2022.10.21.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import pyecharts as pyc\n",
    "import sklearn as sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = list(range(1, 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "홀수 :  1\n",
      "홀수 :  3\n",
      "홀수 :  5\n",
      "홀수 :  7\n",
      "홀수 :  9\n"
     ]
    }
   ],
   "source": [
    "for num in array:\n",
    "\n",
    "    if num % 2 == 0 :\n",
    "        continue\n",
    "\n",
    "    print(\"홀수 : \", num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "midScores = np.array([10, 20, 30])\n",
    "finalScores = np.array([70, 80, 90])\n",
    "\n",
    "total = midScores + finalScores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 80 100 120]\n"
     ]
    }
   ],
   "source": [
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.array([88, 72, 93, 94, 89, 78, 99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([72, 93, 94])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_data = {'a' : 1, 'b' : 2, 'c' : 3}\n",
    "\n",
    "sr = pd.Series(dict_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "이름              동덕\n",
      "생년월일    2022-22-22\n",
      "성별               여\n",
      "재학여부          True\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "tup_data = ('동덕', '2022-22-22', '여', True)\n",
    "sr = pd.Series(tup_data, index = ('이름', '생년월일', '성별', '재학여부'))\n",
    "print(sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2]]\n",
      "\n",
      "\n",
      "생년월일    2022-22-22\n",
      "성별               여\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print([[1,2]])\n",
    "print('\\n')\n",
    "print(sr[['생년월일', '성별']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_data = {'c0' : [1, 2, 3], 'c1' : [4, 5, 6], 'c2' : [7, 8, 9], 'c3' : [10, 11, 12], 'c4' : [13, 14, 15]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dict_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   c0  c1  c2  c3  c4\n",
      "0   1   4   7  10  13\n",
      "1   2   5   8  11  14\n",
      "2   3   6   9  12  15\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[15, '남', '덕동중'], [17, '여', '이치중']],\n",
    "                    index=['동덕', '문화'],\n",
    "                    columns=['나이', '성별', '학교'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>나이</th>\n",
       "      <th>성별</th>\n",
       "      <th>학교</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>동덕</th>\n",
       "      <td>15</td>\n",
       "      <td>남</td>\n",
       "      <td>덕동중</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>문화</th>\n",
       "      <td>17</td>\n",
       "      <td>여</td>\n",
       "      <td>이치중</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    나이 성별   학교\n",
       "동덕  15  남  덕동중\n",
       "문화  17  여  이치중"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['동덕', '문화'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['나이', '성별', '학교'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = ['학생1', '학생2']\n",
    "df.columns = ['연령', '남여', '소속']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>연령</th>\n",
       "      <th>남여</th>\n",
       "      <th>소속</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>학생1</th>\n",
       "      <td>15</td>\n",
       "      <td>남</td>\n",
       "      <td>덕동중</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학생2</th>\n",
       "      <td>17</td>\n",
       "      <td>여</td>\n",
       "      <td>이치중</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연령 남여   소속\n",
       "학생1  15  남  덕동중\n",
       "학생2  17  여  이치중"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "exam_data = {'수학' : [90, 80, 70], '영어' : [98, 89, 95],\n",
    "            '음악' : [85, 95, 100], '체육' : [100, 90, 90]}\n",
    "\n",
    "df = pd.DataFrame(exam_data, index=['서준', '우현', '인아'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>음악</th>\n",
       "      <th>체육</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서준</th>\n",
       "      <td>90</td>\n",
       "      <td>98</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>우현</th>\n",
       "      <td>80</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인아</th>\n",
       "      <td>70</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    수학  영어   음악   체육\n",
       "서준  90  98   85  100\n",
       "우현  80  89   95   90\n",
       "인아  70  95  100   90"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    수학  영어   음악   체육\n",
      "서준  90  98   85  100\n",
      "인아  70  95  100   90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/data_science/lib/python3.7/site-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "df2 = df[:]\n",
    "df2.drop('우현', inplace=True)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/data_science/lib/python3.7/site-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "df3 = df[:]\n",
    "df3.drop(['우현', '인아'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>음악</th>\n",
       "      <th>체육</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서준</th>\n",
       "      <td>90</td>\n",
       "      <td>98</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    수학  영어  음악   체육\n",
       "서준  90  98  85  100"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>음악</th>\n",
       "      <th>체육</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서준</th>\n",
       "      <td>90</td>\n",
       "      <td>98</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>우현</th>\n",
       "      <td>80</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인아</th>\n",
       "      <td>70</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    수학  영어   음악   체육\n",
       "서준  90  98   85  100\n",
       "우현  80  89   95   90\n",
       "인아  70  95  100   90"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "labell = df.loc['서준']\n",
    "positionl = df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "수학     90\n",
       "영어     98\n",
       "음악     85\n",
       "체육    100\n",
       "Name: 서준, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "수학     90\n",
       "영어     98\n",
       "음악     85\n",
       "체육    100\n",
       "Name: 서준, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positionl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "exam = {'이름' : ['서준', '우현', '인아'],\n",
    "        '수학' : [90, 80, 70],\n",
    "        '영어' : [98, 89, 95],\n",
    "        '음악' : [85, 95, 100],\n",
    "        '체육' : [100, 90, 90]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "exam = pd.DataFrame(exam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "exam.set_index('이름',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>음악</th>\n",
       "      <th>체육</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>이름</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서준</th>\n",
       "      <td>90</td>\n",
       "      <td>98</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>우현</th>\n",
       "      <td>80</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인아</th>\n",
       "      <td>70</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    수학  영어   음악   체육\n",
       "이름                  \n",
       "서준  90  98   85  100\n",
       "우현  80  89   95   90\n",
       "인아  70  95  100   90"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')\n",
    "df = titanic.loc[:, ['age', 'fare']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>age</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
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       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     fare\n",
       "0    22.0   7.2500\n",
       "1    38.0  71.2833\n",
       "2    26.0   7.9250\n",
       "3    35.0  53.1000\n",
       "4    35.0   8.0500\n",
       "..    ...      ...\n",
       "886  27.0  13.0000\n",
       "887  19.0  30.0000\n",
       "888   NaN  23.4500\n",
       "889  26.0  30.0000\n",
       "890  32.0   7.7500\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "addition = df+ 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <td>32.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>48.0</td>\n",
       "      <td>81.2833</td>\n",
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       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
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       "      <td>45.0</td>\n",
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       "      <th>4</th>\n",
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       "      <th>...</th>\n",
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       "      <th>887</th>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0000</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33.4500</td>\n",
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       "      <th>889</th>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0000</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>42.0</td>\n",
       "      <td>17.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     fare\n",
       "0    32.0  17.2500\n",
       "1    48.0  81.2833\n",
       "2    36.0  17.9250\n",
       "3    45.0  63.1000\n",
       "4    45.0  18.0500\n",
       "..    ...      ...\n",
       "886  37.0  23.0000\n",
       "887  29.0  40.0000\n",
       "888   NaN  33.4500\n",
       "889  36.0  40.0000\n",
       "890  42.0  17.7500\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"read_csv_sample.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
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       "      <td>4</td>\n",
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       "      <th>1</th>\n",
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       "      <td>8</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>9</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c0  c1  c2  c3\n",
       "0   0   1   4   7\n",
       "1   1   2   5   8\n",
       "2   2   3   6   9"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/data_science/lib/python3.7/site-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_excel(\"./발전_전력량_20221022222347.xlsx\", engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting openpyxl\n",
      "  Downloading openpyxl-3.0.10-py2.py3-none-any.whl (242 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m242.1/242.1 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting et-xmlfile\n",
      "  Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)\n",
      "Installing collected packages: et-xmlfile, openpyxl\n",
      "Successfully installed et-xmlfile-1.1.0 openpyxl-3.0.10\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>시점</th>\n",
       "      <th>북한</th>\n",
       "      <th>북한.1</th>\n",
       "      <th>북한.2</th>\n",
       "      <th>북한.3</th>\n",
       "      <th>북한.4</th>\n",
       "      <th>북한.5</th>\n",
       "      <th>남한</th>\n",
       "      <th>남한.1</th>\n",
       "      <th>남한.2</th>\n",
       "      <th>남한.3</th>\n",
       "      <th>남한.4</th>\n",
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       "      <th>남한.6</th>\n",
       "      <th>남한.7</th>\n",
       "      <th>남한.8</th>\n",
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       "      <th>남한.10</th>\n",
       "      <th>남한.11</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>시점</td>\n",
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       "      <td>수력</td>\n",
       "      <td>수력</td>\n",
       "      <td>화력</td>\n",
       "      <td>화력</td>\n",
       "      <td>합계</td>\n",
       "      <td>합계</td>\n",
       "      <td>수력</td>\n",
       "      <td>수력</td>\n",
       "      <td>화력</td>\n",
       "      <td>화력</td>\n",
       "      <td>원자력</td>\n",
       "      <td>원자력</td>\n",
       "      <td>대체에너지</td>\n",
       "      <td>대체에너지</td>\n",
       "      <td>기타</td>\n",
       "      <td>기타</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>시점</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "      <td>발전 전력량 (억 kWh)</td>\n",
       "      <td>구성비 (%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>202.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>2852.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1689.0</td>\n",
       "      <td>59.2</td>\n",
       "      <td>1121.0</td>\n",
       "      <td>39.3</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>190.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>55.8</td>\n",
       "      <td>84.0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>3065.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>59.4</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>59.7</td>\n",
       "      <td>79.0</td>\n",
       "      <td>40.3</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1859.0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>40.2</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2004</td>\n",
       "      <td>206.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>60.7</td>\n",
       "      <td>81.0</td>\n",
       "      <td>39.3</td>\n",
       "      <td>3421.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2056.0</td>\n",
       "      <td>60.1</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>38.2</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2005</td>\n",
       "      <td>215.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>60.9</td>\n",
       "      <td>84.0</td>\n",
       "      <td>39.1</td>\n",
       "      <td>3646.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>58.3</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>40.3</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006</td>\n",
       "      <td>225.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3812.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2272.0</td>\n",
       "      <td>59.6</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2007</td>\n",
       "      <td>236.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4031.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2551.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>1429.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008</td>\n",
       "      <td>255.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4224.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2645.0</td>\n",
       "      <td>62.6</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2009</td>\n",
       "      <td>235.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4336.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2802.0</td>\n",
       "      <td>64.6</td>\n",
       "      <td>1478.0</td>\n",
       "      <td>34.1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>237.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4747.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3196.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>31.3</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2011</td>\n",
       "      <td>211.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4969.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3343.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012</td>\n",
       "      <td>215.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>62.8</td>\n",
       "      <td>80.0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>5096.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3430.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>221.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>62.9</td>\n",
       "      <td>82.0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>5171.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3581.0</td>\n",
       "      <td>69.3</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014</td>\n",
       "      <td>216.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.2</td>\n",
       "      <td>86.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5220.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3427.0</td>\n",
       "      <td>65.6</td>\n",
       "      <td>1564.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>190.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>52.6</td>\n",
       "      <td>90.0</td>\n",
       "      <td>47.4</td>\n",
       "      <td>5281.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3402.0</td>\n",
       "      <td>64.4</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016</td>\n",
       "      <td>239.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>111.0</td>\n",
       "      <td>46.4</td>\n",
       "      <td>5404.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3522.0</td>\n",
       "      <td>65.2</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017</td>\n",
       "      <td>235.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>50.6</td>\n",
       "      <td>116.0</td>\n",
       "      <td>49.4</td>\n",
       "      <td>5535.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3738.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>243.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018</td>\n",
       "      <td>249.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>51.4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>5706.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4018.0</td>\n",
       "      <td>70.4</td>\n",
       "      <td>1335.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>281.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019</td>\n",
       "      <td>238.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>53.8</td>\n",
       "      <td>5630.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3774.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1459.0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>313.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020</td>\n",
       "      <td>239.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>53.4</td>\n",
       "      <td>112.0</td>\n",
       "      <td>46.6</td>\n",
       "      <td>5522.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3466.0</td>\n",
       "      <td>62.8</td>\n",
       "      <td>1602.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점              북한     북한.1            북한.2     북한.3            북한.4  \\\n",
       "0     시점              합계       합계              수력       수력              화력   \n",
       "1     시점  발전 전력량 (억 kWh)  구성비 (%)  발전 전력량 (억 kWh)  구성비 (%)  발전 전력량 (억 kWh)   \n",
       "2   2001           202.0    100.0           106.0     52.5            96.0   \n",
       "3   2002           190.0    100.0           106.0     55.8            84.0   \n",
       "4   2003           196.0    100.0           117.0     59.7            79.0   \n",
       "5   2004           206.0    100.0           125.0     60.7            81.0   \n",
       "6   2005           215.0    100.0           131.0     60.9            84.0   \n",
       "7   2006           225.0    100.0           126.0     55.0            99.0   \n",
       "8   2007           236.0    100.0           133.0     56.0           103.0   \n",
       "9   2008           255.0    100.0           141.0     55.0           114.0   \n",
       "10  2009           235.0    100.0           125.0     53.0           110.0   \n",
       "11  2010           237.0    100.0           134.0     57.0           103.0   \n",
       "12  2011           211.0    100.0           132.0     63.0            79.0   \n",
       "13  2012           215.0    100.0           135.0     62.8            80.0   \n",
       "14  2013           221.0    100.0           139.0     62.9            82.0   \n",
       "15  2014           216.0    100.0           130.0     60.2            86.0   \n",
       "16  2015           190.0    100.0           100.0     52.6            90.0   \n",
       "17  2016           239.0    100.0           128.0     53.6           111.0   \n",
       "18  2017           235.0    100.0           119.0     50.6           116.0   \n",
       "19  2018           249.0    100.0           128.0     51.4           121.0   \n",
       "20  2019           238.0    100.0           110.0     46.2           128.0   \n",
       "21  2020           239.0    100.0           128.0     53.4           112.0   \n",
       "\n",
       "       북한.5              남한     남한.1            남한.2     남한.3            남한.4  \\\n",
       "0        화력              합계       합계              수력       수력              화력   \n",
       "1   구성비 (%)  발전 전력량 (억 kWh)  구성비 (%)  발전 전력량 (억 kWh)  구성비 (%)  발전 전력량 (억 kWh)   \n",
       "2      47.5          2852.0    100.0            42.0      1.5          1689.0   \n",
       "3      44.2          3065.0    100.0            53.0      1.7          1821.0   \n",
       "4      40.3          3225.0    100.0            69.0      2.1          1859.0   \n",
       "5      39.3          3421.0    100.0            59.0      1.7          2056.0   \n",
       "6      39.1          3646.0    100.0            52.0      1.4          2127.0   \n",
       "7      45.0          3812.0    100.0            52.0      1.4          2272.0   \n",
       "8      44.0          4031.0    100.0            50.0      1.3          2551.0   \n",
       "9      45.0          4224.0    100.0            56.0      1.3          2645.0   \n",
       "10     47.0          4336.0    100.0            56.0      1.3          2802.0   \n",
       "11     43.0          4747.0    100.0            65.0      1.4          3196.0   \n",
       "12     37.0          4969.0    100.0            78.0      1.6          3343.0   \n",
       "13     37.2          5096.0    100.0            77.0      1.5          3430.0   \n",
       "14     37.1          5171.0    100.0            84.0      1.6          3581.0   \n",
       "15     39.8          5220.0    100.0            78.0      1.5          3427.0   \n",
       "16     47.4          5281.0    100.0            58.0      1.1          3402.0   \n",
       "17     46.4          5404.0    100.0            66.0      1.2          3522.0   \n",
       "18     49.4          5535.0    100.0            70.0      1.3          3738.0   \n",
       "19     48.6          5706.0    100.0            73.0      1.3          4018.0   \n",
       "20     53.8          5630.0    100.0            62.0      1.1          3774.0   \n",
       "21     46.6          5522.0    100.0            71.0      1.3          3466.0   \n",
       "\n",
       "       남한.5            남한.6     남한.7            남한.8     남한.9           남한.10  \\\n",
       "0        화력             원자력      원자력           대체에너지    대체에너지              기타   \n",
       "1   구성비 (%)  발전 전력량 (억 kWh)  구성비 (%)  발전 전력량 (억 kWh)  구성비 (%)  발전 전력량 (억 kWh)   \n",
       "2      59.2          1121.0     39.3               -        -               -   \n",
       "3      59.4          1191.0     38.9               -        -               -   \n",
       "4      57.6          1297.0     40.2               -        -               -   \n",
       "5      60.1          1307.0     38.2               -        -               -   \n",
       "6      58.3          1468.0     40.3               -        -               -   \n",
       "7      59.6          1487.0     39.0               -        -               -   \n",
       "8      63.3          1429.0     35.5               -        -               -   \n",
       "9      62.6          1510.0     35.7            14.0      0.3               -   \n",
       "10     64.6          1478.0     34.1               -        -               -   \n",
       "11     67.3          1486.0     31.3               -        -               -   \n",
       "12     67.3          1547.0     31.1               -        -               -   \n",
       "13     67.3          1503.0     29.5            86.0      1.7               -   \n",
       "14     69.3          1388.0     26.8           118.0      2.3               -   \n",
       "15     65.6          1564.0     30.0           151.0      2.9               -   \n",
       "16     64.4          1648.0     31.2           173.0      3.3               -   \n",
       "17     65.2          1620.0     30.0           196.0      3.6               -   \n",
       "18     67.5          1484.0     26.8           243.0      4.4               -   \n",
       "19     70.4          1335.0     23.4           281.0      4.9               -   \n",
       "20     67.0          1459.0     25.9           313.0      5.6            22.0   \n",
       "21     62.8          1602.0     29.0           311.0      5.6            72.0   \n",
       "\n",
       "      남한.11  \n",
       "0        기타  \n",
       "1   구성비 (%)  \n",
       "2         -  \n",
       "3         -  \n",
       "4         -  \n",
       "5         -  \n",
       "6         -  \n",
       "7         -  \n",
       "8         -  \n",
       "9         -  \n",
       "10        -  \n",
       "11        -  \n",
       "12        -  \n",
       "13        -  \n",
       "14        -  \n",
       "15        -  \n",
       "16        -  \n",
       "17        -  \n",
       "18        -  \n",
       "19        -  \n",
       "20      0.4  \n",
       "21      1.3  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ns = df.iloc[[0,5], 3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ns.index = ['south', 'north']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>북한.2</th>\n",
       "      <th>북한.3</th>\n",
       "      <th>북한.4</th>\n",
       "      <th>북한.5</th>\n",
       "      <th>남한</th>\n",
       "      <th>남한.1</th>\n",
       "      <th>남한.2</th>\n",
       "      <th>남한.3</th>\n",
       "      <th>남한.4</th>\n",
       "      <th>남한.5</th>\n",
       "      <th>남한.6</th>\n",
       "      <th>남한.7</th>\n",
       "      <th>남한.8</th>\n",
       "      <th>남한.9</th>\n",
       "      <th>남한.10</th>\n",
       "      <th>남한.11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>south</th>\n",
       "      <td>수력</td>\n",
       "      <td>수력</td>\n",
       "      <td>화력</td>\n",
       "      <td>화력</td>\n",
       "      <td>합계</td>\n",
       "      <td>합계</td>\n",
       "      <td>수력</td>\n",
       "      <td>수력</td>\n",
       "      <td>화력</td>\n",
       "      <td>화력</td>\n",
       "      <td>원자력</td>\n",
       "      <td>원자력</td>\n",
       "      <td>대체에너지</td>\n",
       "      <td>대체에너지</td>\n",
       "      <td>기타</td>\n",
       "      <td>기타</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>north</th>\n",
       "      <td>125.0</td>\n",
       "      <td>60.7</td>\n",
       "      <td>81.0</td>\n",
       "      <td>39.3</td>\n",
       "      <td>3421.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2056.0</td>\n",
       "      <td>60.1</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>38.2</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        북한.2  북한.3  북한.4  북한.5      남한   남한.1  남한.2 남한.3    남한.4  남한.5  \\\n",
       "south     수력    수력    화력    화력      합계     합계    수력   수력      화력    화력   \n",
       "north  125.0  60.7  81.0  39.3  3421.0  100.0  59.0  1.7  2056.0  60.1   \n",
       "\n",
       "         남한.6  남한.7   남한.8   남한.9 남한.10 남한.11  \n",
       "south     원자력   원자력  대체에너지  대체에너지    기타    기타  \n",
       "north  1307.0  38.2      -      -     -     -  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpg = sns.load_dataset(\"mpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</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",
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       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>europe</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0    18.0          8         307.0       130.0    3504          12.0   \n",
       "1    15.0          8         350.0       165.0    3693          11.5   \n",
       "2    18.0          8         318.0       150.0    3436          11.0   \n",
       "3    16.0          8         304.0       150.0    3433          12.0   \n",
       "4    17.0          8         302.0       140.0    3449          10.5   \n",
       "..    ...        ...           ...         ...     ...           ...   \n",
       "393  27.0          4         140.0        86.0    2790          15.6   \n",
       "394  44.0          4          97.0        52.0    2130          24.6   \n",
       "395  32.0          4         135.0        84.0    2295          11.6   \n",
       "396  28.0          4         120.0        79.0    2625          18.6   \n",
       "397  31.0          4         119.0        82.0    2720          19.4   \n",
       "\n",
       "     model_year  origin                       name  \n",
       "0            70     usa  chevrolet chevelle malibu  \n",
       "1            70     usa          buick skylark 320  \n",
       "2            70     usa         plymouth satellite  \n",
       "3            70     usa              amc rebel sst  \n",
       "4            70     usa                ford torino  \n",
       "..          ...     ...                        ...  \n",
       "393          82     usa            ford mustang gl  \n",
       "394          82  europe                  vw pickup  \n",
       "395          82     usa              dodge rampage  \n",
       "396          82     usa                ford ranger  \n",
       "397          82     usa                 chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weight', ylabel='mpg'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg.plot(x = 'weight', y = 'mpg', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg[['mpg', 'cylinders']].plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.13 ('data_science')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "eb7f5e66918d02d82412820c0a4aa3505fa06a6cc37c5a10c65e43bd94ac1a13"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}