4장_판다스_데이터_분석_(4.1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1a439ad5",
   "metadata": {},
   "source": [
    "# 판다스 패키지의 소개"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "479bb09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ecef5b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "서울    9904312\n",
       "부산    3448737\n",
       "인천    2890451\n",
       "대구    2466052\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([9904312, 3448737, 2890451, 2466052], index = [\"서울\", \"부산\", \"인천\", \"대구\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8eba1a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    11\n",
       "2    12\n",
       "3    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(range(10, 14), index = range(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b10ab19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    11\n",
       "2    12\n",
       "3    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(range(10, 14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa0d5b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['서울', '부산', '인천', '대구'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c5a7b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9904312, 3448737, 2890451, 2466052], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66afbd98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "도시\n",
       "서울    9904312\n",
       "부산    3448737\n",
       "인천    2890451\n",
       "대구    2466052\n",
       "Name: 인구, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.name = \"인구\"\n",
    "s.index.name = \"도시\"\n",
    "\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bde071f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "도시\n",
       "서울    9.904312\n",
       "부산    3.448737\n",
       "인천    2.890451\n",
       "대구    2.466052\n",
       "Name: 인구, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s / 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "58f2435e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9904312, 9904312)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0], s[\"서울\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e4f8171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "도시\n",
       "서울    9904312\n",
       "대구    2466052\n",
       "Name: 인구, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[0,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd15de9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "도시\n",
       "서울    9904312\n",
       "대구    2466052\n",
       "부산    3448737\n",
       "Name: 인구, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[\"서울\", \"대구\", \"부산\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "07f8e832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "도시\n",
       "부산    3448737\n",
       "인천    2890451\n",
       "Name: 인구, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[(250e4 < s) & (s <500e4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d7ce1e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "도시\n",
       "부산    3448737\n",
       "인천    2890451\n",
       "대구    2466052\n",
       "Name: 인구, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"부산\":\"대구\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "376d10fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s0 = pd.Series(range(3), index=[\"a\", \"b\", \"c\"])\n",
    "s0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a1551ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s0.a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47ed6180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s0.c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e7a27d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "서울의 인구는 9904312 명입니다.\n",
      "부산의 인구는 3448737 명입니다.\n",
      "인천의 인구는 2890451 명입니다.\n",
      "대구의 인구는 2466052 명입니다.\n"
     ]
    }
   ],
   "source": [
    "for k, v in s.items():\n",
    "    print(\"%s의 인구는 %d 명입니다.\" % (k, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "73b1f47d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "대전    1490158\n",
       "인천    2632035\n",
       "부산    3393191\n",
       "서울    9631482\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series({\"서울\": 9631482, \"부산\": 3393191, \"인천\": 2632035, \"대전\": 1490158}, index = [\"대전\", \"인천\", \"부산\", \"서울\"])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a22edde6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1490158"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6cb53b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9631482"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22243125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "대구         NaN\n",
       "대전         NaN\n",
       "부산     55546.0\n",
       "서울    272830.0\n",
       "인천    258416.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = s - s2\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "acf1259e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8414154,   816702,  -502740, -7165430], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values - s2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "07932944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "부산    1.636984\n",
       "서울    2.832690\n",
       "인천    9.818107\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = (s - s2) / s2 * 100\n",
    "rs = rs[rs.notnull()]\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4a718e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "부산    1.630000\n",
       "서울    2.832690\n",
       "인천    9.818107\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs[\"부산\"] = 1.63\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "98879b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "부산    1.630000\n",
       "서울    2.832690\n",
       "인천    9.818107\n",
       "대구    1.410000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs[\"대구\"] = 1.41\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "361ed379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(rs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "52721a20",
   "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>지역</th>\n",
       "      <th>2015</th>\n",
       "      <th>2010</th>\n",
       "      <th>2005</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010-2015 증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>수도권</td>\n",
       "      <td>9904312</td>\n",
       "      <td>9631482</td>\n",
       "      <td>9762546</td>\n",
       "      <td>9853972</td>\n",
       "      <td>0.0283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>경상권</td>\n",
       "      <td>3448737</td>\n",
       "      <td>3393191</td>\n",
       "      <td>3512547</td>\n",
       "      <td>3655437</td>\n",
       "      <td>0.0163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>수도권</td>\n",
       "      <td>2890451</td>\n",
       "      <td>2632035</td>\n",
       "      <td>2517680</td>\n",
       "      <td>2466338</td>\n",
       "      <td>0.0982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>경상권</td>\n",
       "      <td>2466052</td>\n",
       "      <td>2431774</td>\n",
       "      <td>2456016</td>\n",
       "      <td>2473990</td>\n",
       "      <td>0.0141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역     2015     2010     2005     2000  2010-2015 증가율\n",
       "서울  수도권  9904312  9631482  9762546  9853972         0.0283\n",
       "부산  경상권  3448737  3393191  3512547  3655437         0.0163\n",
       "인천  수도권  2890451  2632035  2517680  2466338         0.0982\n",
       "대구  경상권  2466052  2431774  2456016  2473990         0.0141"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    \"2015\": [9904312, 3448737, 2890451, 2466052],\n",
    "    \"2010\": [9631482, 3393191, 2632035, 2431774],\n",
    "    \"2005\": [9762546, 3512547, 2517680, 2456016],\n",
    "    \"2000\": [9853972, 3655437, 2466338, 2473990],\n",
    "    \"지역\": [\"수도권\", \"경상권\", \"수도권\", \"경상권\"],\n",
    "    \"2010-2015 증가율\": [0.0283, 0.0163, 0.0982, 0.0141]\n",
    "}\n",
    "columns = [\"지역\", \"2015\", \"2010\", \"2005\", \"2000\", \"2010-2015 증가율\"]\n",
    "index = [\"서울\", \"부산\", \"인천\", \"대구\"]\n",
    "df = pd.DataFrame(data, index=index, columns=columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3c0edce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['수도권', 9904312, 9631482, 9762546, 9853972, 0.0283],\n",
       "       ['경상권', 3448737, 3393191, 3512547, 3655437, 0.0163],\n",
       "       ['수도권', 2890451, 2632035, 2517680, 2466338, 0.0982],\n",
       "       ['경상권', 2466052, 2431774, 2456016, 2473990, 0.0141]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bf480232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['지역', '2015', '2010', '2005', '2000', '2010-2015 증가율'], dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e15457a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['서울', '부산', '인천', '대구'], dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1c3050fb",
   "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>League</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Messi</th>\n",
       "      <td>Ligue1</td>\n",
       "      <td>50</td>\n",
       "      <td>47</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ronaldo</th>\n",
       "      <td>EPL</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>EPL</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mane</th>\n",
       "      <td>EPL</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Son</th>\n",
       "      <td>EPL</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         League  2017  2018  2019  2020\n",
       "Messi    Ligue1    50    47    20    32\n",
       "Ronaldo     EPL    40    45    32    12\n",
       "Salah       EPL    43    32    53    73\n",
       "Mane        EPL    28    31    20    29\n",
       "Son         EPL    26    19    12    30"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    \"League\": [\"Ligue1\", \"EPL\", \"EPL\", \"EPL\", \"EPL\"], \n",
    "    \"2017\": [50, 40, 43, 28, 26], \n",
    "    \"2018\": [47, 45, 32, 31, 19], \n",
    "    \"2019\": [20, 32, 53, 20, 12], \n",
    "    \"2020\": [32, 12, 73, 29, 30]\n",
    "}\n",
    "columns = [\"League\", \"2017\", \"2018\", \"2019\", \"2020\"]\n",
    "index = [\"Messi\", \"Ronaldo\", \"Salah\", \"Mane\", \"Son\"]\n",
    "df = pd.DataFrame(data, index = index, columns = columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6756214f",
   "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>지역</th>\n",
       "      <th>2015</th>\n",
       "      <th>2010</th>\n",
       "      <th>2005</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010-2015 증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>수도권</td>\n",
       "      <td>9904312</td>\n",
       "      <td>9631482</td>\n",
       "      <td>9762546</td>\n",
       "      <td>9853972</td>\n",
       "      <td>0.0283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>경상권</td>\n",
       "      <td>3448737</td>\n",
       "      <td>3393191</td>\n",
       "      <td>3512547</td>\n",
       "      <td>3655437</td>\n",
       "      <td>0.0163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>수도권</td>\n",
       "      <td>2890451</td>\n",
       "      <td>2632035</td>\n",
       "      <td>2517680</td>\n",
       "      <td>2466338</td>\n",
       "      <td>0.0982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>경상권</td>\n",
       "      <td>2466052</td>\n",
       "      <td>2431774</td>\n",
       "      <td>2456016</td>\n",
       "      <td>2473990</td>\n",
       "      <td>0.0141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역     2015     2010     2005     2000  2010-2015 증가율\n",
       "서울  수도권  9904312  9631482  9762546  9853972         0.0283\n",
       "부산  경상권  3448737  3393191  3512547  3655437         0.0163\n",
       "인천  수도권  2890451  2632035  2517680  2466338         0.0982\n",
       "대구  경상권  2466052  2431774  2456016  2473990         0.0141"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    \"2015\": [9904312, 3448737, 2890451, 2466052],\n",
    "    \"2010\": [9631482, 3393191, 2632035, 2431774],\n",
    "    \"2005\": [9762546, 3512547, 2517680, 2456016],\n",
    "    \"2000\": [9853972, 3655437, 2466338, 2473990],\n",
    "    \"지역\": [\"수도권\", \"경상권\", \"수도권\", \"경상권\"],\n",
    "    \"2010-2015 증가율\": [0.0283, 0.0163, 0.0982, 0.0141]\n",
    "}\n",
    "columns = [\"지역\", \"2015\", \"2010\", \"2005\", \"2000\", \"2010-2015 증가율\"]\n",
    "index = [\"서울\", \"부산\", \"인천\", \"대구\"]\n",
    "df = pd.DataFrame(data, index=index, columns=columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d4c1427a",
   "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>지역</th>\n",
       "      <th>2015</th>\n",
       "      <th>2010</th>\n",
       "      <th>2005</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010-2015 증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>수도권</td>\n",
       "      <td>9904312</td>\n",
       "      <td>9631482</td>\n",
       "      <td>9762546</td>\n",
       "      <td>9853972</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>경상권</td>\n",
       "      <td>3448737</td>\n",
       "      <td>3393191</td>\n",
       "      <td>3512547</td>\n",
       "      <td>3655437</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>수도권</td>\n",
       "      <td>2890451</td>\n",
       "      <td>2632035</td>\n",
       "      <td>2517680</td>\n",
       "      <td>2466338</td>\n",
       "      <td>9.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>경상권</td>\n",
       "      <td>2466052</td>\n",
       "      <td>2431774</td>\n",
       "      <td>2456016</td>\n",
       "      <td>2473990</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역     2015     2010     2005     2000  2010-2015 증가율\n",
       "서울  수도권  9904312  9631482  9762546  9853972           2.83\n",
       "부산  경상권  3448737  3393191  3512547  3655437           1.63\n",
       "인천  수도권  2890451  2632035  2517680  2466338           9.82\n",
       "대구  경상권  2466052  2431774  2456016  2473990           1.41"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2010-2015 증가율\"] = df[\"2010-2015 증가율\"] * 100\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "53bf4864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역</th>\n",
       "      <th>2015</th>\n",
       "      <th>2010</th>\n",
       "      <th>2005</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010-2015 증가율</th>\n",
       "      <th>2005-2010 증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>수도권</td>\n",
       "      <td>9904312</td>\n",
       "      <td>9631482</td>\n",
       "      <td>9762546</td>\n",
       "      <td>9853972</td>\n",
       "      <td>2.83</td>\n",
       "      <td>-1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>경상권</td>\n",
       "      <td>3448737</td>\n",
       "      <td>3393191</td>\n",
       "      <td>3512547</td>\n",
       "      <td>3655437</td>\n",
       "      <td>1.63</td>\n",
       "      <td>-3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>수도권</td>\n",
       "      <td>2890451</td>\n",
       "      <td>2632035</td>\n",
       "      <td>2517680</td>\n",
       "      <td>2466338</td>\n",
       "      <td>9.82</td>\n",
       "      <td>4.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>경상권</td>\n",
       "      <td>2466052</td>\n",
       "      <td>2431774</td>\n",
       "      <td>2456016</td>\n",
       "      <td>2473990</td>\n",
       "      <td>1.41</td>\n",
       "      <td>-0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역     2015     2010     2005     2000  2010-2015 증가율  2005-2010 증가율\n",
       "서울  수도권  9904312  9631482  9762546  9853972           2.83          -1.34\n",
       "부산  경상권  3448737  3393191  3512547  3655437           1.63          -3.40\n",
       "인천  수도권  2890451  2632035  2517680  2466338           9.82           4.54\n",
       "대구  경상권  2466052  2431774  2456016  2473990           1.41          -0.99"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2005-2010 증가율\"] = ((df[\"2010\"] - df[\"2005\"])/df[\"2005\"] * 100).round(2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "235156ca",
   "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>League</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Messi</th>\n",
       "      <td>Ligue1</td>\n",
       "      <td>50</td>\n",
       "      <td>47</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ronaldo</th>\n",
       "      <td>EPL</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>EPL</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mane</th>\n",
       "      <td>EPL</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Son</th>\n",
       "      <td>EPL</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         League  2017  2018  2019  2020\n",
       "Messi    Ligue1    50    47    20    32\n",
       "Ronaldo     EPL    40    45    32    12\n",
       "Salah       EPL    43    32    53    73\n",
       "Mane        EPL    28    31    20    29\n",
       "Son         EPL    26    19    12    30"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    \"League\": [\"Ligue1\", \"EPL\", \"EPL\", \"EPL\", \"EPL\"], \n",
    "    \"2017\": [50, 40, 43, 28, 26], \n",
    "    \"2018\": [47, 45, 32, 31, 19], \n",
    "    \"2019\": [20, 32, 53, 20, 12], \n",
    "    \"2020\": [32, 12, 73, 29, 30]\n",
    "}\n",
    "columns = [\"League\", \"2017\", \"2018\", \"2019\", \"2020\"]\n",
    "index = [\"Messi\", \"Ronaldo\", \"Salah\", \"Mane\", \"Son\"]\n",
    "df = pd.DataFrame(data, index = index, columns = columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c23c41a2",
   "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>League</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>골 합계</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Messi</th>\n",
       "      <td>Ligue1</td>\n",
       "      <td>50</td>\n",
       "      <td>47</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ronaldo</th>\n",
       "      <td>EPL</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salah</th>\n",
       "      <td>EPL</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mane</th>\n",
       "      <td>EPL</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Son</th>\n",
       "      <td>EPL</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         League  2017  2018  2019  2020  골 합계\n",
       "Messi    Ligue1    50    47    20    32   149\n",
       "Ronaldo     EPL    40    45    32    12   129\n",
       "Salah       EPL    43    32    53    73   201\n",
       "Mane        EPL    28    31    20    29   108\n",
       "Son         EPL    26    19    12    30    87"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"골 합계\"] = df[\"2017\"] + df[\"2018\"] + df[\"2019\"] + df[\"2020\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3f67fbc3",
   "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>지역</th>\n",
       "      <th>2015</th>\n",
       "      <th>2010</th>\n",
       "      <th>2005</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010-2015 증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>수도권</td>\n",
       "      <td>9904312</td>\n",
       "      <td>9631482</td>\n",
       "      <td>9762546</td>\n",
       "      <td>9853972</td>\n",
       "      <td>0.0283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>경상권</td>\n",
       "      <td>3448737</td>\n",
       "      <td>3393191</td>\n",
       "      <td>3512547</td>\n",
       "      <td>3655437</td>\n",
       "      <td>0.0163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>수도권</td>\n",
       "      <td>2890451</td>\n",
       "      <td>2632035</td>\n",
       "      <td>2517680</td>\n",
       "      <td>2466338</td>\n",
       "      <td>0.0982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>경상권</td>\n",
       "      <td>2466052</td>\n",
       "      <td>2431774</td>\n",
       "      <td>2456016</td>\n",
       "      <td>2473990</td>\n",
       "      <td>0.0141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역     2015     2010     2005     2000  2010-2015 증가율\n",
       "서울  수도권  9904312  9631482  9762546  9853972         0.0283\n",
       "부산  경상권  3448737  3393191  3512547  3655437         0.0163\n",
       "인천  수도권  2890451  2632035  2517680  2466338         0.0982\n",
       "대구  경상권  2466052  2431774  2456016  2473990         0.0141"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    \"2015\": [9904312, 3448737, 2890451, 2466052],\n",
    "    \"2010\": [9631482, 3393191, 2632035, 2431774],\n",
    "    \"2005\": [9762546, 3512547, 2517680, 2456016],\n",
    "    \"2000\": [9853972, 3655437, 2466338, 2473990],\n",
    "    \"지역\": [\"수도권\", \"경상권\", \"수도권\", \"경상권\"],\n",
    "    \"2010-2015 증가율\": [0.0283, 0.0163, 0.0982, 0.0141]\n",
    "}\n",
    "columns = [\"지역\", \"2015\", \"2010\", \"2005\", \"2000\", \"2010-2015 증가율\"]\n",
    "index = [\"서울\", \"부산\", \"인천\", \"대구\"]\n",
    "df = pd.DataFrame(data, index=index, columns=columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "744da4f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "서울    수도권\n",
       "부산    경상권\n",
       "인천    수도권\n",
       "대구    경상권\n",
       "Name: 지역, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"지역\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4495e785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "서울    9631482\n",
       "부산    3393191\n",
       "인천    2632035\n",
       "대구    2431774\n",
       "Name: 2010, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2010\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c0bc622a",
   "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>지역</th>\n",
       "      <th>2015</th>\n",
       "      <th>2010</th>\n",
       "      <th>2005</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010-2015 증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>경상권</td>\n",
       "      <td>3448737</td>\n",
       "      <td>3393191</td>\n",
       "      <td>3512547</td>\n",
       "      <td>3655437</td>\n",
       "      <td>0.0163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>수도권</td>\n",
       "      <td>2890451</td>\n",
       "      <td>2632035</td>\n",
       "      <td>2517680</td>\n",
       "      <td>2466338</td>\n",
       "      <td>0.0982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역     2015     2010     2005     2000  2010-2015 증가율\n",
       "부산  경상권  3448737  3393191  3512547  3655437         0.0163\n",
       "인천  수도권  2890451  2632035  2517680  2466338         0.0982"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2a9b9a79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 열 인덱싱은 df[\"열 이름\"]\n",
    "# 행 인덱싱은 df[슬라이싱]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "85ebdf9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9904312"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2015\"][\"서울\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c2239fc6",
   "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>가능</th>\n",
       "      <th>결과</th>\n",
       "      <th>자료형</th>\n",
       "      <th>추가사항</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>라벨</th>\n",
       "      <td>O</td>\n",
       "      <td>열</td>\n",
       "      <td>시리즈</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>라벨 리스트</th>\n",
       "      <td>O</td>\n",
       "      <td>열</td>\n",
       "      <td>데이터프레임</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인덱스데이터(정수)</th>\n",
       "      <td>X</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>열 라벨이 정수인 경우 라벨 인덱싱</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인덱스데이터(정수) 슬라이스</th>\n",
       "      <td>O</td>\n",
       "      <td>행</td>\n",
       "      <td>데이터프레임</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                가능 결과     자료형                 추가사항\n",
       "라벨               O  열     시리즈                    -\n",
       "라벨 리스트           O  열  데이터프레임                    -\n",
       "인덱스데이터(정수)       X  -       -  열 라벨이 정수인 경우 라벨 인덱싱\n",
       "인덱스데이터(정수) 슬라이스  O  행  데이터프레임                    -"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = {\n",
    "    \"가능\": [\"O\", \"O\", \"X\", \"O\"],\n",
    "    \"결과\": [\"열\", \"열\", \"-\", \"행\"],\n",
    "    \"자료형\": [\"시리즈\", \"데이터프레임\", \"-\", \"데이터프레임\"],\n",
    "    \"추가사항\": [\"-\", \"-\", \"열 라벨이 정수인 경우 라벨 인덱싱\", \"-\"]\n",
    "}\n",
    "columns = [\"가능\", \"결과\", \"자료형\", \"추가사항\"]\n",
    "index = [\"라벨\", \"라벨 리스트\", \"인덱스데이터(정수)\", \"인덱스데이터(정수) 슬라이스\"]\n",
    "df3 = pd.DataFrame(data3, index = index, columns = columns)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "08c32632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>가능</th>\n",
       "      <th>결과</th>\n",
       "      <th>자료형</th>\n",
       "      <th>추가사항</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인덱싱 값</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>라벨</th>\n",
       "      <td>O</td>\n",
       "      <td>열</td>\n",
       "      <td>시리즈</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>라벨 리스트</th>\n",
       "      <td>O</td>\n",
       "      <td>열</td>\n",
       "      <td>데이터프레임</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인덱스데이터(정수)</th>\n",
       "      <td>X</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>열 라벨이 정수인 경우 라벨 인덱싱</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인덱스데이터(정수) 슬라이스</th>\n",
       "      <td>O</td>\n",
       "      <td>행</td>\n",
       "      <td>데이터프레임</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                가능 결과     자료형                 추가사항\n",
       "인덱싱 값                                             \n",
       "라벨               O  열     시리즈                    -\n",
       "라벨 리스트           O  열  데이터프레임                    -\n",
       "인덱스데이터(정수)       X  -       -  열 라벨이 정수인 경우 라벨 인덱싱\n",
       "인덱스데이터(정수) 슬라이스  O  행  데이터프레임                    -"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.index.name = \"인덱싱 값\"\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "78eb7f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \"국어\": [80, 90, 70, 30],\n",
    "    \"영어\": [90, 70, 60, 40],\n",
    "    \"수학\": [90, 60, 80, 70],\n",
    "}\n",
    "columns = [\"국어\", \"영어\", \"수학\"]\n",
    "index = [\"춘향\", \"몽룡\", \"향단\", \"방자\"]\n",
    "df = pd.DataFrame(data, index=index, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b11fd309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>춘향</th>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>몽룡</th>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>향단</th>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>방자</th>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    국어  영어  수학\n",
       "춘향  80  90  90\n",
       "몽룡  90  70  60\n",
       "향단  70  60  80\n",
       "방자  30  40  70"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "202f188a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "춘향    90\n",
       "몽룡    60\n",
       "향단    80\n",
       "방자    70\n",
       "Name: 수학, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"수학\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2aded5cf",
   "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>국어</th>\n",
       "      <th>영어</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>춘향</th>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>몽룡</th>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>향단</th>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>방자</th>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    국어  영어\n",
       "춘향  80  90\n",
       "몽룡  90  70\n",
       "향단  70  60\n",
       "방자  30  40"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(data, index = index, columns = [\"국어\", \"영어\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "25564712",
   "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 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>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>평균</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>춘향</th>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>86.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>몽룡</th>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>73.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>향단</th>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>방자</th>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>46.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    국어  영어  수학     평균\n",
       "춘향  80  90  90  86.67\n",
       "몽룡  90  70  60  73.33\n",
       "향단  70  60  80  70.00\n",
       "방자  30  40  70  46.67"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"평균\"] = ((df[\"국어\"] + df[\"영어\"] + df[\"수학\"]) / 3).round(2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8cbb5312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>평균</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>춘향</th>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>86.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>몽룡</th>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>73.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>향단</th>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>방자</th>\n",
       "      <td>30</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    국어  영어  수학     평균\n",
       "춘향  80  90  90  86.67\n",
       "몽룡  90  70  60  73.33\n",
       "향단  70  60  80  70.00\n",
       "방자  30  80  70  60.00"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"영어\"][\"방자\"] == 80\n",
    "df[\"평균\"] = ((df[\"국어\"] + df[\"영어\"] + df[\"수학\"]) / 3).round(2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a002fabc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>춘향</th>\n",
       "      <td>80</td>\n",
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       "      <td>90</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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       "    국어  영어  수학     평균\n",
       "춘향  80  90  90  86.67"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3784f99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "국어    70.0\n",
       "영어    60.0\n",
       "수학    80.0\n",
       "평균    70.0\n",
       "Name: 향단, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T[\"향단\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9366c093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.T[\"향단\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f531024",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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