일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  7, 10],\n",
       "       [13, 16, 19, 22],\n",
       "       [25, 28, 31, 34],\n",
       "       [37, 40, 43, 46]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_on = np.array([False, True, False] * 4)\n",
    "b[:, cols_on]  # 모든 행, 열 1, 4, 7, 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `np.ix_`\n",
    "\n",
    "여러 축에 걸쳐서는 불리언 인덱싱을 사용할 수 없고 `ix_` 함수를 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  7, 10],\n",
       "       [25, 28, 31, 34]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[np.ix_(rows_on, cols_on)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0],\n",
       "        [2]]), array([[ 1,  4,  7, 10]]))"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ix_(rows_on, cols_on)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ndarray`와 같은 크기의 불리언 배열을 사용하면 해당 위치가 `True`인 모든 원소를 담은 1D 배열이 반환됩니다. 일반적으로 조건 연산자와 함께 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[b % 3 == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 반복\n",
    "\n",
    "`ndarray`를 반복하는 것은 일반적인 파이썬 배열을 반복한는 것과 매우 유사합니다. 다차원 배열을 반복하면 첫 번째 축에 대해서 수행됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.arange(24).reshape(2, 3, 4)  # 3D 배열 (두 개의 3x4 행렬로 구성됨)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "아이템:\n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "아이템:\n",
      "[[12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n"
     ]
    }
   ],
   "source": [
    "for m in c:\n",
    "    print(\"아이템:\")\n",
    "    print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "아이템:\n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "아이템:\n",
      "[[12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(c)):  # len(c) == c.shape[0]\n",
    "    print(\"아이템:\")\n",
    "    print(c[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ndarray`에 있는 모든 원소를 반복하려면 `flat` 속성을 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "아이템: 0\n",
      "아이템: 1\n",
      "아이템: 2\n",
      "아이템: 3\n",
      "아이템: 4\n",
      "아이템: 5\n",
      "아이템: 6\n",
      "아이템: 7\n",
      "아이템: 8\n",
      "아이템: 9\n",
      "아이템: 10\n",
      "아이템: 11\n",
      "아이템: 12\n",
      "아이템: 13\n",
      "아이템: 14\n",
      "아이템: 15\n",
      "아이템: 16\n",
      "아이템: 17\n",
      "아이템: 18\n",
      "아이템: 19\n",
      "아이템: 20\n",
      "아이템: 21\n",
      "아이템: 22\n",
      "아이템: 23\n"
     ]
    }
   ],
   "source": [
    "for i in c.flat:\n",
    "    print(\"아이템:\", i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 배열 쌓기\n",
    "\n",
    "종종 다른 배열을 쌓아야 할 때가 있습니다. 넘파이는 이를 위해 몇 개의 함수를 제공합니다. 먼저 배열 몇 개를 만들어 보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = np.full((3,4), 1.0)\n",
    "q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2 = np.full((4,4), 2.0)\n",
    "q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = np.full((3,4), 3.0)\n",
    "q3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `vstack`\n",
    "\n",
    "`vstack` 함수를 사용하여 수직으로 쌓아보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q4 = np.vstack((q1, q2, q3))\n",
    "q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "q1, q2, q3가 모두 같은 크기이므로 가능합니다(수직으로 쌓기 때문에 수직 축은 크기가 달라도 됩니다).\n",
    "\n",
    "## `hstack`\n",
    "\n",
    "`hstack`을 사용해 수평으로도 쌓을 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q5 = np.hstack((q1, q3))\n",
    "q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 8)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q5.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "q1과 q3가 모두 3개의 행을 가지고 있기 때문에 가능합니다. q2는 4개의 행을 가지고 있기 때문에 q1, q3와 수평으로 쌓을 수 없습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all the input array dimensions except for the concatenation axis must match exactly\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    q5 = np.hstack((q1, q2, q3))\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `concatenate`\n",
    "\n",
    "`concatenate` 함수는 지정한 축으로도 배열을 쌓습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7 = np.concatenate((q1, q2, q3), axis=0)  # vstack과 동일\n",
    "q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "예상했겠지만 `hstack`은 `axis=1`으로 `concatenate`를 호출하는 것과 같습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `stack`\n",
    "\n",
    "`stack` 함수는 새로운 축을 따라 배열을 쌓습니다. 모든 배열은 같은 크기를 가져야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[3., 3., 3., 3.],\n",
       "        [3., 3., 3., 3.],\n",
       "        [3., 3., 3., 3.]]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q8 = np.stack((q1, q3))\n",
    "q8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q8.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 배열 분할\n",
    "\n",
    "분할은 쌓기의 반대입니다. 예를 들어 `vsplit` 함수는 행렬을 수직으로 분할합니다.\n",
    "\n",
    "먼저 6x4 행렬을 만들어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = np.arange(24).reshape(6,4)\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "수직으로 동일한 크기로 나누어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1, r2, r3 = np.vsplit(r, 3)\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`split` 함수는 주어진 축을 따라 배열을 분할합니다. `vsplit`는 `axis=0`으로 `split`를 호출하는 것과 같습니다. `hsplit` 함수는 `axis=1`로 `split`를 호출하는 것과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1],\n",
       "       [ 4,  5],\n",
       "       [ 8,  9],\n",
       "       [12, 13],\n",
       "       [16, 17],\n",
       "       [20, 21]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r4, r5 = np.hsplit(r, 2)\n",
    "r4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3],\n",
       "       [ 6,  7],\n",
       "       [10, 11],\n",
       "       [14, 15],\n",
       "       [18, 19],\n",
       "       [22, 23]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 배열 전치\n",
    "\n",
    "`transpose` 메서드는 주어진 순서대로 축을 뒤바꾸어 `ndarray` 데이터에 대한 새로운 뷰를 만듭니다.\n",
    "\n",
    "예를 위해 3D 배열을 만들어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 3,  4,  5]],\n",
       "\n",
       "       [[ 6,  7,  8],\n",
       "        [ 9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14],\n",
       "        [15, 16, 17]],\n",
       "\n",
       "       [[18, 19, 20],\n",
       "        [21, 22, 23]]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = np.arange(24).reshape(4,2,3)\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`0, 1, 2`(깊이, 높이, 너비) 축을 `1, 2, 0` (깊이→너비, 높이→깊이, 너비→높이) 순서로 바꾼 `ndarray`를 만들어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  6, 12, 18],\n",
       "        [ 1,  7, 13, 19],\n",
       "        [ 2,  8, 14, 20]],\n",
       "\n",
       "       [[ 3,  9, 15, 21],\n",
       "        [ 4, 10, 16, 22],\n",
       "        [ 5, 11, 17, 23]]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = t.transpose((1,2,0))\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`transpose` 기본값은 차원의 순서를 역전시킵니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  6, 12, 18],\n",
       "        [ 3,  9, 15, 21]],\n",
       "\n",
       "       [[ 1,  7, 13, 19],\n",
       "        [ 4, 10, 16, 22]],\n",
       "\n",
       "       [[ 2,  8, 14, 20],\n",
       "        [ 5, 11, 17, 23]]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = t.transpose()  # t.transpose((2, 1, 0))와 동일\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 4)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "넘파이는 두 축을 바꾸는 `swapaxes` 함수를 제공합니다. 예를 들어 깊이와 높이를 뒤바꾸어 `t`의 새로운 뷰를 만들어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 6,  7,  8],\n",
       "        [12, 13, 14],\n",
       "        [18, 19, 20]],\n",
       "\n",
       "       [[ 3,  4,  5],\n",
       "        [ 9, 10, 11],\n",
       "        [15, 16, 17],\n",
       "        [21, 22, 23]]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = t.swapaxes(0,1)  # t.transpose((1, 0, 2))와 동일\n",
    "t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4, 3)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 선형 대수학\n",
    "\n",
    "넘파이 2D 배열을 사용하면 파이썬에서 행렬을 효율적으로 표현할 수 있습니다. 주요 행렬 연산을 간단히 둘러 보겠습니다. 선형 대수학, 벡터와 행렬에 관한 자세한 내용은 [Linear Algebra tutorial](math_linear_algebra.ipynb)를 참고하세요.\n",
    "\n",
    "## 행렬 전치\n",
    "\n",
    "`T` 속성은 랭크가 2보다 크거나 같을 때 `transpose()`를 호출하는 것과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = np.arange(10).reshape(2,5)\n",
    "m1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 5],\n",
       "       [1, 6],\n",
       "       [2, 7],\n",
       "       [3, 8],\n",
       "       [4, 9]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`T` 속성은 랭크가 0이거나 1인 배열에는 아무런 영향을 미치지 않습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = np.arange(5)\n",
    "m2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 1D 배열을 하나의 행이 있는 행렬(2D)로 바꾼다음 전치를 수행할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2r = m2.reshape(1,5)\n",
    "m2r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2r.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 행렬 점곱\n",
    "\n",
    "두 개의 행렬을 만들어 `dot` 메서드로 행렬 [점곱](https://en.wikipedia.org/wiki/Dot_product)을 실행해 보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1 = np.arange(10).reshape(2, 5)\n",
    "n1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11],\n",
       "       [12, 13, 14]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2 = np.arange(15).reshape(5,3)\n",
    "n2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 90, 100, 110],\n",
       "       [240, 275, 310]])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1.dot(n2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**주의**: 앞서 언급한 것처럼 `n1*n2`는 점곱이 아니라 원소별 곱셈입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 역행렬과 유사 역행렬\n",
    "\n",
    "`numpy.linalg` 모듈 안에 많은 선형 대수 함수들이 있습니다. 특히 `inv` 함수는 정방 행렬의 역행렬을 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 5,  7, 11],\n",
       "       [21, 29, 31]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy.linalg as linalg\n",
    "\n",
    "m3 = np.array([[1,2,3],[5,7,11],[21,29,31]])\n",
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.31818182,  0.56818182,  0.02272727],\n",
       "       [ 1.72727273, -0.72727273,  0.09090909],\n",
       "       [-0.04545455,  0.29545455, -0.06818182]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.inv(m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pinv` 함수를 사용하여 [유사 역행렬](https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_pseudoinverse)을 계산할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.31818182,  0.56818182,  0.02272727],\n",
       "       [ 1.72727273, -0.72727273,  0.09090909],\n",
       "       [-0.04545455,  0.29545455, -0.06818182]])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.pinv(m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 단위 행렬\n",
    "\n",
    "행렬과 그 행렬의 역행렬을 곱하면 단위 행렬이 됩니다(작은 소숫점 오차가 있습니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00, -5.55111512e-17,  0.00000000e+00],\n",
       "       [-2.98372438e-16,  1.00000000e+00, -5.55111512e-17],\n",
       "       [ 5.78009862e-15,  1.27675648e-15,  1.00000000e+00]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.dot(linalg.inv(m3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`eye` 함수는 NxN 크기의 단위 행렬을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QR 분해\n",
    "\n",
    "`qr` 함수는 행렬을 [QR 분해](https://en.wikipedia.org/wiki/QR_decomposition)합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.04627448,  0.98786672,  0.14824986],\n",
       "       [-0.23137241,  0.13377362, -0.96362411],\n",
       "       [-0.97176411, -0.07889213,  0.22237479]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q, r = linalg.qr(m3)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-21.61018278, -29.89331494, -32.80860727],\n",
       "       [  0.        ,   0.62427688,   1.9894538 ],\n",
       "       [  0.        ,   0.        ,  -3.26149699]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  3.],\n",
       "       [ 5.,  7., 11.],\n",
       "       [21., 29., 31.]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q.dot(r)  # q.r는 m3와 같습니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 행렬식\n",
    "\n",
    "`det` 함수는 [행렬식](https://en.wikipedia.org/wiki/Determinant)을 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.99999999999999"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.det(m3)  # 행렬식 계산"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 고윳값과 고유벡터\n",
    "\n",
    "`eig` 함수는 정방 행렬의 [고윳값과 고유벡터](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors)를 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42.26600592, -0.35798416, -2.90802176])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenvalues, eigenvectors = linalg.eig(m3)\n",
    "eigenvalues # λ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.08381182, -0.76283526, -0.18913107],\n",
       "       [-0.3075286 ,  0.64133975, -0.6853186 ],\n",
       "       [-0.94784057, -0.08225377,  0.70325518]])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenvectors # v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.76996262e-15,  2.22044605e-16, -3.10862447e-15],\n",
       "       [ 7.10542736e-15,  2.02615702e-15, -1.11022302e-15],\n",
       "       [ 2.84217094e-14,  5.11049536e-15, -4.88498131e-15]])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.dot(eigenvectors) - eigenvalues * eigenvectors  # m3.v - λ*v = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 특잇값 분해\n",
    "\n",
    "`svd` 함수는 행렬을 입력으로 받아 그 행렬의 [특잇값 분해](https://en.wikipedia.org/wiki/Singular_value_decomposition)를 반환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 2],\n",
       "       [0, 0, 3, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4 = np.array([[1,0,0,0,2], [0,0,3,0,0], [0,0,0,0,0], [0,2,0,0,0]])\n",
    "m4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  0.,  0.],\n",
       "       [ 1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., -1.],\n",
       "       [ 0.,  0.,  1.,  0.]])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U, S_diag, V = linalg.svd(m4)\n",
    "U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.        , 2.23606798, 2.        , 0.        ])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_diag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`svd` 함수는 Σ의 대각 원소 값만 반환합니다. 전체 Σ 행렬은 다음과 같이 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 2.23606798, 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 2.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = np.zeros((4, 5))\n",
    "S[np.diag_indices(4)] = S_diag\n",
    "S  # Σ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.        ,  0.        ,  1.        ,  0.        ,  0.        ],\n",
       "       [ 0.4472136 ,  0.        ,  0.        ,  0.        ,  0.89442719],\n",
       "       [-0.        ,  1.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  1.        ,  0.        ],\n",
       "       [-0.89442719,  0.        ,  0.        ,  0.        ,  0.4472136 ]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 2.],\n",
       "       [0., 0., 3., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 2., 0., 0., 0.]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U.dot(S).dot(V) # U.Σ.V == m4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 대각원소와 대각합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  7, 31])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(m3)  # m3의 대각 원소입니다(왼쪽 위에서 오른쪽 아래)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.trace(m3)  # np.diag(m3).sum()와 같습니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 선형  방정식 풀기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`solve` 함수는 다음과 같은 선형 방정식을 풉니다:\n",
    "\n",
    "* $2x + 6y = 6$\n",
    "* $5x + 3y = -9$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.,  2.])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs  = np.array([[2, 6], [5, 3]])\n",
    "depvars = np.array([6, -9])\n",
    "solution = linalg.solve(coeffs, depvars)\n",
    "solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "solution을 확인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6., -9.]), array([ 6, -9]))"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs.dot(solution), depvars  # 네 같네요"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다! 다른 방식으로도 solution을 확인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(coeffs.dot(solution), depvars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 벡터화\n",
    "\n",
    "한 번에 하나씩 개별 배열 원소에 대해 연산을 실행하는 대신 배열 연산을 사용하면 훨씬 효율적인 코드를 만들 수 있습니다. 이를 벡터화라고 합니다. 이를 사용하여 넘파이의 최적화된 성능을 활용할 수 있습니다.\n",
    "\n",
    "예를 들어, $sin(xy/40.5)$ 식을 기반으로 768x1024 크기 배열을 생성하려고 합니다. 중첩 반복문 안에 파이썬의 math 함수를 사용하는 것은 **나쁜** 방법입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "data = np.empty((768, 1024))\n",
    "for y in range(768):\n",
    "    for x in range(1024):\n",
    "        data[y, x] = math.sin(x*y/40.5)  # 매우 비효율적입니다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "작동은 하지만 순수한 파이썬 코드로 반복문이 진행되기 때문에 아주 비효율적입니다. 이 알고리즘을 벡터화해 보죠. 먼저 넘파이 `meshgrid` 함수로 좌표 벡터를 사용해 행렬을 만듭니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       ...,\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023]])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_coords = np.arange(0, 1024)  # [0, 1, 2, ..., 1023]\n",
    "y_coords = np.arange(0, 768)   # [0, 1, 2, ..., 767]\n",
    "X, Y = np.meshgrid(x_coords, y_coords)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  1,   1,   1, ...,   1,   1,   1],\n",
       "       [  2,   2,   2, ...,   2,   2,   2],\n",
       "       ...,\n",
       "       [765, 765, 765, ..., 765, 765, 765],\n",
       "       [766, 766, 766, ..., 766, 766, 766],\n",
       "       [767, 767, 767, ..., 767, 767, 767]])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기서 볼 수 있듯이 `X`와 `Y` 모두 768x1024 배열입니다. `X`에 있는 모든 값은 수평 좌표에 해당합니다. `Y`에 있는 모든 값은 수직 좌표에 해당합니다.\n",
    "\n",
    "이제 간단히 배열 연산을 사용해 계산할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.sin(X*Y/40.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "맷플롯립의 `imshow` 함수를 사용해 이 데이터를 그려보죠([matplotlib tutorial](tools_matplotlib.ipynb)을 참조하세요)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "fig = plt.figure(1, figsize=(7, 6))\n",
    "plt.imshow(data, cmap=cm.hot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 저장과 로딩\n",
    "\n",
    "넘파이는 `ndarray`를 바이너리 또는 텍스트 포맷으로 손쉽게 저장하고 로드할 수 있습니다.\n",
    "\n",
    "## 바이너리 `.npy` 포맷\n",
    "\n",
    "랜덤 배열을 만들고 저장해 보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.61301393e-04, 4.46564139e-01, 8.68544670e-02],\n",
       "       [6.04807233e-01, 2.77666582e-01, 3.42502021e-01]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.rand(2,3)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"my_array\", a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "끝입니다! 파일 이름의 확장자를 지정하지 않았기 때문에 넘파이는 자동으로 `.npy`를 붙입니다. 파일 내용을 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f8\\', \\'fortran_order\\': False, \\'shape\\': (2, 3), }                                                          \\n\\x00(\\x0b\\xab\\xa1\\xad7?\\xf0\\xba\\x84\\xc1\\x81\\x94\\xdc?\\xb0\\x994\\'\\x18<\\xb6?\\xa2\\x89\\xc1\\xb2\\x94Z\\xe3?\\xa0l\\xe4\\rJ\\xc5\\xd1?\\x80\\xaf\"\\x99\\x8d\\xeb\\xd5?'"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"my_array.npy\", \"rb\") as f:\n",
    "    content = f.read()\n",
    "\n",
    "content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 파일을 넘파이 배열로 로드하려면 `load` 함수를 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.61301393e-04, 4.46564139e-01, 8.68544670e-02],\n",
       "       [6.04807233e-01, 2.77666582e-01, 3.42502021e-01]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_loaded = np.load(\"my_array.npy\")\n",
    "a_loaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 텍스트 포맷\n",
    "\n",
    "배열을 텍스트 포맷으로 저장해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"my_array.csv\", a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파일 내용을 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.613013932804998163e-04 4.465641393498307465e-01 8.685446698345944405e-02\n",
      "6.048072329834222050e-01 2.776665816942998077e-01 3.425020213569425209e-01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"my_array.csv\", \"rt\") as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 파일은 탭으로 구분된 CSV 파일입니다. 다른 구분자를 지정할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"my_array.csv\", a, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 파일을 로드하려면 `loadtxt` 함수를 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.61301393e-04, 4.46564139e-01, 8.68544670e-02],\n",
       "       [6.04807233e-01, 2.77666582e-01, 3.42502021e-01]])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_loaded = np.loadtxt(\"my_array.csv\", delimiter=\",\")\n",
    "a_loaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 압축된 `.npz` 포맷\n",
    "\n",
    "여러 개의 배열을 압축된 한 파일로 저장하는 것도 가능합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]], dtype=uint8)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(24, dtype=np.uint8).reshape(2, 3, 4)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez(\"my_arrays\", my_a=a, my_b=b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파일 내용을 확인해 보죠. `.npz` 파일 확장자가 자동으로 추가되었습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"b'PK\\\\x03\\\\x04\\\\x14\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00!\\\\x00\\\\xa3\\\\x80\\\\xdc?\\\\xb0\\\\x00\\\\x00\\\\x00\\\\xb0\\\\x00\\\\x00\\\\x00\\\\x08\\\\x00\\\\x00\\\\x00my_a.npy\\\\x93NUMPY\\\\x01\\\\x00v\\\\x00{\\\\'descr\\\\': \\\\'<f8\\\\', \\\\'fortran_order\\\\': [...]\""
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"my_arrays.npz\", \"rb\") as f:\n",
    "    content = f.read()\n",
    "\n",
    "repr(content)[:180] + \"[...]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음과 같이 이 파일을 로드할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<numpy.lib.npyio.NpzFile at 0x7f37cc594908>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays = np.load(\"my_arrays.npz\")\n",
    "my_arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "게으른 로딩을 수행하는 딕셔너리와 유사한 객체입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KeysView(<numpy.lib.npyio.NpzFile object at 0x7f37cc594908>)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.61301393e-04, 4.46564139e-01, 8.68544670e-02],\n",
       "       [6.04807233e-01, 2.77666582e-01, 3.42502021e-01]])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays[\"my_a\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 그 다음은?\n",
    "\n",
    "넘파이 기본 요소를 모두 배웠지만 훨씬 더 많은 기능이 있습니다. 이를 배우는 가장 좋은 방법은 넘파이를 직접 실습해 보고 훌륭한 [넘파이 문서](http://docs.scipy.org/doc/numpy/reference/index.html)에서 필요한 함수와 기능을 찾아 보세요."
   ]
  }
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