Lab_01_1) Numpy,Pandas_TA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab_01-1 Numpy, Pandas 실습\n",
    "\n",
    "### Context\n",
    "#### Numpy\n",
    "+ Scalar, Vector\n",
    "+ Matrix \n",
    "+ Functions\n",
    "\n",
    "#### Pandas\n",
    "+ Series\n",
    "+ DataFrame\n",
    "+ Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "from os.path import join #linux는 구분자가 역슬래시이고, window는 원화인데 이런것을 통합시켜 주는 것이 join!!이다 \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy\n",
    "## Numpy란?\n",
    "넘파이(Numpy)는 행렬이나 일반적으로 대규모 다차원 배열을 쉽게 처리 할 수 있도록 지원하는 파이썬의 라이브러리입니다. <br>\n",
    "데이터 구조 외에도 수치 계산을 위해 효율적으로 구현된 기능을 제공합니다.\n",
    "\n",
    "### 1. Scalar, Vector\n",
    "#### Scalar\n",
    "Scalar는 방향은 없지만, 실수 공간에서 크기를 나타내는 값을 말합니다. <br>\n",
    "간단하게 상수를 생각하시면 됩니다. \n",
    "\n",
    "$ a\\ \\in \\mathbf{R}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "()\n"
     ]
    }
   ],
   "source": [
    "a = np.array(0)#이거 스칼라이다, 지금 차원이 없는 상태임\n",
    "a\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vector\n",
    "Vector는 n차원 공간에서 방향과 크기를 갖는 단위를 말합니다. <br>\n",
    "n차원 공간에서의 벡터 x는 다음과 같이 표기하며, n개의 원소를 가지고 있습니다.<br>\n",
    "정형 데이터에서 어떤 샘플의 데이터를 '특성 벡터'라고도 말합니다.\n",
    "\n",
    "$ \\vec{x}\\ = \\begin{bmatrix} x_1 \\\\ x_2 \\\\ x_3 \\\\ ... \\\\ x_n \\end{bmatrix} $\n",
    "\n",
    "Numpy의 `np.array()` 배열 객체(ndarray)를 사용해 벡터를 생성할 수 있습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.array([1, 2, 3])\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.shape #shape는 그 자료형이 몇 차원인지 알려준다 밑에처럼,\n",
    "#1차원의 데이터이며, 총 3이라는 크기를 갖고 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2 = np.array([[1], [2], [3]])#얘는 행렬 표현!!!!!!\n",
    "x2\n",
    "#2차원의 데이터이며, 3 * 1크기를 갖고 있는 array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy는 1차원 배열 객체도 벡터로 인정하지만, <br> x2처럼 열 벡터(Column Vector)로 나타낸 2차원 배열 객체로 표현한 방식이 올바른 표기입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Matrix\n",
    "Matrix는 행과 열로 이루어진 구조를 이야기하는데, 선형대수학에서는 열 벡터를 모아놓은 것으로도 이야기합니다. <br>\n",
    "mxn 행렬은 다음과 같이 표기하며 m*n개의 원소를 가지고 있습니다. \n",
    "\n",
    "$ \\matrix{A}\\ = \\begin{bmatrix} {x_{11} \\\\ x_{21}\\\\ .. \\\\ x_{m1}} & { x_{12} \\\\ .. \\\\ .. \\\\ .. } & {.. \\\\ .. \\\\ .. \\\\ .. }& {x_{1n} \\\\ .. \\\\ .. \\\\ x_{mn}} \\end{bmatrix} $\n",
    "\n",
    "벡터와 동일하게 Numpy의 `np.array()` 배열 객체를 사용해 행렬을 생성할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n"
     ]
    }
   ],
   "source": [
    "A = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "A\n",
    "print(A.shape)"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Functions\n",
    "자주 사용되는 Numpy의 기능에 대해 알아보겠습니다. \n",
    "\n",
    "#### 1) np.shape, np.reshape\n",
    "##### np.shape\n",
    "행렬의 차원을 확인할 수 있는 메소드이며, `np.shape(행렬)` 뿐만아니라 `행렬.shape` 방식으로도 사용 가능합니다.<br><br>\n",
    "\n",
    "##### np.reshape\n",
    "행렬의 차원을 변경하는 메소드로 변경 이전 차원의 곱과 변경 이후 차원의 곱이 같다면, 변환이 가능합니다.<br>\n",
    "\n",
    "예를 들어 (2, 8) 차원을 가진 행렬 A는 (4, 4) 차원을 가진 행렬로 변환이 가능합니다. 2x8 = 16, 4x4 = 16이기 때문입니다. <br>\n",
    "하지만 (3, 5) 차원을 가진 행렬로는 변환이 불기능합니다. 3x5 = 15이기 때문에 차원이 일치하지 않습니다. \n",
    "\n",
    "`np.reshape(행렬, 변환할 차원)`으로 차원을 변경할 수도 있고, `행렬.reshape(변환할 차원)`으로 변경할 수도 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4, 5, 6, 7, 8],\n",
       "       [8, 7, 6, 5, 4, 3, 2, 1]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1, 2, 3, 4, 5, 6, 7, 8], [8, 7, 6, 5, 4, 3, 2, 1]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[[1]]],\n",
       "\n",
       "\n",
       "        [[[2]]],\n",
       "\n",
       "\n",
       "        [[[3]]],\n",
       "\n",
       "\n",
       "        [[[4]]],\n",
       "\n",
       "\n",
       "        [[[5]]],\n",
       "\n",
       "\n",
       "        [[[6]]],\n",
       "\n",
       "\n",
       "        [[[7]]],\n",
       "\n",
       "\n",
       "        [[[8]]]],\n",
       "\n",
       "\n",
       "\n",
       "       [[[[8]]],\n",
       "\n",
       "\n",
       "        [[[7]]],\n",
       "\n",
       "\n",
       "        [[[6]]],\n",
       "\n",
       "\n",
       "        [[[5]]],\n",
       "\n",
       "\n",
       "        [[[4]]],\n",
       "\n",
       "\n",
       "        [[[3]]],\n",
       "\n",
       "\n",
       "        [[[2]]],\n",
       "\n",
       "\n",
       "        [[[1]]]]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.reshape((2,8,1,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8],\n",
       "       [8, 7, 6, 5],\n",
       "       [4, 3, 2, 1]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(A, (4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 8) 차원의 행렬을 (3, 5) 차원의 행렬로 변환할 수 없습니다.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    np.reshape(A, (3, 5))\n",
    "    print('(3, 5) 행렬로 차원을 변환하였습니다.')\n",
    "except:\n",
    "    print('(2, 8) 차원의 행렬을 (3, 5) 차원의 행렬로 변환할 수 없습니다.')\n",
    "#try 블록 수행 중 오류가 발생하면 except 블록이 수행된다. 하지만 try 블록에서 오류가 발생하지 않는다면 except 블록은 수행되지 않는다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# reshape를 사용해 (n,) 차원으로 구성된 벡터를 (n, 1) 차원으로 구성된 행렬로 변경할 수 있습니다 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([i for i in range(8)])\n",
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 여기에서 -1은 n개의 차원 중 n-1개는 사용자가 지정하고 나머지는 알아서 가능한 차원에 맞추라는 의미입니다. \n",
    "# 해당 예제의 경우 2개의 차원 중 마지막 차원에 1을 할당했으므로 -1에는 8이 들어가 (8, 1) 차원 행렬이 됩니다. \n",
    "A.reshape(-1, 1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) np.concatenate()\n",
    "여러 개의 Numpy 행렬을 특정 방향으로 이어붙이고 싶은 경우에는 np.concatenate() 함수를 사용할 수 있습니다.<br>\n",
    "ndarray.shape로 차원을 확인하고, 차원을 지정해서 행렬을 이어붙일 수 있습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_arr = np.reshape(A, (4, 2))\n",
    "sample_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 행 방향으로 붙이기\n",
    "np.concatenate([sample_arr, sample_arr, sample_arr], axis=0).shape#axis가 축이라는 것이다. 행에 붙이려면 0, 열에 붙이려면 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [2 3]\n",
      " [4 5]\n",
      " [6 7]\n",
      " [0 1]\n",
      " [2 3]\n",
      " [4 5]\n",
      " [6 7]\n",
      " [0 1]\n",
      " [2 3]\n",
      " [4 5]\n",
      " [6 7]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate([sample_arr, sample_arr, sample_arr], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 6)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 열 방향으로 붙이기\n",
    "np.concatenate([sample_arr, sample_arr, sample_arr], axis=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 0 1 0 1]\n",
      " [2 3 2 3 2 3]\n",
      " [4 5 4 5 4 5]\n",
      " [6 7 6 7 6 7]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate([sample_arr, sample_arr, sample_arr], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) np.sum, np.mean, np.var, np.std, np.max, np.min, np.unique\n",
    "더하기, 평균, 분산, 표준 편차 등 기초 통계량을 계산하는 메소드 입니다. 동일한 기능을 하는 파이썬 내장 함수가 있지만, <br>\n",
    "Numpy의 메소드 들은 axis 인자를 할당함으로써 특정 축을 기준으로 연산할 수 있다는 특징이 있습니다.<br>\n",
    "ex) `np.sum(x, axis=0)` `np.mean(x, axis=1)` `np.var(x, axis=2)` `np.std(x, axis=3)` `np.max(x, axis=1)` `np.min(x, axis=2)` `np.unique(x, axis=0)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (1) np.sum()\n",
    "`np.sum(행렬, 축)` 은 주어진 행렬과 축 방향에 대해 합계를 계산해 반환하는 함수입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 18, 21, 24])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0번째 차원 방향으로 모든 원소들의 합\n",
    "np.sum(A, axis=0)\n",
    "#[1+5+9, 2+6+10, 3+7+11, 4+8+12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 26, 42])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1번째 차원 방향으로 모든 원소들의 합\n",
    "np.sum(A, axis=1)\n",
    "#[1+2+3+4, 5+6+7+8, 9+10+11+12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (2) np.mean()\n",
    "`np.mean(행렬, 축)` 은 주어진 행렬과 축 방향에 대해 평균을 계산해 반환하는 함수입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5., 6., 7., 8.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0번째 차원 방향으로 모든 원소들의 평균\n",
    "np.mean(A, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.5,  6.5, 10.5])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1번째 차원 방향으로 모든 원소들의 평균\n",
    "np.mean(A, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (3) np.var()\n",
    "`np.var(행렬, 축)` 은 주어진 행렬과 축 방향에 대해 분산을 계산해 반환하는 함수입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.66666667, 10.66666667, 10.66666667, 10.66666667])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0번째 차원 방향으로 모든 원소들의 분산\n",
    "np.var(A, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.25, 1.25, 1.25])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1번째 차원 방향으로 모든 원소들의 분산\n",
    "np.var(A, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (4) np.std()\n",
    "`np.std(행렬, 축)` 은 주어진 행렬과 축 방향에 대해 표준편차를 계산해 반환하는 함수입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.26598632, 3.26598632, 3.26598632, 3.26598632])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0번째 차원 방향으로 모든 원소들의 표준편차\n",
    "np.std(A, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.11803399, 1.11803399, 1.11803399])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0번째 차원 방향으로 모든 원소들의 표준편차\n",
    "np.std(A, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (5) np.max()\n",
    "`np.max(행렬, 축)` 은 주어진 행렬과 축 방향에 대해 최대값을 반환하는 함수입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 10, 11, 12])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0번째 차원 방향으로 원소들의 최대값\n",
    "np.max(A, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  8, 12])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1번째 차원 방향으로 원소들의 최대값\n",
    "np.max(A, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (6) np.min()\n",
    "`np.min(행렬, 축)` 은 주어진 행렬과 축 방향에 대해 최소값을 반환하는 함수입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0번째 차원 방향으로 원소들의 최소값\n",
    "np.min(A, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 9])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1번째 차원 방향으로 원소들의 최소값\n",
    "np.min(A, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (7) np.unique()\n",
    "`np.unique(행렬)` 은 주어진 행렬에 대해 중복을 제거하여 모든 원소 값을 반환하는 함수입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [1, 2, 2, 0],\n",
       "       [3, 4, 5, 4]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[0, 0, 0, 0], [1, 2, 2, 0], [3, 4, 5, 4]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 원소들 중 고유한 값\n",
    "np.unique(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4) np.log, np.exp\n",
    "값에 자연 로그를 취하거나 자연 상수의 지수곱을 수행하는 함수입니다.<br>\n",
    "\n",
    "일반적으로 데이터가 치우친(Skew) 분포를 가지고 있을 경우 로그 변환을 수행하게 되는데, np.log는 이 때 사용하는 메소드 입니다. <br>\n",
    "자연 로그의 역함수로 사용되는 메소드로 np.exp가 있습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9984235614126774"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 2.714\n",
    "b = np.log(a)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.714"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "## Pandas란?\n",
    "판다스(Pandas)는 데이터 조작 및 분석을 위해 사용하는 라이브러리입니다. <br>\n",
    "특히, 숫자 테이블 및 시계열 데이터를 위한 데이터 구조 및 함수를 제공합니다.\n",
    "\n",
    "### 1. Series\n",
    "시리즈(Series)는 Pandas의 대표적인 데이터 객체 표현으로 라벨(컬럼 이름)을 가진 1차원 배열입니다. <br>\n",
    "간단하게 하나의 열(Column)을 생각하시면 됩니다. \n",
    "\n",
    "$ Series\\ = {\\begin{bmatrix} x_1 \\\\ x_2 \\\\ x_3 \\\\ ... \\\\ x_n \\end{bmatrix}}_{변수 이름} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "5    6\n",
       "6    7\n",
       "7    8\n",
       "Name: 변수 이름, dtype: int32"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = pd.Series(np.array([1, 2, 3, 4, 5, 6, 7, 8]), name='변수 이름')#cf)numpy랑 pandas는 서로 변환이 된다!\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.values #cf)이것도 numpy와 pandas는 서로 변환이 된다는 것의 예시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "5    6\n",
       "6    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#그리고, 걍 python 배열로 넣어도 사용가능!!\n",
    "S = pd.Series([1,2,3,4,5,6,7])\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. DataFrame\n",
    "데이터 프레임(DataFrame)는 Pandas의 대표적인 데이터 객체 표현으로 라벨(컬럼 이름)을 가진 2차원 배열입니다. <br>\n",
    "간단하게 엑셀 데이터를 생각하시면 됩니다. \n",
    "\n",
    "$ DataFrame\\ = {\\begin{bmatrix} x_1 \\\\ x_2 \\\\ x_3 \\\\ ... \\\\ x_n \\end{bmatrix}}_{변수 이름1}  {\\begin{bmatrix} x_1 \\\\ x_2 \\\\ x_3 \\\\ ... \\\\ x_n \\end{bmatrix}}_{변수 이름2}  {\\begin{bmatrix} x_1 \\\\ x_2 \\\\ x_3 \\\\ ... \\\\ x_n \\end{bmatrix}}_{변수 이름3}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</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": [
       "   Var1  Var2  Var3\n",
       "0     1     2     3\n",
       "1     4     5     6\n",
       "2     7     8     9"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['Var1', 'Var2', 'Var3'])\n",
    "DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Functions\n",
    "자주 사용되는 Pandas의 기능에 대해 알아보겠습니다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) DataFrame.shape\n",
    "Numpy의 np.shape 처럼 동일하게 차원을 확인할 수 있는 속성입니다. <br>\n",
    "차원을 변경할 수 있는 reshape 함수는 따로 존재하지 않기 때문에 <br>\n",
    "차원을 변경하고자 할 때는 Numpy 행렬로 변경한 후에 np.reshape 메소드를 사용하여 변경합니다.\n",
    "\n",
    "##### DataFrame.shape\n",
    "Pandas의 DataFrame의 행과 열의 수를 확인할 수 있는 메소드입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3개의 행과 3개의 열로 구성된 것을 확인할 수 있습니다. \n",
    "DF.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) .head()\n",
    "`.head(개수)` 는 Series나 DataFrame 객체가 가진 값의 일부분을 반환하는 메소드 입니다. <br>\n",
    "기본 값으로 5개를 반환하며, 전달한 인자의 숫자에 따라 개수를 조절할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</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": [
       "   Var1  Var2  Var3\n",
       "0     1     2     3\n",
       "1     4     5     6\n",
       "2     7     8     9"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head()#이 함수의 용도는 그냥, 값이 잘 불러졌나 확인용(자료형이 크면, 앞에만 확인하게)\n",
    "#여기서 DF는 3개만 있으니깐 3개만 나온거 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Var1  Var2  Var3\n",
       "0     1     2     3"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) .info()\n",
    "`.info()` 는 Series나 DataFrame 객체가 가진 변수들의 정보를 보여줍니다. <br>\n",
    "일반적으로 변수들의 자료형(수치형 or 범주형)을 확인하기 위해 사용하기도 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   Var1    3 non-null      int32\n",
      " 1   Var2    3 non-null      int32\n",
      " 2   Var3    3 non-null      int32\n",
      "dtypes: int32(3)\n",
      "memory usage: 164.0 bytes\n"
     ]
    }
   ],
   "source": [
    "DF.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) .describe()\n",
    "`.describe()` 는 Series나 DataFrame 객체가 가진 변수들의 최대, 최소, 중간, 평균, 표준편차 등 기초 통계량 값을 확인할 수 있습니다.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Var1  Var2  Var3\n",
       "count   3.0   3.0   3.0\n",
       "mean    4.0   5.0   6.0\n",
       "std     3.0   3.0   3.0\n",
       "min     1.0   2.0   3.0\n",
       "25%     2.5   3.5   4.5\n",
       "50%     4.0   5.0   6.0\n",
       "75%     5.5   6.5   7.5\n",
       "max     7.0   8.0   9.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cf) head, info, describe는 그냥 변수들을 대략적으로 확인하는 용도"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4) .values\n",
    "`.values` 속성은 Series나 DataFrame에서 값을 Numpy 배열(ndarray)로 반환해줍니다. <br>\n",
    "주로 대용량 데이터 처리 시 Pandas 자체 연산보다는 Numpy 배열 연산이 빠르기 때문에 Numpy 배열로 변환 후 연산할 때 사용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5) pd.read_csv()\n",
    "`pd.read_csv(파일명)` 메소드는 csv(comma-separated variables) 파일로 이루어진 데이터를 DataFrame으로 읽어오는 기능을 수행합니다. <br>\n",
    "주로 정형 데이터를 읽어올 때 사용됩니다. <br><br>\n",
    "\n",
    "샘플 데이터 경로: `data/sample_adult_data.csv`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cf)csv파일은 엑셀 파일 같은 것인데, 변수들과 데이터들이 , 와 같은것으 구분되어 나열돼 있는 파일이다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = join('data', 'sample_adult_data.csv')\n",
    "DF = pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country income  \n",
       "0          2174             0              40   United-States  <=50K  \n",
       "1             0             0              13   United-States  <=50K  \n",
       "2             0             0              40   United-States  <=50K  \n",
       "3             0             0              40   United-States  <=50K  \n",
       "4             0             0              40            Cuba  <=50K  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48842 entries, 0 to 48841\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             48842 non-null  int64 \n",
      " 1   workclass       48842 non-null  object\n",
      " 2   fnlwgt          48842 non-null  int64 \n",
      " 3   education       48842 non-null  object\n",
      " 4   education-num   48842 non-null  int64 \n",
      " 5   marital-status  48842 non-null  object\n",
      " 6   occupation      48842 non-null  object\n",
      " 7   relationship    48842 non-null  object\n",
      " 8   race            48842 non-null  object\n",
      " 9   sex             48842 non-null  object\n",
      " 10  capital-gain    48842 non-null  int64 \n",
      " 11  capital-loss    48842 non-null  int64 \n",
      " 12  hours-per-week  48842 non-null  int64 \n",
      " 13  native-country  48842 non-null  object\n",
      " 14  income          48842 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "DF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48842.000000</td>\n",
       "      <td>4.884200e+04</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.643585</td>\n",
       "      <td>1.896641e+05</td>\n",
       "      <td>10.078089</td>\n",
       "      <td>1079.067626</td>\n",
       "      <td>87.502314</td>\n",
       "      <td>40.422382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.710510</td>\n",
       "      <td>1.056040e+05</td>\n",
       "      <td>2.570973</td>\n",
       "      <td>7452.019058</td>\n",
       "      <td>403.004552</td>\n",
       "      <td>12.391444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.175505e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.781445e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.376420e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.490400e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "count  48842.000000  4.884200e+04   48842.000000  48842.000000  48842.000000   \n",
       "mean      38.643585  1.896641e+05      10.078089   1079.067626     87.502314   \n",
       "std       13.710510  1.056040e+05       2.570973   7452.019058    403.004552   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.175505e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.781445e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.376420e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.490400e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours-per-week  \n",
       "count    48842.000000  \n",
       "mean        40.422382  \n",
       "std         12.391444  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39, ' State-gov', 77516, ..., 40, ' United-States', '<=50K'],\n",
       "       [50, ' Self-emp-not-inc', 83311, ..., 13, ' United-States',\n",
       "        '<=50K'],\n",
       "       [38, ' Private', 215646, ..., 40, ' United-States', '<=50K'],\n",
       "       ...,\n",
       "       [38, ' Private', 374983, ..., 50, ' United-States', '<=50K'],\n",
       "       [44, ' Private', 83891, ..., 40, ' United-States', '<=50K'],\n",
       "       [35, ' Self-emp-inc', 182148, ..., 60, ' United-States', '>50K']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6) Series,DataFrame.append(), Pandas.concat()\n",
    "\n",
    "##### Series,DataFrame.append()\n",
    "Series나 DataFrame에 행 방향으로 Series나 DataFrame을 추가하고 싶은 경우에는 append() 메소드를 사용할 수 있습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "5    6\n",
       "6    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series에 하나의 값을 추가할 때\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "S2 = pd.Series([5, 4, 3, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "5    6\n",
       "6    7\n",
       "0    5\n",
       "1    4\n",
       "2    3\n",
       "3    2\n",
       "4    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.append(S2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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",
       "\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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass  fnlwgt   education  education-num  marital-status  \\\n",
       "0   39   State-gov   77516   Bachelors             13   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male          2174             0   \n",
       "\n",
       "   hours-per-week  native-country income  \n",
       "0              40   United-States  <=50K  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame에 행 방향으로 추가할 때\n",
    "tmp_DF = DF.iloc[0:1, :]#iloc은 뒤에 나옴\n",
    "tmp_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
       "\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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass  fnlwgt   education  education-num  marital-status  \\\n",
       "0   39   State-gov   77516   Bachelors             13   Never-married   \n",
       "0   39   State-gov   77516   Bachelors             13   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male          2174             0   \n",
       "0   Adm-clerical   Not-in-family   White   Male          2174             0   \n",
       "\n",
       "   hours-per-week  native-country income  \n",
       "0              40   United-States  <=50K  \n",
       "0              40   United-States  <=50K  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_DF.append(tmp_DF)#자기한테 자기 자신을 뒤에다 붙인 것"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### cf)append는 행만 추가가 가능하고, 열을 추가가 불가능할 것에 비해 concat는 열까지 추가가 가능하다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Pandas.concat()\n",
    "append()는 행 방향으로만 추가가 가능합니다. 축을 지정해서 추가하고 싶은 경우에는 Pandas.concat() 함수를 사용하면 됩니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 15)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97684, 15)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 행 방향으로 붙이기 append()와 동일\n",
    "pd.concat([DF, DF], axis=0).shape#->shape이 행방향으로 붙인것이므로, 행의 개수가 97684개"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 30)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 열 방향으로 붙이기\n",
    "pd.concat([DF, DF], axis=1).shape#->shape이 열방향으로 붙인것이므로, 열의 개수가 30개"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cf)concat을 열방향으로 할때는, 붙이는 것을 index 기준으로 하기 때문에, index가 초기화 돼 있는 것인지, 꼬여 있는 것은 아닌지 확인해주고 concat해줘야 한다. 따라서 index초기화는 항상 해줘야 한다!!!!!!!!!(걍 index초기화 항상 해준다는 것만 기억해둬도 무방)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7) DataFrame.loc DataFrame.iloc\n",
    "DataFrame을 제대로 사용하기 위해서는 필수로 익혀야하는 메소드 2가지 입니다. <br>\n",
    "간단하게 생각하면, 특정 조건에 맞는 행과 열을 추출한다고 말할 수 있습니다.<br>\n",
    "iloc 메소드 보다는 loc 메소드가 더 자주 사용됩니다.\n",
    "\n",
    "##### DataFrame.loc[]\n",
    "`DataFrame.loc[행 조건, 열 조건]`으로 해당 조건에 맞는 행과 열을 찾아냅니다. <br>\n",
    "예를 들어 age 컬럼의 값이 39인 행과 'age, workclass, fnlwgt, education, marital-status' 열로 구성된 데이터만을 추출하고 싶은 경우 <br>\n",
    "DF.loc[DF['age'] == 39, ['age', 'workclass', 'fnlwgt', 'education', 'marital-status']] 와 같이 작성할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>367260</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>365739</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>Divorced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>39</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>235485</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>Never-married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>39</td>\n",
       "      <td>?</td>\n",
       "      <td>157443</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48703</th>\n",
       "      <td>39</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>263690</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48732</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>357118</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48751</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>139057</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48814</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>229647</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Divorced</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1206 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age     workclass  fnlwgt      education       marital-status\n",
       "0       39     State-gov   77516      Bachelors        Never-married\n",
       "28      39       Private  367260        HS-grad             Divorced\n",
       "129     39       Private  365739   Some-college             Divorced\n",
       "166     39   Federal-gov  235485     Assoc-acdm        Never-married\n",
       "297     39             ?  157443        Masters   Married-civ-spouse\n",
       "...    ...           ...     ...            ...                  ...\n",
       "48703   39   Federal-gov  263690        Masters   Married-civ-spouse\n",
       "48732   39       Private  357118      Bachelors        Never-married\n",
       "48751   39       Private  139057    Prof-school   Married-civ-spouse\n",
       "48814   39       Private  229647      Bachelors        Never-married\n",
       "48837   39       Private  215419      Bachelors             Divorced\n",
       "\n",
       "[1206 rows x 5 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.loc[DF['age'] == 39, ['age', 'workclass', 'fnlwgt', 'education', 'marital-status']]\n",
    "#cf)Df.loc[DF['age'] == 39, : ]라고 치면, age가 39살인 사람들을 끌어오고, 모든 column들을 나타내준다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame.iloc[]\n",
    "`DataFrame.iloc[행 인덱스, 열 인덱스]`으로 인덱스에 맞는 행과 열을 찾아냅니다. <br>\n",
    "예를 들어 짝수 인덱스를 갖는 행과 홀수 열로 구성된 데이터만을 추출하고 싶은 경우 <br>\n",
    "DF.iloc[[int(2\\*i) for i in range(int(DF.shape[0]/2))], [int(2\\*i) for i in range(int(DF.shape[1]/2))]] 와 같이 작성할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Private</td>\n",
       "      <td>9th</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Jamaica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48832</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48834</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48836</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>?</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24421 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        workclass   education          marital-status     relationship  \\\n",
       "0       State-gov   Bachelors           Never-married    Not-in-family   \n",
       "2         Private     HS-grad                Divorced    Not-in-family   \n",
       "4         Private   Bachelors      Married-civ-spouse             Wife   \n",
       "6         Private         9th   Married-spouse-absent    Not-in-family   \n",
       "8         Private     Masters           Never-married    Not-in-family   \n",
       "...           ...         ...                     ...              ...   \n",
       "48832     Private     HS-grad      Married-civ-spouse          Husband   \n",
       "48834     Private     HS-grad           Never-married        Own-child   \n",
       "48836     Private   Bachelors           Never-married        Own-child   \n",
       "48838           ?     HS-grad                 Widowed   Other-relative   \n",
       "48840     Private   Bachelors                Divorced        Own-child   \n",
       "\n",
       "           sex  capital-loss  native-country  \n",
       "0         Male             0   United-States  \n",
       "2         Male             0   United-States  \n",
       "4       Female             0            Cuba  \n",
       "6       Female             0         Jamaica  \n",
       "8       Female             0   United-States  \n",
       "...        ...           ...             ...  \n",
       "48832     Male             0   United-States  \n",
       "48834   Female             0   United-States  \n",
       "48836     Male             0   United-States  \n",
       "48838     Male             0   United-States  \n",
       "48840     Male             0   United-States  \n",
       "\n",
       "[24421 rows x 7 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.iloc[[int(2*i) for i in range(int(DF.shape[0]/2))], [int(2*i)+1 for i in range(int(DF.shape[1]/2))]]\n",
    "# !!밑에 block 참고!!!!\n",
    "#iloc은 numpy배열에서 쓰던 것을 그대로 dataframe에서 쓰겠다고 하면, iloc을 쓴다.\n",
    "#bc/numpy는 index가 숫자로만 이루어져 있기 때문에"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 파이썬에는 list를 만드는 신기한 문법이 있다. 밑에 2개의 블럭이 있는데, 위에 있는 블럭과 밑에 있는 블럭이 같은 의미이다\n",
    "\n",
    "#### ->이걸 list comprehension 이라고 한다 https://mingrammer.com/introduce-comprehension-of-python/ 참고"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 978,\n",
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       " 987,\n",
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       " 990,\n",
       " 991,\n",
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       " 994,\n",
       " 995,\n",
       " 996,\n",
       " 997,\n",
       " 998,\n",
       " 999,\n",
       " ...]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_list = list()\n",
    "for i in range(int(DF.shape[0]/2)):\n",
    "    tmp_list.append(i)\n",
    "tmp_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 1800,\n",
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       " 1830,\n",
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       " 1840,\n",
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       " 1848,\n",
       " 1850,\n",
       " 1852,\n",
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       " 1858,\n",
       " 1860,\n",
       " 1862,\n",
       " 1864,\n",
       " 1866,\n",
       " 1868,\n",
       " 1870,\n",
       " 1872,\n",
       " 1874,\n",
       " 1876,\n",
       " 1878,\n",
       " 1880,\n",
       " 1882,\n",
       " 1884,\n",
       " 1886,\n",
       " 1888,\n",
       " 1890,\n",
       " 1892,\n",
       " 1894,\n",
       " 1896,\n",
       " 1898,\n",
       " 1900,\n",
       " 1902,\n",
       " 1904,\n",
       " 1906,\n",
       " 1908,\n",
       " 1910,\n",
       " 1912,\n",
       " 1914,\n",
       " 1916,\n",
       " 1918,\n",
       " 1920,\n",
       " 1922,\n",
       " 1924,\n",
       " 1926,\n",
       " 1928,\n",
       " 1930,\n",
       " 1932,\n",
       " 1934,\n",
       " 1936,\n",
       " 1938,\n",
       " 1940,\n",
       " 1942,\n",
       " 1944,\n",
       " 1946,\n",
       " 1948,\n",
       " 1950,\n",
       " 1952,\n",
       " 1954,\n",
       " 1956,\n",
       " 1958,\n",
       " 1960,\n",
       " 1962,\n",
       " 1964,\n",
       " 1966,\n",
       " 1968,\n",
       " 1970,\n",
       " 1972,\n",
       " 1974,\n",
       " 1976,\n",
       " 1978,\n",
       " 1980,\n",
       " 1982,\n",
       " 1984,\n",
       " 1986,\n",
       " 1988,\n",
       " 1990,\n",
       " 1992,\n",
       " 1994,\n",
       " 1996,\n",
       " 1998,\n",
       " ...]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[int(2*i) for i in range(int(DF.shape[0]/2))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8) map, apply, applymap\n",
    "\n",
    "##### Series.map()\n",
    "map 함수는 Pandas Series(column)에 사용할 수 있는 함수로 Series의 `각 원소에 연산을 적용` 할 수 있습니다. 각 원소를 입력으로 받아 단일 값을 반환합니다.<br>\n",
    "예를들어 인종 컬럼인 'race' 컬럼에 라벨 인코딩을 적용하고 싶은 경우 다음과 같이 할 수 있습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country income  \n",
       "0          2174             0              40   United-States  <=50K  \n",
       "1             0             0              13   United-States  <=50K  \n",
       "2             0             0              40   United-States  <=50K  \n",
       "3             0             0              40   United-States  <=50K  \n",
       "4             0             0              40            Cuba  <=50K  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' White', ' Black', ' Asian-Pac-Islander', ' Amer-Indian-Eskimo', ' Other']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# race 컬럼에 존재하는 값들로 리스트 생성\n",
    "race_lst = DF['race'].unique().tolist()\n",
    "race_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def race_to_label(x):\n",
    "    return race_lst.index(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# race 값 리스트의 인덱스 값으로 new_race 컬럼 생성\n",
    "DF['new_race'] = DF['race'].map(lambda x: race_to_label(x))\n",
    "#절케 lambda를 써주는 이유는, lambda를 써서 race라는 series의 각원소(여기서는 x가 될거임)가 튀어나옴\n",
    "#여기서는 x가 각각 인종인데, 그 값들을 race_to_label로 계산해서 new_race라는 값으로 나온다\n",
    "#lambda를 쓰면 각각의 원소(x)에다 조건을 걸기 쉬움"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>workclass</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country income  new_race  \n",
       "0          2174             0              40   United-States  <=50K         0  \n",
       "1             0             0              13   United-States  <=50K         0  \n",
       "2             0             0              40   United-States  <=50K         0  \n",
       "3             0             0              40   United-States  <=50K         1  \n",
       "4             0             0              40            Cuba  <=50K         1  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Frame에 적용되는 것은 \"apply\", Series 에 적용되는 것은 \"map\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame.apply()\n",
    "apply, applymap 함수는 Pandas DataFrame에 사용할 수 있는 함수로 apply는 `행 또는 열 같은 축에 대해 연산을 적용` 할 수 있습니다. 행 또는 열을 입력으로 받아 단일 값을 반환합니다. <br>\n",
    "예를들어 행에 존재하는 모든 수치형 열의 값을 더해서 새로운 열을 만들고자 한다면 다음과 같이 할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 수치형 컬럼 'age', 'fnlwgt', 'education-num', 'capital-gain', 'hours-per-week' \n",
    "DF['sum_num_var'] = DF.apply(lambda x: x['age'] + x['fnlwgt'] + x['education-num'] + x['capital-gain'] + x['hours-per-week'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sum_num_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
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       "      <td>0</td>\n",
       "      <td>79782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>83387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
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       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>215733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>234821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>338490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country income  \\\n",
       "0          2174             0              40   United-States  <=50K   \n",
       "1             0             0              13   United-States  <=50K   \n",
       "2             0             0              40   United-States  <=50K   \n",
       "3             0             0              40   United-States  <=50K   \n",
       "4             0             0              40            Cuba  <=50K   \n",
       "\n",
       "   new_race  sum_num_var  \n",
       "0         0        79782  \n",
       "1         0        83387  \n",
       "2         0       215733  \n",
       "3         1       234821  \n",
       "4         1       338490  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame.applymap()\n",
    "Pandas DataFrame에 사용할 수 있는 함수로 applymap은 Series의 map과 동일하게 `각 원소에 연산을 적용`할 수 있습니다. 각 원소를 입력으로 받아 단일 값을 반환합니다.<br>\n",
    "예를들어 모든 수치형 값에 log를 취한다면 다음과 같이 할 수 있습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EK Hong\\.conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.663562</td>\n",
       "      <td>11.258240</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>7.684324</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.912023</td>\n",
       "      <td>11.330336</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>-inf</td>\n",
       "      <td>2.564949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.637586</td>\n",
       "      <td>12.281393</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>-inf</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.970292</td>\n",
       "      <td>12.366153</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>-inf</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.332205</td>\n",
       "      <td>12.732011</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>-inf</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>3.663562</td>\n",
       "      <td>12.280340</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>-inf</td>\n",
       "      <td>3.583519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>4.158883</td>\n",
       "      <td>12.680451</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>-inf</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>3.637586</td>\n",
       "      <td>12.834636</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>-inf</td>\n",
       "      <td>3.912023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>3.784190</td>\n",
       "      <td>11.337274</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>8.604288</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>3.555348</td>\n",
       "      <td>12.112575</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>-inf</td>\n",
       "      <td>4.094345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age     fnlwgt  education-num  capital-gain  hours-per-week\n",
       "0      3.663562  11.258240       2.564949      7.684324        3.688879\n",
       "1      3.912023  11.330336       2.564949          -inf        2.564949\n",
       "2      3.637586  12.281393       2.197225          -inf        3.688879\n",
       "3      3.970292  12.366153       1.945910          -inf        3.688879\n",
       "4      3.332205  12.732011       2.564949          -inf        3.688879\n",
       "...         ...        ...            ...           ...             ...\n",
       "48837  3.663562  12.280340       2.564949          -inf        3.583519\n",
       "48838  4.158883  12.680451       2.197225          -inf        3.688879\n",
       "48839  3.637586  12.834636       2.564949          -inf        3.912023\n",
       "48840  3.784190  11.337274       2.564949      8.604288        3.688879\n",
       "48841  3.555348  12.112575       2.564949          -inf        4.094345\n",
       "\n",
       "[48842 rows x 5 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF[['age', 'fnlwgt', 'education-num', 'capital-gain', 'hours-per-week']].applymap(lambda x: np.log(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9) inplace 인자\n",
    "몇몇 Pandas 함수들의 인자를 살펴보면 inplace 인자가 있습니다. <br>\n",
    "inplace란 말 그대로 내부에서 연산을 하고 객체를 반환하지않는 것을 의미합니다. <br>\n",
    "일반적으로 Pandas 함수들은 연산 후 연산이 적용된 새로운 객체를 생성해서 반환하는데, inplace 인자를 True로 전달하면 객체를 반환하지 않습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>215733</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Not-in-family</td>\n",
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       "      <td>0</td>\n",
       "      <td>79782</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass  fnlwgt   education  education-num  \\\n",
       "48841   35       Self-emp-inc  182148   Bachelors             13   \n",
       "48840   44            Private   83891   Bachelors             13   \n",
       "48839   38            Private  374983   Bachelors             13   \n",
       "48838   64                  ?  321403     HS-grad              9   \n",
       "48837   39            Private  215419   Bachelors             13   \n",
       "...    ...                ...     ...         ...            ...   \n",
       "4       28            Private  338409   Bachelors             13   \n",
       "3       53            Private  234721        11th              7   \n",
       "2       38            Private  215646     HS-grad              9   \n",
       "1       50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "0       39          State-gov   77516   Bachelors             13   \n",
       "\n",
       "            marital-status          occupation     relationship  \\\n",
       "48841   Married-civ-spouse     Exec-managerial          Husband   \n",
       "48840             Divorced        Adm-clerical        Own-child   \n",
       "48839   Married-civ-spouse      Prof-specialty          Husband   \n",
       "48838              Widowed                   ?   Other-relative   \n",
       "48837             Divorced      Prof-specialty    Not-in-family   \n",
       "...                    ...                 ...              ...   \n",
       "4       Married-civ-spouse      Prof-specialty             Wife   \n",
       "3       Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "2                 Divorced   Handlers-cleaners    Not-in-family   \n",
       "1       Married-civ-spouse     Exec-managerial          Husband   \n",
       "0            Never-married        Adm-clerical    Not-in-family   \n",
       "\n",
       "                      race      sex  capital-gain  capital-loss  \\\n",
       "48841                White     Male             0             0   \n",
       "48840   Asian-Pac-Islander     Male          5455             0   \n",
       "48839                White     Male             0             0   \n",
       "48838                Black     Male             0             0   \n",
       "48837                White   Female             0             0   \n",
       "...                    ...      ...           ...           ...   \n",
       "4                    Black   Female             0             0   \n",
       "3                    Black     Male             0             0   \n",
       "2                    White     Male             0             0   \n",
       "1                    White     Male             0             0   \n",
       "0                    White     Male          2174             0   \n",
       "\n",
       "       hours-per-week  native-country income  new_race  sum_num_var  \n",
       "48841              60   United-States   >50K         0       182256  \n",
       "48840              40   United-States  <=50K         2        89443  \n",
       "48839              50   United-States  <=50K         0       375084  \n",
       "48838              40   United-States  <=50K         1       321516  \n",
       "48837              36   United-States  <=50K         0       215507  \n",
       "...               ...             ...    ...       ...          ...  \n",
       "4                  40            Cuba  <=50K         1       338490  \n",
       "3                  40   United-States  <=50K         1       234821  \n",
       "2                  40   United-States  <=50K         0       215733  \n",
       "1                  13   United-States  <=50K         0        83387  \n",
       "0                  40   United-States  <=50K         0        79782  \n",
       "\n",
       "[48842 rows x 17 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rrow_DF = pd.DataFrame(DF, index=[i for i in range(DF.shape[0]-1, -1, -1)])\n",
    "rrow_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
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       "  <tbody>\n",
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       "      <td>48841</td>\n",
       "      <td>35</td>\n",
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       "      <td>64</td>\n",
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       "      <td>321403</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
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       "      <th>4</th>\n",
       "      <td>48837</td>\n",
       "      <td>39</td>\n",
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       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
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       "      <td>White</td>\n",
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       "      <td>0</td>\n",
       "      <td>36</td>\n",
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       "      <th>48837</th>\n",
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       "      <td>28</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
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       "      <td>1</td>\n",
       "      <td>338490</td>\n",
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       "      <th>48838</th>\n",
       "      <td>3</td>\n",
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       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
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       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>215733</td>\n",
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       "      <th>48840</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>83387</td>\n",
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       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>79782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  age          workclass  fnlwgt   education  education-num  \\\n",
       "0      48841   35       Self-emp-inc  182148   Bachelors             13   \n",
       "1      48840   44            Private   83891   Bachelors             13   \n",
       "2      48839   38            Private  374983   Bachelors             13   \n",
       "3      48838   64                  ?  321403     HS-grad              9   \n",
       "4      48837   39            Private  215419   Bachelors             13   \n",
       "...      ...  ...                ...     ...         ...            ...   \n",
       "48837      4   28            Private  338409   Bachelors             13   \n",
       "48838      3   53            Private  234721        11th              7   \n",
       "48839      2   38            Private  215646     HS-grad              9   \n",
       "48840      1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "48841      0   39          State-gov   77516   Bachelors             13   \n",
       "\n",
       "            marital-status          occupation     relationship  \\\n",
       "0       Married-civ-spouse     Exec-managerial          Husband   \n",
       "1                 Divorced        Adm-clerical        Own-child   \n",
       "2       Married-civ-spouse      Prof-specialty          Husband   \n",
       "3                  Widowed                   ?   Other-relative   \n",
       "4                 Divorced      Prof-specialty    Not-in-family   \n",
       "...                    ...                 ...              ...   \n",
       "48837   Married-civ-spouse      Prof-specialty             Wife   \n",
       "48838   Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "48839             Divorced   Handlers-cleaners    Not-in-family   \n",
       "48840   Married-civ-spouse     Exec-managerial          Husband   \n",
       "48841        Never-married        Adm-clerical    Not-in-family   \n",
       "\n",
       "                      race      sex  capital-gain  capital-loss  \\\n",
       "0                    White     Male             0             0   \n",
       "1       Asian-Pac-Islander     Male          5455             0   \n",
       "2                    White     Male             0             0   \n",
       "3                    Black     Male             0             0   \n",
       "4                    White   Female             0             0   \n",
       "...                    ...      ...           ...           ...   \n",
       "48837                Black   Female             0             0   \n",
       "48838                Black     Male             0             0   \n",
       "48839                White     Male             0             0   \n",
       "48840                White     Male             0             0   \n",
       "48841                White     Male          2174             0   \n",
       "\n",
       "       hours-per-week  native-country income  new_race  sum_num_var  \n",
       "0                  60   United-States   >50K         0       182256  \n",
       "1                  40   United-States  <=50K         2        89443  \n",
       "2                  50   United-States  <=50K         0       375084  \n",
       "3                  40   United-States  <=50K         1       321516  \n",
       "4                  36   United-States  <=50K         0       215507  \n",
       "...               ...             ...    ...       ...          ...  \n",
       "48837              40            Cuba  <=50K         1       338490  \n",
       "48838              40   United-States  <=50K         1       234821  \n",
       "48839              40   United-States  <=50K         0       215733  \n",
       "48840              13   United-States  <=50K         0        83387  \n",
       "48841              40   United-States  <=50K         0        79782  \n",
       "\n",
       "[48842 rows x 18 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rrow_DF.reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>338490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>234821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>215733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>83387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>79782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass  fnlwgt   education  education-num  \\\n",
       "0       35       Self-emp-inc  182148   Bachelors             13   \n",
       "1       44            Private   83891   Bachelors             13   \n",
       "2       38            Private  374983   Bachelors             13   \n",
       "3       64                  ?  321403     HS-grad              9   \n",
       "4       39            Private  215419   Bachelors             13   \n",
       "...    ...                ...     ...         ...            ...   \n",
       "48837   28            Private  338409   Bachelors             13   \n",
       "48838   53            Private  234721        11th              7   \n",
       "48839   38            Private  215646     HS-grad              9   \n",
       "48840   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "48841   39          State-gov   77516   Bachelors             13   \n",
       "\n",
       "            marital-status          occupation     relationship  \\\n",
       "0       Married-civ-spouse     Exec-managerial          Husband   \n",
       "1                 Divorced        Adm-clerical        Own-child   \n",
       "2       Married-civ-spouse      Prof-specialty          Husband   \n",
       "3                  Widowed                   ?   Other-relative   \n",
       "4                 Divorced      Prof-specialty    Not-in-family   \n",
       "...                    ...                 ...              ...   \n",
       "48837   Married-civ-spouse      Prof-specialty             Wife   \n",
       "48838   Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "48839             Divorced   Handlers-cleaners    Not-in-family   \n",
       "48840   Married-civ-spouse     Exec-managerial          Husband   \n",
       "48841        Never-married        Adm-clerical    Not-in-family   \n",
       "\n",
       "                      race      sex  capital-gain  capital-loss  \\\n",
       "0                    White     Male             0             0   \n",
       "1       Asian-Pac-Islander     Male          5455             0   \n",
       "2                    White     Male             0             0   \n",
       "3                    Black     Male             0             0   \n",
       "4                    White   Female             0             0   \n",
       "...                    ...      ...           ...           ...   \n",
       "48837                Black   Female             0             0   \n",
       "48838                Black     Male             0             0   \n",
       "48839                White     Male             0             0   \n",
       "48840                White     Male             0             0   \n",
       "48841                White     Male          2174             0   \n",
       "\n",
       "       hours-per-week  native-country income  new_race  sum_num_var  \n",
       "0                  60   United-States   >50K         0       182256  \n",
       "1                  40   United-States  <=50K         2        89443  \n",
       "2                  50   United-States  <=50K         0       375084  \n",
       "3                  40   United-States  <=50K         1       321516  \n",
       "4                  36   United-States  <=50K         0       215507  \n",
       "...               ...             ...    ...       ...          ...  \n",
       "48837              40            Cuba  <=50K         1       338490  \n",
       "48838              40   United-States  <=50K         1       234821  \n",
       "48839              40   United-States  <=50K         0       215733  \n",
       "48840              13   United-States  <=50K         0        83387  \n",
       "48841              40   United-States  <=50K         0        79782  \n",
       "\n",
       "[48842 rows x 17 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rrow_DF.reset_index(drop=True)#기존의 index를 살릴건지 말건지를 drop=True, False로 판단한다\n",
    "#drop을 True로 주면, 기존의 index를 죽인다는것"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>index</th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>new_race</th>\n",
       "      <th>sum_num_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48841</td>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>0</td>\n",
       "      <td>182256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48840</td>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>2</td>\n",
       "      <td>89443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48839</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>375084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48838</td>\n",
       "      <td>64</td>\n",
       "      <td>?</td>\n",
       "      <td>321403</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>321516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48837</td>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>215507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>338490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>234821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>215733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>83387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "      <td>79782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  age          workclass  fnlwgt   education  education-num  \\\n",
       "0      48841   35       Self-emp-inc  182148   Bachelors             13   \n",
       "1      48840   44            Private   83891   Bachelors             13   \n",
       "2      48839   38            Private  374983   Bachelors             13   \n",
       "3      48838   64                  ?  321403     HS-grad              9   \n",
       "4      48837   39            Private  215419   Bachelors             13   \n",
       "...      ...  ...                ...     ...         ...            ...   \n",
       "48837      4   28            Private  338409   Bachelors             13   \n",
       "48838      3   53            Private  234721        11th              7   \n",
       "48839      2   38            Private  215646     HS-grad              9   \n",
       "48840      1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "48841      0   39          State-gov   77516   Bachelors             13   \n",
       "\n",
       "            marital-status          occupation     relationship  \\\n",
       "0       Married-civ-spouse     Exec-managerial          Husband   \n",
       "1                 Divorced        Adm-clerical        Own-child   \n",
       "2       Married-civ-spouse      Prof-specialty          Husband   \n",
       "3                  Widowed                   ?   Other-relative   \n",
       "4                 Divorced      Prof-specialty    Not-in-family   \n",
       "...                    ...                 ...              ...   \n",
       "48837   Married-civ-spouse      Prof-specialty             Wife   \n",
       "48838   Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "48839             Divorced   Handlers-cleaners    Not-in-family   \n",
       "48840   Married-civ-spouse     Exec-managerial          Husband   \n",
       "48841        Never-married        Adm-clerical    Not-in-family   \n",
       "\n",
       "                      race      sex  capital-gain  capital-loss  \\\n",
       "0                    White     Male             0             0   \n",
       "1       Asian-Pac-Islander     Male          5455             0   \n",
       "2                    White     Male             0             0   \n",
       "3                    Black     Male             0             0   \n",
       "4                    White   Female             0             0   \n",
       "...                    ...      ...           ...           ...   \n",
       "48837                Black   Female             0             0   \n",
       "48838                Black     Male             0             0   \n",
       "48839                White     Male             0             0   \n",
       "48840                White     Male             0             0   \n",
       "48841                White     Male          2174             0   \n",
       "\n",
       "       hours-per-week  native-country income  new_race  sum_num_var  \n",
       "0                  60   United-States   >50K         0       182256  \n",
       "1                  40   United-States  <=50K         2        89443  \n",
       "2                  50   United-States  <=50K         0       375084  \n",
       "3                  40   United-States  <=50K         1       321516  \n",
       "4                  36   United-States  <=50K         0       215507  \n",
       "...               ...             ...    ...       ...          ...  \n",
       "48837              40            Cuba  <=50K         1       338490  \n",
       "48838              40   United-States  <=50K         1       234821  \n",
       "48839              40   United-States  <=50K         0       215733  \n",
       "48840              13   United-States  <=50K         0        83387  \n",
       "48841              40   United-States  <=50K         0        79782  \n",
       "\n",
       "[48842 rows x 18 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rrow_DF.reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "rrow_DF.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When inplace = True , the data is modified in place, which means it will return nothing and the dataframe is now updated. When inplace = False , which is the default, then the operation is performed and it returns a copy of the object. You then need to save it to something."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy와 Pandas의 브로드캐스팅\n",
    "- 브로드캐스팅이란? Numpy와 Pandas 자료형에 대해 대부분 단항 또는 이항 연산들은 브로드캐스팅을 지원하는데, 차원에 맞게 연산이 알아서 적용되는 것을 말합니다.\n",
    "\n",
    "- 브로드캐스팅이란 어떤 조건만 만족한다면 모양이 다른 배열끼리의 연산도 가능하게 해주며 모양이 부족한 부분은 확장하여 연산을 수행할 수 있도록 한다는 것이라고 생각할 수 있습니다\n",
    "\n",
    "1. (4, 4) 짜리 행렬 A에 단항 연산인 다음과 같이 로그를 씌우면 np.log(A), 알아서 A의 각 원소에 로그가 적용됩니다.\n",
    "2. (4, 4) 짜리 행렬 A에 (4, 1) 행렬 B를 다음과 같이 빼면 행렬 A의 행 방향으로 B 만큼 값이 감소하게 됩니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 3, 2, 6, 7],\n",
       "       [3, 6, 6, 9, 4],\n",
       "       [8, 3, 3, 7, 5],\n",
       "       [7, 9, 5, 6, 7]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.random.randint(2, 10, size=(4, 5))\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.38629436, 1.09861229, 0.69314718, 1.79175947, 1.94591015],\n",
       "       [1.09861229, 1.79175947, 1.79175947, 2.19722458, 1.38629436],\n",
       "       [2.07944154, 1.09861229, 1.09861229, 1.94591015, 1.60943791],\n",
       "       [1.94591015, 2.19722458, 1.60943791, 1.79175947, 1.94591015]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(A)#차원,방향 신경 쓸 필요 없이 그냥 전부다 자기가 알아서 log취해준다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7],\n",
       "       [6],\n",
       "       [2],\n",
       "       [4]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = np.random.randint(2, 10, size=(4, 1))\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3, -4, -5, -1,  0],\n",
       "       [-3,  0,  0,  3, -2],\n",
       "       [ 6,  1,  1,  5,  3],\n",
       "       [ 3,  5,  1,  2,  3]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A - B#이것도 자기가 알아서 연산을 실행해준다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference\n",
    "- Numpy Docs:  https://numpy.org/doc/stable/\n",
    "- Pandas Docs: https://pandas.pydata.org/docs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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