파이썬 라이브러리를 활용한 데이터 분석 4장-7장.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1050ab7a",
   "metadata": {},
   "source": [
    "# 4장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c61d3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "my_arr=np.arange(1000000)\n",
    "\n",
    "my_list= list(range(1000000))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9b498dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.8 ms, sys: 7.21 ms, total: 22 ms\n",
      "Wall time: 19.9 ms\n"
     ]
    }
   ],
   "source": [
    "%time for _ in range(10): my_arr2=my_arr*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16f54cb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 572 ms, sys: 183 ms, total: 755 ms\n",
      "Wall time: 760 ms\n"
     ]
    }
   ],
   "source": [
    "%time for _ in range(10): my_list2= [x*2 for x in my_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549aab16",
   "metadata": {},
   "source": [
    "###### 4.1 numpy ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "76bedc1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.12214882, -0.41338135, -0.46153088],\n",
       "       [ 0.88882599,  2.46393991, -0.58142949]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.random.randn(2,3)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79bedeaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.22148818, -4.13381353, -4.61530884],\n",
       "       [ 8.88825992, 24.63939907, -5.8142949 ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b4ada5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.24429764, -0.82676271, -0.92306177],\n",
       "       [ 1.77765198,  4.92787981, -1.16285898]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data+data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9e175ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#자료형을 알려주는 dtype이라는 객체를 가진다\n",
    "\n",
    "data.shape\n",
    "\n",
    "data.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc95e0a",
   "metadata": {},
   "source": [
    "###### 4.1.1 ndarray 생성하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5c1d5953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6. , 7.5, 8. , 0. , 1. ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#배열 생성하기 위해 array 함수 사용\n",
    "\n",
    "\n",
    "data1=[6,7.5,8,0,1]\n",
    "\n",
    "arr1=np.array(data1)\n",
    "\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "13aeb055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#같은 길이를 가지는 리스트\n",
    "\n",
    "data2=[[1,2,3,4],[5,6,7,8]]\n",
    "\n",
    "arr2=np.array(data2)\n",
    "\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c23be57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "903cd1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.shape #2차원 형태"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec87cf1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6d86b1f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "9fcde94b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "id": "dcfa0e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "cb92240b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.e-323, 2.e-323, 2.e-323],\n",
       "       [2.e-323, 2.e-323, 2.e-323]])"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((2,3))\n",
    "\n",
    "\n",
    "#np.empty는 0으로 초기화된 배열을 반환하지 않는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "ad6163e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.e-323, 5.e-324],\n",
       "        [0.e+000, 5.e-324],\n",
       "        [1.e-323, 0.e+000]],\n",
       "\n",
       "       [[0.e+000, 5.e-324],\n",
       "        [1.e-323, 1.e-323],\n",
       "        [5.e-324, 0.e+000]]])"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((2,3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "e07fc445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e4c5a7",
   "metadata": {},
   "source": [
    "#### 4.1.2 ndarray의 dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3b092bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1= np.array([1,2,3], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8386abf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.array([1, 2, 3], dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2d707e9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f769ad88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "438773bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ndarray의 astype 메서드를 사용해 배열의 dtype을 다른 형으로 명시적으로 변환 가능\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "    \n",
    "arr.dtype\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "39875db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_arr = arr.astype(np.float64)\n",
    "\n",
    "float_arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4c7bf907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.7, -1.2, -2.6,  0.5, 12.9, 10.1])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1])\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d244381d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, -1, -2,  0, 12, 10], dtype=int32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d52807b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.25, -9.6 , 42.  ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#문자열 배열을 숫자로 변환\n",
    "\n",
    "numeric_strings = np.array(['1.25', '-9.6', '42'], dtype=np.string_)\n",
    "\n",
    "numeric_strings.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1b97b394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([         0, 1075314688,          0, 1075707904,          0,\n",
       "       1075838976,          0, 1072693248], dtype=uint32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#축약 코드\n",
    "\n",
    "empty_uint32 = np.empty(8, dtype='u4')\n",
    "\n",
    "empty_uint32"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ab1f0d",
   "metadata": {},
   "source": [
    "###### 4.1.3 numpy 배열의 산술 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8091a5ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#백터화: 포 문을 작성하지 않고 데이터를 일괄 처리할 수 있다\n",
    "\n",
    "arr= np.array([[1.,2.,3.],[4.,5.,6.]])\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "95e6138e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  4.,  9.],\n",
       "       [16., 25., 36.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr*arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "00331a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr-arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "74644466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.5       , 0.33333333],\n",
       "       [0.25      , 0.2       , 0.16666667]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2216faab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5, 1. , 1.5],\n",
       "       [2. , 2.5, 3. ]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fe9153f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  4.,  1.],\n",
       "       [ 7.,  2., 12.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#크기가 같은 배열간의 비교 연산-> 불리언 배열을 반환\n",
    "arr2 = np.array([[0., 4., 1.], [7., 2., 12.]])\n",
    "\n",
    "\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9f4f92bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True, False],\n",
       "       [ True, False,  True]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2>arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4c3e6acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#크기가 다른 배열간의 연산: 브로드캐스팅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eaf3edb",
   "metadata": {},
   "source": [
    "###### 4.1.4 색인과 슬라이싱 기초"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "287c0b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ef4680b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[5:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "727a6d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4, 12, 12, 12,  8,  9])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[5:8]=12\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8c0e7181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 12, 12])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_slice=arr[5:8]\n",
    "\n",
    "arr_slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9f627c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2,     3,     4,    12, 12345,    12,     8,\n",
       "           9])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_slice[1]=12345\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8b284673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4, 64, 64, 64,  8,  9])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_slice[:]=64\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9a6ef7e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "\n",
    "arr2d[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d6e46504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fad5b2c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3],\n",
       "        [ 4,  5,  6]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\n",
    "arr3d\n",
    "                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "330cce0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1d89c89d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[42, 42, 42],\n",
       "        [42, 42, 42]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_values=arr3d[0].copy()\n",
    "\n",
    "arr3d[0]=42\n",
    "\n",
    "arr3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fe93ce5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a353b541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3],\n",
       "        [ 4,  5,  6]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d[0]=old_values\n",
    "arr3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3eac8bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a2cd4fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7014634b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=arr3d[1]\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "19af505c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "17a93466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#슬라이싱\n",
    "\n",
    "arr2d[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7db236a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "150bb2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f922c721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[1,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6b6e5971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 6])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "44e50c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [4, 0, 0],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2,1:]=0\n",
    "\n",
    "arr2d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4326e3",
   "metadata": {},
   "source": [
    "#### 4.1.5 불리언값으로 선택하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8d3d128b",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])\n",
    "\n",
    "data=np.random.randn(7,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "42d82fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'], dtype='<U4')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "89b2c09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.01085996,  0.94511658, -0.5822428 , -0.70022049],\n",
       "       [-0.29218931, -0.39343345, -1.54929854,  0.25354452],\n",
       "       [-0.91688966, -1.50003879,  0.0611466 , -0.99106948],\n",
       "       [ 0.56958213, -0.26287356,  0.95980213, -0.61981634],\n",
       "       [ 1.36591495,  0.32204411,  0.76762959,  2.07652819],\n",
       "       [-1.27627468, -2.14570944, -1.4819925 ,  0.89603138],\n",
       "       [-0.35047812,  0.49586889,  0.24382591,  0.59757278]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1fa5546a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False,  True, False, False, False])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names=='Bob'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "259161b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.01085996,  0.94511658, -0.5822428 , -0.70022049],\n",
       "       [ 0.56958213, -0.26287356,  0.95980213, -0.61981634]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[names==\"Bob\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "97504590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.5822428 , -0.70022049],\n",
       "       [ 0.95980213, -0.61981634]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[names=='Bob', 2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "70445698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.70022049, -0.61981634])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[names=='Bob',3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "50da2ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, False,  True,  True,  True])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bob 가 아닌 요소를 선택하기 위해 != 아니면 ~을 사용\n",
    "\n",
    "names != 'Bob'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1d86efa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.42454697,  2.26989104,  0.05392397,  1.53659966],\n",
       "       [ 0.75464798,  1.51153997, -1.44917634, -0.79350864],\n",
       "       [-0.02492095,  0.52051725,  0.67001875,  0.02073892],\n",
       "       [ 0.20609775, -1.27224161,  0.96789429,  1.08319295],\n",
       "       [ 0.47620477,  1.86391261,  1.17126289,  2.36930937]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[~(names == 'Bob')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3b145746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.42454697,  2.26989104,  0.05392397,  1.53659966],\n",
       "       [ 0.75464798,  1.51153997, -1.44917634, -0.79350864],\n",
       "       [-0.02492095,  0.52051725,  0.67001875,  0.02073892],\n",
       "       [ 0.20609775, -1.27224161,  0.96789429,  1.08319295],\n",
       "       [ 0.47620477,  1.86391261,  1.17126289,  2.36930937]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = names == 'Bob'\n",
    "\n",
    "data[~cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c04fda70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True,  True,  True, False, False])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#이름 중에서 두 가지 이름을 선택하려면 &(and)나 |(or)\n",
    "\n",
    "mask = (names == 'Bob') | (names == 'Will')\n",
    "\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b7c18f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.46028967, -0.45640008,  2.54652455,  0.20634994],\n",
       "       [ 0.75464798,  1.51153997, -1.44917634, -0.79350864],\n",
       "       [ 1.0806961 , -1.38187203, -0.60231398, -0.33862516],\n",
       "       [-0.02492095,  0.52051725,  0.67001875,  0.02073892]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "de82e19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.46028967, 0.        , 2.54652455, 0.20634994],\n",
       "       [1.42454697, 2.26989104, 0.05392397, 1.53659966],\n",
       "       [0.75464798, 1.51153997, 0.        , 0.        ],\n",
       "       [1.0806961 , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.52051725, 0.67001875, 0.02073892],\n",
       "       [0.20609775, 0.        , 0.96789429, 1.08319295],\n",
       "       [0.47620477, 1.86391261, 1.17126289, 2.36930937]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data < 0] = 0\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b863f5a",
   "metadata": {},
   "source": [
    "###### 4.1.6 팬시 색인 fancy indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bc2bcb70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [4., 4., 4., 4.],\n",
       "       [5., 5., 5., 5.],\n",
       "       [6., 6., 6., 6.],\n",
       "       [7., 7., 7., 7.]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.empty((8, 4))\n",
    "\n",
    "for i in range(8):\n",
    "    arr[i]=i\n",
    "    \n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b97a2a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4., 4., 4., 4.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [6., 6., 6., 6.]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[4, 3, 0, 6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "32006e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 5., 5., 5.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#끝에서부터 로우를 선택\n",
    "arr[[-3, -5, -7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "77cfb1d3",
   "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],\n",
       "       [24, 25, 26, 27],\n",
       "       [28, 29, 30, 31]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(32).reshape((8, 4))\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "1ea7384a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 23, 29, 10])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[1, 5, 7, 2], [0, 3, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "03f76475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  7,  5,  6],\n",
       "       [20, 23, 21, 22],\n",
       "       [28, 31, 29, 30],\n",
       "       [ 8, 11,  9, 10]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[1, 5, 7, 2]][:,[0, 3, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "571e00ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#팬시 색인은 선택된 데이터를 새로운 배열로 복사"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada991d9",
   "metadata": {},
   "source": [
    "###### 4.1.7 배열 전치와 축 바꾸기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "32322491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(15).reshape((3, 5))\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "b44dff92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  5, 10],\n",
       "       [ 1,  6, 11],\n",
       "       [ 2,  7, 12],\n",
       "       [ 3,  8, 13],\n",
       "       [ 4,  9, 14]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transpose method\n",
    "arr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "59c0a276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.29108439, -0.96741962,  1.00549493],\n",
       "       [-0.16407935,  0.21806018,  1.55012303],\n",
       "       [-0.72211214,  1.26520415, -0.21221545],\n",
       "       [-0.83176256,  0.51151672, -0.75875031],\n",
       "       [ 0.90380097, -0.13592061, -0.20032486],\n",
       "       [-0.72190196, -0.39100795, -0.75734227]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#행렬의 내적은 np.dot 이용해서 구한다\n",
    "\n",
    "arr=np.random.randn(6,3)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "a03dffa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.66292569, -1.49703543,  1.18835722],\n",
       "       [-1.49703543,  3.01720349, -0.96796963],\n",
       "       [ 1.18835722, -0.96796963,  4.64833627]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(arr.T, arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "020e9d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [12, 13, 14, 15]]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr=np.arange(16).reshape((2,2,4))\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "459f915c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[ 4,  5,  6,  7],\n",
       "        [12, 13, 14, 15]]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다차원 배열에서 transpose 메서드는 튜플로 축 번호를 받아서 치환\n",
    "\n",
    "arr.transpose((1,0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "53499a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  4],\n",
       "        [ 1,  5],\n",
       "        [ 2,  6],\n",
       "        [ 3,  7]],\n",
       "\n",
       "       [[ 8, 12],\n",
       "        [ 9, 13],\n",
       "        [10, 14],\n",
       "        [11, 15]]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#swapaxe 메서드는 두개의 축 번호를 받아서 배열을 뒤바꾼다\n",
    "\n",
    "arr.swapaxes(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "e331e986",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  4],\n",
       "        [ 1,  5],\n",
       "        [ 2,  6],\n",
       "        [ 3,  7]],\n",
       "\n",
       "       [[ 8, 12],\n",
       "        [ 9, 13],\n",
       "        [10, 14],\n",
       "        [11, 15]]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.swapaxes(2,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b39c184",
   "metadata": {},
   "source": [
    "## 4.2 유니버설 함수: 배열의 각 원소를 빠르게 처리하는 함수 ufunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "3dbd9081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 1.        , 1.41421356, 1.73205081, 2.        ,\n",
       "       2.23606798, 2.44948974, 2.64575131, 2.82842712, 3.        ])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10)\n",
    "\n",
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "24148d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01,\n",
       "       5.45981500e+01, 1.48413159e+02, 4.03428793e+02, 1.09663316e+03,\n",
       "       2.98095799e+03, 8.10308393e+03])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "25fb50d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.18716349,  0.57577098, -0.43048979, -0.66881565, -1.58371379,\n",
       "       -0.7470241 , -0.77586804, -0.32743519])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.random.randn(8)\n",
    "\n",
    "y=np.random.randn(8)\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "f656abbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.89312939, -0.73781865,  0.3566522 ,  1.510479  ,  0.54306704,\n",
       "       -0.69847369,  0.47248521, -1.51652809])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "65b6fbb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.89312939,  0.57577098,  0.3566522 ,  1.510479  ,  0.54306704,\n",
       "       -0.69847369,  0.47248521, -0.32743519])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#x and y의 원소별로 가장 큰 값\n",
    "np.maximum(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "6f804cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.33745126,  3.83906361,  4.48745339,  5.48838131,  4.58444047,\n",
       "       -0.68054986,  2.88756635])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(7) * 5\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "1c69c802",
   "metadata": {},
   "outputs": [],
   "source": [
    "#.modf는 분수를 받아서 몫과 나머지를 함께 반환\n",
    "remainder, whole_part= np.modf(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "30957516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.33745126,  0.83906361,  0.48745339,  0.48838131,  0.58444047,\n",
       "       -0.68054986,  0.88756635])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remainder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "b97219c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.,  3.,  4.,  5.,  4., -0.,  2.])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whole_part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "4ec4d67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.33745126,  3.83906361,  4.48745339,  5.48838131,  4.58444047,\n",
       "       -0.68054986,  2.88756635])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "a57fb6ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-161-b58949107b3d>:1: RuntimeWarning: invalid value encountered in sqrt\n",
      "  np.sqrt(arr)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([       nan, 1.95935286, 2.11836101, 2.34272946, 2.14113065,\n",
       "              nan, 1.69928407])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "ba329b39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-162-e3ca18b15869>:1: RuntimeWarning: invalid value encountered in sqrt\n",
      "  np.sqrt(arr,arr)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([       nan, 1.95935286, 2.11836101, 2.34272946, 2.14113065,\n",
       "              nan, 1.69928407])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(arr,arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "1a83f52e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([       nan, 1.95935286, 2.11836101, 2.34272946, 2.14113065,\n",
       "              nan, 1.69928407])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ab5fe6",
   "metadata": {},
   "source": [
    "#단항 유니버셜 함수들\n",
    "\n",
    "#ceil: 각 원소의 소수자리를 올린다.\n",
    "#floor: 각 원소의 소수자리를 내린다\n",
    "#rint: ,,소수자리를 반올림한다. dtype은 유지\n",
    "#isnan: 숫자가 아닌지를 나타내는 불리언 배열을 반환한다.\n",
    "#isfinite, isinf: 원소가 유한한지 무한한지 나타내는 불리언 배열을 반환\n",
    "#logical_not:각 원소의 논리 부정 (not)값을 계산한다. ~arr과 동일하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2953364",
   "metadata": {},
   "outputs": [],
   "source": [
    "#이항 유니버셜 함수들\n",
    "\n",
    "#fmax: NaN을 무시한 원소중 큰값을 반환\n",
    "#fmin: NaN을 무시한원소중 작은 값을 반환\n",
    "#copysign: 첫 번째 배열의 원소의 기호를 두 번째 배열의 원소의 기호로 바꾼다.\n",
    "#logical_xor: 각각 두 원소 간의 ^ 논리 연산 결과를 반환한다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ad23eb",
   "metadata": {},
   "source": [
    "## 4.3 배열을 이용한 배열지향 프로그래밍"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "86f1ef66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.  , -5.  , -5.  , ..., -5.  , -5.  , -5.  ],\n",
       "       [-4.99, -4.99, -4.99, ..., -4.99, -4.99, -4.99],\n",
       "       [-4.98, -4.98, -4.98, ..., -4.98, -4.98, -4.98],\n",
       "       ...,\n",
       "       [ 4.97,  4.97,  4.97, ...,  4.97,  4.97,  4.97],\n",
       "       [ 4.98,  4.98,  4.98, ...,  4.98,  4.98,  4.98],\n",
       "       [ 4.99,  4.99,  4.99, ...,  4.99,  4.99,  4.99]])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we want to calculate sqrt(x^2+y^2)\n",
    "#np.meshgrid는 두개의 1차원 배열을 받아서 가능한 모든 (x,y) 짝을 만들 수 있는 2차원 배열 두개를 반환\n",
    "\n",
    "points=np.arange(-5,5,0.01) #-5부터 4.99까지 0.01씩 증가하는 값들\n",
    "\n",
    "xs, ys= np.meshgrid(points, points)\n",
    "\n",
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "6ae198cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.07106781, 7.06400028, 7.05693985, ..., 7.04988652, 7.05693985,\n",
       "        7.06400028],\n",
       "       [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815,\n",
       "        7.05692568],\n",
       "       [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354,\n",
       "        7.04985815],\n",
       "       ...,\n",
       "       [7.04988652, 7.04279774, 7.03571603, ..., 7.0286414 , 7.03571603,\n",
       "        7.04279774],\n",
       "       [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354,\n",
       "        7.04985815],\n",
       "       [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815,\n",
       "        7.05692568]])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z= np.sqrt(xs**2+ ys**2)\n",
    "\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "263e5c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Image plot of $\\\\sqrt{x^2 + y^2}$ for a grid of values')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplotlib을 이용해서 2차원 배열을 시각화\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(z, cmap=plt.cm.gray); plt.colorbar()\n",
    "plt.title(\"Image plot of $\\sqrt{x^2 + y^2}$ for a grid of values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ced47ea",
   "metadata": {},
   "source": [
    "#### 4.3.1 배열 연산으로 조건절 표현하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f1c4a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#numpy.where: x if 조건 else y 같은 삼항식의 벡터화된 버전, 234"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "06fa85d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5])\n",
    "\n",
    "yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5])\n",
    "\n",
    "cond = np.array([True, False, True, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "16517072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1, 2.2, 1.3, 1.4, 2.5]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 컨디션 값이 참일때는 xarr 의 값을 취하고 아니면 yarr의 값을 취하고 싶다면 리스트 표기법을 이용\n",
    "\n",
    "\n",
    "result = [(x if c else y)\n",
    "          for x, y, c in zip(xarr, yarr, cond)]\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9da109bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "??? 책 pg 164\n",
    "\n",
    "\n",
    "#큰 배열을 빠르게 처리하지 못함+ 다차원 배열에서는 사용할 수 없는 문제가 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "1fc3aa56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.1, 2.2, 1.3, 1.4, 2.5])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.where를 사용해서 위에를 간결하게 작성 가능\n",
    "\n",
    "result = np.where(cond, xarr, yarr)\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "8362642a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.47972487, -1.16742063,  0.30625815,  0.28571847],\n",
       "       [-0.98543448,  0.65603316, -1.00283866,  0.32815079],\n",
       "       [ 0.31129287,  1.30088119, -0.15218115, -0.30904791],\n",
       "       [-1.20785044,  1.49783623, -1.42663559, -0.13061084]])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#양수는 2로 음수는 -2로 바꾸기\n",
    "arr = np.random.randn(4, 4)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "b0a49919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False,  True,  True],\n",
       "       [False,  True, False,  True],\n",
       "       [ True,  True, False, False],\n",
       "       [False,  True, False, False]])"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "9e1b82b0",
   "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": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(arr > 0, 2, -2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "1af924ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.47972487, -1.16742063,  2.        ,  2.        ],\n",
       "       [-0.98543448,  2.        , -1.00283866,  2.        ],\n",
       "       [ 2.        ,  2.        , -0.15218115, -0.30904791],\n",
       "       [-1.20785044,  2.        , -1.42663559, -0.13061084]])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#양수인 경우에만 2 \n",
    "np.where(arr > 0, 2, arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6db2732",
   "metadata": {},
   "source": [
    "###### 4.3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "db023d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.03638276,  1.21308677, -1.03775383,  2.07203782],\n",
       "       [ 1.82894802, -1.74588181, -1.74000093,  0.69149394],\n",
       "       [ 0.05194534,  0.15558274,  0.89110321,  0.68182587],\n",
       "       [-0.92006112,  0.9946656 ,  0.83214814, -1.28027942],\n",
       "       [-0.73387556, -0.23376924,  0.67464129,  1.07360201]])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(5, 4)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "b70f342b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2252920798433494"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "336061ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2252920798433494"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "b56e1741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.505841596866988"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "e1761f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.82093838, -0.2413602 ,  0.44511429, -0.0933817 ,  0.19514962])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#axis에 대한 통계를 계산하여 낮은 배열 반환\n",
    "arr.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "878a6a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.26333944,  0.38368406, -0.37986212,  3.23868021])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "d6edfcb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  3,  6, 10, 15, 21, 28])"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cumsum과 cumprod 메서드는 중간 계산값을 담고 있는 배열을 반환\n",
    "\n",
    "arr = np.array([0, 1, 2, 3, 4, 5, 6, 7])\n",
    "\n",
    "arr.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "0f90708a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "e9c0fbf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  5,  7],\n",
       "       [ 9, 12, 15]])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다차원 배열에서 cumsum은 같은 크기의 배열을 반환함 축을 지정하여 부분적으로 계산하면 낮은 차수의 슬라이스를 반환함.\n",
    "arr.cumsum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "68adaa73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  3],\n",
       "       [ 3,  7, 12],\n",
       "       [ 6, 13, 21]])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.cumsum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "084c12a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0],\n",
       "       [  3,  12,  60],\n",
       "       [  6,  42, 336]])"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#곱하기\n",
    "arr.cumprod(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fcafc0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#argmin, argmax: 최소 원소의 색인값과 최대 원소의 색인값"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87174ab4",
   "metadata": {},
   "source": [
    "###### 4.3.3 불리언 배열을 위한 메서드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "4a51c1a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(100)\n",
    "\n",
    "(arr > 0).sum() # 양수인 원소의 개수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "3bf89872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools = np.array([False, False, True, False])\n",
    "bools.any() #하나 이상의 값이 true인지 검사             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "26c4d363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools.all()#모든 원소가 true인지 검사\n",
    "\n",
    "\n",
    "# 0이 아닌 원소는 모두 True로 간주한다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87484f77",
   "metadata": {},
   "source": [
    "###### 4.3.4 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "130a587f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.2595646 , -0.22110612,  0.73439192, -1.19415316, -2.68375942,\n",
       "       -0.10904876])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort 메서드를 이용해서 정렬\n",
    "\n",
    "arr = np.random.randn(6)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "eaeb661a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.68375942, -1.19415316, -0.2595646 , -0.22110612, -0.10904876,\n",
       "        0.73439192])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sort()\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "923af454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.35369331,  0.1409829 , -0.04817751],\n",
       "       [-0.58987283, -1.33476822,  0.80722099],\n",
       "       [ 2.13882614, -0.25886802, -1.37419464],\n",
       "       [-0.49016617,  0.52210306,  0.90677672],\n",
       "       [-0.38960545,  1.72813653, -1.03156008]])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#다차원 배열의 정렬은 sort 메서드에 넘긴 축의 값에 따라 1차원 부분을 정렬한다\n",
    "\n",
    "arr = np.random.randn(5, 3)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "b4a5bf28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.35369331, -0.04817751,  0.1409829 ],\n",
       "       [-1.33476822, -0.58987283,  0.80722099],\n",
       "       [-1.37419464, -0.25886802,  2.13882614],\n",
       "       [-0.49016617,  0.52210306,  0.90677672],\n",
       "       [-1.03156008, -0.38960545,  1.72813653]])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sort(1)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "7827d68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.6612322000458013"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.sort 메서드는 배열을 직접 변경하지 않고 정렬된 결과를 가지고 있는 복사본을 반환\n",
    "\n",
    "large_arr = np.random.randn(1000)\n",
    "large_arr.sort()\n",
    "\n",
    "large_arr[int(0.05 * len(large_arr))] # 5% 분위수"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82c51b1",
   "metadata": {},
   "source": [
    "###### 4.3.5 집합 관련 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "f9a799fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bob', 'Joe', 'Will'], dtype='<U4')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])\n",
    "\n",
    "np.unique(names)# 배열 내에서 중복된 원소는 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "4e7d65d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bob', 'Joe', 'Will']"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(set(names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "1ecd382c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False,  True,  True, False,  True])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.in1d 함수는 두 개의 배열을 인자로 받아서 첫 번째 배열의 원소가 두 번째 배열의 원소를 포함하는지 나타낸다\n",
    "values = np.array([6, 0, 0, 3, 2, 5, 6])\n",
    "\n",
    "np.in1d(values, [2, 3, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a635d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique(x)배열 x에서 중복된 원소를 제거한 뒤 정렬하여 반환한다.\n",
    "intersect1d(x, y)배열 x와 y에 공통적으로 존재하는 원소를 정렬하여 반환한다.\n",
    "union1d(x, y)두 배열의 합집합을 반환한다.\n",
    "in1d(x, y)x의 원소가 y의 원소에 포함되는지 나타내는 불리언 배열을 반환한다.\n",
    "setdiff1d(x, y)x와 y의 차집합을 반환한다.s\n",
    "etxor1d(x, y)한 배열에는 포함되지만 두 배열 모두에는 포함되지 않는 원소들의 집합인 대칭차집합을 반환한다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d864815",
   "metadata": {},
   "source": [
    "## 4.4 배열 데이터의 파일 입출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "52b0d139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.save와 np.load는 배열 데이터를 효과적으로 디스크에 저장하고 불러오기 위한 함수\n",
    "arr = np.arange(10)\n",
    "\n",
    "np.save('some_array', arr)\n",
    "\n",
    "np.load('some_array.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "ab3c5d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.savez 함수를 이용하면 여러 개의 배열을 압축된 형식으로 저장할 수 있는데, 저장하려는 배열을 키워드 인자 형태로 전달함\n",
    "\n",
    "np.savez('array_archive.npz', a=arr, b=arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "3f5517e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#npz 파일을 불러올 때는 각각의 배열을 필요할 때 불러올 수 있도록 사전 형식의 객체에 저장한다\n",
    "arch = np.load('array_archive.npz')\n",
    "\n",
    "arch['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "cdb0ca48",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed('arrays_compressed.npz', a=arr, b=arr) #압축이 잘되는 형식의 데이터는 numpy.sazex_compresssed 사용"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd8089b",
   "metadata": {},
   "source": [
    "## 4.5 선형대수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "52f7bca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1., 2., 3.], [4., 5., 6.]])\n",
    "\n",
    "y = np.array([[6., 23.], [-1, 7], [8, 9]])\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "17be446f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6., 23.],\n",
       "       [-1.,  7.],\n",
       "       [ 8.,  9.]])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "0be046dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 28.,  64.],\n",
       "       [ 67., 181.]])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dot(y)#행렬 곱셈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "8dd9ef74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 28.,  64.],\n",
       "       [ 67., 181.]])"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(x, y) #x.dot(y)와 동일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "c7f031c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6., 15.])"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2차원 배열과 곱셈이 가능한 크기의 1차원 배열 간의 행렬 곱셈의 결과는 1차원 배열이다\n",
    "np.dot(x, np.ones(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "11b9e317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6., 15.])"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@기호는 행렬 곱셈을 수행\n",
    "x @ np.ones(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "bd257415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.96686812, -1.0454726 , -1.57857077,  0.38524331, -1.43735039],\n",
       "       [-1.0454726 ,  1.18933887, -1.53992378,  1.13226695, -0.11119731],\n",
       "       [-1.57857077, -1.53992378,  6.87673317, -2.45170175,  1.8952002 ],\n",
       "       [ 0.38524331,  1.13226695, -2.45170175,  3.57154732, -2.06109701],\n",
       "       [-1.43735039, -0.11119731,  1.8952002 , -2.06109701,  2.07323527]])"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#numpy.linalg는 행렬의 분할과 역행렬, 행렬식과 같은 것들을 포함하고 있다\n",
    "\n",
    "from numpy.linalg import inv, qr\n",
    "\n",
    "X = np.random.randn(5, 5)\n",
    "mat = X.T.dot(X)\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "6e291fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 555.54989741, 1152.45437174,  320.67019096, -273.00311692,\n",
       "        -117.57007955],\n",
       "       [1152.45437174, 2394.385444  ,  666.00156134, -568.50918759,\n",
       "        -246.58510118],\n",
       "       [ 320.67019096,  666.00156134,  185.46700284, -157.97265345,\n",
       "         -68.55037803],\n",
       "       [-273.00311692, -568.50918759, -157.97265345,  136.1308561 ,\n",
       "          59.97914484],\n",
       "       [-117.57007955, -246.58510118,  -68.55037803,   59.97914484,\n",
       "          28.03855818]])"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "570cd740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00, -2.89790013e-13, -2.64899585e-14,\n",
       "         1.80407266e-13,  7.21195034e-15],\n",
       "       [ 2.62277277e-13,  1.00000000e+00,  2.38869780e-14,\n",
       "        -4.95688351e-14, -5.08239421e-14],\n",
       "       [ 1.05325844e-13,  3.41309330e-13,  1.00000000e+00,\n",
       "        -2.86386386e-13,  6.66166629e-14],\n",
       "       [ 4.96739521e-13,  3.99916615e-13,  1.07467418e-13,\n",
       "         1.00000000e+00, -1.11086817e-13],\n",
       "       [-1.10391526e-13,  3.42106479e-14, -3.57590436e-15,\n",
       "        -3.28696968e-14,  1.00000000e+00]])"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.dot(inv(mat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "5b3012c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.82120316e+00,  3.44994503e-01,  4.60520962e+00,\n",
       "        -2.13750217e+00,  2.85613505e+00],\n",
       "       [ 0.00000000e+00, -2.46054409e+00,  6.23694481e+00,\n",
       "        -3.95436178e+00,  2.07173926e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -1.28370521e+00,\n",
       "        -2.05092578e+00,  1.26123368e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         1.82110636e-01, -4.19781874e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00, -3.45630438e-03]])"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q, r = qr(mat)\n",
    "\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95f7f463",
   "metadata": {},
   "outputs": [],
   "source": [
    "diag: 정사각 행렬의 대각/비대각 원소를 1차원 배열로 반환하거나, 1차원 배열을 대각선 원소로 하고 나머지는 0으로 채운 단위행렬을 반환한다.\n",
    "dot:행렬 곱셈\n",
    "trace: 행렬의 대각선 원소의 합을 계산한다.\n",
    "det: 행렬식을 계산한다.\n",
    "eig: 정사각 행렬의 고윳값과 고유벡터를 계산한다.\n",
    "inv: 정사각 행렬의 역행렬을 계산한다.\n",
    "pinv: 정사각 행렬의 무어-펜로즈 유사역원 역행렬을 구한다.\n",
    "qr: QR 분해를 계산한다.\n",
    "svd: 특잇값 분해 (SVD)를 계산한다.\n",
    "solve: A가 정사각 행렬일 때 Ax = b를 만족하는 x를 구한다.\n",
    "lstsq: Ax = b를 만족하는 최소제곱해를 구한다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5a2484",
   "metadata": {},
   "source": [
    "## 4.6 난수 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "a1d84df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.58795094,  0.50846118, -1.10988153, -0.16557171],\n",
       "       [ 0.3685606 ,  1.39958327,  0.58047487, -2.26023031],\n",
       "       [-0.21683938, -1.82037392,  0.25616855, -0.34407688],\n",
       "       [ 1.39843607,  1.5968076 , -0.97078491,  0.14454788]])"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = np.random.normal(size=(4, 4))\n",
    "\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "704f1f43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "610 ms ± 6.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "from random import normalvariate\n",
    "\n",
    "N=1000000\n",
    "\n",
    "%timeit samples = [normalvariate(0, 1) for _ in range(N)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "c455c8ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.2 ms ± 537 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.random.normal(size=N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "4448a2d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.47143516, -1.19097569,  1.43270697, -0.3126519 , -0.72058873,\n",
       "        0.88716294,  0.85958841, -0.6365235 ,  0.01569637, -2.24268495])"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 파이썬 내장 random 모듈은 한 번에 하나의 값만 생성할 수 있다\n",
    "\n",
    "#NumPy 난수 생성기의 시드값, 난수 생성기의 시드값에 따라 정해진 난수를 알고리즘으로 생성\n",
    "rng = np.random.RandomState(1234) #다른 난수 생성기로부터 격리된 난수 생성기 aksema\n",
    "\n",
    "rng.randn(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b05c3e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed 난수 생성기의 시드를 지정한다.\n",
    "permutation 순서를 임의로 바꾸거나 임의의 순열을 반환한다.\n",
    "shuffle 리스트나 배열의 순서를 뒤섞는다.\n",
    "rand 균등분포에서 표본을 추출한다.\n",
    "randint 주어진 최소/최대 범위 안에서 임의의 난수를 추출한다.\n",
    "randn 표준편차가 1이고 평균값이 0인 정규분포 (매트랩과 같은 방식 )에서 표본을 추출한다.\n",
    "binomial 이항분포에서 표본을 추출한다.\n",
    "normal 정규분포 (가우시안 )에서 표본을 추출한다.\n",
    "beta 베타분포에서 표본을 추출한다.\n",
    "chisquare 카이제곱분포에서 표본을 추출한다.\n",
    "gamma 감마분포에서 표본을 추출한다.\n",
    "uniform 균등 [0, 1) 분포에서 표본을 추출한다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef672fb",
   "metadata": {},
   "source": [
    "## 4.7 계단 오르내리기 예제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "3d0ebaad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#계단 중간에서 같은 확률로 한 계단 올라가거나 내려간다고 가정\n",
    "\n",
    "import random\n",
    "position=0\n",
    "walk=[position]\n",
    "steps=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "34540cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(steps):\n",
    "    step=1 if random.randint(0,1) else -1 #randint:주어진 최소/최대 범위 안에서 임의의 난수를 추출\n",
    "    position += step\n",
    "    walk.append(position)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "8686ae7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1259ab6d0>]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WM4NetNiTvfMyZz1Yzz/W15WxAtR6vbYWO04ccKcdN9Mxl8YXc851KVn8Ob2tJ/a70Z42O4aZP1Mi4Pxwk/fIicgN4I0APgoAQoiIEGKx3PMyVmvGgtZsks3Nk9qc9tTX5fbqNisHZW24lTx+btiLFvvmf9mzw2Y6Jl+eXA0asBFwLi1AWemYWsuTT+iruJRnEzB9JYJ/vDNf9HmEEPjbazOIxBLbPzhJDRo5N856VIke+SEA8wA+TkSXiOgjRJSzPIqInicilYjU+fnifziM1YqAZuDMoCdnUOz7T/ThaF8n/N3tZZ3/NQd98LS14K0n+jKOH+3rgtzdjrc+nnm8s9WBx/d34eV8gVzT8fh+NzpbHRnHR/w+3JwJYylPOqZafuPL1/GeTwRyFit9+Ft38XMfexkLy8XVcn/pgY5//acX8cXLubtS5hNPCFwMGjseoK4llQjkDgDnAXxYCHEOwAqAD2Q/SAjxghBCEUIoPT09FXhZxvbOWiSOa1P554n/2zcfxtd++U1lrwrs7mzFlV//AbzlWG/GcbuN8K3/8Ba85/UHc56j+H24PLGIaNoujNFU8efcto7KUsF0TDUkEmZ1emM1invzmYuVXtbMQsjZG4cVEnhg3tCKnSt/azaM5Y1Y3efHgcoE8kkAk0KIl5JffxZmYGesYWwOitVW721U9mE9msCN6XDq2M2ZMNai8bw5+3PDEmyEmsmTW6tVgcw58WuROK5Pmbs1FtvWQNBaUFVc4LceV8uVf4pVdiAXQswCmCCiY8lDTwO4Ue55Gasl1uq/WhsUy7di05r5ouSZRWOmY9w1kye3gmmrw5ZxDdaNs9VhK6qt8YTApaC5sOf+wgoeFZGOUYMG+t0uDHjbdn4BNaJSs1b+HYBPEdErAM4C+M0KnZexmhDQDBzt64S33VntpmToc7sw5GvL6IWqmo5BqQ39Hlfe54zKPlwaz0zHVIuq6djX6TQ3IMu6BgD48ZFBXJsKYS2ydVGMV2eXsLQRw08og+bztwn+QggEHuhQZKluN8pKV5FALoS4nMx/PyGE+BEhRG3c7hmrAGtQbCf7qOyFUb8vtQujEGbOeau8ryJLWIvGM9Ix1RII6lD85iZg4/oq5sLryePmbJKnj/ciGhe4Mrm45XmsjcV+/nUH4XTYtk3HTC2uYTa83hD5cYBXdjK2rdsPzd5ereXHLYrsw8JyBMFHqxjXV7GwvLFl3tdKuVQ7vfIwvJ6qTq+k7R1jpUkUWUrNKNluwFPVzDTJoX0dODPo2fbaUnvH1+jPtFQcyBnbhpradbA2e2/pefKt8uOWfo8Lg1Jb1Qc8NwcbfTh5wA1Xi5knt9IkiizB2+7E0b7ObWeiqJqOkWSaZMTv2zYdE9B0dLY6cLy/fjfKSseBnLFtBDQDfe5WDEq1OSh2uKczVa1I1XS4XQ4c6e3c8jmjslleziqKUQ0BTYerxYaTB9xosdtwdshrXkMw88apyD6MJXvq+UwtrmE6tI5Rv7XRmLRtOkbVDJxLLqhqBBzIGdtGrVePsdkIil+CGtShJnP52cWfsymyhIXlDQQfre5RK3OpQR3nhqTUatVR2YcbM2F8+/Y8+t2u1I1zVJbMGqUP89coTX1iSqZnrHRMoU8cobUoXn241DD5cYADOWNbyu7t1SpF9uHe/Aruzi0XlfctZj/z3bS8EcON6XDGuIMi+xBPCLx4ay5jNsl2OX1VM5Jpki4ASKVjCj3+4ri50KhR8uMAB3LGtpTd26tV6UGpmFz+4Z5OuF2OquXJLyc39RpJe1/PDXtBBDPIpt04B6U29LkLF8UIaDrODW9u1wuYWxEUSseomg67jXC2xNqqtYwDOWNbUDUDHU57qrdXq04PeOC02+C02/DEoGfbx9tsBEX25R1EFELg/nxlanvOLa3n3dcloOmwEXB+2Js65na1pAYf02+cRGZb863YtNIk2TevrdIxAc3AqQNutDsdOd+rVxzIGduCGjRwbljK6O3VIleLHaMHJSiyVHTxZ0WWcG9+BfpKJOP4N27N4fv+9z/gWnKJfDl+6k/+Gf/5i9dzjo8FDRzrd6PLlbnr4OsPd6O7w5lz4xwtUKP0UoE0SXplpXSRWAJXJhbruj5nPrX928lYFYXXo7g1G66bQbE/+ukRfPhnRop+vHVd2XO0v3v3EQDge/cWymrP1OIaHiys4Lt3FzJmx8TiCVwcN3AhT476V37gGL76/jfk3DiVAjl9VTNgtxHOpfXsgbR0TNbjr02HsBFL4MLBxsmPAxzIGSvoYrC+BsU87S0l7attpWOye63W9L+tysgVwzrv3NIGJvTNnvTNmSWsRuIZ+XGLq8WOPnfu1gLH+7vQkacoRkDTcTJPmoSIoPhz0zHW87lHzliTsHp7jTQols7VYsfpQU9GnnxlI4br02EQmT31cuaZW9V3gOxNvcx/l7JS1mG34bxfyri5RGIJXJ5YLDi4q8i56ZiAZkDubkdP185qq9YqDuSMFaAGzd5eR2vjDIplU2QJV6dCWI+aqyCvTCwinhB426l+6CsR3F9Y2fG51aCBpx7rRpfLkZHiGAsaGPC2Yb+ntAVW2TVKryfTJIVuCNlTLIUQGKvhPXPKwYGcsTys3l4jVI/ZiuL3IRoXeGXSHNgMJHvR/+oNhwDsfN/y9PGFEb+UOo+5qZe+o3SVIksQaUUxrLTJSIFzHe/vyqhRen/BHNhVGvBnyoGcsTyuT4ewHk3UzUDnTlk3KivdoQZ1HOvrwtkhL6T2lh3nya3xhVHZ3NnwztwyFlcjmNDXMLe0saNe8dkhc0m9FZgDmg5/dzt6u/Jv1+uw23B+eDMdUy9rAnaCAzljeaR2x2vA3ls6X4cTh3s7MRY0zNkkQXMLXGvudrFl1rKNBTfHF5S0HQytgdSd7CTZ0erAyQNuBDR9M02yzaClIkt4NZmOUTUDUnsLHuvJKSlc9ziQM5ZHQNMx7GtHb54ZFI1GSaY+bs4sYSWyWSJO8Ut4sLCC+aXiih+nC2g6Tuw3xxfODHnRYicENAMBzUCXy4GjvTtbYDXil3B5YhG3Hy7j0Upk2xSN4velapSqQQMj/trdM6ccHMgZyyKEgKoZdTPtsFyK7EN4PYY/e3k89XX631aZu2KlZpMk3z9Xix2nBjxQNd3cbtYvbbupVyFWjdJPfE9Lfr31z+hscofDv702gwcLKzW7p3y5OJAzluXBwgoerUQaPj9usYLb58YmccCzWcPy1IA7WUuztPRKvvGFUdmHK5OLuDO3XNb7aqVpPjc2mUyTbL1db2erAyf2u/G5sSnz+Q36M61YICciOxFdIqKvVOqcjFWDNV2tUXtv2YZ95rzqSDyREehaHXacGfKWvENivvEFxW/uEZ59vFS9bhf83e2IxBNFp0kUWUIknkCrw4ZTA41RSCJbJXvk7wdws4Lna1iJAhvks9qgajq87S04tG/r3l6jMFdBJvPiWTcvxS/h+lQIq5FY0efLN75gzY5psRPOlLnAaqRAWwuxBkTPDHrR6ihuH5p6U5FATkSDAH4IwEcqcb5G9vUbD3HmN76GR8ulDyCxvaFqBkaGd57HrUcXDprBLjvtMSr7EEsIXJ5YLOo8m7NJMoNsd2crDvd24vSAp+hNvQp5TYG2FjIqSyACRhtsf5V0lVqy9rsAfhVAwaFoInoewPMAMDw8XKGXrT8v3nyIpY0YApqOZ07tr3ZzWJZHyxu4v7CCnxwdqnZT9tRzF4Yhd3fg8f2ZqYfzw2YQHNMMPPXYvm3Poz1axcJyBKMHc4PsH/70ebTYy785/ui5QezrbM3YAncrvW4XPvXe1+DkwPbb+9arsnvkRPQOAHNCiLGtHieEeEEIoQghlJ6ennJftm4FtMpsSMR2h9ok88ezuVrseMvx3pzjnvYWHO3tQqDIPLn1+53v/TvW34VD2wxOFsPpsOHpx/tKmkb41OF98LQVv6FYvalEauV1AH6YiDQAnwHwfUT0pxU4b8N5tLyBe/Pm3hXVrmDO8lM1HU6HDaeLKM7QLBRZwsUtih+ns8YXtptNwiqr7EAuhPigEGJQCCEDeCeAbwghfrbsljUgazT/wkEfrk2HSxpAYnsjoBk4M+hp2EGxnRiVfVjeiOHWbHjbx6qamR9vpvGFWsDzyPfQWNCA027De153EPESBpDY3liLxHF9OtSwc413ypodst1yfWt8gd+/vVfRQC6E+JYQ4h2VPGcjCWg6Tg968NrHukGEvDUIWfVcmVxENC6aLj++nQFvG/rdrm3HdZp1fKEWcI98j6xH47g6FYIiS/C0teBYX1fewresejarx3AgSmduoCVtO67D4wvVw4F8j7wyGUI0LjDqt/axkHBpfLGoASS2N9SggaN9nfC2O6vdlJozKvswE1rHVFbx43RqkMcXqoUD+R4JZPX2ShlAYrsvnjAXsjRaLcdKsX5vC/XK1yJxXJsK8ftXJRzI94iq6Tjc2wmpw+ztbf7H4Dx5Lbj9cAlL67Gm2V+lVMf7u9DZ6iiYDrTGF/j9qw4O5Hsgkeztpf+SmzULXZwnrxFWT7NZdjwslcNuw7lhb8GOB48vVBcH8j1wZ24Z4fVYRjUTqwKLqpVXqZxVhho00OduxaBUWkHgZjIq+/DqwyWE1qI53+PxheriQL4HUsuW8+wsNxtex6SROYCkr0Tw7B9+Nyd/Hosn8K6PvoRv3Hq4uw1uUGuROH7yj/8JLz/I/RRkLmRpzOoxlaL4zeLHF7Pmk/P4QvVxIN8Dqqajp6sVw772jOOFFlp85+4Crkws4qtXZzOO35pdwj/eWcAXLk3vboMb1MVxAy9rOr50ZSrj+NTiGqYW1zgtsA2r2o6aVTHIGl/g+ePVw4F8DwQ0I7mVZmZv73i/O+8AkpVvzJ4hEChwnBVn8/3LvHFa7+eFPDv2sU3tTgdOHXDnLAzi96/6OJDvspmQ2dvLV+3bbiOc90s5PXIr0FyeWEQ0ntg8nnzcdvN5WX7W+5yd5x0LGuhw2nG8f2cFgZuJIvtwZWIRkVjm7yWPL1QXB/JdZgXlQtVMFL9kBpZVM7CE16O4NRvGkd5OrEbiuDlj5snNgsA6jvR2Js/LvfJSxOIJXAwaONLbaeZ5xzdvngHNwLlhCQ47/3fYjuKXsBFL4Np0KHWMxxeqj39zd5mq6Wh32nFif/5agYosZQSWS+OLSAjg+TceArC5b/mksYaH4Q389GuGt5zPy/K7NbuElUgc7339QTPPm3z/rBtnsWXDmt2InLkwaDo5vsDvX3VxIN9latDAuWFvwd7e2SEvHGkDSGOaDruN8LbT+zEotWEsedz6/pOHurecz8vyswLPG4/24NQBd+r9uzS+CCF4/nixertckLvbU+/fZqFqfv+qiQP5Llpaj+LmTHjLaVntTgdOpg0gBTQDJ/abg6Cjsg+B5DzzgGagy+XA0b6uLefzsvwCQQMD3jYc8LZBkX24nMzzqskb59kyCwI3kxG/D2rQSKX72nl8oeo4kO8iK02y3bJlawBpNRLDpQkjNQ1uxC9hfmkD4/oqVE3H+WEJdhttzucd5155MayAk6q+npbnDWg6Tux3o6O1UuVrG9+oLEFfieD+wgoCmoHzPL5Qdfzu7yJV02Ej4Nzw1oF8VDYDy58HJrAeTaQ+plp///3NOdx+uJy6IaTm83KevCjW+IL1/ll53n+69wiXJxY5v1siq3DEN2/N8fhCjeBAvovUoIETB8w0yVas1MtH/vEBgM0ZLkd6O+F2OfCx71jHzcdZ83k5T14ca3zBev+sPO+fvTSeceNkxXmspwNSews+/l2NxxdqRNmBnIiGiOibRHSTiK4T0fsr0bB6F40ncGl8Me/88Ww9Xa2Qu9sxtbiGIV8b+twuAIDNRhjxS5haXIPDRjgz6E09Z8S/medlWwtoBrpazfEFy4jfl5qLzysSS0NEqfePxxdqQyV65DEAvyKEeBzAkwB+gYhOVOC8de3GdBhr0XjRHzut3uJoVuC3jp8a8KDNublhv5WOSZ/Py/JTNR3n/eb4gsVKswz72tGbvHGy4lnvH48v1IayA7kQYkYIcTH57yUANwEMlHveerdZv7C4j53Wf4zswrWb+fLMG0L2fN5aEE8I/JcvXU8tYqoFodVoxviCxXqfOb+7M/z+1ZaK5siJSAZwDsBLeb73PBGpRKTOz89X8mVrkqrpGJTa0O8prrf31sf78PbT/fj+E30Zx88OefGj5wbw4yODGcez5/PWgluzYXziexo+8/J4tZuSMjZu7ZOdeYN8rKcDz10YxnMXhqvRrLr3xKAHP3Z+AD8xMlTtpjAAFftMRESdAD4H4JeEEDldMiHECwBeAABFURp6A25r3vcbjuwr+jndna34o58ZyTnudNjwf37qbN7njPh9+OarcxBC1MTyaDVtLnytCGgGHHnyuESE//Fjp6vTqAbQYrfhd37ybLWbwZIq0iMnohaYQfxTQojPV+Kc9Sz4aBULyxu7/rEzfT5vLbC2Dbg1G8bSem0sVlI1PWd8gbFGU4lZKwTgowBuCiF+p/wm1b+9WrZs5SnHaqAHbC66MXfBSwhzMVS1bcTiuDIZ4jqSrOFVokf+OgDvAvB9RHQ5+eftFThv3VI1HW6XA4d7Onf1daz5vLWwgdbU4hpmw+t491NyzSxWujYVQiSW4Mo1rOGVnSMXQnwHQPUTtDUkoOlQZB9stt19W6z5vGqw+j1yKz/+pqM9+OrVmZrIkwe22UKYsUbBKzsrTF+J4N78yp4Fj1FZwoOFFSwsb+zJ6xWiBnV0tjpwvN8NJblYKb0oRlXapBk4tK8D+zpbq9oOxnYbB/IKG9vjbT2tPHm1pyGqmpFadDMq+7AWjePGdPXmkycSAmNBnXvjrClwIK8wVdPhtNtwesCzJ693asANp8NW1Zx0aDWKVx8upZa6W8Gzmrn7+wvLMFajRS/IYqyecSCvsICm4/SgB66WvZnu1uqw4+ygF4Eq5skvjhsQYjOA97ldGPZVd7ES58dZM+FAXkHr0TiuToX2PHgosoTrUyGsReJ7+roWNajnLLpR/FKq+EBV2qQZ6O5w4uC+jqq8PmN7iQN5Bb0yGUI0Lvb847wiS4glBC5PLO7p61oCmoGTB9xod25OglJkHxaWNxB8tFqVNqlBs5BELax4ZWy3cSDfwt9cncHth0tFP97KCY/s8baoI8PWgGduTlrVdHzv7kLZr7EejeNj33mQMxMlEkvgysRins2+CufJv/XqHK6UcNPRFlbwxctTRT9+bmkdwUervE82axocyAtYj8bx/s9cxu987XbRzxkLGnispwO+DucutiyXp70Fx/q68ubJ/9MXr+PXPv9K2a/x16/M4L9+5Qa+cWsu4/i16RA2YomcPb0f6+mEt70lJ08uhMC//4sr+G9/faPo1/6Db97FL/35ZYRWi1v2b610HeH8OGsSHMgLeGUyhEg8UXSeN5Ew60JeOFidXqAiS7gUNBBPbLY1vB7FrdkwJvQ1PAyvl3V+q8rOWNbNwvoUkN0jtyVri1rPs9xfWIG+EsGVyRA2YsXl9FVNL6lGaUAz4Gqx4dSBvZk5xFi1cSAvwEoJFJvnvTO3jPB6rGrLwRVZwtJGDK/ObqaCLgbN2SRA+fPMA6mdDfWc43J3O3q6chfdjPh9uDe/gkdpi5WswB+JJXBtavuiGHNL69CS73+x0xnVoI4zg144HfzrzZoD/6YXoGp6qtZmMQHEeky1NmiyBljTe8CqZsBuI7habGXN6TZWIrg7t4zOVgeuTYWwHjV70kIIjAWNnN64xXov0nvxAc1AR3InwmKW8Vtpks5WR1E3o5WNGK5Phzk/zpoKB/I8zFWBBn7o9H542lpy0gn5jAUN9HS1YtjXvgctzDUotaHf7coIdmpQx8kDbpwbkoq6hkKs5/7Mk8OIxkVqoNJKkxS6eZ0e9JiLldJeeyxo4KnD+3BoX0dRgVkNGmh12PDj5wdwZXJx23TMlYlFxBOC54+zpsKBPA8rTXLhoA+KXyq6R65UcbobEWFEljJSF5cnzOLPo7KE69MhLG/EdnTuQNBcrfqe1x0EsLlNr6rlr75jaXXY8cSAJ/W4+aUNPFhYwagsQZEljAV1JBJbjz+omo6zQ1689rF9Zo3Sqa2X/Qc0A0TAeS6ozJoIB/I8AqkBPAkjspST5802E1rDpLFWMMWwV0b9EqZD65haXMP16RDWo4nkNfiQEMDlHe4RrmoGTg240ed24UhvZ+r9CWgGpPYWPNZTeNGNIvtwNZmOGQtuBn7F74OxGsX9heWCz12NxHBtOmxeg7+4GqVqUMexvi64XS2lXiZjdYsDeR6qpqfSJFaudavUhJUiqHYBg80NtPRUexW/hPPDXthoZ3ufrEfjuDoZSr0PiuzDWNBIpZ8U2bflp5BRWUqlY1TNTJOcGnCn7cdS+H29PG6lSXzo6WrFwX0dWz4+Fk/gYtDg/DhrOhzI81CDBkZlM01yesADp922ZSAfCxpod9pxYr97D1uZ63h/V2pQMKDp8He3o9ftQperBcf73TvKk1+dMqdhWjeJUVnC0noM37v3KJUm2UqqJx00EAgaODPkRavDjoP7OtDd4dwyT64Gk2mS4eRmXH4zHVNoOuit2SWsROKcH2dNhwN5FitNYuV9XS12nB70bNmbDSTzuA57dd9Oh92Gc8NeBDQdqmZkrDBVZAkXxw3EStwjPHu1qjU75o//4V7y+Na9X2+7E0d6O/EPt+dxfSqUWjhkFsXInWee/drH+rrgaWtJXYOxGsW9+fw1SgvNaWes0VWq+PIzRPQqEd0log9U4pzVki9NoshSKs+bbXkjhpsz4ZoJHorfh1uzS3i0EslIMSiyD6uROG7OFL/lAGC+H+mrVYd8bejtasV37i6k0iTbtkn24eUHOmIJkdGmUdmH4KNVzC3lLlaKJwQujS9m9K7TU0f5BIIGDnhcGPC2lXSNjNW7ShRftgP4QwBvA3ACwHNEdKLc81ZLvjTJqN+HaFzglcncBSyXxg0kRPXz45b0duT791Y94GxWHjw9+BJR6msrTVJsm9LTJMDmFrP5ikffmg1jeSOW8dqH9pk3lHyl7cziz3rN3FAZ20uV6JFfAHBXCHFfCBEB8BkAz1bgvDmuTYVK2jxpKy/df5S3FFlA03FuODNNYqUV8qVXApoBGwHnhmsjkJ8d9sJuo+Rsks3iz/s9bRjwtpW0wvPu/DJCa9Gc4GgF4GJvXlYwPtbXBU/75mySkwc8ycVKuW1SU/uJZ95EFL+Ut0c+aazhYXijZm6ojO2lSgTyAQATaV9PJo9lIKLniUglInV+fn5HL/QX6gQ++PmrJed5s92aDeOnXvhn/KU6mXF8aT2KmzPhnLyv1OHE4bRpd+lUTcfj+92pVaDV1u504MlDPrzpaE/ObBJFlvCyVniwMFtqGmbWnOw3HNmHFjvhTUd7izrPoNSGx3o68KZjPRnHnQ4bzgx6C9wgdezPkyZRZAlannSMGtx6TjtjjawSgTzf3LOcSCGEeEEIoQghlJ6enjxP2Z6V5701W1qeN9tL983/9C89eJRx/NL4YsE0yagspabdWaLxBC6NL9bcdLeP/8sL+K2fOJNzXJF9mF/awIS+VtR5VM3Avs5W+LszV6se7u3ClV//gaI3CCMi/PX73oBf/cHjOd8blX24MRPGStpiJTNNkn/pv3UsOx0T0Ax0tTpwrL+rqDYx1kgqEcgnAQylfT0IYLoC582hbJHiKMXmykQj53ihNIni92FpPYbbc5s3kZszYaxFa2+6m9NhQ0ueGTSl5snVoJ6ahpktvYhEMVwtdthtuedRZAnxhMjYn3xqcQ2z4fW8N9RTBzxozVr2D5iB3Sr+zFizqUQgDwA4QkQHicgJ4J0AvlSB8+Y44C09z5tNCIHAA7M02dTiGqYXN3unW6VJRlMzJjI3gAJQNwV+j/Z2ocvlKGqzqtnQOib0tV0vknHeL4Eoc2GQ9R7ne22nw4YzQ96MPHl28WfGmk3ZgVwIEQPwiwD+DsBNAH8hhLhe7nkLUWRz7vFOa0Favb0fOWem8a2e3XZpEmvaXXoAUTUdQ7429HtcO2rLXrPZknO3i/hEY/Xadztt5HaZRTHSPyUEkjtPHu/PP7VxVJZwbTqM1YiZjhkb5/njrLlVZB65EOKrQoijQojHhBD/vRLnLESRfXgY3sCkUVyeN5u1uvHnXutHu9OeCmo3prdOkxARFFlK9RyFEAhoRt30xi2jsg935paxuBrZ8nGqZqCtxY4TB3Z/teqo7MPF4OZiJXWbNIki+xBPiNTeMQHNyCn+zFgzqbuVneXmya3e3skDHpwfllIf49Xg9mkSxe9LpWPG9VUsLG/UXH58O9b7t91yfTVoTsPMl2uveJtkCSvJQezQahS355YwukWa5PywmY6xfmZjmoFTAx60Obef085YI6q7QH60r/g8bz6qZuBccq71iF/CrdkwwutRqJpu7um9RZoklScPGqnXr7UZK9s5M+RFi522fP+WN2K4MR3es5xz+orNi+NmVaOt6m162pI1SjUdG7E4Lk8ucn6cNbXamPxcAisAj5WwQtESWjMHxd5+ej8AMwgnhDntMKAZeMORfVs+//H9XWh32jGm6diIJeBpa8HhtEU39cDVYseptD3C87FWq+5VznnA24YDHhcCQQNzSxtFpUkUWcJfXZrGlYkQIrEE58dZU6u7HjlgBuDbD7fP82azentWOsRaBfn5i5NFpUk2N6UyoAbNTalsdTjdbVT24ZXJ/HvHAOanFnMapnfP2qTIPqjJzb5ODni2nd44KvuwvBHDp14KJp/PPXLWvOoykI8UmefNpmo67Gm9vc5WBx7f34UvXzGnvRczcKn4fbg5G8bdueW6DR4jfgmReOHix2pQx/F+N7r2sDiDIkt4GN6AGtSLSpNYvwNfvjKNg/s6sK8zt/gzY82iLgP5mcHt87z5BDQDpw64M3p7it9Mr7hdDhzp3T5NMir7UpXp6y0/btkcMM59/2KpaZh7e5OybqLFbkA24G3Dfo/LTAFxfpw1uboM5G1OM89bSp48EkvgysRiTi41vfJNMWkSKx3jtNtwesBTWsNrRHdnKw71dOR9/27OLGE1Et/znPOx/i50JRdiFbNfijkd1Cp2UZ83VMYqpS4DOWD+570yUTjPm+3adAgbsURO721UNucrP3mouGDQ2erAE4MenBv2wtVSv9PdRv0+BDQjp/hxer3SvWS3EV5zyIfDvZ3o6SouTfLaQ90gQtF7vjDWqOpu1oplxC/hhW/fx7WpUFG9x0LVY3rdLnz1fW+AvK8939Py+vDPjKAOxzgzjMgS/lydwL35ZRzp29xoSg3qybTF3hdn+F8//gTWY8XvbPmTyiDODnkh7ytc/JmxZlC3PfKt8rz5BDQDcnd73t7esf6uogokWPo9LvS662NZfiFWOiL9/bNWq1ZrT+/uztaSqvs47LY9WXnKWK2r20C+VZ43mxCbFd+ZSe5ux75OZ8YeJxP6GuaXNvh9YqzO1G0gB8xeuRrMzfNmu7+wAn0lwrMb0qSKH2fs5lid/DhjrDz1HchlHxZXo7g3v7zl47i6en6jsg/j+irmwma1HTWoo8vlwNFeLs7AWD2p60CeL8+bT0AzkjUseVAsnZK2dwxgruhU6nS1KmPNrK4Deb48bz5WfjxfpZtmdvKAO1n8WIexEsGduWX+1MJYHarrQJ4vz5ttfmkDDxZWOD+eR4vdhrNDXqiakdrugN8nxupPXQdyIDfPm82a1cI9zfys4sf/cHseLXbCGS7OwFjdqftAnp3nzaZqBlodNpwa4PnG+VjVdj47NonTA566Xq3KWLMqK5AT0W8R0S0ieoWIvkBE3gq1q2jped58AkEDZwa9JS34aSbnhr0gQrLMHX9qYaweldsj/zqAU0KIJwDcBvDB8ptUmvQ8b7bVSAzXp0I8L3oLbldLqsgx58cZq09lBXIhxNeEELHkl/8MYLD8JpVuVPbh+nQIKxuxjOOXJxYRSwgO5NuwluSPcCBnrC5VMkf+HgB/U+ibRPQ8EalEpM7Pz1fwZc08b0KYgTvdmGaACBgZ5pTBVn7hLYfxwrtG0M3FGRirS9sGciL6eyK6lufPs2mP+RCAGIBPFTqPEOIFIYQihFB6enoq0/okK8+bnScPBA0c7e2Cp33vKt3Uoz63Cz9wsr/azWCM7dC229gKId661feJ6N0A3gHgaSHE1pue7BIrz5ueJ48nBC4GDTx79kA1msQYY3um3FkrzwD4NQA/LIRYrUyTdmZUlnBx3EAsbu5nfWs2jOWNGFePYYw1vHJz5H8AoAvA14noMhH9cQXatCOK7MNqJI5bs0sANgsz80AnY6zRlVUhSAhxuFINKddmoQkdpwY8CGgG+t2ukgoVMMZYPar7lZ2WA942DHjbUnlyVdOhyBJvlMUYa3gNE8gBM42iBnVMGquYCa1zfpwx1hQaK5D7JTwMb+CvLk0B4AUujLHm0FiBPNkD//h3NXS2OnC8nyvdMMYaX0MF8qN9XehyOfBoJYJzw1447A11eYwxlldDRTq7jVLpFM6PM8aaRUMFcmBzGiLv5McYaxZlzSOvRf9iZAjh9Rjvrc0YaxoNF8j7PS78x7c/Xu1mMMbYnmm41ApjjDUbDuSMMVbnOJAzxlid40DOGGN1jgM5Y4zVOQ7kjDFW5ziQM8ZYneNAzhhjdY6qUS+ZiOYBBHf49H0AFirYnHrRjNfdjNcMNOd1N+M1A6Vft18I0ZN9sCqBvBxEpAohlGq3Y68143U34zUDzXndzXjNQOWum1MrjDFW5ziQM8ZYnavHQP5CtRtQJc143c14zUBzXnczXjNQoeuuuxw5Y4yxTPXYI2eMMZaGAzljjNW5ugrkRPQMEb1KRHeJ6APVbs9uIKIhIvomEd0koutE9P7kcR8RfZ2I7iT/brhadkRkJ6JLRPSV5NfNcM1eIvosEd1K/sxf2+jXTUS/nPzdvkZEnyYiVyNeMxF9jIjmiOha2rGC10lEH0zGtleJ6AdLea26CeREZAfwhwDeBuAEgOeI6ER1W7UrYgB+RQjxOIAnAfxC8jo/AOBFIcQRAC8mv2407wdwM+3rZrjm3wPwt0KI4wDOwLz+hr1uIhoA8D4AihDiFAA7gHeiMa/5EwCeyTqW9zqT/8ffCeBk8jl/lIx5RambQA7gAoC7Qoj7QogIgM8AeLbKbao4IcSMEOJi8t9LMP9jD8C81k8mH/ZJAD9SlQbuEiIaBPBDAD6SdrjRr9kN4I0APgoAQoiIEGIRDX7dMEtMthGRA0A7gGk04DULIb4NQM86XOg6nwXwGSHEhhDiAYC7MGNeUeopkA8AmEj7ejJ5rGERkQzgHICXAPQJIWYAM9gD6K1i03bD7wL4VQCJtGONfs2HAMwD+HgypfQRIupAA1+3EGIKwG8DGAcwAyAkhPgaGviasxS6zrLiWz0FcspzrGHnThJRJ4DPAfglIUS42u3ZTUT0DgBzQoixardljzkAnAfwYSHEOQAraIyUQkHJnPCzAA4COACgg4h+trqtqgllxbd6CuSTAIbSvh6E+ZGs4RBRC8wg/ikhxOeThx8S0f7k9/cDmKtW+3bB6wD8MBFpMFNm30dEf4rGvmbA/J2eFEK8lPz6szADeyNf91sBPBBCzAshogA+D+ApNPY1pyt0nWXFt3oK5AEAR4joIBE5YQ4MfKnKbao4IiKYOdObQojfSfvWlwC8O/nvdwP44l63bbcIIT4ohBgUQsgwf67fEEL8LBr4mgFACDELYIKIjiUPPQ3gBhr7uscBPElE7cnf9adhjgM18jWnK3SdXwLwTiJqJaKDAI4AeLnoswoh6uYPgLcDuA3gHoAPVbs9u3SNr4f5keoVAJeTf94OoBvmKPed5N++ard1l67/zQC+kvx3w18zgLMA1OTP+68ASI1+3QB+A8AtANcA/D8ArY14zQA+DXMcIAqzx/3era4TwIeSse1VAG8r5bV4iT5jjNW5ekqtMMYYy4MDOWOM1TkO5IwxVuc4kDPGWJ3jQM4YY3WOAzljjNU5DuSMMVbn/j+9SsBrjhYkcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(walk[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "a8419022",
   "metadata": {},
   "outputs": [],
   "source": [
    "nsteps = 1000\n",
    "\n",
    "draws = np.random.randint(0,2,size=nsteps)\n",
    "\n",
    "steps = np.where(draws>0, 1, -1)\n",
    "walk = steps.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "f76f0b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-9"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walk.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "b13bb132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walk.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638de6ac",
   "metadata": {},
   "source": [
    "# CHAPTER 5 pandas 시작하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "3f3638eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c819330",
   "metadata": {},
   "source": [
    "## 5.1 pandas 자료구조 소개"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f26b75",
   "metadata": {},
   "source": [
    "###### 5.1.1 Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "791f4d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#index: 색인\n",
    "\n",
    "obj = pd.Series([4, 7, -5, 3]) #Series 객체의 문자열 표현은 왼쪽에 색인을 보여주고 오른쪽에 해당 색인의 값\n",
    "\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "d5692058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  7, -5,  3])"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "a422c37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.index #range(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "cda3d46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "a   -5\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#index 지정하기\n",
    "obj2 = pd.Series([4, 7, -5, 3], index=['d', 'b', 'a', 'c'])\n",
    "\n",
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "6fbeb786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['d', 'b', 'a', 'c'], dtype='object')"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "0c073622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "fdfe92fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2['d']=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "60fc7cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    6\n",
       "b    7\n",
       "a   -5\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "44f1db0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a   -5\n",
       "d    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[['c','a','d']] #색인의 배열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "477e628f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    6\n",
       "b    7\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[obj2 > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "f66a2c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    12\n",
       "b    14\n",
       "a   -10\n",
       "c     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "ba14cea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d     403.428793\n",
       "b    1096.633158\n",
       "a       0.006738\n",
       "c      20.085537\n",
       "dtype: float64"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(obj2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "004bb3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'b' in obj2 #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "603a82ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'e' in obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "f5529c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio      35000\n",
       "Texas     71000\n",
       "Oregon    16000\n",
       "Utah       5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}\n",
    "\n",
    "obj3 = pd.Series(sdata)\n",
    "\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "8702d47d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#사전 객체만 가지고 Series 객체를 생성하면 생성된 Series 객체의 색인에는 사전의 키값이 순서대로 들어감\n",
    "\n",
    "states = ['California', 'Ohio', 'Oregon', 'Texas']\n",
    "\n",
    "obj4 = pd.Series(sdata, index=states)\n",
    "\n",
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "6ad9a627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "7af6e804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    False\n",
       "Ohio           True\n",
       "Oregon         True\n",
       "Texas          True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "57169d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "0c46296c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio      35000\n",
       "Texas     71000\n",
       "Oregon    16000\n",
       "Utah       5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "a65b8ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "a70b2c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California         NaN\n",
       "Ohio           70000.0\n",
       "Oregon         32000.0\n",
       "Texas         142000.0\n",
       "Utah               NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3+obj4 #안겹치는건 NaN으로 나옴?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "9c8e083d",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4.name='population'\n",
    "\n",
    "obj4.index.name='state'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "65990284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "2c801289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "6d92f544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bob      4\n",
       "Steve    7\n",
       "Jeff    -5\n",
       "Ryan     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.index=['Bob', 'Steve', 'Jeff', 'Ryan']\n",
    "\n",
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bd389d",
   "metadata": {},
   "source": [
    "###### 5.1.2 dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "774cbbcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    Ohio  2000  1.5\n",
       "1    Ohio  2001  1.7\n",
       "2    Ohio  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9\n",
       "5  Nevada  2003  3.2"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2002, 2001, 2002, 2003], 'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}\n",
    "frame = pd.DataFrame(data)\n",
    "\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "21620fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    Ohio  2000  1.5\n",
       "1    Ohio  2001  1.7\n",
       "2    Ohio  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head() #first 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "40c19010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    Ohio  2000  1.5\n",
       "1    Ohio  2001  1.7\n",
       "2    Ohio  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9\n",
       "5  Nevada  2003  3.2"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns 순서 지정하기\n",
    "pd.DataFrame(data, columns=['state', 'year', 'pop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "4c8cb46e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop debt\n",
       "one    2000    Ohio  1.5  NaN\n",
       "two    2001    Ohio  1.7  NaN\n",
       "three  2002    Ohio  3.6  NaN\n",
       "four   2001  Nevada  2.4  NaN\n",
       "five   2002  Nevada  2.9  NaN\n",
       "six    2003  Nevada  3.2  NaN"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2 = pd.DataFrame(data, columns=['year', 'state', 'pop', 'debt'],\n",
    "                      index= ['one', 'two', 'three', 'four',\n",
    "                              'five', 'six'])\n",
    "\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "55649317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'pop', 'debt'], dtype='object')"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "e9a874fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one        Ohio\n",
       "two        Ohio\n",
       "three      Ohio\n",
       "four     Nevada\n",
       "five     Nevada\n",
       "six      Nevada\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "b3d38886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      2000\n",
       "two      2001\n",
       "three    2002\n",
       "four     2001\n",
       "five     2002\n",
       "six      2003\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "cbba0b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year     2002\n",
       "state    Ohio\n",
       "pop       3.6\n",
       "debt      NaN\n",
       "Name: three, dtype: object"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loc통해 이름을 통해 접근\n",
    "frame2.loc['three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "b7de8550",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2['debt'] = 16.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "8eeaaf9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>three</th>\n",
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       "      <td>3.6</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
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       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5  16.5\n",
       "two    2001    Ohio  1.7  16.5\n",
       "three  2002    Ohio  3.6  16.5\n",
       "four   2001  Nevada  2.4  16.5\n",
       "five   2002  Nevada  2.9  16.5\n",
       "six    2003  Nevada  3.2  16.5"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "0a00a78d",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2['debt'] = np.arange(6.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "8eb22b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
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       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
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       "      <td>2.9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5   0.0\n",
       "two    2001    Ohio  1.7   1.0\n",
       "three  2002    Ohio  3.6   2.0\n",
       "four   2001  Nevada  2.4   3.0\n",
       "five   2002  Nevada  2.9   4.0\n",
       "six    2003  Nevada  3.2   5.0"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "bd213fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>one</th>\n",
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       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
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       "      <td>-1.2</td>\n",
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       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
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       "    </tr>\n",
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       "      <th>four</th>\n",
       "      <td>2001</td>\n",
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       "    <tr>\n",
       "      <th>five</th>\n",
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       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5   NaN\n",
       "two    2001    Ohio  1.7  -1.2\n",
       "three  2002    Ohio  3.6   NaN\n",
       "four   2001  Nevada  2.4  -1.5\n",
       "five   2002  Nevada  2.9  -1.7\n",
       "six    2003  Nevada  3.2   NaN"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = pd.Series([-1.2, -1.5, -1.7], index=['two', 'four', 'five'])\n",
    "frame2['debt']=val\n",
    "\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "69b2975c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>eastern</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
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       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>-1.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt  eastern\n",
       "one    2000    Ohio  1.5   NaN     True\n",
       "two    2001    Ohio  1.7  -1.2     True\n",
       "three  2002    Ohio  3.6   NaN     True\n",
       "four   2001  Nevada  2.4  -1.5    False\n",
       "five   2002  Nevada  2.9  -1.7    False\n",
       "six    2003  Nevada  3.2   NaN    False"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#del로 컬럼 삭제\n",
    "frame2['eastern'] = frame2.state == 'Ohio'\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "617876de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'pop', 'debt'], dtype='object')"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " del frame2['eastern']\n",
    "    \n",
    "frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "8b2a3864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
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      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2000     NaN   1.5\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6"
      ]
     },
     "execution_count": 352,
     "metadata": {},
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   ],
   "source": [
    "pop = {'Nevada': {2001: 2.4, 2002: 2.9},\n",
    "       'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}\n",
    "\n",
    "frame3=pd.DataFrame(pop, index=[2000, 2001, 2002])\n",
    "\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "d4d9f963",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.9</td>\n",
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       "      <th>Ohio</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2000  2001  2002\n",
       "Nevada   NaN   2.4   2.9\n",
       "Ohio     1.5   1.7   3.6"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.T #transpose/전치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "6e48ba9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6\n",
       "2003     NaN   NaN"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pop, index=[2001, 2002, 2003])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "4d147b08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1.7</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ohio  Nevada\n",
       "2000   1.5     NaN\n",
       "2001   1.7     2.4"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata = {'Ohio': frame3['Ohio'][:-1],\n",
    "         'Nevada': frame3['Nevada'][:2]}\n",
    "\n",
    "pd.DataFrame(pdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "b1918e23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  Nevada  Ohio\n",
       "year               \n",
       "2000      NaN   1.5\n",
       "2001      2.4   1.7\n",
       "2002      2.9   3.6"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.index.name = 'year'; frame3.columns.name = 'state'\n",
    "\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "d6db66fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[nan, 1.5],\n",
       "       [2.4, 1.7],\n",
       "       [2.9, 3.6]])"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "29448b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2000, 'Ohio', 1.5, nan],\n",
       "       [2001, 'Ohio', 1.7, -1.2],\n",
       "       [2002, 'Ohio', 3.6, nan],\n",
       "       [2001, 'Nevada', 2.4, -1.5],\n",
       "       [2002, 'Nevada', 2.9, -1.7],\n",
       "       [2003, 'Nevada', 3.2, nan]], dtype=object)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed00b962",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame 생성을 위한 입력 데이터의 종류\n",
    "\n",
    "2차원 ndarray: 데이터를 담고 있는 행렬. 선택적으로 행 (로우 )과 열 (컬럼 )의 이름을 전달할 수 있다.\n",
    "배열, 리스트, 튜플의 사전: 사전의 모든 항목은 같은 길이를 가져야 하며, 각 항목의 내용이 DataFrame의 컬럼이 된다.\n",
    "NumPy의 구조화 배열: 배열의 사전과 같은 방식으로 취급된다.\n",
    "Series의 사전: Series의 각 값이 컬럼이 된다. 명시적으로 색인을 넘겨주지 않으면 각 Series의 색인이 하나로 합쳐져서 로우의 색인이 된다.\n",
    "사전의 사전: 내부에 있는 사전이 컬럼이 된다. 키값은 ‘Series의 사전’과 마찬가지로 합쳐져서 로우의 색인이 된다.\n",
    "사전이나 Series의 리스트: 리스트의 각 항목이 DataFrame의 로우가 된다. 합쳐진 사전의 키값이나 Series의 색인이 DataFrame의 컬럼 이름이 된다.\n",
    "리스트나 튜플의 리스트: ‘2차원 ndarray’의 경우와 같은 방식으로 취급된다.\n",
    "다른 DataFrame: 색인을 따로 지정하지 않으면 DataFrame의 색인이 그대로 사용된다.\n",
    "NumPy MaskedArray: ‘2차원 ndarray’의  경우와  같은 방식으로  취급되지만 마스크값은  반환되는 DataFrame에서 NA 값이 된다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411d8d52",
   "metadata": {},
   "source": [
    "###### 5.1.3 색인 객체"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "0652c56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c'], dtype='object')"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series(range(3), index=['a', 'b', 'c'])\n",
    "\n",
    "index = obj.index\n",
    "\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "6569a3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['b', 'c'], dtype='object')"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "d5dff77b",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Index does not support mutable operations",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-362-8be6e68dba2d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'd'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4275\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4277\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4279\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Index does not support mutable operations"
     ]
    }
   ],
   "source": [
    "index[1]='d' --> TypeError 발생함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "313105ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2], dtype='int64')"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = pd.Index(np.arange(3))\n",
    "\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "e1c2775d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1   -2.5\n",
       "2    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 = pd.Series([1.5, -2.5, 0], index=labels)\n",
    "\n",
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "d78e312c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index is labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "54939938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  Nevada  Ohio\n",
       "year               \n",
       "2000      NaN   1.5\n",
       "2001      2.4   1.7\n",
       "2002      2.9   3.6"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "5d303f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Nevada', 'Ohio'], dtype='object', name='state')"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "040efe44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Ohio' in frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "7ad50878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2003 in frame3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "a9ce90e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['foo', 'foo', 'bar', 'bar'], dtype='object')"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_labels = pd.Index(['foo', 'foo', 'bar', 'bar'])\n",
    "\n",
    "dup_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e899f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#색인 메서드와 속성\n",
    "\n",
    "append: 추가적인 색인 객체를 덧붙여 새로운 색인을 반환한다.\n",
    "difference: 색인의 차집합을 반환한다.\n",
    "intersection: 색인의 교집합을 반환한다.\n",
    "union: 색인의 합집합을 반환한다.\n",
    "isin: 색인이 넘겨받은 색인에 존재하는지 알려주는 불리언 배열을 반환한다.\n",
    "delete: i 위치의 색인이 삭제된 새로운 색인을 반환한다.\n",
    "drop: 넘겨받은 값이 삭제된 새로운 색인을 반환한다.\n",
    "insert: i 위치에 색인이 추가된 새로운 색인을 반환한다.\n",
    "is_monotonic: 색인이 단조성을 가진다면 True를 반환한다.\n",
    "is_unique: 중복되는 색인이 없다면 True를 반환한다.\n",
    "unique: 색인에서 중복되는 요소를 제거하고 유일한 값만 반환한다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7ffc76",
   "metadata": {},
   "source": [
    "## 5.2 핵심 기능"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e47294",
   "metadata": {},
   "source": [
    "###### 5.2.1 재색인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "603ed18a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4.5\n",
       "b    7.2\n",
       "a   -5.3\n",
       "c    3.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])\n",
    "\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "1e5c5b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])\n",
    "\n",
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "2668f035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "2    purple\n",
       "4    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])\n",
    "\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "6bd3bb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(range(6), method='ffill') #누락된 값을 직전의 값으로 채워넣는다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "9d1e0e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.arange(9).reshape((3, 3)),\n",
    "                     index=['a', 'c', 'd'],\n",
    "                     columns=['Ohio', 'Texas', 'California'])\n",
    "\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "daecaf05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a   0.0    1.0         2.0\n",
       "b   NaN    NaN         NaN\n",
       "c   3.0    4.0         5.0\n",
       "d   6.0    7.0         8.0"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2 = frame.reindex(['a', 'b', 'c', 'd'])\n",
    "\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "910bee5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a      1   NaN           2\n",
       "c      4   NaN           5\n",
       "d      7   NaN           8"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = ['Texas', 'Utah', 'California']\n",
    "\n",
    "frame.reindex(columns=states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "931f96c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "4c0bdc5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.loc[['a', 'd']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "28b6c95c",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"Passing list-likes to .loc or [] with any missing labels is no longer supported. The following labels were missing: Index(['b'], dtype='object'). See https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-392-8f4ee4dd579c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#loc를 이용해서 라벨로 색인하면 좀 더 간결하게 할 수 있으며 대부분의 사용자는 명시적으로 이 방식을 사용하는 것을 선호\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'd'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    887\u001b[0m                     \u001b[0;31m# AttributeError for IntervalTree get_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1065\u001b[0m         \u001b[0;31m# ugly hack for GH #836\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1066\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_multi_take_opportunity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1067\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_multi_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1069\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple_same_dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_multi_take\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1017\u001b[0m         \"\"\"\n\u001b[1;32m   1018\u001b[0m         \u001b[0;31m# GH 836\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1019\u001b[0;31m         d = {\n\u001b[0m\u001b[1;32m   1020\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXIS_ORDERS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1018\u001b[0m         \u001b[0;31m# GH 836\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m         d = {\n\u001b[0;32m-> 1020\u001b[0;31m             \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1021\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXIS_ORDERS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         }\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1264\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1266\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1267\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0moption_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"display.max_seq_items\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"display.width\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m80\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1321\u001b[0;31m                 raise KeyError(\n\u001b[0m\u001b[1;32m   1322\u001b[0m                     \u001b[0;34m\"Passing list-likes to .loc or [] with any missing labels \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m                     \u001b[0;34m\"is no longer supported. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"Passing list-likes to .loc or [] with any missing labels is no longer supported. The following labels were missing: Index(['b'], dtype='object'). See https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\""
     ]
    }
   ],
   "source": [
    "#loc를 이용해서 라벨로 색인하면 좀 더 간결하게 할 수 있으며 대부분의 사용자는 명시적으로 이 방식을 사용하는 것을 선호\n",
    "frame.loc[['a', 'b', 'c', 'd'], states]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0aec1c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "index: 색인으로 사용할 새로운 순서. Index 인스턴스나 다른 순차적인 자료구조가 사용 가능하다. Index는 복사가 이루어지지 않고 그대로 사용된다.\n",
    "method: 채움 메서드. ffill은 직전 값을 채워 넣고 bfill은 다음 값을 채워 넣는다.\n",
    "fill_value: 재색인 과정 중에 새롭게 나타나는 비어 있는 데이터를 채우기 위한 값\n",
    "limit: 전/후 보간 시에 사용할 최대 갭 크기 (채워넣을 원소의 수)\n",
    "tolerance: 전/후 보간 시에 사용할 최대 갭 크기 (값의 차이)\n",
    "level: MultiIndex의 단계(level)에 단순 색인을 맞춘다. 그렇지 않으면 MultiIndex의 하위집합에 맞춘다.\n",
    "copy: True인 경우 새로운 색인이 이전 색인과 동일하더라도 데이터를 복사한다. False인 경우 새로운 색인이 이전 색인과 동일할 경우 복사하지 않는다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca58c3bc",
   "metadata": {},
   "source": [
    "###### 5.2.2 하나의 로우나 컬럼 삭제하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "2acbaab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])\n",
    "\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "93813270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop 메서드를 사용하여 선택한 값들이 삭제된 새로운 객체를 얻는다\n",
    "new_obj = obj.drop('c')\n",
    "\n",
    "new_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "8cccf69b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.drop(['d', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "3d08070a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFrame에서는 로우와 컬럼 모두에서 값을 삭제할 수 있다\n",
    "data = pd.DataFrame(np.arange(16).reshape((4, 4)),\n",
    "                    index=['Ohio', 'Colorado', 'Utah', 'New York'],\n",
    "                    columns=['one', 'two', 'three', 'four'])\n",
    "\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "bc9e4909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop 함수에 인자로 로우 이름을 넘기면 해당 로우 (axis 0)의 값을 모두 삭제한다.\n",
    "\n",
    "data.drop(['Colorado', 'Ohio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "21b71014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three  four\n",
       "Ohio        0      2     3\n",
       "Colorado    4      6     7\n",
       "Utah        8     10    11\n",
       "New York   12     14    15"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#컬럼의 값을 삭제할 때는 axis=1 또는 axis='columns'를 인자로 넘긴다\n",
    "\n",
    "data.drop('two', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "ece2846f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three\n",
       "Ohio        0      2\n",
       "Colorado    4      6\n",
       "Utah        8     10\n",
       "New York   12     14"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['two', 'four'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "e2091059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.drop('c', inplace=True)\n",
    "#inplace 옵션을 사용하는 경우 버려지는 값을 모두 삭제\n",
    "\n",
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8976bac3",
   "metadata": {},
   "source": [
    "###### 5.2.3 색인하기, 선택하기, 거르기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "23e2c561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series(np.arange(4.), index=['a', 'b', 'c', 'd'])\n",
    "\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "4774e4b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "e19ef939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "40e87cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    2.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "a27db499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "a    0.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[['b', 'a', 'd']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "28c240fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[obj < 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "dde146b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['b':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "1bddcd67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    5.0\n",
       "c    5.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['b':'c'] = 5\n",
    "\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "d6096aa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#색인으로 DataFrame에서 하나 이상의 컬럼 값을 가져올 수 있다\n",
    "\n",
    "data = pd.DataFrame(np.arange(16).reshape((4, 4)),\n",
    "                    index=['Ohio', 'Colorado', 'Utah', 'New York'],\n",
    "                    columns=['one', 'two', 'three', 'four'])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "09a848c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio         1\n",
       "Colorado     5\n",
       "Utah         9\n",
       "New York    13\n",
       "Name: two, dtype: int64"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "3791a675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>three</th>\n",
       "      <th>one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          three  one\n",
       "Ohio          2    0\n",
       "Colorado      6    4\n",
       "Utah         10    8\n",
       "New York     14   12"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['three', 'one']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "6babaeab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "9eadf5c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['three'] > 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "17416804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one    two  three   four\n",
       "Ohio       True   True   True   True\n",
       "Colorado   True  False  False  False\n",
       "Utah      False  False  False  False\n",
       "New York  False  False  False  False"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data < 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "65939548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    0      0     0\n",
       "Colorado    0    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data < 5] = 0\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "fba0093a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "two      5\n",
       "three    6\n",
       "Name: Colorado, dtype: int64"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['Colorado', ['two', 'three']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "c874af6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    11\n",
       "one      8\n",
       "two      9\n",
       "Name: Utah, dtype: int64"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[2, [3, 0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "e17d29d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>four</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          four  one  two\n",
       "Colorado     7    0    5\n",
       "Utah        11    8    9"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[[1, 2], [3, 0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "50287211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio        0\n",
       "Colorado    5\n",
       "Utah        9\n",
       "Name: two, dtype: int64"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:'Utah', 'two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "456f9f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three\n",
       "Colorado    0    5      6\n",
       "Utah        8    9     10\n",
       "New York   12   13     14"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " data.iloc[:, :3][data.three > 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d91e2e",
   "metadata": {},
   "source": [
    "###### 5.2.4 정수 색인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "6e103a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "2    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series(np.arange(3.))\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "79e4bbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2 = pd.Series(np.arange(3.), index=['a', 'b', 'c'])\n",
    "\n",
    "ser2[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "c19fcb95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "8307aac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.loc[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "a7d73f1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.iloc[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba3bdba",
   "metadata": {},
   "source": [
    "###### 5.2.5 산술 연산과 데이터 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "120a5492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    7.3\n",
       "c   -2.5\n",
       "d    3.4\n",
       "e    1.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([7.3, -2.5, 3.4, 1.5], index=['a', 'c', 'd', 'e'])\n",
    "\n",
    "s2 = pd.Series([-2.1, 3.6, -1.5, 4, 3.1], index=['a', 'c', 'e', 'f', 'g'])\n",
    "\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "cd244372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -2.1\n",
       "c    3.6\n",
       "e   -1.5\n",
       "f    4.0\n",
       "g    3.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "0712a882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.2\n",
       "c    1.1\n",
       "d    NaN\n",
       "e    0.0\n",
       "f    NaN\n",
       "g    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 + s2 #겹치는 색인이 없는 경우 데이터는 NA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "0444bb89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b    c    d\n",
       "Ohio      0.0  1.0  2.0\n",
       "Texas     3.0  4.0  5.0\n",
       "Colorado  6.0  7.0  8.0"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.arange(9.).reshape((3, 3)), columns=list('bcd'),index=['Ohio', 'Texas', 'Colorado'])\n",
    "\n",
    "df2 = pd.DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'),index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "2a874e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b     d     e\n",
       "Utah    0.0   1.0   2.0\n",
       "Ohio    3.0   4.0   5.0\n",
       "Texas   6.0   7.0   8.0\n",
       "Oregon  9.0  10.0  11.0"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "a33af1b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b   c     d   e\n",
       "Colorado  NaN NaN   NaN NaN\n",
       "Ohio      3.0 NaN   6.0 NaN\n",
       "Oregon    NaN NaN   NaN NaN\n",
       "Texas     9.0 NaN  12.0 NaN\n",
       "Utah      NaN NaN   NaN NaN"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1+df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "750275fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A\n",
       "0  1\n",
       "1  2"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'A': [1, 2]})\n",
    "\n",
    "df2 = pd.DataFrame({'B': [3, 4]})\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "867405a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   B\n",
       "0  3\n",
       "1  4"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "1766a3e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B\n",
       "0 NaN NaN\n",
       "1 NaN NaN"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 - df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "b92e893a",
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   "outputs": [
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     "execution_count": 441,
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   "source": [
    "df1 = pd.DataFrame(np.arange(12.).reshape((3, 4)), columns=list('abcd'))\n",
    "\n",
    "df2 = pd.DataFrame(np.arange(20.).reshape((4, 5)),columns=list('abcde'))\n",
    "df2.loc[1, 'b'] = np.nan\n",
    "\n",
    "\n",
    "df1"
   ]
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   "cell_type": "code",
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     "execution_count": 442,
     "metadata": {},
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   "source": [
    "df2"
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  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "64092e02",
   "metadata": {},
   "outputs": [
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     "execution_count": 443,
     "metadata": {},
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   ],
   "source": [
    "df1+df2                                                           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "7e6431b9",
   "metadata": {
    "scrolled": true
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       "      a     b     c     d     e\n",
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     "execution_count": 444,
     "metadata": {},
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   ],
   "source": [
    "df1.add(df2, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "3a02faf0",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.250</td>\n",
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       "      <td>0.142857</td>\n",
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       "       a         b         c         d\n",
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     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
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    "1/df1"
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  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "436f43e2",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     a    b     c     d  e\n",
       "0  0.0  1.0   2.0   3.0  0\n",
       "1  4.0  5.0   6.0   7.0  0\n",
       "2  8.0  9.0  10.0  11.0  0"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1.reindex(columns=df2.columns, fill_value=0) #Series나 DataFrame을 재색인할 때도 fill_valu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "c3520394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.,  7.],\n",
       "       [ 8.,  9., 10., 11.]])"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(12.).reshape((3, 4))\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "fa65ac46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3.])"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "771c78d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [4., 4., 4., 4.],\n",
       "       [8., 8., 8., 8.]])"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr - arr[0]                                                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "ccb66fd8",
   "metadata": {},
   "outputs": [],
   "source": [
    " frame = pd.DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "ccecaea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "series = frame.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "6ab57ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
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       "      <td>11.0</td>\n",
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      "text/plain": [
       "          b     d     e\n",
       "Utah    0.0   1.0   2.0\n",
       "Ohio    3.0   4.0   5.0\n",
       "Texas   6.0   7.0   8.0\n",
       "Oregon  9.0  10.0  11.0"
      ]
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     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "frame"
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  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "24e24ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0.0\n",
       "d    1.0\n",
       "e    2.0\n",
       "Name: Utah, dtype: float64"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "06e67f30",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b    d    e\n",
       "Utah    0.0  0.0  0.0\n",
       "Ohio    3.0  3.0  3.0\n",
       "Texas   6.0  6.0  6.0\n",
       "Oregon  9.0  9.0  9.0"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame - series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "56baf9c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b   d     e   f\n",
       "Utah    0.0 NaN   3.0 NaN\n",
       "Ohio    3.0 NaN   6.0 NaN\n",
       "Texas   6.0 NaN   9.0 NaN\n",
       "Oregon  9.0 NaN  12.0 NaN"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series2 = pd.Series(range(3), index=['b', 'e', 'f'])\n",
    "\n",
    "frame + series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "8b77334a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b     d     e\n",
       "Utah    0.0   1.0   2.0\n",
       "Ohio    3.0   4.0   5.0\n",
       "Texas   6.0   7.0   8.0\n",
       "Oregon  9.0  10.0  11.0"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series3 = frame['d']\n",
    "\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "7e960383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah       1.0\n",
       "Ohio       4.0\n",
       "Texas      7.0\n",
       "Oregon    10.0\n",
       "Name: d, dtype: float64"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "e0c6cfb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b    d    e\n",
       "Utah   -1.0  0.0  1.0\n",
       "Ohio   -1.0  0.0  1.0\n",
       "Texas  -1.0  0.0  1.0\n",
       "Oregon -1.0  0.0  1.0"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sub(series3, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5b92e4",
   "metadata": {},
   "source": [
    "###### 5.2.6 함수 적용과 매핑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "7aa1c412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-0.195472</td>\n",
       "      <td>-0.463419</td>\n",
       "      <td>0.989415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1.388647</td>\n",
       "      <td>1.087714</td>\n",
       "      <td>0.438801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-0.961602</td>\n",
       "      <td>1.245610</td>\n",
       "      <td>0.502926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-0.631399</td>\n",
       "      <td>-0.704252</td>\n",
       "      <td>-0.240483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b         d         e\n",
       "Utah   -0.195472 -0.463419  0.989415\n",
       "Ohio    1.388647  1.087714  0.438801\n",
       "Texas  -0.961602  1.245610  0.502926\n",
       "Oregon -0.631399 -0.704252 -0.240483"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'),index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "ec7c8fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.195472</td>\n",
       "      <td>0.463419</td>\n",
       "      <td>0.989415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1.388647</td>\n",
       "      <td>1.087714</td>\n",
       "      <td>0.438801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.961602</td>\n",
       "      <td>1.245610</td>\n",
       "      <td>0.502926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>0.631399</td>\n",
       "      <td>0.704252</td>\n",
       "      <td>0.240483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b         d         e\n",
       "Utah    0.195472  0.463419  0.989415\n",
       "Ohio    1.388647  1.087714  0.438801\n",
       "Texas   0.961602  1.245610  0.502926\n",
       "Oregon  0.631399  0.704252  0.240483"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "e5399075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    2.350250\n",
       "d    1.949862\n",
       "e    1.229897\n",
       "dtype: float64"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = lambda x: x.max() - x.min()\n",
    "\n",
    "frame.apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "46cdf013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah      1.452834\n",
       "Ohio      0.949846\n",
       "Texas     2.207212\n",
       "Oregon    0.463770\n",
       "dtype: float64"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "id": "3841435c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.961602</td>\n",
       "      <td>-0.704252</td>\n",
       "      <td>-0.240483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.388647</td>\n",
       "      <td>1.245610</td>\n",
       "      <td>0.989415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b         d         e\n",
       "min -0.961602 -0.704252 -0.240483\n",
       "max  1.388647  1.245610  0.989415"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x):\n",
    "    return pd.Series([x.min(), x.max()], index=['min', 'max'])\n",
    "\n",
    "frame.apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "96bcdd7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-0.96</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b      d      e\n",
       "Utah    -0.20  -0.46   0.99\n",
       "Ohio     1.39   1.09   0.44\n",
       "Texas   -0.96   1.25   0.50\n",
       "Oregon  -0.63  -0.70  -0.24"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format = lambda x: '%.2f' % x\n",
    "\n",
    "frame.applymap(format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "df442475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah       0.99\n",
       "Ohio       0.44\n",
       "Texas      0.50\n",
       "Oregon    -0.24\n",
       "Name: e, dtype: object"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['e'].map(format)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59b6326",
   "metadata": {},
   "source": [
    "###### 5.2.7 정렬과 순위"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "b3c05d93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series(range(4), index=['d', 'a', 'b', 'c'])\n",
    "\n",
    "obj.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "5b7de59a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>d</th>\n",
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       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       d  a  b  c\n",
       "one    4  5  6  7\n",
       "three  0  1  2  3"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.arange(8).reshape((2, 4)),index=['three', 'one'],columns=['d', 'a', 'b', 'c'])\n",
    "\n",
    "frame.sort_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "1a7ed16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>one</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       a  b  c  d\n",
       "three  1  2  3  0\n",
       "one    5  6  7  4"
      ]
     },
     "execution_count": 472,
     "metadata": {},
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   "source": [
    "frame.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "a73f8c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>d</th>\n",
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       "      <th>b</th>\n",
       "      <th>a</th>\n",
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       "  </thead>\n",
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       "      <th>three</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       d  c  b  a\n",
       "three  0  3  2  1\n",
       "one    4  7  6  5"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "b3fc01f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2   -3\n",
       "3    2\n",
       "0    4\n",
       "1    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series([4, 7, -3, 2])\n",
    "\n",
    "obj.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "766429b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4   -3.0\n",
       "5    2.0\n",
       "0    4.0\n",
       "2    7.0\n",
       "1    NaN\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series([4, np.nan, 7, np.nan, -3, 2])\n",
    "\n",
    "obj.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "88cbffd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   b  a\n",
       "0  4  0\n",
       "1  7  1\n",
       "2 -3  0\n",
       "3  2  1"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame({'b': [4, 7, -3, 2], 'a': [0, 1, 0, 1]})\n",
    "\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "977c13a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   b  a\n",
       "2 -3  0\n",
       "3  2  1\n",
       "0  4  0\n",
       "1  7  1"
      ]
     },
     "execution_count": 477,
     "metadata": {},
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   "source": [
    " frame.sort_values(by='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "069dab9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   b  a\n",
       "2 -3  0\n",
       "0  4  0\n",
       "3  2  1\n",
       "1  7  1"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "frame.sort_values(by=['a', 'b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "d083c4bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.5\n",
       "1    1.0\n",
       "2    6.5\n",
       "3    4.5\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    4.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series([7, -5, 7, 4, 2, 0, 4])\n",
    "\n",
    "obj.rank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "7dd53536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.0\n",
       "1    1.0\n",
       "2    7.0\n",
       "3    4.0\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.rank(method='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "92ca08d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    7.0\n",
       "2    2.0\n",
       "3    4.0\n",
       "4    5.0\n",
       "5    6.0\n",
       "6    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.rank(ascending=False, method='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "b8a0d95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     b  a    c\n",
       "0  4.3  0 -2.0\n",
       "1  7.0  1  5.0\n",
       "2 -3.0  0  8.0\n",
       "3  2.0  1 -2.5"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame({'b': [4.3, 7, -3, 2], 'a': [0, 1, 0, 1],\n",
    "                      'c': [-2, 5, 8, -2.5]})\n",
    "\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "d1c032f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     b    a    c\n",
       "0  3.0  2.0  1.0\n",
       "1  3.0  1.0  2.0\n",
       "2  1.0  2.0  3.0\n",
       "3  3.0  2.0  1.0"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.rank(axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe1ffae",
   "metadata": {},
   "source": [
    "###### 5.2.8 중복 색인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "fa8ecd12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    1\n",
       "b    2\n",
       "b    3\n",
       "c    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series(range(5), index=['a', 'a', 'b', 'b', 'c'])\n",
    "\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "0ade977e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "8e5b7b99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "cb2bef09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.585599</td>\n",
       "      <td>0.352801</td>\n",
       "      <td>-1.815558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-0.910124</td>\n",
       "      <td>-0.748528</td>\n",
       "      <td>-0.108125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.274779</td>\n",
       "      <td>0.237350</td>\n",
       "      <td>-0.259844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.312442</td>\n",
       "      <td>0.448510</td>\n",
       "      <td>-0.059532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "a  0.585599  0.352801 -1.815558\n",
       "a -0.910124 -0.748528 -0.108125\n",
       "b  0.274779  0.237350 -0.259844\n",
       "b -0.312442  0.448510 -0.059532"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(4, 3), index=['a', 'a', 'b', 'b'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "4c9ef84e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.274779</td>\n",
       "      <td>0.23735</td>\n",
       "      <td>-0.259844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.312442</td>\n",
       "      <td>0.44851</td>\n",
       "      <td>-0.059532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0        1         2\n",
       "b  0.274779  0.23735 -0.259844\n",
       "b -0.312442  0.44851 -0.059532"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79c649e",
   "metadata": {},
   "source": [
    "## 5.3 기술 통계 계산과 요약"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "d342085b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.10</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  7.10 -4.5\n",
       "c   NaN  NaN\n",
       "d  0.75 -1.3"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[1.4, np.nan], [7.1, -4.5],[np.nan, np.nan], [0.75, -1.3]],\n",
    "                  index=['a', 'b', 'c', 'd'],columns=['one', 'two'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "ffff0cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    9.25\n",
       "two   -5.80\n",
       "dtype: float64"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "6bd6829b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.40\n",
       "b    2.60\n",
       "c    0.00\n",
       "d   -0.55\n",
       "dtype: float64"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "3119beb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      NaN\n",
       "b    1.300\n",
       "c      NaN\n",
       "d   -0.275\n",
       "dtype: float64"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis='columns', skipna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "f593b414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    b\n",
       "two    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "0771cd66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>8.50</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>9.25</td>\n",
       "      <td>-5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  8.50 -4.5\n",
       "c   NaN  NaN\n",
       "d  9.25 -5.8"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "f1f2dfbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.083333</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.493685</td>\n",
       "      <td>2.262742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>-4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.075000</td>\n",
       "      <td>-3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.250000</td>\n",
       "      <td>-2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>-1.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one       two\n",
       "count  3.000000  2.000000\n",
       "mean   3.083333 -2.900000\n",
       "std    3.493685  2.262742\n",
       "min    0.750000 -4.500000\n",
       "25%    1.075000 -3.700000\n",
       "50%    1.400000 -2.900000\n",
       "75%    4.250000 -2.100000\n",
       "max    7.100000 -1.300000"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "6bb85529",
   "metadata": {},
   "outputs": [],
   "source": [
    "#수치 데이터가 아닐 경우 describe는 다른 요약 통계\n",
    "obj = pd.Series(['a', 'a', 'b', 'c'] * 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c066fa",
   "metadata": {},
   "source": [
    "###### 5.3.1 상관관계와 공분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "ad74e483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.12.0\n",
      "  latest version: 22.11.1\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /opt/anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - pandas-datareader\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    pandas-datareader-0.10.0   |     pyhd3eb1b0_0          71 KB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:          71 KB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  pandas-datareader  pkgs/main/noarch::pandas-datareader-0.10.0-pyhd3eb1b0_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "pandas-datareader-0. | 71 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "38bcea10",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "string indices must be integers",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-539-16ccc31f28b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas_datareader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mweb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m all_data = {ticker: web.get_data_yahoo(ticker)\n\u001b[0m\u001b[1;32m      3\u001b[0m             for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']}\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m price = pd.DataFrame({ticker: data['Adj Close'] \n",
      "\u001b[0;32m<ipython-input-539-16ccc31f28b1>\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas_datareader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mweb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m all_data = {ticker: web.get_data_yahoo(ticker)\n\u001b[0m\u001b[1;32m      3\u001b[0m             for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']}\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m price = pd.DataFrame({ticker: data['Adj Close'] \n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas_datareader/data.py\u001b[0m in \u001b[0;36mget_data_yahoo\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_data_yahoo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mYahooDailyReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas_datareader/base.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;31m# If a single symbol, (e.g., 'GOOG')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m             \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_one_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m         \u001b[0;31m# Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas_datareader/yahoo/daily.py\u001b[0m in \u001b[0;36m_read_one_data\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptrn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDOTALL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"context\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dispatcher\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"stores\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"HistoricalPriceStore\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"No data fetched for symbol {} using {}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
     ]
    }
   ],
   "source": [
    "import pandas_datareader.data as web\n",
    "all_data = {ticker: web.get_data_yahoo(ticker)\n",
    "            for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']}\n",
    "\n",
    "price = pd.DataFrame({ticker: data['Adj Close'] \n",
    "                      for ticker, data in all_data.items()})\n",
    "\n",
    "\n",
    "volume = pd.DataFrame({ticker: data['Volume'] \n",
    "                       for ticker, data in all_data.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a72504ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cf558e69",
   "metadata": {},
   "source": [
    "###### 5.3.2 유일값, 값 세기, 멤버십"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "26effe8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['c', 'a', 'd', 'b'], dtype=object)"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])\n",
    "\n",
    "uniques = obj.unique()\n",
    "\n",
    "uniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "314622e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a    3\n",
       "b    2\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "4878f075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "d    1\n",
       "b    2\n",
       "a    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(obj.values, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "234c5696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    c\n",
       "1    a\n",
       "2    d\n",
       "3    a\n",
       "4    a\n",
       "5    b\n",
       "6    b\n",
       "7    c\n",
       "8    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "93dcb195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = obj.isin(['b', 'c'])\n",
    "\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "1050e041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    c\n",
       "5    b\n",
       "6    b\n",
       "7    c\n",
       "8    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "18179290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 1, 0, 2])"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_match = pd.Series(['c', 'a', 'b', 'b', 'c', 'a'])\n",
    "\n",
    "unique_vals = pd.Series(['c', 'b', 'a'])\n",
    "\n",
    "pd.Index(unique_vals).get_indexer(to_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "fbdaabc8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qu1</th>\n",
       "      <th>Qu2</th>\n",
       "      <th>Qu3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Qu1  Qu2  Qu3\n",
       "0    1    2    1\n",
       "1    3    3    5\n",
       "2    4    1    2\n",
       "3    3    2    4\n",
       "4    4    3    4"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'Qu1': [1, 3, 4, 3, 4],\n",
    "                     'Qu2': [2, 3, 1, 2, 3],\n",
    "                     'Qu3': [1, 5, 2, 4, 4]})\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "c052c51f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qu1</th>\n",
       "      <th>Qu2</th>\n",
       "      <th>Qu3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Qu1  Qu2  Qu3\n",
       "1  1.0  1.0  1.0\n",
       "2  0.0  2.0  1.0\n",
       "3  2.0  2.0  0.0\n",
       "4  2.0  0.0  2.0\n",
       "5  0.0  0.0  1.0"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = data.apply(pd.value_counts).fillna(0)\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687f2ae2",
   "metadata": {},
   "source": [
    "# CHAPTER 6 데이터 로딩과 저장, 파일 형식"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c7a21a3",
   "metadata": {},
   "source": [
    "## 6.1 텍스트 파일에서 데이터를 읽고 쓰는 법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fc03e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "read_csv: 파일, URL 또는 파일과 유사한 객체로부터 구분된 데이터를 읽어온다. 데이터 구분자는 쉼표(,)를 기본으로 한다.\n",
    "read_table: 파일, URL 또는 파일과 유사한 객체로부터 구분된 데이터를 읽어온다. 데이터 구분자는 탭('\\t')을 기본으로 한다.\n",
    "read_fwf: 고정폭 컬럼 형식에서 데이터를 읽어온다 (구분자가 없는 데이터 ).\n",
    "read_clipboard: 클립보드에 있는 데이터를 읽어오는 read_table 함수. 웹페이지에서 표를 읽어올 때 유용하다\n",
    "    ;;;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "id": "1c5483d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>world</td>\n",
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       "      <td>9</td>\n",
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      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('/Users/yuna/Documents/ex1.csv')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "id": "890f1a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "�\u0005��\u0003\u0000\u0000\u0000\u0000\u0000\u0000�\u0011pandas.core.frame��\tDataFrame���)��}�(�\u0004_mgr��\u001e",
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      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000�h\u001e",
      "�\u0002i8�����R�(K\u0003�\u0001<�NNNJ����J����K\u0000t�bK\u0004K\u0003���\u0001C�t�R�h\u0014h\u0017K\u0000��h\u0019��R�(K\u0001K\u0001K\u0003��h!�]�(�\u0005hello��\u0005world��\u0003foo�et�be]�(h\r",
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     ]
    }
   ],
   "source": [
    "!cat /Users/yuna/Documents/ex1.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "id": "5c1d856d",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_table('/Users/yuna/Documents/ex1.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "7375fd0b",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   0   1   2   3      4\n",
       "0  1   2   3   4  hello\n",
       "1  5   6   7   8  world\n",
       "2  9  10  11  12    foo"
      ]
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     "execution_count": 554,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.read_csv('/Users/yuna/Documents/ex2.csv',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "6dc7d57e",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/Users/yuna/Documents/ex2.csv',names=['a','b','c','d','message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "ac692cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "         a   b   c   d\n",
       "message               \n",
       "hello    1   2   3   4\n",
       "world    5   6   7   8\n",
       "foo      9  10  11  12"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 메세지 컬럼을 색인으로 하는 데이터프레임 만들려면, index_col안에 4번째 컬럼에 'message' 이름을 가진 컬럼을 지정 후 색인으로 만든다\n",
    "names = ['a', 'b', 'c', 'd', 'message']\n",
    "\n",
    "pd.read_csv('/Users/yuna/Documents/ex2.csv',names=names, index_col=\"message\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "id": "98f3aab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">one</th>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">two</th>\n",
       "      <th>a</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           value1  value2\n",
       "key1 key2                \n",
       "one  a          1       2\n",
       "     b          3       4\n",
       "     c          5       6\n",
       "     d          7       8\n",
       "two  a          9      10\n",
       "     b         11      12\n",
       "     c         13      14\n",
       "     d         15      16"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed= pd.read_csv('/Users/yuna/Documents/csv_mindex.csv',index_col=['key1', 'key2'])\n",
    "\n",
    "parsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "id": "52b1006b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1,2,3,4,hello\\n', '5,6,7,8,world\\n', '9,10,11,12,foo\\n']"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(open('/Users/yuna/Documents/ex2.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "id": "e85b1377",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th># hey!</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>a</th>\n",
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       "    <tr>\n",
       "      <th># just wanted to make things more difficult for you</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th># who reads CSV files with computers</th>\n",
       "      <th>anyway?</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      # hey!\n",
       "a                                                  b        c   d    message\n",
       "# just wanted to make things more difficult for... NaN      NaN NaN      NaN\n",
       "# who reads CSV files with computers                anyway? NaN NaN      NaN\n",
       "1                                                  2        3   4      hello\n",
       "5                                                  6        7   8      world\n",
       "9                                                  10       11  12       foo"
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/Users/yuna/Documents/ex4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "id": "ec6a132b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#skiprows로 로우를 건너뛸 수 있다\n",
    "pd.read_csv('/Users/yuna/Documents/ex4.csv',skiprows=[0,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "id": "e8b382b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
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       "      <th>0</th>\n",
       "      <td>one</td>\n",
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       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  something  a   b     c   d message\n",
       "0       one  1   2   3.0   4     NaN\n",
       "1       two  5   6   NaN   8   world\n",
       "2     three  9  10  11.0  12     foo"
      ]
     },
     "execution_count": 572,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=pd.read_csv('/Users/yuna/Documents/ex5.csv')\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "id": "e046d03f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   something      a      b      c      d  message\n",
       "0      False  False  False  False  False     True\n",
       "1      False  False  False   True  False    False\n",
       "2      False  False  False  False  False    False"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "id": "573314d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>something</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  something  a   b     c   d message\n",
       "0       one  1   2   3.0   4     NaN\n",
       "1       two  5   6   NaN   8   world\n",
       "2     three  9  10  11.0  12     foo"
      ]
     },
     "execution_count": 577,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#na_values 옵션은 리스트나 문자열 집합을 받아서 누락된 값을 처리함\n",
    "\n",
    "result=pd.read_csv('/Users/yuna/Documents/ex5.csv', na_values=['NULL'])\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "id": "9a0d5e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>something</th>\n",
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       "      <th>b</th>\n",
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       "      <th>message</th>\n",
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       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  something  a   b     c   d message\n",
       "0       one  1   2   3.0   4     NaN\n",
       "1       NaN  5   6   NaN   8   world\n",
       "2     three  9  10  11.0  12     NaN"
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinels = {'message': ['foo', 'NA'], 'something': ['two']}\n",
    "\n",
    "\n",
    "pd.read_csv('/Users/yuna/Documents/ex5.csv', na_values=sentinels) #??"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c054086",
   "metadata": {},
   "source": [
    "###### 6.1.1 텍스트 파일 조금씩 읽어오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "id": "7441bfc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one       two     three      four key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0     0.467976 -0.038649 -0.295344 ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1    -0.358893  1.404453  0.704965 ...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2    -0.501840  0.659254 -0.421691 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3     0.204886  1.074134  1.388361 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4     0.354628 -0.133116  0.283763 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9995  2.311896 -0.417070 -1.409599 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9996 -0.479893 -0.650419  0.745152 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9997  0.523331  0.787112  0.486066 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9998 -0.362559  0.598894 -1.843201 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9999 -0.096376 -1.012999 -0.657431 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               one       two     three      four key\n",
       "0             0     0.467976 -0.038649 -0.295344 ...\n",
       "1             1    -0.358893  1.404453  0.704965 ...\n",
       "2             2    -0.501840  0.659254 -0.421691 ...\n",
       "3             3     0.204886  1.074134  1.388361 ...\n",
       "4             4     0.354628 -0.133116  0.283763 ...\n",
       "5             9995  2.311896 -0.417070 -1.409599 ...\n",
       "6             9996 -0.479893 -0.650419  0.745152 ...\n",
       "7             9997  0.523331  0.787112  0.486066 ...\n",
       "8             9998 -0.362559  0.598894 -1.843201 ...\n",
       "9             9999 -0.096376 -1.012999 -0.657431 ..."
      ]
     },
     "execution_count": 582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#여러 파일 중에서 몇개의 파일만 읽어서 확인하기\n",
    "\n",
    "pd.options.display.max_rows=10\n",
    "\n",
    "result=pd.read_csv('/Users/yuna/Documents/ex6.csv')\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "id": "8d81992c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one       two     three      four key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0     0.467976 -0.038649 -0.295344 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1    -0.358893  1.404453  0.704965 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2    -0.501840  0.659254 -0.421691 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3     0.204886  1.074134  1.388361 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4     0.354628 -0.133116  0.283763 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               one       two     three      four key\n",
       "0             0     0.467976 -0.038649 -0.295344 ...\n",
       "1             1    -0.358893  1.404453  0.704965 ...\n",
       "2             2    -0.501840  0.659254 -0.421691 ...\n",
       "3             3     0.204886  1.074134  1.388361 ...\n",
       "4             4     0.354628 -0.133116  0.283763 ..."
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/Users/yuna/Documents/ex6.csv', nrows=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "id": "e9507901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.io.parsers.TextFileReader at 0x1269ede80>"
      ]
     },
     "execution_count": 593,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#파일을 여러 조각으로 나누어서 읽기\n",
    "\n",
    "chunker= pd.read_csv('/Users/yuna/Documents/ex6.csv',chunksize=2)\n",
    "\n",
    "chunker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "id": "07af27e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: float64)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-591-b3614119a58f>:2: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  tot = pd.Series([])\n"
     ]
    }
   ],
   "source": [
    "chunker = pd.read_csv('/Users/yuna/Documents/ex6.csv', chunksize=2)\n",
    "tot = pd.Series([])\n",
    "\n",
    "print(tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a3d7b798",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'key'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'key'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-56-27e089359716>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m'int64'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpiece\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunker\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mtot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpiece\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'key'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'key'"
     ]
    }
   ],
   "source": [
    "chunker = pd.read_csv('/Users/yuna/Documents/ex6.csv', chunksize=2)\n",
    "tot = pd.Series([],dtype= 'int64')\n",
    "for piece in chunker:\n",
    "    tot = tot.add(piece['key'].value_counts(), fill_value=0)\n",
    "\n",
    "tot = tot.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01068eb2",
   "metadata": {},
   "source": [
    "###### 6.1.2 데이터를 텍스트 형식으로 기록하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "id": "c832c8fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>something</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  something  a   b     c   d message\n",
       "0       one  1   2   3.0   4     NaN\n",
       "1       two  5   6   NaN   8   world\n",
       "2     three  9  10  11.0  12     foo"
      ]
     },
     "execution_count": 598,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#데이터를 구분자로 구분한 형식으로 내보내기\n",
    "\n",
    "data = pd.read_csv('/Users/yuna/Documents/ex5.csv')\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "id": "22dfd329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",something,a,b,c,d,message\r\n",
      "0,one,1,2,3.0,4,\r\n",
      "1,two,5,6,,8,world\r\n",
      "2,three,9,10,11.0,12,foo\r\n"
     ]
    }
   ],
   "source": [
    "#데이터를 쉼표로 구분된 형식으로 파일에 쓰기\n",
    "\n",
    "data.to_csv('/Users/yuna/Documents/out.csv')\n",
    "\n",
    "!cat /Users/yuna/Documents/out.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "id": "1f1c9a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|something|a|b|c|d|message\n",
      "0|one|1|2|3.0|4|\n",
      "1|two|5|6||8|world\n",
      "2|three|9|10|11.0|12|foo\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "data.to_csv(sys.stdout, sep =\"|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "id": "00c5ff7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",something,a,b,c,d,message\n",
      "0,one,1,2,3.0,4,NULL\n",
      "1,two,5,6,NULL,8,world\n",
      "2,three,9,10,11.0,12,foo\n"
     ]
    }
   ],
   "source": [
    "data.to_csv(sys.stdout, na_rep=\"NULL\") #누락된 값을 원하는 값으로 지정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "id": "f9ba38a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one,1,2,3.0,4,\n",
      "two,5,6,,8,world\n",
      "three,9,10,11.0,12,foo\n"
     ]
    }
   ],
   "source": [
    "data.to_csv(sys.stdout, index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "id": "2a2a40ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a,b,c\n",
      "1,2,3.0\n",
      "5,6,\n",
      "9,10,11.0\n"
     ]
    }
   ],
   "source": [
    "data.to_csv(sys.stdout, index=False, columns=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "id": "a55358e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates= pd.date_range('1/1/2000', periods=7)\n",
    "\n",
    "ts= pd.Series(np.arange(7), index=dates)\n",
    "\n",
    "ts.to_csv('/Users/yuna/Documents/tseries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "id": "75f2331b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",0\r\n",
      "2000-01-01,0\r\n",
      "2000-01-02,1\r\n",
      "2000-01-03,2\r\n",
      "2000-01-04,3\r\n",
      "2000-01-05,4\r\n",
      "2000-01-06,5\r\n",
      "2000-01-07,6\r\n"
     ]
    }
   ],
   "source": [
    "!cat /Users/yuna/Documents/tseries.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48db0f9b",
   "metadata": {},
   "source": [
    "###### 6.1.3 구분자 형식 다루기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e1af21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pandas_read_table 함수를 이용해서 디스크에 표 형태로 저장된 대부분의 파일 형식을 불러온다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "id": "9aaa785f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"a\",\"b\",\"c\"\r\n",
      "\"1\",\"2\",\"3\"\r\n",
      "\"1\",\"2\",\"3\"\r\n"
     ]
    }
   ],
   "source": [
    "!cat /Users/yuna/Documents/ex7.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "id": "ad3b5cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "f = open('/Users/yuna/Documents/ex7.csv')\n",
    "\n",
    "reader=csv.reader(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "id": "92fe6879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b', 'c']\n",
      "['1', '2', '3']\n",
      "['1', '2', '3']\n"
     ]
    }
   ],
   "source": [
    "for line in reader:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "id": "8c753e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#원하는 형태로 넣기\n",
    "\n",
    "with open('/Users/yuna/Documents/ex7.csv') as f:\n",
    "    lines=list(csv.reader(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "id": "ea9daf3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "header, values= lines[0], lines[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "id": "c7764eb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': ('1', '1'), 'b': ('2', '2'), 'c': ('3', '3')}"
      ]
     },
     "execution_count": 637,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#사전 표기법과 로우를 컬럼으로 전치해주는 zip(*values)\n",
    "\n",
    "data_dict={h: v for h, v in zip(header,zip(*values))}\n",
    "\n",
    "data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "id": "cd46f781",
   "metadata": {},
   "outputs": [],
   "source": [
    "class my_dialect(csv.Dialect):\n",
    "    lineterminator = '\\n'    \n",
    "    delimiter = ';'    \n",
    "    quotechar = '\"'    \n",
    "    quoting = csv.QUOTE_MINIMAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "id": "6c767d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = csv.reader(f, delimiter='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "id": "35f6e9ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_csv.reader at 0x127f3e4a0>"
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader = csv.reader(f, dialect=my_dialect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "id": "b27afbe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#csv 관련 옵션\n",
    "\n",
    "delimiter: 필드를 구분하기 위한 한 문자로 된 구분자. 기본값은 ','\n",
    "lineterminator: 파일을 저장할 때 사용할 개행 문자. 기본값은 '\\r\\n'. 파일을 읽을 때는 이 값을 무시하며, 자동으로 플랫폼별 개행 문자를 인식한다.\n",
    "quotechar: 각 필드에서 값을 둘러싸고 있는 문자. 기본값은 ' \" '\n",
    "quoting:값을 읽거나 쓸 때 둘러쌀 문자 컨벤션. csv.QUOTE_ALL(모든 필드에 적용 ), csv.QUOTE_MINIMAL(구분자 같은 특별한 문자가 포함된 필드만 적용 ), csv.QUOTE_NONE(값을 둘러싸지 않음 ) 옵션이 있다. 자세한 내용은 파이썬 문서를 참고하자. 기본값은 QUOTE_MINIMAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "452cc092",
   "metadata": {},
   "outputs": [],
   "source": [
    "#contd\n",
    "skipinitialspace: 구분자 뒤에 있는 공백 문자를 무시할지 여부. 기본값은 False\n",
    "doublequote: 값을 둘러싸는 문자가 필드 내에 존재할 경우 처리 여부. True일 경우 그 문자까지 모두 둘러싼다. 자세한 내용은 온라인 문서를 참고하자. http://docs.python.org/2/library/csv.html\n",
    "escapechar: quoting이 csv.QUOTE_NONE일 때 값에 구분자와 같은 문자가 있을 경우 구별할 수 있도록 해주는 이스케이프 문자 ('\\' 같은 ). 기본값은 None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "id": "a62dd896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one;two;three\r\n",
      "1;2;3\r\n",
      "4;5;6\r\n",
      "7;8;9\r\n"
     ]
    }
   ],
   "source": [
    "# csv처럼 구분자로 구분된 파일을 기록하기 위해서는 csv.writer\n",
    "\n",
    "with open('mydata.csv', 'w') as f:    \n",
    "    writer = csv.writer(f, dialect=my_dialect)  #이미 열린, 쓰기가 가능한 파일 객체  \n",
    "    writer.writerow(('one', 'two', 'three'))    \n",
    "    writer.writerow(('1', '2', '3'))    \n",
    "    writer.writerow(('4', '5', '6'))    \n",
    "    writer.writerow(('7', '8', '9'))\n",
    "    \n",
    "!cat mydata.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddaa4c08",
   "metadata": {},
   "source": [
    "###### 6.1.4 JSON 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "id": "866c4cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#JSON: 웹브라우저와 다른 애플리케이션이 HTTP 요청으로 데이터를 보낼때 널리 사용하는 표준 파일 형식 중 하나."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "id": "77694ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = \"\"\"\n",
    "{\"name\": \"Wes\", \n",
    "\"places_lived\": [\"United States\", \"Spain\", \"Germany\"], \n",
    "\"pet\": null,\n",
    "\"siblings\": [{\"name\": \"Scott\", \"age\": 30, \"pets\": [\"Zeus\", \"Zuko\"]},\n",
    "{\"name\": \"Katie\", \"age\": 38,\n",
    "\"pets\": [\"Sixes\", \"Stache\", \"Cisco\"]}]}\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "id": "5119890e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Wes',\n",
       " 'places_lived': ['United States', 'Spain', 'Germany'],\n",
       " 'pet': None,\n",
       " 'siblings': [{'name': 'Scott', 'age': 30, 'pets': ['Zeus', 'Zuko']},\n",
       "  {'name': 'Katie', 'age': 38, 'pets': ['Sixes', 'Stache', 'Cisco']}]}"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "result= json.loads(obj)\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "id": "6b7862a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "asjson=json.dumps(result) #json.dumps:파이썬 객체를 json 형태로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "id": "d512d532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scott</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Katie</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  age\n",
       "0  Scott   30\n",
       "1  Katie   38"
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siblings= pd.DataFrame(result['siblings'], columns=['name', 'age'])\n",
    "\n",
    "siblings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e6c36db",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pandas.read_json은 자동으로 제이선 데이터셋을 series나 데이터 프레임으로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "id": "8f050f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"a\": 1, \"b\": 2, \"c\": 3},\r\n",
      " {\"a\": 4, \"b\": 5, \"c\": 6},\r\n",
      " {\"a\": 7, \"b\": 8, \"c\": 9}]\r\n"
     ]
    }
   ],
   "source": [
    "!cat /Users/yuna/Documents/example.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "id": "baf98c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  1  2  3\n",
       "1  4  5  6\n",
       "2  7  8  9"
      ]
     },
     "execution_count": 668,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pd.read_json:json배열에 담긴 각 객체를 테이블의 로우로 간주\n",
    "data = pd.read_json('/Users/yuna/Documents/example.json')\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "id": "bf7632b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"a\":{\"0\":1,\"1\":4,\"2\":7},\"b\":{\"0\":2,\"1\":5,\"2\":8},\"c\":{\"0\":3,\"1\":6,\"2\":9}}\n"
     ]
    }
   ],
   "source": [
    "print(data.to_json())#pandas의 데이터를 json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "id": "7b10e6d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"a\":1,\"b\":2,\"c\":3},{\"a\":4,\"b\":5,\"c\":6},{\"a\":7,\"b\":8,\"c\":9}]\n"
     ]
    }
   ],
   "source": [
    "print(data.to_json(orient='records'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f7a22a",
   "metadata": {},
   "source": [
    "###### 6.1.5 XML과 HTML: 웹 스크래핑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f50faa6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read_html: lxml이나 Beautiful Soup 라이버리를 사용해서 자동으로 html 파일을 파싱하여 데이터프레임으로 변환해준다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "id": "ea67c9fc",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-676-c6e82c16ad3e>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-676-c6e82c16ad3e>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    pip install beautifulsoup4 html5lib #오잉?\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#read_html을 사용하기 위해서 아래 라이버리들을 설치하자\n",
    "\n",
    "conda install lxml\n",
    "pip install beautifulsoup4 html5lib #오잉?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "id": "e3409fbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.12.0\n",
      "  latest version: 22.11.1\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /opt/anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - lxml\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    conda-4.13.0               |   py38hecd8cb5_0         988 KB  anaconda\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         988 KB\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  conda              pkgs/main::conda-4.12.0-py38hecd8cb5_0 --> anaconda::conda-4.13.0-py38hecd8cb5_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "conda-4.13.0         | 988 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c anaconda lxml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "id": "48482f43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: beautifulsoup4 in /opt/anaconda3/lib/python3.8/site-packages (4.9.3)\n",
      "Requirement already satisfied: html5lib in /opt/anaconda3/lib/python3.8/site-packages (1.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in /opt/anaconda3/lib/python3.8/site-packages (from beautifulsoup4) (2.2.1)\n",
      "Requirement already satisfied: webencodings in /opt/anaconda3/lib/python3.8/site-packages (from html5lib) (0.5.1)\n",
      "Requirement already satisfied: six>=1.9 in /opt/anaconda3/lib/python3.8/site-packages (from html5lib) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install beautifulsoup4 html5lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "id": "bbff7163",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No tables found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-697-d9ee9ec29f6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/about/governance/index.html'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    297\u001b[0m                 )\n\u001b[1;32m    298\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36mread_html\u001b[0;34m(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, thousands, encoding, decimal, converters, na_values, keep_default_na, displayed_only)\u001b[0m\n\u001b[1;32m   1083\u001b[0m     \u001b[0mio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1085\u001b[0;31m     return _parse(\n\u001b[0m\u001b[1;32m   1086\u001b[0m         \u001b[0mflavor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflavor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1087\u001b[0m         \u001b[0mio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse\u001b[0;34m(flavor, io, match, attrs, encoding, displayed_only, **kwargs)\u001b[0m\n\u001b[1;32m    911\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mretained\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# for mypy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 913\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mretained\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse\u001b[0;34m(flavor, io, match, attrs, encoding, displayed_only, **kwargs)\u001b[0m\n\u001b[1;32m    891\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    892\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 893\u001b[0;31m             \u001b[0mtables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    894\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcaught\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m             \u001b[0;31m# if `io` is an io-like object, check if it's seekable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36mparse_tables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mparsed\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfooter\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mtuples\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m         \"\"\"\n\u001b[0;32m--> 213\u001b[0;31m         \u001b[0mtables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_doc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_thead_tbody_tfoot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse_tables\u001b[0;34m(self, doc, match, attrs)\u001b[0m\n\u001b[1;32m    541\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    542\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 543\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No tables found\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No tables found"
     ]
    }
   ],
   "source": [
    "tables = pd.read_html('/about/governance/index.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aad7811",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d6d4e9a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5f6598d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1672b8cc",
   "metadata": {},
   "source": [
    "## 6.2 이진 데이터 형식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "id": "bd284fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 699,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.read_csv('/Users/yuna/Documents/ex1.csv')\n",
    "\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "id": "cc78a739",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickle 직렬화: 데이터를 이진 형식(binary file)으로 저장\n",
    "\n",
    "frame.to_pickle('/Users/yuna/Documents/ex1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "id": "4faf4e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 706,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_pickle('/Users/yuna/Documents/ex1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc323760",
   "metadata": {},
   "source": [
    "###### 6.2.1 HDF5 형식 사용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "id": "2ff62061",
   "metadata": {},
   "outputs": [],
   "source": [
    "# HDF5:과학 계산용 배열 데이터를 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bf8591ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: mydata.h5"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame= pd.DataFrame({'a': np.random.randn(100)})\n",
    "\n",
    "store=pd.HDFStore('mydata.h5')\n",
    "\n",
    "store['obj1']=frame\n",
    "\n",
    "store['obj1_col']=frame['a']\n",
    "\n",
    "store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fdcb4353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.246037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.506853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.135885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.753441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.289285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-0.659678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-0.397067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-0.018229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.634103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>-0.682290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           a\n",
       "0   0.246037\n",
       "1  -0.506853\n",
       "2  -0.135885\n",
       "3  -1.753441\n",
       "4  -1.289285\n",
       "..       ...\n",
       "95 -0.659678\n",
       "96 -0.397067\n",
       "97 -0.018229\n",
       "98  0.634103\n",
       "99 -0.682290\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store['obj1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4db8f31e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install pytables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8ad74fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.102143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.177794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.136964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.243320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.250380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.314967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           a\n",
       "10  1.102143\n",
       "11  0.177794\n",
       "12  0.136964\n",
       "13 -0.243320\n",
       "14 -0.250380\n",
       "15  0.314967"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.put('obj2', frame, format='table')\n",
    "\n",
    "store.select('obj2', where=['index >= 10 and index <= 15'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d3625c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "store.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d29b7289",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame.to_hdf('mydata.h5', 'obj3', format='table')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a6bdc108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.246037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.506853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.135885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.753441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.289285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a\n",
       "0  0.246037\n",
       "1 -0.506853\n",
       "2 -0.135885\n",
       "3 -1.753441\n",
       "4 -1.289285"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf('mydata.h5', 'obj3', where=['index < 5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d0bf79",
   "metadata": {},
   "source": [
    "###### 6.2.2 마이크로소프트 엑셀 파일에서 데이터 읽어오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "id": "f57ee797",
   "metadata": {},
   "outputs": [],
   "source": [
    "xlsx = pd.ExcelFile('/Users/yuna/Downloads/ex1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "id": "146c2c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d message\n",
       "0  1   2   3   4   hello\n",
       "1  5   6   7   8   world\n",
       "2  9  10  11  12     foo"
      ]
     },
     "execution_count": 733,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame= pd.read_excel('/Users/yuna/Downloads/ex1.xlsx','Sheet1')\n",
    "\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "id": "ac14cc23",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pandas data를 엑셀 파일로 저장하기\n",
    "\n",
    "writer=pd.ExcelWriter('/Users/yuna/Documents/ex2.xlsx')#생성 데이터를 기록하고\n",
    "\n",
    "frame.to_excel(writer, 'Sheet1')\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "id": "08e711d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#excel writer 사용하지 않고 to_excel 메서드에 파일 경로만 넘기기\n",
    "\n",
    "frame.to_excel('/Users/yuna/Documents/ex2.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1edead2",
   "metadata": {},
   "source": [
    "###### 6.3 웹 API와 함께 사용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "id": "a9343fc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 737,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "url='https://api.github.com/repos/pandas-dev/pandas/issues'\n",
    "\n",
    "resp = requests.get(url)\n",
    "\n",
    "resp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "id": "e0badbc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CLN: Remove 3rd party warning filters'"
      ]
     },
     "execution_count": 738,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=resp.json()\n",
    "\n",
    "data[0]['title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "id": "2dce0483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number</th>\n",
       "      <th>title</th>\n",
       "      <th>labels</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50674</td>\n",
       "      <td>CLN: Remove 3rd party warning filters</td>\n",
       "      <td>[{'id': 127685, 'node_id': 'MDU6TGFiZWwxMjc2OD...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50673</td>\n",
       "      <td>API/BUG: pd.concat doesn't copy indexes if wit...</td>\n",
       "      <td>[{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50672</td>\n",
       "      <td>BUG: is_[int|float]_dtypes functions not recog...</td>\n",
       "      <td>[{'id': 3303158446, 'node_id': 'MDU6TGFiZWwzMz...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50671</td>\n",
       "      <td>ENH: Add a test for fillna in PeriodArray</td>\n",
       "      <td>[{'id': 4851593007, 'node_id': 'LA_kwDOAA0YD88...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50670</td>\n",
       "      <td>BUG: read_sas(..) interprets some zeros as 5.3...</td>\n",
       "      <td>[{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>50638</td>\n",
       "      <td>DEPR: Add FutureWarning for pandas.io.sql.execute</td>\n",
       "      <td>[]</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>50636</td>\n",
       "      <td>TST: Get tests to run and fix them to pass</td>\n",
       "      <td>[{'id': 127685, 'node_id': 'MDU6TGFiZWwxMjc2OD...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>50632</td>\n",
       "      <td>REF/PERF: ArrowExtensionArray.__setitem__</td>\n",
       "      <td>[{'id': 127681, 'node_id': 'MDU6TGFiZWwxMjc2OD...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50630</td>\n",
       "      <td>BUG: Fix bug in putmask for CoW</td>\n",
       "      <td>[{'id': 2085877452, 'node_id': 'MDU6TGFiZWwyMD...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>50628</td>\n",
       "      <td>BUG: Inconsistent result dtypes for aggregatio...</td>\n",
       "      <td>[{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=...</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    number                                              title  \\\n",
       "0    50674              CLN: Remove 3rd party warning filters   \n",
       "1    50673  API/BUG: pd.concat doesn't copy indexes if wit...   \n",
       "2    50672  BUG: is_[int|float]_dtypes functions not recog...   \n",
       "3    50671          ENH: Add a test for fillna in PeriodArray   \n",
       "4    50670  BUG: read_sas(..) interprets some zeros as 5.3...   \n",
       "..     ...                                                ...   \n",
       "25   50638  DEPR: Add FutureWarning for pandas.io.sql.execute   \n",
       "26   50636         TST: Get tests to run and fix them to pass   \n",
       "27   50632          REF/PERF: ArrowExtensionArray.__setitem__   \n",
       "28   50630                    BUG: Fix bug in putmask for CoW   \n",
       "29   50628  BUG: Inconsistent result dtypes for aggregatio...   \n",
       "\n",
       "                                               labels state  \n",
       "0   [{'id': 127685, 'node_id': 'MDU6TGFiZWwxMjc2OD...  open  \n",
       "1   [{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=...  open  \n",
       "2   [{'id': 3303158446, 'node_id': 'MDU6TGFiZWwzMz...  open  \n",
       "3   [{'id': 4851593007, 'node_id': 'LA_kwDOAA0YD88...  open  \n",
       "4   [{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=...  open  \n",
       "..                                                ...   ...  \n",
       "25                                                 []  open  \n",
       "26  [{'id': 127685, 'node_id': 'MDU6TGFiZWwxMjc2OD...  open  \n",
       "27  [{'id': 127681, 'node_id': 'MDU6TGFiZWwxMjc2OD...  open  \n",
       "28  [{'id': 2085877452, 'node_id': 'MDU6TGFiZWwyMD...  open  \n",
       "29  [{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=...  open  \n",
       "\n",
       "[30 rows x 4 columns]"
      ]
     },
     "execution_count": 740,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issues = pd.DataFrame(data, columns=['number', 'title', \n",
    "                                     'labels', 'state'])\n",
    "\n",
    "issues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984f8930",
   "metadata": {},
   "source": [
    "###### 6.4 데이터베이스와 함께 사용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4a2688a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "\n",
    "query = \"\"\"\n",
    "CREATE TABLE test\n",
    "(a VARCHAR(20), b VARCHAR(20),\n",
    "c REAL,d INTEGER\n",
    ");\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7a743871",
   "metadata": {},
   "outputs": [],
   "source": [
    "con = sqlite3.connect('mydata.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dadb7b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlite3.Cursor at 0x11d7960a0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.execute(query)# create a table, 한번만 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0a1a6073",
   "metadata": {},
   "outputs": [],
   "source": [
    "con.commit() #데이터 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "79462d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [('Atlanta', 'Georgia', 1.25, 6),\n",
    "        ('Tallahassee', 'Florida', 2.6, 3),\n",
    "        ('Sacramento', 'California', 1.7, 5)]\n",
    "\n",
    "stmt = \"INSERT INTO test VALUES(?, ?, ?, ?)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2243749a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlite3.Cursor at 0x11d7963b0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.executemany(stmt, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "69666f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "con.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a6baac44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Atlanta', 'Georgia', 1.25, 6),\n",
       " ('Tallahassee', 'Florida', 2.6, 3),\n",
       " ('Sacramento', 'California', 1.7, 5)]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cursor = con.execute(\"SELECT * FROM test\") #사용자의 커서\n",
    "\n",
    "rows = cursor.fetchall() #list of TUPLes를 반환\n",
    "\n",
    "rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a9760cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('a', None, None, None, None, None, None),\n",
       " ('b', None, None, None, None, None, None),\n",
       " ('c', None, None, None, None, None, None),\n",
       " ('d', None, None, None, None, None, None))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cursor.description #컬럼 이름"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cf45b7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1.25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tallahassee</td>\n",
       "      <td>Florida</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>California</td>\n",
       "      <td>1.70</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a           b     c  d\n",
       "0      Atlanta     Georgia  1.25  6\n",
       "1  Tallahassee     Florida  2.60  3\n",
       "2   Sacramento  California  1.70  5"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(rows, columns=[x[0]for x in cursor.description])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "57c5f38e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install sqlalchemy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2ea1e913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1.25</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tallahassee</td>\n",
       "      <td>Florida</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>California</td>\n",
       "      <td>1.70</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a           b     c  d\n",
       "0      Atlanta     Georgia  1.25  6\n",
       "1  Tallahassee     Florida  2.60  3\n",
       "2   Sacramento  California  1.70  5"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sqlalchemy as sqla\n",
    "\n",
    "db = sqla.create_engine(\"sqlite:///mydata.sqlite\") #외부 DB 연동을 통해 확장시키기\n",
    "\n",
    "pd.read_sql(\"SELECT * FROM test\", db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63c4b186",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e403b3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3afda9c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bd8789d9",
   "metadata": {},
   "source": [
    "# 7장 데이터 정제 및 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b5aa301",
   "metadata": {},
   "outputs": [],
   "source": [
    "#결측치, 중복 데이터, 문자열 처리, 다른 분석적 데이터 변환에 대한 도구들"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9a1ef67",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0666bf25",
   "metadata": {},
   "source": [
    "## 7.1 누락된 데이터 처리하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49072f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NA: 잘못된 값, \n",
    "#NULL: 아직 정해지지 않은 값\n",
    "#NaN: 아직 정해지지 않은 값= NA & NULL을 포함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ceec7ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    aardvark\n",
       "1         NaN\n",
       "2        None\n",
       "3     avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data = pd.Series(['aardvark', np.nan, None, 'avocado'])\n",
    "\n",
    "string_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "282bcda7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8edf04b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data[0]=None\n",
    "\n",
    "\n",
    "string_data.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "568b007b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NA처리 메서드\n",
    "\n",
    "dropna: 누락된 데이터가 있는 축(로우, 컬럼 )을 제외시킨다. 어느 정도의 누락 데이터까지 용인할 것인지 지정할 수 있다.\n",
    "fillna: 누락된 데이터를 대신할 값을 채우거나 'ffill'이나 'bfill' 같은 보간 메서드를 적용한다.\n",
    "isnull: 누락되거나 NA인 값을 알려주는 불리언값이 저장된 같은 형의 객체를 반환한다.\n",
    "notnull: isnull과 반대되는 메서드"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22232ae9",
   "metadata": {},
   "source": [
    "###### 7.1.1 누락된 데이터 골라내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08fc0ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropna로 널이 아닌 데이터와 색인값만 들어있는 series 반환\n",
    "from numpy import nan as NA\n",
    "\n",
    "data = pd.Series([1, NA, 3.5, NA, 7])\n",
    "\n",
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30d89d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "814b0097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
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       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
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       "2  NaN  NaN  NaN\n",
       "3  NaN  6.5  3.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#dataFrame 객체의 경우, 모두 na값인 우나 컬럼을 제외하거나 na값을 하나라도 포함하고 있는 로우나 컬럼을 제외\n",
    "#dropna는 기본적으로 na값을 하나라도 포함하고 있는 로우를 제외시킨다\n",
    "data = pd.DataFrame([[1., 6.5, 3.], [1., NA, NA],\n",
    "                     [NA, NA, NA], [NA, 6.5, 3.]])\n",
    "\n",
    "\n",
    "cleaned=data.dropna()\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "25cb2c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     0    1    2\n",
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   "source": [
    "cleaned"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0f370ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.dropna(how='all')#모두 NA값인 로우만 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "837f1592",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "     0    1    2   4\n",
       "0  1.0  6.5  3.0 NaN\n",
       "1  1.0  NaN  NaN NaN\n",
       "2  NaN  NaN  NaN NaN\n",
       "3  NaN  6.5  3.0 NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data[4] = NA #added column\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1ba70a1b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
       "1  1.0  NaN  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  NaN  6.5  3.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(axis=1, how='all')#remove column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c39f32dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.498604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.178665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.714598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.385400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.917451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.736266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.411923</td>\n",
       "      <td>-1.604248</td>\n",
       "      <td>-0.776265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.142645</td>\n",
       "      <td>-1.491900</td>\n",
       "      <td>-0.277562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.305983</td>\n",
       "      <td>-1.153344</td>\n",
       "      <td>-0.626078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.498604       NaN       NaN\n",
       "1  0.178665       NaN       NaN\n",
       "2 -0.714598       NaN -0.385400\n",
       "3 -0.917451       NaN  0.736266\n",
       "4 -0.411923 -1.604248 -0.776265\n",
       "5  0.142645 -1.491900 -0.277562\n",
       "6  1.305983 -1.153344 -0.626078"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataframe의 로우를 제외시키는 방법 (시계열에서 많이 쓰임)\n",
    "\n",
    "#몇개 이상의 값이 들어 있는 로우만 살펴보기 thresh\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(7, 3))\n",
    "\n",
    "df.iloc[:4, 1] = NA\n",
    "\n",
    "df.iloc[:2, 2] = NA\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b6eb7099",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <td>-0.411923</td>\n",
       "      <td>-1.604248</td>\n",
       "      <td>-0.776265</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.142645</td>\n",
       "      <td>-1.491900</td>\n",
       "      <td>-0.277562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.305983</td>\n",
       "      <td>-1.153344</td>\n",
       "      <td>-0.626078</td>\n",
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       "          0         1         2\n",
       "4 -0.411923 -1.604248 -0.776265\n",
       "5  0.142645 -1.491900 -0.277562\n",
       "6  1.305983 -1.153344 -0.626078"
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     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3044d5f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.634450</td>\n",
       "      <td>1.081153</td>\n",
       "      <td>1.283565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.657713</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-0.440190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.417601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.884731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.126480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.634450  1.081153  1.283565\n",
       "1  0.657713  1.167576 -0.440190\n",
       "2 -0.417601       NaN -0.884731\n",
       "3 -1.126480       NaN -1.618636"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(thresh=2) #thresh: thresh takes integer value which tells minimum amount of na values to drop.?\n",
    "#트레쉬가 3이면 3개 이상을 가지고 있는 값들\n",
    "\n",
    "#rows containing at most a certain number of 멀쩡한 value가 몇개 이상 있어야되는지"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4b964a",
   "metadata": {},
   "source": [
    "###### 7.1.2 결측치 채우기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "520c5b58",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.178665</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.714598</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.385400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.917451</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.736266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.411923</td>\n",
       "      <td>-1.604248</td>\n",
       "      <td>-0.776265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.142645</td>\n",
       "      <td>-1.491900</td>\n",
       "      <td>-0.277562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.305983</td>\n",
       "      <td>-1.153344</td>\n",
       "      <td>-0.626078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.498604  0.000000  0.000000\n",
       "1  0.178665  0.000000  0.000000\n",
       "2 -0.714598  0.000000 -0.385400\n",
       "3 -0.917451  0.000000  0.736266\n",
       "4 -0.411923 -1.604248 -0.776265\n",
       "5  0.142645 -1.491900 -0.277562\n",
       "6  1.305983 -1.153344 -0.626078"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)#채우고 싶은 값=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "642bd295",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>-0.714598</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.385400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.917451</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.736266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.411923</td>\n",
       "      <td>-1.604248</td>\n",
       "      <td>-0.776265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.142645</td>\n",
       "      <td>-1.491900</td>\n",
       "      <td>-0.277562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.305983</td>\n",
       "      <td>-1.153344</td>\n",
       "      <td>-0.626078</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.498604  0.500000  0.000000\n",
       "1  0.178665  0.500000  0.000000\n",
       "2 -0.714598  0.500000 -0.385400\n",
       "3 -0.917451  0.500000  0.736266\n",
       "4 -0.411923 -1.604248 -0.776265\n",
       "5  0.142645 -1.491900 -0.277562\n",
       "6  1.305983 -1.153344 -0.626078"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({1: 0.5, 2: 0}) #각 칼럼마다 다른 값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2c613853",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.385400</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.411923</td>\n",
       "      <td>-1.604248</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.142645</td>\n",
       "      <td>-1.491900</td>\n",
       "      <td>-0.277562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.305983</td>\n",
       "      <td>-1.153344</td>\n",
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      "text/plain": [
       "          0         1         2\n",
       "0  0.498604  0.000000  0.000000\n",
       "1  0.178665  0.000000  0.000000\n",
       "2 -0.714598  0.000000 -0.385400\n",
       "3 -0.917451  0.000000  0.736266\n",
       "4 -0.411923 -1.604248 -0.776265\n",
       "5  0.142645 -1.491900 -0.277562\n",
       "6  1.305983 -1.153344 -0.626078"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " _ = df.fillna(0, inplace=True)#기존 객체를 변경 가능\n",
    "    \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "89e53b94",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.283565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.657713</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-0.440190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.417601</td>\n",
       "      <td>0.194746</td>\n",
       "      <td>-0.884731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.126480</td>\n",
       "      <td>-0.271570</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.367870</td>\n",
       "      <td>-1.194802</td>\n",
       "      <td>-1.709385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.191927</td>\n",
       "      <td>-1.013455</td>\n",
       "      <td>2.082800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.634450  1.081153  1.283565\n",
       "1  0.657713  1.167576 -0.440190\n",
       "2 -0.417601  0.194746 -0.884731\n",
       "3 -1.126480 -0.271570 -1.618636\n",
       "4 -0.367870 -1.194802 -1.709385\n",
       "5  1.191927 -1.013455  2.082800"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6, 3))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4dd7436e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.417601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.884731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.126480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.367870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.191927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.634450  1.081153  1.283565\n",
       "1  0.657713  1.167576 -0.440190\n",
       "2 -0.417601       NaN -0.884731\n",
       "3 -1.126480       NaN -1.618636\n",
       "4 -0.367870       NaN       NaN\n",
       "5  1.191927       NaN       NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:, 1] = NA\n",
    "\n",
    "df.iloc[4:, 2] = NA\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6dfad6d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.167576</td>\n",
       "      <td>-0.440190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.417601</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-0.884731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.126480</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.367870</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.191927</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.634450  1.081153  1.283565\n",
       "1  0.657713  1.167576 -0.440190\n",
       "2 -0.417601  1.167576 -0.884731\n",
       "3 -1.126480  1.167576 -1.618636\n",
       "4 -0.367870  1.167576 -1.618636\n",
       "5  1.191927  1.167576 -1.618636"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "51677fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>0.657713</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-0.440190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.417601</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-0.884731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.126480</td>\n",
       "      <td>1.167576</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.367870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.191927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.618636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.634450  1.081153  1.283565\n",
       "1  0.657713  1.167576 -0.440190\n",
       "2 -0.417601  1.167576 -0.884731\n",
       "3 -1.126480  1.167576 -1.618636\n",
       "4 -0.367870       NaN -1.618636\n",
       "5  1.191927       NaN -1.618636"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill', limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "602f9c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.000000\n",
       "1    3.833333\n",
       "2    3.500000\n",
       "3    3.833333\n",
       "4    7.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#series의 평균값과 중간값\n",
    "\n",
    "data = pd.Series([1., NA, 3.5, NA, 7])\n",
    "\n",
    "data.fillna(data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "347ea7d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#limit 함수 인자\n",
    "\n",
    "value: 비어 있는 값을 채울 스칼라값이나 사전 형식의 객체\n",
    "method: 보간 방식. 기본적으로 'ffill'을 사용한다.\n",
    "axis: 값을 채워 넣을 축. 기본값은 axis=0이다.\n",
    "inplace: 복사본을 생성하지 않고 호출한 객체를 변경한다. 기본값은 False다.\n",
    "limit: 값을 앞 혹은 뒤에서부터 몇 개까지 채울지 지정한다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0167f8a",
   "metadata": {},
   "source": [
    "## 7.2 데이터 변형"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde7104b",
   "metadata": {},
   "source": [
    "###### 7.2.1 중복 제거하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "25a7086b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>k1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "1  two   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "4  one   3\n",
       "5  two   4\n",
       "6  two   4"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'k1': ['one', 'two'] * 3 + ['two'],\n",
    "                     'k2': [1, 1, 2, 3, 3, 4, 4]})\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "93d8d678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()#로우가 중복인지 알려줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "69041db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "1  two   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "4  one   3\n",
       "5  two   4"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()#drop_duplicates는 duplicated 배열이 false일때 반환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "68bc7cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>k2</th>\n",
       "      <th>v1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
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       "</table>\n",
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      "text/plain": [
       "    k1  k2  v1\n",
       "0  one   1   0\n",
       "1  two   1   1\n",
       "2  one   2   2\n",
       "3  two   3   3\n",
       "4  one   3   4\n",
       "5  two   4   5\n",
       "6  two   4   6"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"v1\"] = range(7)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "aae0c511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "      <th>v1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2  v1\n",
       "0  one   1   0\n",
       "1  two   1   1"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(subset=[\"k1\"]) #filter duplicated based on \"k1\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "296677ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "      <th>v1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2  v1\n",
       "0  one   1   0\n",
       "1  two   1   1\n",
       "2  one   2   2\n",
       "3  two   3   3\n",
       "4  one   3   4\n",
       "6  two   4   6"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates([\"k1\", \"k2\"], keep=\"last\")\n",
    "#duplicate 중 마지막"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0a066d",
   "metadata": {},
   "source": [
    "###### 7.2.2 Transforming Data Using a Function or Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e89190c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>food</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bacon</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bacon</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces\n",
       "0        bacon     4.0\n",
       "1  pulled pork     3.0\n",
       "2        bacon    12.0\n",
       "3     pastrami     6.0\n",
       "4  corned beef     7.5\n",
       "5        bacon     8.0\n",
       "6     pastrami     3.0\n",
       "7    honey ham     5.0\n",
       "8     nova lox     6.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({\"food\": [\"bacon\", \"pulled pork\", \"bacon\",\n",
    "                              \"pastrami\", \"corned beef\", \"bacon\",\n",
    "                              \"pastrami\", \"honey ham\", \"nova lox\"],\n",
    "                     \"ounces\": [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2527bdbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# distinct meat type to the kind of animal:\n",
    "\n",
    "meat_to_animal = {\n",
    "  \"bacon\": \"pig\",\n",
    "  \"pulled pork\": \"pig\",\n",
    "  \"pastrami\": \"cow\",\n",
    "  \"corned beef\": \"cow\",\n",
    "  \"honey ham\": \"pig\",\n",
    "  \"nova lox\": \"salmon\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4db2cba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>food</th>\n",
       "      <th>ounces</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bacon</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>6.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bacon</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "      <td>salmon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces  animal\n",
       "0        bacon     4.0     pig\n",
       "1  pulled pork     3.0     pig\n",
       "2        bacon    12.0     pig\n",
       "3     pastrami     6.0     cow\n",
       "4  corned beef     7.5     cow\n",
       "5        bacon     8.0     pig\n",
       "6     pastrami     3.0     cow\n",
       "7    honey ham     5.0     pig\n",
       "8     nova lox     6.0  salmon"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"animal\"] = data[\"food\"].map(meat_to_animal)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3c6d8b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       pig\n",
       "1       pig\n",
       "2       pig\n",
       "3       cow\n",
       "4       cow\n",
       "5       pig\n",
       "6       cow\n",
       "7       pig\n",
       "8    salmon\n",
       "Name: food, dtype: object"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_animal(x):\n",
    "    return meat_to_animal[x]\n",
    "\n",
    "data[\"food\"].map(get_animal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee4fa7d",
   "metadata": {},
   "source": [
    "###### 7.2.3 Replacing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "015c49a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1    -999.0\n",
       "2       2.0\n",
       "3    -999.0\n",
       "4   -1000.0\n",
       "5       3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1., -999., 2., -999., -1000., 3.])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d19b799d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1       NaN\n",
       "2       2.0\n",
       "3       NaN\n",
       "4   -1000.0\n",
       "5       3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace(-999, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d2a03b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999, -1000], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "71a96ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    0.0\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999, -1000], [np.nan, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "bb453773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    0.0\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace({-999: np.nan, -1000: 0})#as a dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9daa194c",
   "metadata": {},
   "source": [
    "###### 7.2.4 Renaming Axis Indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "0922c018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "New York    8    9     10    11"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.arange(12).reshape((3, 4)),\n",
    "                    index=[\"Ohio\", \"Colorado\", \"New York\"],\n",
    "                    columns=[\"one\", \"two\", \"three\", \"four\"])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9a9f83d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['OHIO', 'COLO', 'NEW '], dtype='object')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def transform(x):\n",
    "    return x[:4].upper()#capitalized\n",
    "\n",
    "data.index.map(transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "96f5f9cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OHIO</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLO</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      one  two  three  four\n",
       "OHIO    0    1      2     3\n",
       "COLO    4    5      6     7\n",
       "NEW     8    9     10    11"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = data.index.map(transform)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "6efde662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ONE</th>\n",
       "      <th>TWO</th>\n",
       "      <th>THREE</th>\n",
       "      <th>FOUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colo</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ONE  TWO  THREE  FOUR\n",
       "Ohio    0    1      2     3\n",
       "Colo    4    5      6     7\n",
       "New     8    9     10    11"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index=str.title, columns=str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "353f7244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>peekaboo</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INDIANA</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLO</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         one  two  peekaboo  four\n",
       "INDIANA    0    1         2     3\n",
       "COLO       4    5         6     7\n",
       "NEW        8    9        10    11"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index={\"OHIO\": \"INDIANA\"},\n",
    "            columns={\"three\": \"peekaboo\"})\n",
    "#conjunction with a dictionary-like object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503bb36c",
   "metadata": {},
   "source": [
    "###### 7.2.5 Discretization and Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ea77c9fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "bfda05d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let’s divide these into bins of 18 to 25, 26 to 35, 36 to 60, and finally 61 and older\n",
    "\n",
    "bins = [18, 25, 35, 60, 100]\n",
    "\n",
    "age_categories = pd.cut(ages, bins)\n",
    "\n",
    "age_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "2c6ffae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_categories.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f7c87820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IntervalIndex([(18, 25], (25, 35], (35, 60], (60, 100]],\n",
       "              closed='right',\n",
       "              dtype='interval[int64]')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_categories.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b28f6811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Interval(18, 25, closed='right')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_categories.categories[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "b0fa63ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 25]     5\n",
       "(25, 35]     3\n",
       "(35, 60]     3\n",
       "(60, 100]    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(age_categories) # bin counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "adf7fa7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[18, 25), [18, 25), [25, 35), [25, 35), [18, 25), ..., [25, 35), [60, 100), [35, 60), [35, 60), [25, 35)]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [[18, 25) < [25, 35) < [35, 60) < [60, 100)]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(ages, bins, right=False) #You can change which side is closed by passing right=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "969ca0f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Youth', 'Youth', 'Youth', 'YoungAdult', 'Youth', ..., 'YoungAdult', 'Senior', 'MiddleAged', 'MiddleAged', 'YoungAdult']\n",
       "Length: 12\n",
       "Categories (4, object): ['Youth' < 'YoungAdult' < 'MiddleAged' < 'Senior']"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_names = [\"Youth\", \"YoungAdult\", \"MiddleAged\", \"Senior\"]\n",
    "\n",
    "pd.cut(ages, bins, labels=group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "9aa6575b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.011, 0.25], (0.25, 0.49], (0.25, 0.49], (0.011, 0.25], (0.25, 0.49], ..., (0.25, 0.49], (0.011, 0.25], (0.49, 0.74], (0.25, 0.49], (0.49, 0.74]]\n",
       "Length: 20\n",
       "Categories (4, interval[float64]): [(0.011, 0.25] < (0.25, 0.49] < (0.49, 0.74] < (0.74, 0.98]]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.uniform(size=20)\n",
    "\n",
    "pd.cut(data, 4, precision=2) #precision=2--> limits the decimal precision to two digits\n",
    "\n",
    "#If you pass an integer number of bins to pandas.cut instead of explicit bin edges, it will compute equal-length bins based on the minimum and maximum values in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "1ed9e67d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.66, 2.83], (-0.0077, 0.66], (-0.0077, 0.66], (-0.0077, 0.66], (0.66, 2.83], ..., (-0.67, -0.0077], (-0.0077, 0.66], (-0.67, -0.0077], (-2.78, -0.67], (-2.78, -0.67]]\n",
       "Length: 1000\n",
       "Categories (4, interval[float64]): [(-2.78, -0.67] < (-0.67, -0.0077] < (-0.0077, 0.66] < (0.66, 2.83]]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas.qcut, bins the data based on sample quantiles. \n",
    "\n",
    "data = np.random.standard_normal(1000)\n",
    "\n",
    "quartiles = pd.qcut(data, 4, precision=2)\n",
    "\n",
    "quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "b728893b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.78, -0.67]      250\n",
       "(-0.67, -0.0077]    250\n",
       "(-0.0077, 0.66]     250\n",
       "(0.66, 2.83]        250\n",
       "dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(quartiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "07e57505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.776, -1.234]      100\n",
       "(-1.234, -0.00771]    400\n",
       "(-0.00771, 1.333]     400\n",
       "(1.333, 2.834]        100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(data, [0, 0.1, 0.5, 0.9, 1.]).value_counts() #my own quantiles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89ee86e",
   "metadata": {},
   "source": [
    "###### 7.2.6 Detecting and Filtering Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "98d4da41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.018108</td>\n",
       "      <td>-0.046392</td>\n",
       "      <td>-0.017302</td>\n",
       "      <td>-0.002289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.001020</td>\n",
       "      <td>1.013586</td>\n",
       "      <td>1.005487</td>\n",
       "      <td>1.027103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.410632</td>\n",
       "      <td>-2.857389</td>\n",
       "      <td>-3.180617</td>\n",
       "      <td>-3.258373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.681762</td>\n",
       "      <td>-0.719327</td>\n",
       "      <td>-0.679421</td>\n",
       "      <td>-0.702644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.010188</td>\n",
       "      <td>-0.013521</td>\n",
       "      <td>0.032127</td>\n",
       "      <td>-0.027899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.737776</td>\n",
       "      <td>0.629702</td>\n",
       "      <td>0.667878</td>\n",
       "      <td>0.680120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.236665</td>\n",
       "      <td>3.395353</td>\n",
       "      <td>3.381727</td>\n",
       "      <td>3.869108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean      0.018108    -0.046392    -0.017302    -0.002289\n",
       "std       1.001020     1.013586     1.005487     1.027103\n",
       "min      -3.410632    -2.857389    -3.180617    -3.258373\n",
       "25%      -0.681762    -0.719327    -0.679421    -0.702644\n",
       "50%       0.010188    -0.013521     0.032127    -0.027899\n",
       "75%       0.737776     0.629702     0.667878     0.680120\n",
       "max       3.236665     3.395353     3.381727     3.869108"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.random.standard_normal((1000, 4)))\n",
    "\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c549962c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34     3.381727\n",
       "872   -3.180617\n",
       "969    3.002927\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = data[2]\n",
    "\n",
    "col[col.abs() > 3] #values in one of the columns exceeding 3 in abs.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ff7f6dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.350967</td>\n",
       "      <td>1.360912</td>\n",
       "      <td>3.381727</td>\n",
       "      <td>-1.027063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.130672</td>\n",
       "      <td>3.346842</td>\n",
       "      <td>-0.247005</td>\n",
       "      <td>-0.516276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>-3.410632</td>\n",
       "      <td>-0.513349</td>\n",
       "      <td>-2.207808</td>\n",
       "      <td>-0.345499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1.041085</td>\n",
       "      <td>3.395353</td>\n",
       "      <td>-1.406781</td>\n",
       "      <td>-1.466011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>-3.064379</td>\n",
       "      <td>0.942459</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>-0.887983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1.734428</td>\n",
       "      <td>3.375971</td>\n",
       "      <td>-2.459930</td>\n",
       "      <td>-1.719038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>-1.188378</td>\n",
       "      <td>-0.345329</td>\n",
       "      <td>0.988788</td>\n",
       "      <td>-3.258373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>0.276858</td>\n",
       "      <td>3.189294</td>\n",
       "      <td>-0.076950</td>\n",
       "      <td>-1.442130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>3.236665</td>\n",
       "      <td>-0.311384</td>\n",
       "      <td>0.613403</td>\n",
       "      <td>0.809488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>-3.098208</td>\n",
       "      <td>0.286066</td>\n",
       "      <td>2.331012</td>\n",
       "      <td>-1.030558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>0.174532</td>\n",
       "      <td>0.704147</td>\n",
       "      <td>0.219917</td>\n",
       "      <td>3.434625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>3.127668</td>\n",
       "      <td>0.070772</td>\n",
       "      <td>0.942471</td>\n",
       "      <td>1.583969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>-0.266193</td>\n",
       "      <td>-1.575323</td>\n",
       "      <td>-3.180617</td>\n",
       "      <td>-1.397579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>-0.857262</td>\n",
       "      <td>-2.170613</td>\n",
       "      <td>0.721727</td>\n",
       "      <td>3.869108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1.087441</td>\n",
       "      <td>-0.768510</td>\n",
       "      <td>3.002927</td>\n",
       "      <td>0.690449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "34   0.350967  1.360912  3.381727 -1.027063\n",
       "64   0.130672  3.346842 -0.247005 -0.516276\n",
       "104 -3.410632 -0.513349 -2.207808 -0.345499\n",
       "117  1.041085  3.395353 -1.406781 -1.466011\n",
       "158 -3.064379  0.942459  0.000811 -0.887983\n",
       "268  1.734428  3.375971 -2.459930 -1.719038\n",
       "316 -1.188378 -0.345329  0.988788 -3.258373\n",
       "460  0.276858  3.189294 -0.076950 -1.442130\n",
       "476  3.236665 -0.311384  0.613403  0.809488\n",
       "481 -3.098208  0.286066  2.331012 -1.030558\n",
       "522  0.174532  0.704147  0.219917  3.434625\n",
       "746  3.127668  0.070772  0.942471  1.583969\n",
       "872 -0.266193 -1.575323 -3.180617 -1.397579\n",
       "950 -0.857262 -2.170613  0.721727  3.869108\n",
       "969  1.087441 -0.768510  3.002927  0.690449"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.abs() > 3).any(axis=\"columns\")] #all rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "edacb78a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1000.000000</td>\n",
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       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.018317</td>\n",
       "      <td>-0.047699</td>\n",
       "      <td>-0.017506</td>\n",
       "      <td>-0.003335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.998072</td>\n",
       "      <td>1.009419</td>\n",
       "      <td>1.003705</td>\n",
       "      <td>1.022027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-2.857389</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.681762</td>\n",
       "      <td>-0.719327</td>\n",
       "      <td>-0.679421</td>\n",
       "      <td>-0.702644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.010188</td>\n",
       "      <td>-0.013521</td>\n",
       "      <td>0.032127</td>\n",
       "      <td>-0.027899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.737776</td>\n",
       "      <td>0.629702</td>\n",
       "      <td>0.667878</td>\n",
       "      <td>0.680120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean      0.018317    -0.047699    -0.017506    -0.003335\n",
       "std       0.998072     1.009419     1.003705     1.022027\n",
       "min      -3.000000    -2.857389    -3.000000    -3.000000\n",
       "25%      -0.681762    -0.719327    -0.679421    -0.702644\n",
       "50%       0.010188    -0.013521     0.032127    -0.027899\n",
       "75%       0.737776     0.629702     0.667878     0.680120\n",
       "max       3.000000     3.000000     3.000000     3.000000"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.abs() > 3] = np.sign(data) * 3\n",
    "\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "e9c8896c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.318195</td>\n",
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       "      <td>-0.250738</td>\n",
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       "      <td>-0.618297</td>\n",
       "      <td>-0.033125</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-0.408528</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.877408</td>\n",
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       "      <td>-0.745245</td>\n",
       "      <td>-1.220850</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.564997</td>\n",
       "      <td>1.193074</td>\n",
       "      <td>-0.144322</td>\n",
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       "          0         1         2         3\n",
       "0  1.592259  1.064421  0.318195  0.266681\n",
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       "4  0.564997  1.193074 -0.144322  0.474458"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "12091636",
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <td>-1.0</td>\n",
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      "text/plain": [
       "     0    1    2    3\n",
       "0  1.0  1.0  1.0  1.0\n",
       "1 -1.0  1.0 -1.0 -1.0\n",
       "2 -1.0 -1.0  1.0 -1.0\n",
       "3  1.0 -1.0 -1.0 -1.0\n",
       "4  1.0  1.0 -1.0  1.0"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sign(data).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004e0ea3",
   "metadata": {},
   "source": [
    "###### 7.2.7 치환과 임의 샘플링 Permutation and Random Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bdaf841",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Permuting (randomly reordering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "69867598",
   "metadata": {},
   "outputs": [
    {
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       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
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       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
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       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6\n",
       "0   0   1   2   3   4   5   6\n",
       "1   7   8   9  10  11  12  13\n",
       "2  14  15  16  17  18  19  20\n",
       "3  21  22  23  24  25  26  27\n",
       "4  28  29  30  31  32  33  34"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.arange(5 * 7).reshape((5, 7)))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "460ffe0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3, 4])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampler = np.random.permutation(5) # produces an array of integers indicating the new ordering\n",
    "\n",
    "sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "6313e4ca",
   "metadata": {},
   "outputs": [
    {
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       "    0   1   2   3   4   5   6\n",
       "0   0   1   2   3   4   5   6\n",
       "2  14  15  16  17  18  19  20\n",
       "1   7   8   9  10  11  12  13\n",
       "3  21  22  23  24  25  26  27\n",
       "4  28  29  30  31  32  33  34"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.take(sampler) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "e2b9cebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6\n",
       "0   0   1   2   3   4   5   6\n",
       "2  14  15  16  17  18  19  20\n",
       "1   7   8   9  10  11  12  13\n",
       "3  21  22  23  24  25  26  27\n",
       "4  28  29  30  31  32  33  34"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[sampler]#indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "f885c66d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3, 5, 4, 6, 2])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_sampler = np.random.permutation(7)\n",
    "\n",
    "column_sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "3f021ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   3   5   4   6   2\n",
       "0   0   1   3   5   4   6   2\n",
       "1   7   8  10  12  11  13   9\n",
       "2  14  15  17  19  18  20  16\n",
       "3  21  22  24  26  25  27  23\n",
       "4  28  29  31  33  32  34  30"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#axis=\"columns\" -> select a permutation of columns\n",
    "\n",
    "df.take(column_sampler, axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "f0e125ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>3</th>\n",
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       "      <th>6</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
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       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6\n",
       "2  14  15  16  17  18  19  20\n",
       "3  21  22  23  24  25  26  27\n",
       "1   7   8   9  10  11  12  13"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=3) #select random subset without replacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "5b0cb0ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    7\n",
       "0    5\n",
       "1    7\n",
       "3    6\n",
       "3    6\n",
       "1    7\n",
       "2   -1\n",
       "0    5\n",
       "1    7\n",
       "3    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sample with replacement\n",
    "\n",
    "choices = pd.Series([5, 7, -1, 6, 4])\n",
    "\n",
    "choices.sample(n=10, replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1f6743",
   "metadata": {},
   "source": [
    "###### 7.2.8 Computing Indicator/ Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "5a046620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>b</td>\n",
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       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1\n",
       "0   b      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   c      3\n",
       "4   a      4\n",
       "5   b      5"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#k distinct values of column in DataFrame-> derive dataframe with k columns containing all 1s and 0s.\n",
    "\n",
    "df = pd.DataFrame({\"key\": [\"b\", \"b\", \"a\", \"c\", \"a\", \"b\"],\n",
    "                   \"data1\": range(6)})\n",
    "\n",
    "df\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "a6c1614a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>b</th>\n",
       "      <th>c</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  0  1  0\n",
       "1  0  1  0\n",
       "2  1  0  0\n",
       "3  0  0  1\n",
       "4  1  0  0\n",
       "5  0  1  0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df[\"key\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "24aacb25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key_a</th>\n",
       "      <th>key_b</th>\n",
       "      <th>key_c</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  key_a  key_b  key_c\n",
       "0      0      0      1      0\n",
       "1      1      0      1      0\n",
       "2      2      1      0      0\n",
       "3      3      0      0      1\n",
       "4      4      1      0      0\n",
       "5      5      0      1      0"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add a prefix to the columns in the indicator DataFrame, which can then be merged with the other data\n",
    "\n",
    "dummies = pd.get_dummies(df[\"key\"], prefix=\"key\")\n",
    "\n",
    "df_with_dummy = df[[\"data1\"]].join(dummies)\n",
    "\n",
    "df_with_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "962c91d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96b48dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                               title                        genres\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy\n",
       "5         6                         Heat (1995)         Action|Crime|Thriller\n",
       "6         7                      Sabrina (1995)                Comedy|Romance\n",
       "7         8                 Tom and Huck (1995)          Adventure|Children's\n",
       "8         9                 Sudden Death (1995)                        Action\n",
       "9        10                    GoldenEye (1995)     Action|Adventure|Thriller"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#when row in a DataFrame belongs to multiple categories\n",
    "\n",
    "mnames = [\"movie_id\", \"title\", \"genres\"]\n",
    "\n",
    "movies = pd.read_table(\"datasets/movielens/movies.dat\", sep=\"::\",\n",
    "                       header=None, names=mnames, engine=\"python\")\n",
    "\n",
    "movies[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87fce04c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Action  Adventure  Animation  Children's  Comedy  Crime\n",
       "0       0          0          1           1       1      0\n",
       "1       0          1          0           1       0      0\n",
       "2       0          0          0           0       1      0\n",
       "3       0          0          0           0       1      0\n",
       "4       0          0          0           0       1      0\n",
       "5       1          0          0           0       0      1\n",
       "6       0          0          0           0       1      0\n",
       "7       0          1          0           1       0      0\n",
       "8       1          0          0           0       0      0\n",
       "9       1          1          0           0       0      0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#str.get_dummies handles this scenario of multiple group membership encoded as a delimited string\n",
    "\n",
    "dummies = movies[\"genres\"].str.get_dummies(\"|\")\n",
    "\n",
    "dummies.iloc[:10, :6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "670dab44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                                       1\n",
       "title                           Toy Story (1995)\n",
       "genres               Animation|Children's|Comedy\n",
       "Genre_Action                                   0\n",
       "Genre_Adventure                                0\n",
       "Genre_Animation                                1\n",
       "Genre_Children's                               1\n",
       "Genre_Comedy                                   1\n",
       "Genre_Crime                                    0\n",
       "Genre_Documentary                              0\n",
       "Genre_Drama                                    0\n",
       "Genre_Fantasy                                  0\n",
       "Genre_Film-Noir                                0\n",
       "Genre_Horror                                   0\n",
       "Genre_Musical                                  0\n",
       "Genre_Mystery                                  0\n",
       "Genre_Romance                                  0\n",
       "Genre_Sci-Fi                                   0\n",
       "Genre_Thriller                                 0\n",
       "Genre_War                                      0\n",
       "Genre_Western                                  0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_windic = movies.join(dummies.add_prefix(\"Genre_\"))\n",
    "\n",
    "movies_windic.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f1b318b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92961609, 0.31637555, 0.18391881, 0.20456028, 0.56772503,\n",
       "       0.5955447 , 0.96451452, 0.6531771 , 0.74890664, 0.65356987])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining with a discretization function like pandas.cut\n",
    "\n",
    "np.random.seed(12345) # to make the example repeatable\n",
    "\n",
    "values = np.random.uniform(size=10)\n",
    "\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b17532d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(0.0, 0.2]</th>\n",
       "      <th>(0.2, 0.4]</th>\n",
       "      <th>(0.4, 0.6]</th>\n",
       "      <th>(0.6, 0.8]</th>\n",
       "      <th>(0.8, 1.0]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   (0.0, 0.2]  (0.2, 0.4]  (0.4, 0.6]  (0.6, 0.8]  (0.8, 1.0]\n",
       "0           0           0           0           0           1\n",
       "1           0           1           0           0           0\n",
       "2           1           0           0           0           0\n",
       "3           0           1           0           0           0\n",
       "4           0           0           1           0           0\n",
       "5           0           0           1           0           0\n",
       "6           0           0           0           0           1\n",
       "7           0           0           0           1           0\n",
       "8           0           0           0           1           0\n",
       "9           0           0           0           1           0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 0.2, 0.4, 0.6, 0.8, 1]\n",
    "\n",
    "pd.get_dummies(pd.cut(values, bins))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c91f527",
   "metadata": {},
   "source": [
    "## 7.3 Extension Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9373ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a Series of integers with a missing value\n",
    "s = pd.Series([1, 2, 3, None])\n",
    "\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2fdca405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cac3886c",
   "metadata": {},
   "outputs": [],
   "source": [
    "s= pd.Series([1, 2, 3, None], dtype=pd.Int64Dtype())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0de53ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2       3\n",
       "3    <NA>\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69ddd259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99368be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Dtype()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "813b8479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " s[3] #<NA> indicates that a value is missing for an extension type array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "505ca400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[3] is pd.NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3443f3ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#can use \"Int64\" instead of above\n",
    "\n",
    "s = pd.Series([1, 2, 3, None], dtype=\"Int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "248230c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      one\n",
       "1      two\n",
       "2     <NA>\n",
       "3    three\n",
       "dtype: string"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['one', 'two', None, 'three'], dtype=pd.StringDtype())\n",
    "\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a629ee8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B      C\n",
       "0  1.0    one  False\n",
       "1  2.0    two   None\n",
       "2  NaN  three  False\n",
       "3  4.0   None   True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#categorical\n",
    "\n",
    "df = pd.DataFrame({\"A\": [1, 2, None, 4],\n",
    "                   \"B\": [\"one\", \"two\", \"three\", None],\n",
    "                   \"C\": [False, None, False, True]})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d7ad64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>one</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>three</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A      B      C\n",
       "0     1    one  False\n",
       "1     2    two   <NA>\n",
       "2  <NA>  three  False\n",
       "3     4   <NA>   True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\"] = df[\"A\"].astype(\"Int64\")\n",
    "df[\"B\"] = df[\"B\"].astype(\"string\")\n",
    "df[\"C\"] = df[\"C\"].astype(\"boolean\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf7676e",
   "metadata": {},
   "source": [
    "## 7.4 String Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5315f641",
   "metadata": {},
   "source": [
    "###### 7.4.1 Python Built-In string object methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a0c187f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', '  guido']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = \"a,b,  guido\"\n",
    "\n",
    "val.split(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "01e4c62d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'guido']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#strip used to trim white-space\n",
    "\n",
    "pieces = [x.strip() for x in val.split(\",\")]\n",
    "\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a21e5e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first, second, third = pieces\n",
    "\n",
    "first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "05da2013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a::b::guido'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first + \"::\" + second + \"::\" + third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "26fe3085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a::b::guido'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"::\".join(pieces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd3e7e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"guido\" in val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "107de581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a,b,  guido'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "227af9b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.index(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e5442c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.find(\":\") #없으면 -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "34554496",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "substring not found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-bea4c4c30248>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\":\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: substring not found"
     ]
    }
   ],
   "source": [
    "val.index(\":\") #없으면 안돌아감"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4300f4c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.count(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1b291acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.count(\":\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "06b12f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a::b::  guido'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.replace(\",\", \"::\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "237b3996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ab  guido'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.replace(\",\", \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca525ab2",
   "metadata": {},
   "source": [
    "###### 7.4.2 Regular Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5fd8f450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'qux']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "text = \"foo    bar\\t baz  \\tqux\"\n",
    "\n",
    "re.split(r\"\\s+\", text) #\"\\s+\" represents one or more of any whitespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0ad2681d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', '', '', '', 'bar\\t', 'baz', '', '\\tqux']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.split(\" \", text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fdda9d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'qux']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex = re.compile(r\"\\s+\")\n",
    "\n",
    "regex.split(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8e181ce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['    ', '\\t ', '  \\t']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "95fc3ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"Dave dave@google.com\n",
    "Steve steve@gmail.com\n",
    "Rob rob@gmail.com\n",
    "Ryan ryan@yahoo.com\"\"\"\n",
    "pattern = r\"[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,4}\" #??\n",
    "\n",
    "# re.IGNORECASE makes the regex case insensitive\n",
    "regex = re.compile(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b4b7ff2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "re.compile(r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\\.([A-Z]{2,4})',\n",
       "re.IGNORECASE|re.UNICODE)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dc4b7cde",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dave@google.com', 'steve@gmail.com', 'rob@gmail.com', 'ryan@yahoo.com']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8648c61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<re.Match object; span=(5, 20), match='dave@google.com'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = regex.search(text)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a63eb402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dave@google.com'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[m.start():m.end()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "49c26103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(regex.match(text)) #없으면 none\n",
    "\n",
    "#the pattern occurs at the start of the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9cdd867a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dave REDACTED\n",
      "Steve REDACTED\n",
      "Rob REDACTED\n",
      "Ryan REDACTED\n"
     ]
    }
   ],
   "source": [
    "print(regex.sub(\"REDACTED\", text)) #return new string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "26c197d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = r\"([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\\.([A-Z]{2,4})\"\n",
    "\n",
    "regex = re.compile(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "be04b82c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('wesm', 'bright', 'net')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = regex.match(\"wesm@bright.net\")\n",
    "\n",
    "m.groups()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4a2966e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('dave', 'google', 'com'),\n",
       " ('steve', 'gmail', 'com'),\n",
       " ('rob', 'gmail', 'com'),\n",
       " ('ryan', 'yahoo', 'com')]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text) #returns a list of tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b4a72e67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dave Username: dave, Domain: google, Suffix: com\n",
      "Steve Username: steve, Domain: gmail, Suffix: com\n",
      "Rob Username: rob, Domain: gmail, Suffix: com\n",
      "Ryan Username: ryan, Domain: yahoo, Suffix: com\n"
     ]
    }
   ],
   "source": [
    "print(regex.sub(r\"Username: \\1, Domain: \\2, Suffix: \\3\", text))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "178cf9c4",
   "metadata": {},
   "source": [
    "###### 7.4.3 String Functions in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7796b69d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     dave@google.com\n",
       "Steve    steve@gmail.com\n",
       "Rob        rob@gmail.com\n",
       "Wes                  NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"Dave\": \"dave@google.com\", \"Steve\": \"steve@gmail.com\",\n",
    "        \"Rob\": \"rob@gmail.com\", \"Wes\": np.nan}\n",
    "\n",
    "data = pd.Series(data)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "75ec1837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     False\n",
       "Steve    False\n",
       "Rob      False\n",
       "Wes       True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a373d72d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     False\n",
       "Steve     True\n",
       "Rob       True\n",
       "Wes        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.str.contains(\"gmail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b67cb312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     dave@google.com\n",
       "Steve    steve@gmail.com\n",
       "Rob        rob@gmail.com\n",
       "Wes                 <NA>\n",
       "dtype: string"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_as_string_ext = data.astype('string')\n",
    "\n",
    "data_as_string_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cc658cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     False\n",
       "Steve     True\n",
       "Rob       True\n",
       "Wes       <NA>\n",
       "dtype: boolean"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_as_string_ext.str.contains(\"gmail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c50acd23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     [(dave, google, com)]\n",
       "Steve    [(steve, gmail, com)]\n",
       "Rob        [(rob, gmail, com)]\n",
       "Wes                        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern = r\"([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\\.([A-Z]{2,4})\"\n",
    "\n",
    "data.str.findall(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6fcb8508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     (dave, google, com)\n",
       "Steve    (steve, gmail, com)\n",
       "Rob        (rob, gmail, com)\n",
       "Wes                      NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = data.str.findall(pattern, flags=re.IGNORECASE).str[0]\n",
    "\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a99ec628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     google\n",
       "Steve     gmail\n",
       "Rob       gmail\n",
       "Wes         NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d072676c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     dave@\n",
       "Steve    steve\n",
       "Rob      rob@g\n",
       "Wes        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.str[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a392d127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dave</th>\n",
       "      <td>dave</td>\n",
       "      <td>google</td>\n",
       "      <td>com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>steve</td>\n",
       "      <td>gmail</td>\n",
       "      <td>com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rob</th>\n",
       "      <td>rob</td>\n",
       "      <td>gmail</td>\n",
       "      <td>com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0       1    2\n",
       "Dave    dave  google  com\n",
       "Steve  steve   gmail  com\n",
       "Rob      rob   gmail  com\n",
       "Wes      NaN     NaN  NaN"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#str.extract => return the captured groups of a regular expression as a dataframe\n",
    "\n",
    "data.str.extract(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389b9846",
   "metadata": {},
   "source": [
    "## 7.5 Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd425d91",
   "metadata": {},
   "source": [
    "###### 7.5.1 Background and Motivation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f94f8f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique and value_counts that extract the distinct values from an array and compute their frequencies\n",
    "values = pd.Series(['apple', 'orange', 'apple',\n",
    "                    'apple'] * 2)\n",
    "\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4705f115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['apple', 'orange'], dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fbfd94c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     6\n",
       "orange    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "10a0d536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    1\n",
       "6    0\n",
       "7    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dimension tables containing the distinct values and storing the primary observations as integer keys referencing the dimension table\n",
    "\n",
    "values = pd.Series([0, 1, 0, 0] * 2)\n",
    "\n",
    "dim = pd.Series(['apple', 'orange'])\n",
    "\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4af1cf98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6da929e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "0     apple\n",
       "0     apple\n",
       "0     apple\n",
       "1    orange\n",
       "0     apple\n",
       "0     apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take method to restore the original series of string\n",
    "\n",
    "dim.take(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7cf0ac0",
   "metadata": {},
   "source": [
    "###### 7.5.2 Categorical Extension Type in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2aaea38d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>basket_id</th>\n",
       "      <th>fruit</th>\n",
       "      <th>count</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>apple</td>\n",
       "      <td>11</td>\n",
       "      <td>1.564438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>5</td>\n",
       "      <td>1.331256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>apple</td>\n",
       "      <td>12</td>\n",
       "      <td>2.393235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>apple</td>\n",
       "      <td>6</td>\n",
       "      <td>0.746937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>apple</td>\n",
       "      <td>5</td>\n",
       "      <td>2.691024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>orange</td>\n",
       "      <td>12</td>\n",
       "      <td>3.767211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>apple</td>\n",
       "      <td>10</td>\n",
       "      <td>0.992983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>apple</td>\n",
       "      <td>11</td>\n",
       "      <td>3.795525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   basket_id   fruit  count    weight\n",
       "0          0   apple     11  1.564438\n",
       "1          1  orange      5  1.331256\n",
       "2          2   apple     12  2.393235\n",
       "3          3   apple      6  0.746937\n",
       "4          4   apple      5  2.691024\n",
       "5          5  orange     12  3.767211\n",
       "6          6   apple     10  0.992983\n",
       "7          7   apple     11  3.795525"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data compression techinque for data with many occurrences of similar values\n",
    "\n",
    "fruits = ['apple', 'orange', 'apple', 'apple'] * 2\n",
    "\n",
    "N = len(fruits)\n",
    "\n",
    "rng = np.random.default_rng(seed=12345)\n",
    "\n",
    "df = pd.DataFrame({'fruit': fruits,\n",
    "                   'basket_id': np.arange(N),\n",
    "                   'count': rng.integers(3, 15, size=N),\n",
    "                   'weight': rng.uniform(0, 4, size=N)},\n",
    "                  columns=['basket_id', 'fruit', 'count', 'weight'])\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0bc98ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "Name: fruit, dtype: category\n",
       "Categories (2, object): ['apple', 'orange']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df['fruit'] is an array of Python string objects\n",
    "#converting it to categorical\n",
    "\n",
    "fruit_cat = df['fruit'].astype('category')\n",
    "\n",
    "fruit_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c0b06d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.arrays.categorical.Categorical"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = fruit_cat.array\n",
    "\n",
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8e8c7c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'orange', 'apple', 'apple', 'apple', 'orange', 'apple', 'apple']\n",
       "Categories (2, object): ['apple', 'orange']"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e351bd9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'orange'], dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2666ae09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 0, 1, 0, 0], dtype=int8)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2a6c8d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'apple', 1: 'orange'}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(enumerate(c.categories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "569783f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "Name: fruit, dtype: category\n",
       "Categories (2, object): ['apple', 'orange']"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert a dataframe column to categorical by assigning the converted result\n",
    "\n",
    "df['fruit'] = df['fruit'].astype('category')\n",
    "\n",
    "df[\"fruit\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "899d1074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'foo', 'bar']\n",
       "Categories (3, object): ['bar', 'baz', 'foo']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_categories = pd.Categorical(['foo', 'bar', 'baz', 'foo', 'bar'])\n",
    "\n",
    "my_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "379d4559",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ['foo', 'bar', 'baz']\n",
    "\n",
    "codes = [0, 1, 2, 0, 0, 1]\n",
    "\n",
    "my_cats_2 = pd.Categorical.from_codes(codes, categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "482fa8bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'foo', 'foo', 'bar']\n",
       "Categories (3, object): ['foo', 'bar', 'baz']"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cats_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "35dbb8d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'foo', 'foo', 'bar']\n",
       "Categories (3, object): ['foo' < 'bar' < 'baz']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_cat = pd.Categorical.from_codes(codes, categories,\n",
    "                                        ordered=True)\n",
    "\n",
    "ordered_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "84e2a1a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'foo', 'foo', 'bar']\n",
       "Categories (3, object): ['foo' < 'bar' < 'baz']"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cats_2.as_ordered()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4be04c3",
   "metadata": {},
   "source": [
    "###### 7.5.3 Computations with Categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d790c93f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.42382504,  1.26372846, -0.87066174, -0.25917323, -0.07534331])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.default_rng(seed=12345)\n",
    "\n",
    "draws = rng.standard_normal(1000)\n",
    "\n",
    "draws[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9bdc707f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-3.121, -0.675], (0.687, 3.211], (-3.121, -0.675], (-0.675, 0.0134], (-0.675, 0.0134], ..., (0.0134, 0.687], (0.0134, 0.687], (-0.675, 0.0134], (0.0134, 0.687], (-0.675, 0.0134]]\n",
       "Length: 1000\n",
       "Categories (4, interval[float64]): [(-3.121, -0.675] < (-0.675, 0.0134] < (0.0134, 0.687] < (0.687, 3.211]]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.qcut(draws, 4)\n",
    "\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8a663cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q1', 'Q4', 'Q1', 'Q2', 'Q2', ..., 'Q3', 'Q3', 'Q2', 'Q3', 'Q2']\n",
       "Length: 1000\n",
       "Categories (4, object): ['Q1' < 'Q2' < 'Q3' < 'Q4']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.qcut(draws, 4, labels=['Q1', 'Q2', 'Q3', 'Q4'])\n",
    "\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b22e7dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 0, 1, 1, 0, 0, 2, 2, 0], dtype=int8)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins.codes[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "6b21e4c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quartile</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q1</td>\n",
       "      <td>250</td>\n",
       "      <td>-3.119609</td>\n",
       "      <td>-0.678494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q2</td>\n",
       "      <td>250</td>\n",
       "      <td>-0.673305</td>\n",
       "      <td>0.008009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q3</td>\n",
       "      <td>250</td>\n",
       "      <td>0.018753</td>\n",
       "      <td>0.686183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q4</td>\n",
       "      <td>250</td>\n",
       "      <td>0.688282</td>\n",
       "      <td>3.211418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  quartile  count       min       max\n",
       "0       Q1    250 -3.119609 -0.678494\n",
       "1       Q2    250 -0.673305  0.008009\n",
       "2       Q3    250  0.018753  0.686183\n",
       "3       Q4    250  0.688282  3.211418"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.Series(bins, name='quartile')\n",
    "\n",
    "results = (pd.Series(draws)\n",
    "           .groupby(bins)\n",
    "           .agg(['count', 'min', 'max'])\n",
    "           .reset_index())\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ee0f30df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Q1\n",
       "1    Q2\n",
       "2    Q3\n",
       "3    Q4\n",
       "Name: quartile, dtype: category\n",
       "Categories (4, object): ['Q1' < 'Q2' < 'Q3' < 'Q4']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['quartile']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c722cea5",
   "metadata": {},
   "source": [
    "###### 7.5.3 Better performance with categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7cc07c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorical types can improve performeance and memory use\n",
    "\n",
    "N = 10_000_000\n",
    "labels = pd.Series(['foo', 'bar', 'baz', 'qux'] * (N // 4))\n",
    "\n",
    "categories = labels.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cfe4a086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600000128"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#labels uses significantly more memory than categories\n",
    "\n",
    "labels.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "863ab861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000540"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f69c1cb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 409 ms, sys: 31.6 ms, total: 441 ms\n",
      "Wall time: 519 ms\n"
     ]
    }
   ],
   "source": [
    "%time _ = labels.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5084c2ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537 ms ± 8.45 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit labels.value_counts()#internally uses the groupby machinery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e9640bd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.6 ms ± 1.96 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit categories.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd821c7",
   "metadata": {},
   "source": [
    "###### 7.5.4 Categorical Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c0488375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    a\n",
       "5    b\n",
       "6    c\n",
       "7    d\n",
       "dtype: category\n",
       "Categories (4, object): ['a', 'b', 'c', 'd']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['a', 'b', 'c', 'd'] * 2)\n",
    "\n",
    "cat_s = s.astype('category')\n",
    "\n",
    "cat_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ba35ceb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    0\n",
       "5    1\n",
       "6    2\n",
       "7    3\n",
       "dtype: int8"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3f00cd6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "90bbccb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    a\n",
       "5    b\n",
       "6    c\n",
       "7    d\n",
       "dtype: category\n",
       "Categories (5, object): ['a', 'b', 'c', 'd', 'e']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using set_categories to change \n",
    "\n",
    "actual_categories = ['a', 'b', 'c', 'd', 'e']\n",
    "\n",
    "cat_s2 = cat_s.cat.set_categories(actual_categories)\n",
    "\n",
    "cat_s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "fcfb0b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2\n",
       "b    2\n",
       "c    2\n",
       "d    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s.value_counts() #respects the categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "8e509fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2\n",
       "b    2\n",
       "c    2\n",
       "d    2\n",
       "e    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "870f9aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "4    a\n",
       "5    b\n",
       "dtype: category\n",
       "Categories (4, object): ['a', 'b', 'c', 'd']"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s3 = cat_s[cat_s.isin(['a', 'b'])]\n",
    "\n",
    "cat_s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1e8a8707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "4    a\n",
       "5    b\n",
       "dtype: category\n",
       "Categories (2, object): ['a', 'b']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s3.cat.remove_unused_categories()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6a76b6",
   "metadata": {},
   "source": [
    "###### 7.5.5 Creating dummy variables for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "5e726c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_s = pd.Series(['a', 'b', 'c', 'd'] * 2, dtype='category')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a5d32f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d\n",
       "0  1  0  0  0\n",
       "1  0  1  0  0\n",
       "2  0  0  1  0\n",
       "3  0  0  0  1\n",
       "4  1  0  0  0\n",
       "5  0  1  0  0\n",
       "6  0  0  1  0\n",
       "7  0  0  0  1"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(cat_s) #converts the one-dimensional categorical data into dataframe containing many dummy variable\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58ce4496",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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