3. 운동 데이터 분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3장 운동량 데이터 분석해 보기\n",
    "\n",
    "## 3-1. 파이썬의 기본 데이터 구조인 list 형에 대해 학습해요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = [1, 2, 3, 4, 5]\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 1, 2, 3]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list2 = ['a', 'b', 1, 2, 3]\n",
    "list2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 1]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1.append(1)\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1.pop()\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 1, 2, 3, 'aa', 'bb', 'cc']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list2.extend(['aa', 'bb', 'cc'])\n",
    "list2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b', 1, 2, 3, 'aa', 'bb', 'cc']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list2.remove('a')\n",
    "list2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 5]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1.remove(1)\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['파이썬 머신러닝 완벽 가이드',\n",
       " '파이썬 철저 입문',\n",
       " '파이썬 코딩 도장',\n",
       " '파이썬 라이브러리를 활용한 머신러닝',\n",
       " '파이썬 프로그래밍']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_book = ['파이썬 머신러닝 완벽 가이드', '파이썬 철저 입문', '파이썬 코딩 도장', \n",
    "           '파이썬 라이브러리를 활용한 머신러닝', '파이썬 프로그래밍']\n",
    "py_book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['파이썬 머신러닝 완벽 가이드',\n",
       " '파이썬 철저 입문',\n",
       " '파이썬 코딩 도장',\n",
       " '파이썬 라이브러리를 활용한 머신러닝',\n",
       " '파이썬 프로그래밍',\n",
       " '모두의 파이썬']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_book.append('모두의 파이썬')\n",
    "py_book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['파이썬 머신러닝 완벽 가이드',\n",
       " '파이썬 철저 입문',\n",
       " '파이썬 코딩 도장',\n",
       " '파이썬 라이브러리를 활용한 머신러닝',\n",
       " '파이썬 프로그래밍',\n",
       " '모두의 파이썬',\n",
       " '파이썬으로 데이터 주무르기']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_book.append('파이썬으로 데이터 주무르기')\n",
    "py_book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['파이썬 머신러닝 완벽 가이드',\n",
       " 34200,\n",
       " '파이썬 철저 입문',\n",
       " '파이썬 코딩 도장',\n",
       " '파이썬 라이브러리를 활용한 머신러닝',\n",
       " '파이썬 프로그래밍',\n",
       " '모두의 파이썬',\n",
       " '파이썬으로 데이터 주무르기']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_book.insert(1, 34200)\n",
    "py_book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['파이썬 머신러닝 완벽 가이드',\n",
       " 34200,\n",
       " '파이썬 철저 입문',\n",
       " 24300,\n",
       " '파이썬 코딩 도장',\n",
       " 22500,\n",
       " '파이썬 라이브러리를 활용한 머신러닝',\n",
       " 27000,\n",
       " '파이썬 프로그래밍',\n",
       " 25000,\n",
       " '모두의 파이썬',\n",
       " 10800,\n",
       " '파이썬으로 데이터 주무르기']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_book.insert(3, 24300)\n",
    "py_book.insert(5, 22500)\n",
    "py_book.insert(7, 27000)\n",
    "py_book.insert(9, 25000)\n",
    "py_book.insert(11, 10800)\n",
    "py_book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['파이썬 머신러닝 완벽 가이드',\n",
       " 34200,\n",
       " '파이썬 철저 입문',\n",
       " 24300,\n",
       " '파이썬 코딩 도장',\n",
       " 22500,\n",
       " '파이썬 라이브러리를 활용한 머신러닝',\n",
       " 27000,\n",
       " '파이썬 프로그래밍',\n",
       " 25000,\n",
       " '모두의 파이썬',\n",
       " 10800,\n",
       " '파이썬으로 데이터 주무르기',\n",
       " 24750]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_book.append(24750)\n",
    "py_book"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3-2. 데이터를 저장하고 읽어 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>운동</th>\n",
       "      <th>양</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [날짜, 운동, 양]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "practice = pd.DataFrame({'날짜':[], '운동':[], '양':[]})\n",
    "practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
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      "text/plain": [
       "       날짜   운동    양\n",
       "0  19-3-1  달리기  1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice.loc[0] = ['19-3-1', '달리기', 1.]\n",
    "practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>양</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>걷기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>계단오르기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       날짜     운동    양\n",
       "0  19-3-1    달리기  1.0\n",
       "1  19-3-2     걷기  1.0\n",
       "2  19-3-2    달리기  1.0\n",
       "3  19-3-2  계단오르기  1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice.loc[1] = ['19-3-2', '걷기', 1.]\n",
    "practice.loc[2] = ['19-3-2', '달리기', 1.]\n",
    "practice.loc[3] = ['19-3-2', '계단오르기', 1.]\n",
    "practice"
   ]
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  {
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   "execution_count": 18,
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       "      <th>양</th>\n",
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       "      <td>걷기</td>\n",
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       "      <td>달리기</td>\n",
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       "       날짜     운동    양\n",
       "0  19-3-1    달리기  1.0\n",
       "1  19-3-2     걷기  1.0\n",
       "2  19-3-2    달리기  1.0\n",
       "3  19-3-2  계단오르기  1.0\n",
       "4  19-3-3     걷기  1.5\n",
       "5  19-3-3    달리기  1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice.loc[4] = ['19-3-3', '걷기', 1.5]\n",
    "practice.loc[5] = ['19-3-3', '달리기', 1.]\n",
    "practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "practice.to_csv(\"./data/practice.csv\", encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
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       "      <td>4</td>\n",
       "      <td>19-3-3</td>\n",
       "      <td>걷기</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>19-3-3</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      날짜     운동    양\n",
       "0           0  19-3-1    달리기  1.0\n",
       "1           1  19-3-2     걷기  1.0\n",
       "2           2  19-3-2    달리기  1.0\n",
       "3           3  19-3-2  계단오르기  1.0\n",
       "4           4  19-3-3     걷기  1.5\n",
       "5           5  19-3-3    달리기  1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"./data/practice.csv\", encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>운동</th>\n",
       "      <th>양</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19-3-1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>걷기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>계단오르기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19-3-3</td>\n",
       "      <td>걷기</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19-3-3</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       날짜     운동    양\n",
       "0  19-3-1    달리기  1.0\n",
       "1  19-3-2     걷기  1.0\n",
       "2  19-3-2    달리기  1.0\n",
       "3  19-3-2  계단오르기  1.0\n",
       "4  19-3-3     걷기  1.5\n",
       "5  19-3-3    달리기  1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"./data/practice.csv\", index_col=0, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>운동</th>\n",
       "      <th>양</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19-3-1</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>걷기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>계단오르기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19-3-3</td>\n",
       "      <td>걷기</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19-3-3</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       날짜     운동    양\n",
       "0  19-3-1    달리기  1.0\n",
       "1  19-3-2     걷기  1.0\n",
       "2  19-3-2    달리기  1.0\n",
       "3  19-3-2  계단오르기  1.0\n",
       "4  19-3-3     걷기  1.5\n",
       "5  19-3-3    달리기  1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice = pd.read_csv(\"./data/practice.csv\", index_col=0, encoding='utf-8')\n",
    "practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>운동</th>\n",
       "      <th>양</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19-3-1</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>걷기</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19-3-2</td>\n",
       "      <td>계단오르기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19-3-3</td>\n",
       "      <td>걷기</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19-3-3</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19-3-4</td>\n",
       "      <td>걷기</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19-3-5</td>\n",
       "      <td>달리기</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19-3-6</td>\n",
       "      <td>걷기</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19-3-6</td>\n",
       "      <td>달리기</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       날짜     운동    양\n",
       "0  19-3-1    달리기  1.0\n",
       "1  19-3-2     걷기  1.0\n",
       "2  19-3-2    달리기  1.0\n",
       "3  19-3-2  계단오르기  1.0\n",
       "4  19-3-3     걷기  1.5\n",
       "5  19-3-3    달리기  1.0\n",
       "6  19-3-4     걷기  1.5\n",
       "7  19-3-5    달리기  1.0\n",
       "8  19-3-6     걷기  2.5\n",
       "9  19-3-6    달리기  0.5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice.loc[6] = ['19-3-4', '걷기', 1.5]\n",
    "practice.loc[7] = ['19-3-5', '달리기', 1.]\n",
    "practice.loc[8] = ['19-3-6', '걷기', 2.5]\n",
    "practice.loc[9] = ['19-3-6', '달리기', 0.5]\n",
    "practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3-3. 데이터를 정리하는 마법같은 단어 pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>양</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>운동</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>걷기</th>\n",
       "      <td>1.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>계단오르기</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>달리기</th>\n",
       "      <td>0.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           양\n",
       "운동          \n",
       "걷기     1.625\n",
       "계단오르기  1.000\n",
       "달리기    0.900"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice.pivot_table(index=\"운동\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>양</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>운동</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>걷기</th>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>계단오르기</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>달리기</th>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         양\n",
       "운동        \n",
       "걷기     6.5\n",
       "계단오르기  1.0\n",
       "달리기    4.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "practice.pivot_table(index=\"운동\", aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th colspan=\"2\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>양</th>\n",
       "      <th>날짜</th>\n",
       "      <th>양</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>운동</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>걷기</th>\n",
       "      <td>6.5</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>계단오르기</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>달리기</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sum len     \n",
       "         양  날짜    양\n",
       "운동                 \n",
       "걷기     6.5   4  4.0\n",
       "계단오르기  1.0   1  1.0\n",
       "달리기    4.5   5  5.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice.pivot_table(index=\"운동\", aggfunc=[np.sum, len])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>운동</th>\n",
       "      <th>걷기</th>\n",
       "      <th>계단오르기</th>\n",
       "      <th>달리기</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>날짜</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19-3-1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-4</th>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-6</th>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "운동       걷기  계단오르기  달리기\n",
       "날짜                     \n",
       "19-3-1  NaN    NaN  1.0\n",
       "19-3-2  1.0    1.0  1.0\n",
       "19-3-3  1.5    NaN  1.0\n",
       "19-3-4  1.5    NaN  NaN\n",
       "19-3-5  NaN    NaN  1.0\n",
       "19-3-6  2.5    NaN  0.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "practice.pivot('날짜','운동','양')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>운동</th>\n",
       "      <th>걷기</th>\n",
       "      <th>계단오르기</th>\n",
       "      <th>달리기</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>날짜</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>19-3-1</th>\n",
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       "    <tr>\n",
       "      <th>19-3-2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-4</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-3-6</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "운동       걷기  계단오르기  달리기\n",
       "날짜                     \n",
       "19-3-1  0.0    0.0  1.0\n",
       "19-3-2  1.0    1.0  1.0\n",
       "19-3-3  1.5    0.0  1.0\n",
       "19-3-4  1.5    0.0  0.0\n",
       "19-3-5  0.0    0.0  1.0\n",
       "19-3-6  2.5    0.0  0.5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prac_pivot = practice.pivot('날짜','운동','양')\n",
    "prac_pivot.fillna(0, inplace=True)\n",
    "prac_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3-4. 운동을 하지 않는 이유라는 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>기간</th>\n",
       "      <th>대분류</th>\n",
       "      <th>분류</th>\n",
       "      <th>운동을 할 충분한 시간이 없어서</th>\n",
       "      <th>함께 운동을 할 사람이 없어서</th>\n",
       "      <th>운동을 할 만한 장소가 없어서</th>\n",
       "      <th>운동을 싫어해서</th>\n",
       "      <th>기타</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울시</td>\n",
       "      <td>서울시</td>\n",
       "      <td>49.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>55.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>연령별</td>\n",
       "      <td>10대</td>\n",
       "      <td>55.7</td>\n",
       "      <td>6.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>연령별</td>\n",
       "      <td>20대</td>\n",
       "      <td>54.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     기간  대분류   분류  운동을 할 충분한 시간이 없어서 함께 운동을 할 사람이 없어서  운동을 할 만한 장소가 없어서  \\\n",
       "0  2017  서울시  서울시               49.7              8.1               5.8   \n",
       "1  2017   성별   남자               55.2              7.9               5.5   \n",
       "2  2017   성별   여자               45.0              8.3               6.0   \n",
       "3  2017  연령별  10대               55.7              6.1               5.6   \n",
       "4  2017  연령별  20대               54.8              6.9               5.2   \n",
       "\n",
       "   운동을 싫어해서   기타  \n",
       "0      34.7  1.7  \n",
       "1      29.8  1.6  \n",
       "2      38.8  1.8  \n",
       "3      32.2  0.3  \n",
       "4      32.9  0.1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_exercise = pd.read_excel(\"./data/reason_not_exercise.xls\")\n",
    "not_exercise.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>대분류</th>\n",
       "      <th>분류</th>\n",
       "      <th>운동을 할 충분한 시간이 없어서</th>\n",
       "      <th>함께 운동을 할 사람이 없어서</th>\n",
       "      <th>운동을 할 만한 장소가 없어서</th>\n",
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       "      <th>0</th>\n",
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       "      <td>49.7</td>\n",
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       "      <td>5.8</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1.7</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
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       "      <th>2</th>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38.8</td>\n",
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       "      <th>3</th>\n",
       "      <td>연령별</td>\n",
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       "      <td>32.2</td>\n",
       "      <td>0.3</td>\n",
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       "      <th>4</th>\n",
       "      <td>연령별</td>\n",
       "      <td>20대</td>\n",
       "      <td>54.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>연령별</td>\n",
       "      <td>30대</td>\n",
       "      <td>58.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>연령별</td>\n",
       "      <td>40대</td>\n",
       "      <td>57.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>29.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>연령별</td>\n",
       "      <td>50대</td>\n",
       "      <td>50.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>연령별</td>\n",
       "      <td>60대 이상</td>\n",
       "      <td>27.5</td>\n",
       "      <td>12.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>46.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>학력별</td>\n",
       "      <td>중졸 이하</td>\n",
       "      <td>26.1</td>\n",
       "      <td>13.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>학력별</td>\n",
       "      <td>고졸 이하</td>\n",
       "      <td>46.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>36.5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>학력별</td>\n",
       "      <td>대졸 이하</td>\n",
       "      <td>57.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>30.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>학력별</td>\n",
       "      <td>대학원 이상</td>\n",
       "      <td>65.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>소득별</td>\n",
       "      <td>100만원 미만</td>\n",
       "      <td>22.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>50.7</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>소득별</td>\n",
       "      <td>100-200만원 미만</td>\n",
       "      <td>34.3</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>41.5</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>소득별</td>\n",
       "      <td>200-300만원 미만</td>\n",
       "      <td>48.8</td>\n",
       "      <td>12.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>소득별</td>\n",
       "      <td>300-400만원 미만</td>\n",
       "      <td>54.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>소득별</td>\n",
       "      <td>400-500만원 미만</td>\n",
       "      <td>56.1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>소득별</td>\n",
       "      <td>500만원 이상</td>\n",
       "      <td>52.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>34.9</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>혼인상태별</td>\n",
       "      <td>기혼</td>\n",
       "      <td>50.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>34.9</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>혼인상태별</td>\n",
       "      <td>미혼</td>\n",
       "      <td>54.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>32.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>혼인상태별</td>\n",
       "      <td>이혼/별거</td>\n",
       "      <td>47.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>34.9</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>혼인상태별</td>\n",
       "      <td>사별</td>\n",
       "      <td>25.2</td>\n",
       "      <td>14.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>42.9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>지역대분류</td>\n",
       "      <td>도심권</td>\n",
       "      <td>50.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>36.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>지역대분류</td>\n",
       "      <td>동북권</td>\n",
       "      <td>47.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>36.1</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>지역대분류</td>\n",
       "      <td>서북권</td>\n",
       "      <td>49.4</td>\n",
       "      <td>13.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>지역대분류</td>\n",
       "      <td>서남권</td>\n",
       "      <td>50.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>지역대분류</td>\n",
       "      <td>동남권</td>\n",
       "      <td>52.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>종로구</td>\n",
       "      <td>종로구</td>\n",
       "      <td>41.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>중구</td>\n",
       "      <td>중구</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>40.5</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>용산구</td>\n",
       "      <td>용산구</td>\n",
       "      <td>55.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>성동구</td>\n",
       "      <td>성동구</td>\n",
       "      <td>50.5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.8</td>\n",
       "      <td>37.8</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>광진구</td>\n",
       "      <td>광진구</td>\n",
       "      <td>49.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>32.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>동대문구</td>\n",
       "      <td>동대문구</td>\n",
       "      <td>47.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>43.6</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>중랑구</td>\n",
       "      <td>중랑구</td>\n",
       "      <td>47.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>성북구</td>\n",
       "      <td>성북구</td>\n",
       "      <td>43.4</td>\n",
       "      <td>16.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>33.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>강북구</td>\n",
       "      <td>강북구</td>\n",
       "      <td>48.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>37.7</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>도봉구</td>\n",
       "      <td>도봉구</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>노원구</td>\n",
       "      <td>노원구</td>\n",
       "      <td>44.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>32.5</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>은평구</td>\n",
       "      <td>은평구</td>\n",
       "      <td>44.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>서대문구</td>\n",
       "      <td>서대문구</td>\n",
       "      <td>58.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>마포구</td>\n",
       "      <td>마포구</td>\n",
       "      <td>47.7</td>\n",
       "      <td>15.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>양천구</td>\n",
       "      <td>양천구</td>\n",
       "      <td>55.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>10.6</td>\n",
       "      <td>22.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>강서구</td>\n",
       "      <td>강서구</td>\n",
       "      <td>59.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>구로구</td>\n",
       "      <td>구로구</td>\n",
       "      <td>42.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>금천구</td>\n",
       "      <td>금천구</td>\n",
       "      <td>42.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>46.1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>영등포구</td>\n",
       "      <td>영등포구</td>\n",
       "      <td>53.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>동작구</td>\n",
       "      <td>동작구</td>\n",
       "      <td>46.0</td>\n",
       "      <td>-</td>\n",
       "      <td>1.1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>관악구</td>\n",
       "      <td>관악구</td>\n",
       "      <td>50.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>33.6</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>서초구</td>\n",
       "      <td>서초구</td>\n",
       "      <td>59.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>강남구</td>\n",
       "      <td>강남구</td>\n",
       "      <td>59.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>송파구</td>\n",
       "      <td>송파구</td>\n",
       "      <td>42.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>44.3</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>강동구</td>\n",
       "      <td>강동구</td>\n",
       "      <td>53.1</td>\n",
       "      <td>9.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      대분류            분류  운동을 할 충분한 시간이 없어서 함께 운동을 할 사람이 없어서  운동을 할 만한 장소가 없어서  \\\n",
       "0     서울시           서울시               49.7              8.1               5.8   \n",
       "1      성별            남자               55.2              7.9               5.5   \n",
       "2      성별            여자               45.0              8.3               6.0   \n",
       "3     연령별           10대               55.7              6.1               5.6   \n",
       "4     연령별           20대               54.8              6.9               5.2   \n",
       "5     연령별           30대               58.1              5.5               4.6   \n",
       "6     연령별           40대               57.7              6.8               5.2   \n",
       "7     연령별           50대               50.1              9.2               7.2   \n",
       "8     연령별        60대 이상               27.5             12.8               6.9   \n",
       "9     학력별         중졸 이하               26.1             13.7               6.6   \n",
       "10    학력별         고졸 이하               46.3              8.5               6.9   \n",
       "11    학력별         대졸 이하               57.4              6.6               4.9   \n",
       "12    학력별        대학원 이상               65.3              3.8               0.6   \n",
       "13    소득별      100만원 미만               22.7             10.3               3.6   \n",
       "14    소득별  100-200만원 미만               34.3             11.5               7.1   \n",
       "15    소득별  200-300만원 미만               48.8             12.3               6.8   \n",
       "16    소득별  300-400만원 미만               54.9              7.1               6.5   \n",
       "17    소득별  400-500만원 미만               56.1                5               5.3   \n",
       "18    소득별      500만원 이상               52.3              7.1               5.1   \n",
       "19  혼인상태별            기혼               50.7              7.8               5.4   \n",
       "20  혼인상태별            미혼               54.3              7.1               5.4   \n",
       "21  혼인상태별         이혼/별거               47.4              8.3               6.8   \n",
       "22  혼인상태별            사별               25.2             14.9               8.9   \n",
       "23  지역대분류           도심권               50.5              6.9               4.5   \n",
       "24  지역대분류           동북권               47.4              7.9               5.9   \n",
       "25  지역대분류           서북권               49.4             13.9               7.4   \n",
       "26  지역대분류           서남권               50.7              6.7               5.7   \n",
       "27  지역대분류           동남권               52.2              6.9               4.9   \n",
       "28    종로구           종로구               41.4             18.2               7.8   \n",
       "29     중구            중구               51.0              1.2               1.9   \n",
       "30    용산구           용산구               55.9              3.8               4.3   \n",
       "31    성동구           성동구               50.5                5               6.8   \n",
       "32    광진구           광진구               49.1              8.5               5.6   \n",
       "33   동대문구          동대문구               47.2              1.7               3.4   \n",
       "34    중랑구           중랑구               47.9              3.9               5.7   \n",
       "35    성북구           성북구               43.4             16.1               6.1   \n",
       "36    강북구           강북구               48.4              7.2               6.7   \n",
       "37    도봉구           도봉구               52.0              8.6               5.1   \n",
       "38    노원구           노원구               44.6              7.8               7.2   \n",
       "39    은평구           은평구               44.8             17.7               7.1   \n",
       "40   서대문구          서대문구               58.6              5.8               7.9   \n",
       "41    마포구           마포구               47.7             15.6               7.5   \n",
       "42    양천구           양천구               55.2             11.5              10.6   \n",
       "43    강서구           강서구               59.0              8.3               4.0   \n",
       "44    구로구           구로구               42.9              6.1               7.3   \n",
       "45    금천구           금천구               42.4              7.8               3.2   \n",
       "46   영등포구          영등포구               53.8              3.4               4.0   \n",
       "47    동작구           동작구               46.0                -               1.1   \n",
       "48    관악구           관악구               50.7              7.1               6.9   \n",
       "49    서초구           서초구               59.8              3.4               3.2   \n",
       "50    강남구           강남구               59.1              5.5               2.6   \n",
       "51    송파구           송파구               42.6              7.2               5.8   \n",
       "52    강동구           강동구               53.1              9.6               6.8   \n",
       "\n",
       "    운동을 싫어해서    기타  \n",
       "0       34.7   1.7  \n",
       "1       29.8   1.6  \n",
       "2       38.8   1.8  \n",
       "3       32.2   0.3  \n",
       "4       32.9   0.1  \n",
       "5       31.6   0.2  \n",
       "6       29.4     1  \n",
       "7       32.2   1.4  \n",
       "8       46.8     6  \n",
       "9       46.2   7.4  \n",
       "10      36.5   1.7  \n",
       "11      30.7   0.4  \n",
       "12      29.6   0.6  \n",
       "13      50.7  12.7  \n",
       "14      41.5   5.6  \n",
       "15      30.7   1.4  \n",
       "16      31.2   0.3  \n",
       "17      33.0   0.6  \n",
       "18      34.9   0.6  \n",
       "19      34.9   1.2  \n",
       "20      32.2     1  \n",
       "21      34.9   2.7  \n",
       "22      42.9     8  \n",
       "23      36.4   1.7  \n",
       "24      36.1   2.7  \n",
       "25      28.5   0.8  \n",
       "26      35.0   1.9  \n",
       "27      35.4   0.6  \n",
       "28      32.4   0.2  \n",
       "29      40.5   5.5  \n",
       "30      36.0     -  \n",
       "31      37.8     -  \n",
       "32      32.5   4.4  \n",
       "33      43.6   4.2  \n",
       "34      42.0   0.4  \n",
       "35      33.4     1  \n",
       "36      37.7     -  \n",
       "37      32.0   2.3  \n",
       "38      32.5   7.9  \n",
       "39      29.7   0.8  \n",
       "40      27.3   0.5  \n",
       "41      28.0   1.2  \n",
       "42      22.3   0.3  \n",
       "43      27.1   1.6  \n",
       "44      43.3   0.4  \n",
       "45      46.1   0.5  \n",
       "46      34.8     4  \n",
       "47      47.0   5.9  \n",
       "48      33.6   1.7  \n",
       "49      32.0   1.5  \n",
       "50      31.5   1.3  \n",
       "51      44.3   0.1  \n",
       "52      30.4   0.1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_exercise.drop(columns='기간', inplace=True)\n",
    "not_exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>대분류</th>\n",
       "      <th>분류</th>\n",
       "      <th>운동을 할 충분한 시간이 없어서</th>\n",
       "      <th>함께 운동을 할 사람이 없어서</th>\n",
       "      <th>운동을 할 만한 장소가 없어서</th>\n",
       "      <th>운동을 싫어해서</th>\n",
       "      <th>기타</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울시</td>\n",
       "      <td>서울시</td>\n",
       "      <td>49.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>55.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>연령별</td>\n",
       "      <td>10대</td>\n",
       "      <td>55.7</td>\n",
       "      <td>6.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>연령별</td>\n",
       "      <td>20대</td>\n",
       "      <td>54.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>연령별</td>\n",
       "      <td>30대</td>\n",
       "      <td>58.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>연령별</td>\n",
       "      <td>40대</td>\n",
       "      <td>57.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>29.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>연령별</td>\n",
       "      <td>50대</td>\n",
       "      <td>50.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>연령별</td>\n",
       "      <td>60대 이상</td>\n",
       "      <td>27.5</td>\n",
       "      <td>12.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>46.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>학력별</td>\n",
       "      <td>중졸 이하</td>\n",
       "      <td>26.1</td>\n",
       "      <td>13.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>학력별</td>\n",
       "      <td>고졸 이하</td>\n",
       "      <td>46.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>36.5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>학력별</td>\n",
       "      <td>대졸 이하</td>\n",
       "      <td>57.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>30.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>학력별</td>\n",
       "      <td>대학원 이상</td>\n",
       "      <td>65.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>소득별</td>\n",
       "      <td>100만원 미만</td>\n",
       "      <td>22.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>50.7</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>소득별</td>\n",
       "      <td>100-200만원 미만</td>\n",
       "      <td>34.3</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>41.5</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>소득별</td>\n",
       "      <td>200-300만원 미만</td>\n",
       "      <td>48.8</td>\n",
       "      <td>12.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>소득별</td>\n",
       "      <td>300-400만원 미만</td>\n",
       "      <td>54.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>소득별</td>\n",
       "      <td>400-500만원 미만</td>\n",
       "      <td>56.1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>소득별</td>\n",
       "      <td>500만원 이상</td>\n",
       "      <td>52.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>34.9</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>혼인상태별</td>\n",
       "      <td>기혼</td>\n",
       "      <td>50.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>34.9</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>혼인상태별</td>\n",
       "      <td>미혼</td>\n",
       "      <td>54.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>32.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>혼인상태별</td>\n",
       "      <td>이혼/별거</td>\n",
       "      <td>47.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>34.9</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      대분류            분류  운동을 할 충분한 시간이 없어서 함께 운동을 할 사람이 없어서  운동을 할 만한 장소가 없어서  \\\n",
       "0     서울시           서울시               49.7              8.1               5.8   \n",
       "1      성별            남자               55.2              7.9               5.5   \n",
       "2      성별            여자               45.0              8.3               6.0   \n",
       "3     연령별           10대               55.7              6.1               5.6   \n",
       "4     연령별           20대               54.8              6.9               5.2   \n",
       "5     연령별           30대               58.1              5.5               4.6   \n",
       "6     연령별           40대               57.7              6.8               5.2   \n",
       "7     연령별           50대               50.1              9.2               7.2   \n",
       "8     연령별        60대 이상               27.5             12.8               6.9   \n",
       "9     학력별         중졸 이하               26.1             13.7               6.6   \n",
       "10    학력별         고졸 이하               46.3              8.5               6.9   \n",
       "11    학력별         대졸 이하               57.4              6.6               4.9   \n",
       "12    학력별        대학원 이상               65.3              3.8               0.6   \n",
       "13    소득별      100만원 미만               22.7             10.3               3.6   \n",
       "14    소득별  100-200만원 미만               34.3             11.5               7.1   \n",
       "15    소득별  200-300만원 미만               48.8             12.3               6.8   \n",
       "16    소득별  300-400만원 미만               54.9              7.1               6.5   \n",
       "17    소득별  400-500만원 미만               56.1                5               5.3   \n",
       "18    소득별      500만원 이상               52.3              7.1               5.1   \n",
       "19  혼인상태별            기혼               50.7              7.8               5.4   \n",
       "20  혼인상태별            미혼               54.3              7.1               5.4   \n",
       "21  혼인상태별         이혼/별거               47.4              8.3               6.8   \n",
       "\n",
       "    운동을 싫어해서    기타  \n",
       "0       34.7   1.7  \n",
       "1       29.8   1.6  \n",
       "2       38.8   1.8  \n",
       "3       32.2   0.3  \n",
       "4       32.9   0.1  \n",
       "5       31.6   0.2  \n",
       "6       29.4     1  \n",
       "7       32.2   1.4  \n",
       "8       46.8     6  \n",
       "9       46.2   7.4  \n",
       "10      36.5   1.7  \n",
       "11      30.7   0.4  \n",
       "12      29.6   0.6  \n",
       "13      50.7  12.7  \n",
       "14      41.5   5.6  \n",
       "15      30.7   1.4  \n",
       "16      31.2   0.3  \n",
       "17      33.0   0.6  \n",
       "18      34.9   0.6  \n",
       "19      34.9   1.2  \n",
       "20      32.2     1  \n",
       "21      34.9   2.7  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_exercise.drop(index=range(22,53), inplace=True)\n",
    "not_exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1      True\n",
       "2      True\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18    False\n",
       "19    False\n",
       "20    False\n",
       "21    False\n",
       "Name: 대분류, dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_exercise['대분류']=='성별'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>대분류</th>\n",
       "      <th>분류</th>\n",
       "      <th>운동을 할 충분한 시간이 없어서</th>\n",
       "      <th>함께 운동을 할 사람이 없어서</th>\n",
       "      <th>운동을 할 만한 장소가 없어서</th>\n",
       "      <th>운동을 싫어해서</th>\n",
       "      <th>기타</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>55.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  대분류  분류  운동을 할 충분한 시간이 없어서 함께 운동을 할 사람이 없어서  운동을 할 만한 장소가 없어서  운동을 싫어해서   기타\n",
       "1  성별  남자               55.2              7.9               5.5      29.8  1.6\n",
       "2  성별  여자               45.0              8.3               6.0      38.8  1.8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_ex_sex = not_exercise[not_exercise['대분류']=='성별'].copy()\n",
    "not_ex_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a = [1, 2, 3, 4]\n",
    "test_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_b = test_a\n",
    "test_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 0, 4]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_b[2] = 0\n",
    "test_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 0, 4]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 0, 4]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_c = test_a.copy()\n",
    "test_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 100, 4]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_c[2] = 100\n",
    "test_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 0, 4]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>분류</th>\n",
       "      <th>운동을 할 충분한 시간이 없어서</th>\n",
       "      <th>함께 운동을 할 사람이 없어서</th>\n",
       "      <th>운동을 할 만한 장소가 없어서</th>\n",
       "      <th>운동을 싫어해서</th>\n",
       "      <th>기타</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>남자</td>\n",
       "      <td>55.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>여자</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   분류  운동을 할 충분한 시간이 없어서 함께 운동을 할 사람이 없어서  운동을 할 만한 장소가 없어서  운동을 싫어해서   기타\n",
       "1  남자               55.2              7.9               5.5      29.8  1.6\n",
       "2  여자               45.0              8.3               6.0      38.8  1.8"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_ex_sex.drop(columns='대분류', inplace=True)\n",
    "not_ex_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>운동을 할 충분한 시간이 없어서</th>\n",
       "      <th>함께 운동을 할 사람이 없어서</th>\n",
       "      <th>운동을 할 만한 장소가 없어서</th>\n",
       "      <th>운동을 싫어해서</th>\n",
       "      <th>기타</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>분류</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>남자</th>\n",
       "      <td>55.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>여자</th>\n",
       "      <td>45.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    운동을 할 충분한 시간이 없어서 함께 운동을 할 사람이 없어서  운동을 할 만한 장소가 없어서  운동을 싫어해서   기타\n",
       "분류                                                                     \n",
       "남자               55.2              7.9               5.5      29.8  1.6\n",
       "여자               45.0              8.3               6.0      38.8  1.8"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_ex_sex.set_index('분류', inplace=True)\n",
    "not_ex_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import font_manager, rc\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "f_path = \"/Library/Fonts/AppleGothic.ttf\"\n",
    "# f_path = \"C:/Windows/Fonts/malgun.ttf\"\n",
    "font_name = font_manager.FontProperties(fname=f_path).get_name()\n",
    "rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,2, figsize=(16,8))\n",
    "\n",
    "not_ex_sex['운동을 할 충분한 시간이 없어서'].plot.pie(explode=[0,0.02], ax=ax[0], autopct='%1.1f%%')\n",
    "ax[0].set_title('운동을 할 충분한 시간이 없어서')\n",
    "ax[0].set_ylabel('')\n",
    "\n",
    "not_ex_sex['함께 운동을 할 사람이 없어서'].plot.pie(explode=[0,0.02], ax=ax[1], autopct='%1.1f%%')\n",
    "ax[1].set_title('함께 운동을 할 사람이 없어서')\n",
    "ax[1].set_ylabel('')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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>운동을 할 충분한 시간이 없어서</th>\n",
       "      <th>함께 운동을 할 사람이 없어서</th>\n",
       "      <th>운동을 할 만한 장소가 없어서</th>\n",
       "      <th>운동을 싫어해서</th>\n",
       "      <th>기타</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>분류</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10대</th>\n",
       "      <td>55.7</td>\n",
       "      <td>6.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20대</th>\n",
       "      <td>54.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30대</th>\n",
       "      <td>58.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40대</th>\n",
       "      <td>57.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>29.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50대</th>\n",
       "      <td>50.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60대 이상</th>\n",
       "      <td>27.5</td>\n",
       "      <td>12.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>46.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        운동을 할 충분한 시간이 없어서 함께 운동을 할 사람이 없어서  운동을 할 만한 장소가 없어서  운동을 싫어해서   기타\n",
       "분류                                                                         \n",
       "10대                  55.7              6.1               5.6      32.2  0.3\n",
       "20대                  54.8              6.9               5.2      32.9  0.1\n",
       "30대                  58.1              5.5               4.6      31.6  0.2\n",
       "40대                  57.7              6.8               5.2      29.4    1\n",
       "50대                  50.1              9.2               7.2      32.2  1.4\n",
       "60대 이상               27.5             12.8               6.9      46.8    6"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_ex_age = not_exercise[not_exercise['대분류']=='연령별'].copy()\n",
    "not_ex_age.drop(columns='대분류', inplace=True)\n",
    "not_ex_age.set_index('분류', inplace=True)\n",
    "not_ex_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,3, figsize=(16,8))\n",
    "\n",
    "explode_setting = [0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
    "\n",
    "not_ex_age['운동을 할 충분한 시간이 없어서'].plot.pie(explode=explode_setting, \n",
    "                                         ax=ax[0], \n",
    "                                         autopct='%1.1f%%')\n",
    "ax[0].set_title('운동을 할 충분한 시간이 없어서')\n",
    "ax[0].set_ylabel('')\n",
    "\n",
    "not_ex_age['함께 운동을 할 사람이 없어서'].plot.pie(explode=explode_setting, \n",
    "                                        ax=ax[1], \n",
    "                                        autopct='%1.1f%%')\n",
    "ax[1].set_title('함께 운동을 할 사람이 없어서')\n",
    "ax[1].set_ylabel('')\n",
    "\n",
    "not_ex_age['운동을 할 만한 장소가 없어서'].plot.pie(explode=explode_setting, \n",
    "                                        ax=ax[2], \n",
    "                                        autopct='%1.1f%%')\n",
    "ax[2].set_title('운동을 할 만한 장소가 없어서')\n",
    "ax[2].set_ylabel('')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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