exNumpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9822c7c",
   "metadata": {},
   "source": [
    "### Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6367bbc8",
   "metadata": {},
   "source": [
    "넘파이(Numpy)는 수치 데이터를 다루는 파이썬 패키지입니다. Numpy의 핵심이라고 불리는 다차원 행렬 자료구조인 ndarray를 통해 벡터 및 행렬을 사용하는 선형 대수 계산에서 주로 사용됩니다. Numpy는 편의성뿐만 아니라, 속도면에서도 순수 파이썬에 비해 압도적으로 빠르다는 장점이 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f87410",
   "metadata": {},
   "source": [
    "Numpy의 주요 모듈은 아래와 같습니다.\n",
    "1. np.array() # 리스트, 튜플, 배열로 부터 ndarray를 생성\n",
    "2. np.asarray() # 기존의 array로 부터 ndarray를 생성\n",
    "3. np.arange() # range와 비슷\n",
    "4. np.linspace(start, end, num) # [start, end] 균일한 간격으로 num개 생성\n",
    "5. np.logspace(start, end, num) # [start, end] log scale 간격으로 num개 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a743ffdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03c05bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.20.2'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012044a8",
   "metadata": {},
   "source": [
    "#### ndarray 생성"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb222727",
   "metadata": {},
   "source": [
    "앞서 Numpy의 핵심은 ndarray라고 언급한 바 있습니다. np.array()는 리스트, 튜플, 배열로 부터 ndarray를 생성합니다. 또한 인덱스가 항상 0으로 시작한다는 특징을 갖고 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "792d637e",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1, 2, 3, 4], dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d59943f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adbce5d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "71253e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1차원 리스트\n",
    "myList = [1, 2, 3, 4]    # python List\n",
    "arr = np.array(myList, dtype=float)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "672fdef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dc20b48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "myList = [1.3, 2.3, 3.3, 4.3]\n",
    "arr = np.array(myList, dtype=int)  # 강제 casting이 일어남"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5139ce3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "51182c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2차원리스트\n",
    "myList2 = [[1.3, 2, 3, 4],\n",
    "          [5, 6, 7, 8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f1c57a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3, 4], [5, 6, 7, 8]]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myList2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "37fd8457",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-50d7efb68d2b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmyList2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "myList2.shape   # shape는 numpy의 내장함수임"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1819fa49",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array(myList2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ad074f30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.3, 2. , 3. , 4. ],\n",
       "       [5. , 6. , 7. , 8. ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f2ba7d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "485e4e16",
   "metadata": {},
   "outputs": [],
   "source": [
    "myList3 = [1, 3.14, 'hello', '1234']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "967cbbcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3.14, 'hello', '1234']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myList3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "064d33a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array(myList3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c9b89263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '3.14', 'hello', '1234'], dtype='<U32')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "90a76392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'13.14'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0]+arr[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "19d26637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.140000000000001"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(arr[0])+float(arr[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4965883",
   "metadata": {},
   "source": [
    "#### 슬라이싱(Slicing)\n",
    "배열을 부분선택"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab0745c",
   "metadata": {},
   "source": [
    "1차원 배열의 부분선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6fa896f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b072d6bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "12a7f283",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 10 is out of bounds for axis 0 with size 10",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-ff656e92d79c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0marr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: index 10 is out of bounds for axis 0 with size 10"
     ]
    }
   ],
   "source": [
    "arr[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d9d7e7e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3400b3b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[-10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "da558e36",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index -11 is out of bounds for axis 0 with size 10",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-91f133f07612>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0marr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: index -11 is out of bounds for axis 0 with size 10"
     ]
    }
   ],
   "source": [
    "arr[-11]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10bb73fe",
   "metadata": {},
   "source": [
    "2차원 배열의 부분선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b96b0954",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "65d75e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11,  2]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dbe60dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.arange(12) : 시작값은 0이고, 종료조건이 12미만\n",
    "arr1d = np.array(np.arange(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ad763440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "954933ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1d.reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c8657cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.arange(1, 13) : 시작값이 1이고, 종료조건이 13미만\n",
    "arr2d = np.array(np.arange(1, 13)).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bb293992",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9d5d50f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0df453e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1af08b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1d = np.array(np.arange(1, 13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e428b417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d7a1a018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3~8\n",
    "# 시작값(이상) : 종료값(index미만)\n",
    "arr1d[2:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f551f830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2~12\n",
    "# 시작값 : 종료값(생략시 끝까지)\n",
    "arr1d[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "820241a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1d[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8ebc8059",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d = np.array(np.arange(1, 13)).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a3cdd432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0acd1eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 한 행을 지정한 뒤 모두 가져오기\n",
    "# 행.열 (: 모든 열)\n",
    "arr2d[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a3199068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[1, :].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3ed8464c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 11])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[: ,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "dad6cf6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2, 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "639908de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5x5 배열, 1부터 1씩 증가하는 배열(1~25)\n",
    "\n",
    "my2dArr = np.array(np.arange(1, 26), dtype=int).reshape(5, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "679d8de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10],\n",
       "       [11, 12, 13, 14, 15],\n",
       "       [16, 17, 18, 19, 20],\n",
       "       [21, 22, 23, 24, 25]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my2dArr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "4508906d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 13, 14],\n",
       "       [17, 18, 19],\n",
       "       [22, 23, 24]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my2dArr[2:, 1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8602b02",
   "metadata": {},
   "source": [
    "#### Fancy Indexing\n",
    "범위가 아닌 특정 index값을 선택해서 추출하고 싶을 때"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10d6537",
   "metadata": {},
   "source": [
    "1차원 Fancy Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "184138d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1d = np.array(np.arange(1, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9701140d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4f8b5d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [0, 2, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8bf3e240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1d[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "896a658e",
   "metadata": {},
   "source": [
    "2차원 Fancy Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "72845cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d = np.array(np.arange(1, 13)).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "6049570f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7f8a2064",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [0,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "26011ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3, 4],\n",
       "       [5, 7, 8]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2, index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb1450f",
   "metadata": {},
   "source": [
    "#### Boolean Indexing\n",
    "조건 필터링을 통해서 Boolean 값을 이용한 색인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "2fa41b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array(np.arange(1, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e9666edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a86102a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d = np.array(np.arange(1, 13)).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f4512d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "2e4a539b",
   "metadata": {},
   "outputs": [],
   "source": [
    "myTrueFalse = [True, False, True, False, False, False, False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ce2f9703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[myTrueFalse]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "073f49ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "myTrueFalse = [[True, False, True, False], [True, False, True, False], [True, False, True, False]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1228c035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False,  True, False],\n",
       "       [ True, False,  True, False],\n",
       "       [ True, False,  True, False]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(myTrueFalse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11859099",
   "metadata": {},
   "source": [
    "arr2d[np.array(myTrueFalse)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "9dc6a906",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 조건 필터\n",
    "TrueFalse = arr2d > 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "e826be7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[TrueFalse]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "f08ec27f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[arr2d > 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298a9b6f",
   "metadata": {},
   "source": [
    "#### arange"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9187e7de",
   "metadata": {},
   "source": [
    "array + range = arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "5f6cc861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# start(이상), strop(미만)\n",
    "arr = np.arange(1, 11)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ca8cbde3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 키워드를 사용해서 순서 없이\n",
    "arr = np.arange(stop=11, start=1)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "2b11e1a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 홀수만 생성\n",
    "arr = np.arange(start=1, stop=11, step=2)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c48a60",
   "metadata": {},
   "source": [
    "#### range (numpy와는 상관없는 python 문법)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "85f0441a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(1, 11)\n"
     ]
    }
   ],
   "source": [
    "# range는 조건을 말할 뿐, 아직 숫자가 생성된 것은 아니다\n",
    "test = range(1, 11)\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "bbab2d50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 11, 2):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579d9cf1",
   "metadata": {},
   "source": [
    "#### 1차원 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "3bcab4c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1, 10, 5, 8, 2, 4, 3, 6, 7, 9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "799d6f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 10,  5,  8,  2,  4,  3,  6,  7,  9])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "4ecd7f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(arr)   # 출력만 정렬해서 보여줄뿐, update 된 것은 아님"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "b3faeedc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 10,  5,  8,  2,  4,  3,  6,  7,  9])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "ebcaa64c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mysort = np.sort(arr)   # update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "fed00da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "bac0b7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  9,  8,  7,  6,  5,  4,  3,  2,  1])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(arr)[::-1]   # desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "7fae249e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(arr[::])   # asc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f09e2301",
   "metadata": {},
   "source": [
    "#### 2차원 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "a4109ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d = np.array([[5, 6, 7, 8],\n",
    "                [4, 3, 2, 1],\n",
    "                [10, 9, 12, 11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "71b58825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  6,  7,  8],\n",
       "       [ 4,  3,  2,  1],\n",
       "       [10,  9, 12, 11]])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "d020ea87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  6,  7,  8],\n",
       "       [ 1,  2,  3,  4],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axis는 축이 행인지, 열인지를 가리킨다.\n",
    "# axis=1 하면 같은 행안에서 정렬이 된다 (default : asc)\n",
    "np.sort(arr2d, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "e14abbf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  3,  2,  1],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [10,  9, 12, 11]])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axis=0 하면 같은 열끼리 비교하고 정렬된다.\n",
    "np.sort(arr2d, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fed18c8",
   "metadata": {},
   "source": [
    "#### Index를 반환하는 argsort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "f912202b",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d = np.array([[5, 6, 7, 8],\n",
    "                [4, 3, 2, 1],\n",
    "                [10, 9, 12, 11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "554cbb63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  6,  7,  8],\n",
       "       [ 4,  3,  2,  1],\n",
       "       [10,  9, 12, 11]])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "a220fbf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [3, 2, 1, 0],\n",
       "       [1, 0, 3, 2]], dtype=int64)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 열 정렬(왼쪽에서 오른쪽으로)\n",
    "# argsort를 통해 출력되는 배열의 데이터는 index값을 의미한다\n",
    "np.argsort(arr2d, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae43509",
   "metadata": {},
   "source": [
    "#### numpy를 이용한 행렬 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "e83e1744",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 행렬의 덧셈\n",
    "a = np.array([[1,2,3], \n",
    "              [2,3,4]])\n",
    "b = np.array([[1,2],\n",
    "             [3,4],\n",
    "             [5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "236d1e5a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (2,3) (3,2) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-201-bd58363a63fc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (3,2) "
     ]
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "60a8b26d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10],\n",
       "       [11, 12, 13, 14, 15],\n",
       "       [16, 17, 18, 19, 20],\n",
       "       [21, 22, 23, 24, 25]])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.sum\n",
    "a = np.array(np.arange(1, 26)).reshape(5, 5)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "cdb32634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([55, 60, 65, 70, 75])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 같은 열끼리 더함\n",
    "np.sum(a, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "7ae0b31b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15,  40,  65,  90, 115])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 같은 행끼리 더함\n",
    "np.sum(a, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "bdcfe4e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 행렬의 곱셈\n",
    "a = np.array([[1,2,3], \n",
    "              [2,3,4]])\n",
    "b = np.array([[3,4,5],\n",
    "             [1,2,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "4f6c422f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 3), (2, 3))"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape, b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "a180c614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  8, 15],\n",
       "       [ 2,  6, 12]])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 아다마르 곱 : 배열의 사이즈가 같아야 한다\n",
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "095bb7ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(np.arange(1, 10)).reshape(3, 3)\n",
    "b = np.array(np.arange(1, 7)).reshape(3, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "00b5e5bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[1, 2, 3],\n",
       "        [4, 5, 6],\n",
       "        [7, 8, 9]]),\n",
       " array([[1, 2],\n",
       "        [3, 4],\n",
       "        [5, 6]]))"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "1e8a484a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 3), (3, 2))"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape, b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "45ed7dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 22,  28],\n",
       "       [ 49,  64],\n",
       "       [ 76, 100]])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "ea97d940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 22,  28],\n",
       "       [ 49,  64],\n",
       "       [ 76, 100]])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dot(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b729b8",
   "metadata": {},
   "source": [
    "#### BroadCasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "12476945",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2,3], \n",
    "              [2,3,4]])\n",
    "b = np.array([[3,3,3],\n",
    "             [3,3,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "c2c985f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 5, 6],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "8e2ab0bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 5, 6],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# broadcasting 연산\n",
    "a + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "fb9aaaad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  6,  9],\n",
       "       [ 6,  9, 12]])"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 아다마르 곱\n",
    "a * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "e08a62a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2,3], \n",
    "              [2,3,4]])\n",
    "b = np.array([[1],\n",
    "             [2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "b3a9bd76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 3), (2, 1))"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape, b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "658a1350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 6, 8]])"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 행단위로 broadcasting 연산\n",
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "81eab95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2,3], \n",
    "              [2,3,4]])\n",
    "b = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "8d2a428e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  9],\n",
       "       [ 2,  6, 12]])"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 열단위로 broadcasting 연산\n",
    "a * b"
   ]
  }
 ],
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