pandas_ds.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a58f6dbe-d911-42a9-8727-24d2e46d2d8b",
   "metadata": {},
   "source": [
    "# 1. 라이브러리 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68d5337b-f193-4629-9c72-8baefc1f4bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a8f49dd-4df3-4ee0-ba0b-b5facb2ff766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\euhki\\\\Desktop\\\\python_basic_1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "582b3d56-b544-4200-a433-80d721ebdd4e",
   "metadata": {},
   "source": [
    "# 2. 데이터셋 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a9cf601-9a91-4825-9cc6-3c41a35bcc49",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          country continent  year  lifeExp       pop   gdpPercap\n",
      "0     Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
      "1     Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
      "2     Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
      "3     Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
      "4     Afghanistan      Asia  1972   36.088  13079460  739.981106\n",
      "...           ...       ...   ...      ...       ...         ...\n",
      "1699     Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
      "1700     Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
      "1701     Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
      "1702     Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
      "1703     Zimbabwe    Africa  2007   43.487  12311143  469.709298\n",
      "\n",
      "[1704 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"gapminder.tsv\", sep=\"\\t\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda6f3a5-de39-4132-a3dd-2bf6628873e9",
   "metadata": {},
   "source": [
    "# 3. 데이터(데이터프레임) 이해하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe8314bd-20e3-41a5-a947-f14709be9899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# df의 자료형 확인\n",
    "print(type(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd43b648-3720-49bd-a3bd-c53e3fee3832",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1704, 6)\n"
     ]
    }
   ],
   "source": [
    "# 데이터프레임의 행과 열의 개수 확인\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8de6de38-1924-4883-bd43-9d463ec4d0aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 데이터프레임의 열 정보 확인\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d65482e4-7b03-4e1e-8333-77731f8027e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country       object\n",
      "continent     object\n",
      "year           int64\n",
      "lifeExp      float64\n",
      "pop            int64\n",
      "gdpPercap    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 데이터프레임의 각 열이 어떤 자료형인지 확인\n",
    "# 데이터프레임 객체의 각 열은 동일 자료형\n",
    "# 데이터프레임 객체의 각 행은 이종의 자료형\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d00d27a-a6f6-4c71-9e88-944f0eba03c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1704 entries, 0 to 1703\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   country    1704 non-null   object \n",
      " 1   continent  1704 non-null   object \n",
      " 2   year       1704 non-null   int64  \n",
      " 3   lifeExp    1704 non-null   float64\n",
      " 4   pop        1704 non-null   int64  \n",
      " 5   gdpPercap  1704 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 80.0+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1564630b-e0f4-4697-9ff0-db1a026d28c4",
   "metadata": {},
   "source": [
    "# 4. 데이터 추출 : 열 데이터 추출하기\n",
    "* df['특정_열_이름']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "604e6ff9-0cff-486b-868b-010ec81b4878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Afghanistan\n",
       "1       Afghanistan\n",
       "2       Afghanistan\n",
       "3       Afghanistan\n",
       "4       Afghanistan\n",
       "           ...     \n",
       "1699       Zimbabwe\n",
       "1700       Zimbabwe\n",
       "1701       Zimbabwe\n",
       "1702       Zimbabwe\n",
       "1703       Zimbabwe\n",
       "Name: country, Length: 1704, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df에서 country 열 데이터 추출하고 그 결과를 country_df 변수에 저장\n",
    "country_df = df['country']\n",
    "country_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9759a5b-a29f-453c-8727-ce41d07f815d",
   "metadata": {},
   "source": [
    "## 4.1 특정 열 탐색하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7541bd55-0d2d-4e9f-8166-34435f700ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 특정 열 데이터 탐색하기\n",
    "type(country_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44ba9208-94a5-4b60-b440-358b7d59682e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Afghanistan\n",
       "1    Afghanistan\n",
       "2    Afghanistan\n",
       "3    Afghanistan\n",
       "4    Afghanistan\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6f4d75b-1fb8-4141-afa8-b3097a84471d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1699    Zimbabwe\n",
       "1700    Zimbabwe\n",
       "1701    Zimbabwe\n",
       "1702    Zimbabwe\n",
       "1703    Zimbabwe\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "960ca339-ed03-433b-a20a-590deeb3b10e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1704,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e79e31-b55e-49d4-8f40-7a42fa28fdbe",
   "metadata": {},
   "source": [
    "## 4.2. 리스트로 여러 열 데이터 추출하기\n",
    "* df[['특정_열_이름_리스트']]\n",
    "  * 바깥쪽 대괄호 : 데이터프레임에서 열을 추출\n",
    "  * 안쪽 대괄호 : 추출할 열 목록을 지정한 파이썬 리스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c8ac988a-ce0c-4eac-ace7-09fb5163ce08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year\n",
       "0     Afghanistan      Asia  1952\n",
       "1     Afghanistan      Asia  1957\n",
       "2     Afghanistan      Asia  1962\n",
       "3     Afghanistan      Asia  1967\n",
       "4     Afghanistan      Asia  1972\n",
       "...           ...       ...   ...\n",
       "1699     Zimbabwe    Africa  1987\n",
       "1700     Zimbabwe    Africa  1992\n",
       "1701     Zimbabwe    Africa  1997\n",
       "1702     Zimbabwe    Africa  2002\n",
       "1703     Zimbabwe    Africa  2007\n",
       "\n",
       "[1704 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df에서 country, continent, year 3개의 열 데이터 추출하고 그 결과를 sub_df에 저장하기\n",
    "sub_df = df[['country','continent','year']]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da12fc3c-eb45-463b-be24-e2799d1ae42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # 탐색하기\n",
    "type(sub_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4dee7acb-2946-4376-a321-faea8cdf9827",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       country continent  year\n",
       "0  Afghanistan      Asia  1952\n",
       "1  Afghanistan      Asia  1957\n",
       "2  Afghanistan      Asia  1962"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dadf56a1-0138-41ef-a2ff-2557c0715f4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       country continent  year\n",
       "1701  Zimbabwe    Africa  1997\n",
       "1702  Zimbabwe    Africa  2002\n",
       "1703  Zimbabwe    Africa  2007"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "beee3fe0-a5c7-4daf-84b6-860a53f2d28d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1704 entries, 0 to 1703\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   country    1704 non-null   object\n",
      " 1   continent  1704 non-null   object\n",
      " 2   year       1704 non-null   int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 40.1+ KB\n"
     ]
    }
   ],
   "source": [
    "sub_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "48bcef33-b754-451f-ae06-a00a46bc8fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1704.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1979.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.26533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1952.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1965.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1979.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1993.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2007.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year\n",
       "count  1704.00000\n",
       "mean   1979.50000\n",
       "std      17.26533\n",
       "min    1952.00000\n",
       "25%    1965.75000\n",
       "50%    1979.50000\n",
       "75%    1993.25000\n",
       "max    2007.00000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80fb54ba-0b1e-4df5-b0a2-9a1af5c548fb",
   "metadata": {},
   "source": [
    "# 4. 데이터 추출 : 특정 행 데이터 추출하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "edd5e17a-472d-4afd-aafb-3a868c2b8d37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          country continent  year  lifeExp       pop   gdpPercap\n",
      "0     Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
      "1     Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
      "2     Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
      "3     Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
      "4     Afghanistan      Asia  1972   36.088  13079460  739.981106\n",
      "...           ...       ...   ...      ...       ...         ...\n",
      "1699     Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
      "1700     Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
      "1701     Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
      "1702     Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
      "1703     Zimbabwe    Africa  2007   43.487  12311143  469.709298\n",
      "\n",
      "[1704 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fbcc65d3-3e20-4ebc-86fc-f2dbc800dcb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      Afghanistan\n",
       "continent           Asia\n",
       "year                1952\n",
       "lifeExp           28.801\n",
       "pop              8425333\n",
       "gdpPercap     779.445314\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 첫 번째 행 데이터 추출: df.loc[인덱스]\n",
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a4e56154-178e-4cc2-b85a-0bd3c31410b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      Bangladesh\n",
       "continent          Asia\n",
       "year               1967\n",
       "lifeExp          43.453\n",
       "pop            62821884\n",
       "gdpPercap    721.186086\n",
       "Name: 99, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 100번째 행 데이터 추출하기\n",
    "df.loc[99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8fb82a2d-3997-47bb-9133-a77db0820ab3",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "-1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32m~\\Desktop\\pydata-book\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:413\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    412\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 413\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_range\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    414\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[1;31mValueError\u001b[0m: -1 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\pydata-book\\venv\\Lib\\site-packages\\pandas\\core\\indexing.py:1191\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1189\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m   1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[1;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\pydata-book\\venv\\Lib\\site-packages\\pandas\\core\\indexing.py:1431\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1429\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[0;32m   1430\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1431\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_label\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\pydata-book\\venv\\Lib\\site-packages\\pandas\\core\\indexing.py:1381\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1379\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: AxisInt):\n\u001b[0;32m   1380\u001b[0m     \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[1;32m-> 1381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\pydata-book\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:4298\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   4296\u001b[0m             new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[0;32m   4297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4298\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4300\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m   4301\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n",
      "File \u001b[1;32m~\\Desktop\\pydata-book\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:415\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    413\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m    414\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 415\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[0;32m    417\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: -1"
     ]
    }
   ],
   "source": [
    "df.loc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "508de295-ba28-4960-b90f-3a713a0819a3",
   "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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>43.453</td>\n",
       "      <td>62821884</td>\n",
       "      <td>721.186086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>51.253</td>\n",
       "      <td>1149500</td>\n",
       "      <td>1226.041130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country continent  year  lifeExp       pop    gdpPercap\n",
       "0    Afghanistan      Asia  1952   28.801   8425333   779.445314\n",
       "99    Bangladesh      Asia  1967   43.453  62821884   721.186086\n",
       "999     Mongolia      Asia  1967   51.253   1149500  1226.041130"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 리스트로 행 데이터 여러 개 추출하기\n",
    "sub_df = df.loc[[0, 99, 999]]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "470795a8-02e1-42b5-9a3e-bf940c1fa269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 탐색하기\n",
    "type(sub_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9bfc4cc0-81b2-43a7-9902-8eae2ff1e3f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap'], dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 컬럼 확인\n",
    "sub_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6fb592e2-10be-404f-88c8-9b8142bce0b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3 entries, 0 to 999\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   country    3 non-null      object \n",
      " 1   continent  3 non-null      object \n",
      " 2   year       3 non-null      int64  \n",
      " 3   lifeExp    3 non-null      float64\n",
      " 4   pop        3 non-null      int64  \n",
      " 5   gdpPercap  3 non-null      float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 168.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# 행 데이터 타입 확인\n",
    "sub_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80d257eb-8ac3-408b-b3d4-4148a9a186ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1962.000000</td>\n",
       "      <td>41.169000</td>\n",
       "      <td>2.413224e+07</td>\n",
       "      <td>908.890844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.660254</td>\n",
       "      <td>11.398928</td>\n",
       "      <td>3.370313e+07</td>\n",
       "      <td>276.200584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1952.000000</td>\n",
       "      <td>28.801000</td>\n",
       "      <td>1.149500e+06</td>\n",
       "      <td>721.186086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1959.500000</td>\n",
       "      <td>36.127000</td>\n",
       "      <td>4.787416e+06</td>\n",
       "      <td>750.315700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1967.000000</td>\n",
       "      <td>43.453000</td>\n",
       "      <td>8.425333e+06</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1967.000000</td>\n",
       "      <td>47.353000</td>\n",
       "      <td>3.562361e+07</td>\n",
       "      <td>1002.743222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1967.000000</td>\n",
       "      <td>51.253000</td>\n",
       "      <td>6.282188e+07</td>\n",
       "      <td>1226.041130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year    lifeExp           pop    gdpPercap\n",
       "count     3.000000   3.000000  3.000000e+00     3.000000\n",
       "mean   1962.000000  41.169000  2.413224e+07   908.890844\n",
       "std       8.660254  11.398928  3.370313e+07   276.200584\n",
       "min    1952.000000  28.801000  1.149500e+06   721.186086\n",
       "25%    1959.500000  36.127000  4.787416e+06   750.315700\n",
       "50%    1967.000000  43.453000  8.425333e+06   779.445314\n",
       "75%    1967.000000  47.353000  3.562361e+07  1002.743222\n",
       "max    1967.000000  51.253000  6.282188e+07  1226.041130"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터프레임 기술 통계 확인\n",
    "sub_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468f6b85-8f5a-4fc7-87ea-4c730bc6698d",
   "metadata": {},
   "source": [
    "## 5. 모든 행과 특정 열 데이터을 함께 추출하기\n",
    "* df.loc[[행_이름],[열_이름]]\n",
    "* df.iloc[[행_위치],[열_위치]]\n",
    "  * 슬라이싱 구문 사용\n",
    "    * df.loc[:,[열]]은 특정 열의 모든 행 데이터을 의미\n",
    "  * range함수 사용"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43942106-1d79-4a84-9452-88e3fc81daf5",
   "metadata": {},
   "source": [
    "### 5.1 슬라이싱 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7bb738f5-a298-4e21-ab02-cdac46c05f6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year       pop\n",
       "0     1952   8425333\n",
       "1     1957   9240934\n",
       "2     1962  10267083\n",
       "3     1967  11537966\n",
       "4     1972  13079460\n",
       "...    ...       ...\n",
       "1699  1987   9216418\n",
       "1700  1992  10704340\n",
       "1701  1997  11404948\n",
       "1702  2002  11926563\n",
       "1703  2007  12311143\n",
       "\n",
       "[1704 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc로 year와 pop 열 데이터 추출하여 변수 sub_df에 저장하기\n",
    "# 이때 콜론으로 행을 지정하여 year와 pop 열의 모든 행 데이터을 가져오기\n",
    "sub_df = df.loc[:,['year','pop']]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c08df4a7-96d2-49c2-965c-de85ac58abda",
   "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>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year       pop   gdpPercap\n",
       "0     1952   8425333  779.445314\n",
       "1     1957   9240934  820.853030\n",
       "2     1962  10267083  853.100710\n",
       "3     1967  11537966  836.197138\n",
       "4     1972  13079460  739.981106\n",
       "...    ...       ...         ...\n",
       "1699  1987   9216418  706.157306\n",
       "1700  1992  10704340  693.420786\n",
       "1701  1997  11404948  792.449960\n",
       "1702  2002  11926563  672.038623\n",
       "1703  2007  12311143  469.709298\n",
       "\n",
       "[1704 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 3, 5번째와 마지막 열 데이터 추출하기\n",
    "# iloc로 콜론을 사용하여 모든 행 데이터 가져오기\n",
    "sub_df = df.iloc[: ,[2,4,-1]]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5725023-689a-45df-99ed-7fea96c54c39",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index([2, 4, -1], dtype='int32')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Note:\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m sub_df \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m      3\u001b[0m sub_df\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1184\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[0;32m   1183\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[1;32m-> 1184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1185\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1186\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[0;32m   1187\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1377\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1374\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take_opportunity(tup):\n\u001b[0;32m   1375\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take(tup)\n\u001b[1;32m-> 1377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple_same_dim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1020\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_tuple_same_dim\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1017\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_null_slice(key):\n\u001b[0;32m   1018\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m-> 1020\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mretval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1021\u001b[0m \u001b[38;5;66;03m# We should never have retval.ndim < self.ndim, as that should\u001b[39;00m\n\u001b[0;32m   1022\u001b[0m \u001b[38;5;66;03m#  be handled by the _getitem_lowerdim call above.\u001b[39;00m\n\u001b[0;32m   1023\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m retval\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1420\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1417\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m key\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1418\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index with multidimensional key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1420\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1422\u001b[0m \u001b[38;5;66;03m# nested tuple slicing\u001b[39;00m\n\u001b[0;32m   1423\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_nested_tuple(key, labels):\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1360\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1357\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m   1359\u001b[0m \u001b[38;5;66;03m# A collection of keys\u001b[39;00m\n\u001b[1;32m-> 1360\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1361\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[0;32m   1362\u001b[0m     {axis: [keyarr, indexer]}, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   1363\u001b[0m )\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1558\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1555\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[0;32m   1556\u001b[0m axis_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis_name(axis)\n\u001b[1;32m-> 1558\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1560\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6198\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m   6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m   6204\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[0;32m   6248\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[1;32m-> 6249\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6251\u001b[0m     not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m   6252\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index([2, 4, -1], dtype='int32')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "# Note:\n",
    "sub_df = df.loc[: ,[2,4,-1]]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ea68a8c4-2e3d-42e4-8647-992fae0451a0",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": ".iloc requires numeric indexers, got ['year' 'pop']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[30], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Note: \u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m sub_df \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43myear\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpop\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m      3\u001b[0m sub_df\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1184\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[0;32m   1183\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[1;32m-> 1184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1185\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1186\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[0;32m   1187\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1690\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1689\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_tuple\u001b[39m(\u001b[38;5;28mself\u001b[39m, tup: \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m-> 1690\u001b[0m     tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_tuple_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1691\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[0;32m   1692\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_lowerdim(tup)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:966\u001b[0m, in \u001b[0;36m_LocationIndexer._validate_tuple_indexer\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    964\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(key):\n\u001b[0;32m    965\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 966\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_key\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    967\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m    968\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    969\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLocation based indexing can only have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    970\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_valid_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] types\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    971\u001b[0m         ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1608\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_key\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1606\u001b[0m \u001b[38;5;66;03m# check that the key has a numeric dtype\u001b[39;00m\n\u001b[0;32m   1607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_numeric_dtype(arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[1;32m-> 1608\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.iloc requires numeric indexers, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00marr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1610\u001b[0m \u001b[38;5;66;03m# check that the key does not exceed the maximum size of the index\u001b[39;00m\n\u001b[0;32m   1611\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(arr) \u001b[38;5;129;01mand\u001b[39;00m (arr\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmin() \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis):\n",
      "\u001b[1;31mIndexError\u001b[0m: .iloc requires numeric indexers, got ['year' 'pop']"
     ]
    }
   ],
   "source": [
    "#Note: \n",
    "sub_df = df.iloc[: ,['year','pop']]\n",
    "sub_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abb4c57-753e-41e3-94da-47692fd46d96",
   "metadata": {},
   "source": [
    "### 5.2 iloc 속성과 range함수 사용하여 데이터 추출\n",
    "* range()에 시작값과 끝값을 입력하면 자동으로 시작값 이상, 끝값 미만인 값을 생성\n",
    "* 간격을 지정하여 생성하는 숫자를 조절할 수 있다.\n",
    "* range()함수는 리스트가 아닌 정해진 범위의 수를 반환하는 제너레이터.\n",
    "* 제너레이터는 그때마다 값을 생성하며 한 번 사용한 값은 메모리에서 사라짐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4235207f-97f2-419f-98ca-27641e4473e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop\n",
       "0     Afghanistan      Asia  1952   28.801   8425333\n",
       "1     Afghanistan      Asia  1957   30.332   9240934\n",
       "2     Afghanistan      Asia  1962   31.997  10267083\n",
       "3     Afghanistan      Asia  1967   34.020  11537966\n",
       "4     Afghanistan      Asia  1972   36.088  13079460\n",
       "...           ...       ...   ...      ...       ...\n",
       "1699     Zimbabwe    Africa  1987   62.351   9216418\n",
       "1700     Zimbabwe    Africa  1992   60.377  10704340\n",
       "1701     Zimbabwe    Africa  1997   46.809  11404948\n",
       "1702     Zimbabwe    Africa  2002   39.989  11926563\n",
       "1703     Zimbabwe    Africa  2007   43.487  12311143\n",
       "\n",
       "[1704 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. range(n)의 반환값 0이상 n 미만의 정수를 반환하는 제너레이터 생성\n",
    "# 2. 제너레이터를 리스트로 변환\n",
    "# 3. iloc 속성에 사용\n",
    "small_range = list(range(5))\n",
    "sub_df = df.iloc[:, small_range]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "09684373-24dc-4203-8fb9-81e85c24fbe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.088</td>\n",
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       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      lifeExp       pop   gdpPercap\n",
       "0      28.801   8425333  779.445314\n",
       "1      30.332   9240934  820.853030\n",
       "2      31.997  10267083  853.100710\n",
       "3      34.020  11537966  836.197138\n",
       "4      36.088  13079460  739.981106\n",
       "...       ...       ...         ...\n",
       "1699   62.351   9216418  706.157306\n",
       "1700   60.377  10704340  693.420786\n",
       "1701   46.809  11404948  792.449960\n",
       "1702   39.989  11926563  672.038623\n",
       "1703   43.487  12311143  469.709298\n",
       "\n",
       "[1704 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모든 행의 3,4,5 번 열을 추출하기 : range함수\n",
    "small_range = list(range(3,6))\n",
    "sub_df = df.iloc[:, small_range]\n",
    "sub_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "339229f4-51d4-4de1-a562-af6f1de2e7d1",
   "metadata": {},
   "source": [
    "### 5.3 iloc 속성과 슬라이싱 구문을 사용하여 데이터 추출\n",
    "* range함수에 전달한 인자를 : 기호로 구분하여 대괄호에 작성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9c060a40-be46-4b33-ae42-cb1005da7cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1957</td>\n",
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       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
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       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>1967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year\n",
       "0     Afghanistan      Asia  1952\n",
       "1     Afghanistan      Asia  1957\n",
       "2     Afghanistan      Asia  1962\n",
       "3     Afghanistan      Asia  1967\n",
       "4     Afghanistan      Asia  1972\n",
       "...           ...       ...   ...\n",
       "1699     Zimbabwe    Africa  1987\n",
       "1700     Zimbabwe    Africa  1992\n",
       "1701     Zimbabwe    Africa  1997\n",
       "1702     Zimbabwe    Africa  2002\n",
       "1703     Zimbabwe    Africa  2007\n",
       "\n",
       "[1704 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 슬라이싱 구문 으로 추출\n",
    "sub_df = df.iloc[:, :3]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9a185a13-3885-48a9-9ea9-83d35e4e338c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>853.100710</td>\n",
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       "      <th>3</th>\n",
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       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1699</th>\n",
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       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      lifeExp       pop   gdpPercap\n",
       "0      28.801   8425333  779.445314\n",
       "1      30.332   9240934  820.853030\n",
       "2      31.997  10267083  853.100710\n",
       "3      34.020  11537966  836.197138\n",
       "4      36.088  13079460  739.981106\n",
       "...       ...       ...         ...\n",
       "1699   62.351   9216418  706.157306\n",
       "1700   60.377  10704340  693.420786\n",
       "1701   46.809  11404948  792.449960\n",
       "1702   39.989  11926563  672.038623\n",
       "1703   43.487  12311143  469.709298\n",
       "\n",
       "[1704 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모든 행의 3 이상과 6 미만의 범위에 해당하는 열 데이터 추출\n",
    "sub_df = df.iloc[:, 3:6]\n",
    "sub_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125d14b0-4735-47e5-9493-6f2a78b0962e",
   "metadata": {},
   "source": [
    "### 5.4 여러 행과 열 지정하여 데이터 추출하기\n",
    "* 열 데이터 추출은 모든 행을 대상 \n",
    "    * 모두 loc와iloc 의 대괄호에서 쉼표 왼쪽에 : 기호 사용\n",
    "* 특정 행과 열에 있는 데이터 추출\n",
    "    * 모두 loc와iloc 의 대괄호에서 쉼표 왼쪽에 값 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ef1b4c8-e623-40c1-b10a-34b0d8ebe3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>43.453</td>\n",
       "      <td>721.186086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>51.253</td>\n",
       "      <td>1226.041130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  lifeExp    gdpPercap\n",
       "0    Afghanistan   28.801   779.445314\n",
       "99    Bangladesh   43.453   721.186086\n",
       "999     Mongolia   51.253  1226.041130"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc 사용 : 첫 번째, 4번째, 6번째 열의 첫 번째, 100번째, 1000번째 행 데이터 추출하기\n",
    "sub_df = df.iloc[[0,99,999],[0,3,5]]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a594af95-b52b-4346-9d13-d7307a637da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>country</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>43.453</td>\n",
       "      <td>721.186086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>51.253</td>\n",
       "      <td>1226.041130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  lifeExp    gdpPercap\n",
       "0    Afghanistan   28.801   779.445314\n",
       "99    Bangladesh   43.453   721.186086\n",
       "999     Mongolia   51.253  1226.041130"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc 사용 : 첫 번째, 4번째, 6번째 열의 첫 번째, 100번째, 1000번째 행 데이터 추출하기\n",
    "sub_df = df.loc[[0,99,999],['country','lifeExp','gdpPercap']]\n",
    "sub_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "20b5a451-6439-4521-8709-4721d9441425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        country continent  year  lifeExp       pop    gdpPercap\n",
      "10  Afghanistan      Asia  2002   42.129  25268405   726.734055\n",
      "11  Afghanistan      Asia  2007   43.828  31889923   974.580338\n",
      "12      Albania    Europe  1952   55.230   1282697  1601.056136\n",
      "13      Albania    Europe  1957   59.280   1476505  1942.284244\n",
      "\n",
      "        country continent  year  lifeExp       pop    gdpPercap\n",
      "10  Afghanistan      Asia  2002   42.129  25268405   726.734055\n",
      "11  Afghanistan      Asia  2007   43.828  31889923   974.580338\n",
      "12      Albania    Europe  1952   55.230   1282697  1601.056136\n"
     ]
    }
   ],
   "source": [
    "# iloc & loc 사용 : 10번째부터 13번째 행의 모든 열 데이터 추출하기\n",
    "#<주의> \n",
    "# loc는 콜론 오른쪽 번호를 결과에 포함\n",
    "# iloc는 콜론 오른쪽 번호를 결과에에서 제외\n",
    "sub1_df = df.loc[10:13, :]\n",
    "print(sub1_df)\n",
    "print()\n",
    "sub2_df = df.iloc[10:13, :]\n",
    "print(sub2_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4451053c-063d-4a1b-8ac6-4b9b9bfb94d0",
   "metadata": {},
   "source": [
    "# 6. 데이터 집계\n",
    "* 추출한 데이터을 대상으로 기초 통계 계산\n",
    "* 그룹화한 데이터의 평균\n",
    "* 2 개 이상의 열 그룹화 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "88584a6c-c3e5-4e43-904a-8c14676e00f4",
   "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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop   gdpPercap\n",
       "0     Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1     Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2     Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3     Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4     Afghanistan      Asia  1972   36.088  13079460  739.981106\n",
       "...           ...       ...   ...      ...       ...         ...\n",
       "1699     Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
       "1700     Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
       "1701     Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
       "1702     Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
       "1703     Zimbabwe    Africa  2007   43.487  12311143  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7125e97b-6036-49d5-adfd-563e08a61db3",
   "metadata": {},
   "source": [
    "### <1. 열 그룹화 하기>\n",
    "* groupby메서드 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e101ac2e-9124-45b3-a2e0-94f3b3ce9dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1952    49.057620\n",
       "1957    51.507401\n",
       "1962    53.609249\n",
       "1967    55.678290\n",
       "1972    57.647386\n",
       "1977    59.570157\n",
       "1982    61.533197\n",
       "1987    63.212613\n",
       "1992    64.160338\n",
       "1997    65.014676\n",
       "2002    65.694923\n",
       "2007    67.007423\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연도별 기대 수명 계산\n",
    "# 1. 데이터를 연도별 그룹화\n",
    "grouped_year_df = df.groupby('year') #DataFrameGroupBy\n",
    "type(grouped_year_df)\n",
    "\n",
    "# 2. lifeExp 열을 선택\n",
    "grouped_year_df_lifeExp = grouped_year_df['lifeExp'] # SeriesGroupBy\n",
    "type(grouped_year_df_lifeExp)\n",
    "\n",
    "# 3. 평균 구하기\n",
    "mean_lifeExp_by_year = grouped_year_df_lifeExp.mean()\n",
    "mean_lifeExp_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a81c0cc4-f696-4098-a0bb-6385b67bce05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1952    49.057620\n",
       "1957    51.507401\n",
       "1962    53.609249\n",
       "1967    55.678290\n",
       "1972    57.647386\n",
       "1977    59.570157\n",
       "1982    61.533197\n",
       "1987    63.212613\n",
       "1992    64.160338\n",
       "1997    65.014676\n",
       "2002    65.694923\n",
       "2007    67.007423\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 한 줄 명령 구문\n",
    "mean_lifeExp_by_year = df.groupby('year')['lifeExp'].mean()\n",
    "mean_lifeExp_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f559ab6d-4154-4f1c-b63c-8e3743ef4585",
   "metadata": {},
   "source": [
    "### <2. 2개 이상 열 그룹화 하기>\n",
    "* 여러 열에 같은 연산을 적용\n",
    "* 다층 계층 구조로 구성\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5aa0216c-dd83-4a6e-b2b0-2ae4341ebe54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1952</th>\n",
       "      <th>Africa</th>\n",
       "      <td>39.135500</td>\n",
       "      <td>1252.572466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>53.279840</td>\n",
       "      <td>4079.062552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>46.314394</td>\n",
       "      <td>5195.484004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>64.408500</td>\n",
       "      <td>5661.057435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>69.255000</td>\n",
       "      <td>10298.085650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1957</th>\n",
       "      <th>Africa</th>\n",
       "      <td>41.266346</td>\n",
       "      <td>1385.236062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>55.960280</td>\n",
       "      <td>4616.043733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>49.318544</td>\n",
       "      <td>5787.732940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>66.703067</td>\n",
       "      <td>6963.012816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>70.295000</td>\n",
       "      <td>11598.522455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1962</th>\n",
       "      <th>Africa</th>\n",
       "      <td>43.319442</td>\n",
       "      <td>1598.078825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>58.398760</td>\n",
       "      <td>4901.541870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>51.563223</td>\n",
       "      <td>5729.369625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>68.539233</td>\n",
       "      <td>8365.486814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.085000</td>\n",
       "      <td>12696.452430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1967</th>\n",
       "      <th>Africa</th>\n",
       "      <td>45.334538</td>\n",
       "      <td>2050.363801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>60.410920</td>\n",
       "      <td>5668.253496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>54.663640</td>\n",
       "      <td>5971.173374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>69.737600</td>\n",
       "      <td>10143.823757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.310000</td>\n",
       "      <td>14495.021790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1972</th>\n",
       "      <th>Africa</th>\n",
       "      <td>47.450942</td>\n",
       "      <td>2339.615674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>62.394920</td>\n",
       "      <td>6491.334139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>57.319269</td>\n",
       "      <td>8187.468699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>70.775033</td>\n",
       "      <td>12479.575246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.910000</td>\n",
       "      <td>16417.333380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1977</th>\n",
       "      <th>Africa</th>\n",
       "      <td>49.580423</td>\n",
       "      <td>2585.938508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>64.391560</td>\n",
       "      <td>7352.007126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>59.610556</td>\n",
       "      <td>7791.314020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>71.937767</td>\n",
       "      <td>14283.979110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>72.855000</td>\n",
       "      <td>17283.957605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1982</th>\n",
       "      <th>Africa</th>\n",
       "      <td>51.592865</td>\n",
       "      <td>2481.592960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>66.228840</td>\n",
       "      <td>7506.737088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>62.617939</td>\n",
       "      <td>7434.135157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>72.806400</td>\n",
       "      <td>15617.896551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>74.290000</td>\n",
       "      <td>18554.709840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1987</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.344788</td>\n",
       "      <td>2282.668991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>68.090720</td>\n",
       "      <td>7793.400261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>64.851182</td>\n",
       "      <td>7608.226508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>73.642167</td>\n",
       "      <td>17214.310727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>75.320000</td>\n",
       "      <td>20448.040160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1992</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.629577</td>\n",
       "      <td>2281.810333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>69.568360</td>\n",
       "      <td>8044.934406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>66.537212</td>\n",
       "      <td>8639.690248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>74.440100</td>\n",
       "      <td>17061.568084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>76.945000</td>\n",
       "      <td>20894.045885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1997</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.598269</td>\n",
       "      <td>2378.759555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>71.150480</td>\n",
       "      <td>8889.300863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>68.020515</td>\n",
       "      <td>9834.093295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>75.505167</td>\n",
       "      <td>19076.781802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>78.190000</td>\n",
       "      <td>24024.175170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2002</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.325231</td>\n",
       "      <td>2599.385159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>72.422040</td>\n",
       "      <td>9287.677107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>69.233879</td>\n",
       "      <td>10174.090397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>76.700600</td>\n",
       "      <td>21711.732422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>79.740000</td>\n",
       "      <td>26938.778040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2007</th>\n",
       "      <th>Africa</th>\n",
       "      <td>54.806038</td>\n",
       "      <td>3089.032605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>73.608120</td>\n",
       "      <td>11003.031625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>70.728485</td>\n",
       "      <td>12473.026870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>77.648600</td>\n",
       "      <td>25054.481636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>80.719500</td>\n",
       "      <td>29810.188275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lifeExp     gdpPercap\n",
       "year continent                         \n",
       "1952 Africa     39.135500   1252.572466\n",
       "     Americas   53.279840   4079.062552\n",
       "     Asia       46.314394   5195.484004\n",
       "     Europe     64.408500   5661.057435\n",
       "     Oceania    69.255000  10298.085650\n",
       "1957 Africa     41.266346   1385.236062\n",
       "     Americas   55.960280   4616.043733\n",
       "     Asia       49.318544   5787.732940\n",
       "     Europe     66.703067   6963.012816\n",
       "     Oceania    70.295000  11598.522455\n",
       "1962 Africa     43.319442   1598.078825\n",
       "     Americas   58.398760   4901.541870\n",
       "     Asia       51.563223   5729.369625\n",
       "     Europe     68.539233   8365.486814\n",
       "     Oceania    71.085000  12696.452430\n",
       "1967 Africa     45.334538   2050.363801\n",
       "     Americas   60.410920   5668.253496\n",
       "     Asia       54.663640   5971.173374\n",
       "     Europe     69.737600  10143.823757\n",
       "     Oceania    71.310000  14495.021790\n",
       "1972 Africa     47.450942   2339.615674\n",
       "     Americas   62.394920   6491.334139\n",
       "     Asia       57.319269   8187.468699\n",
       "     Europe     70.775033  12479.575246\n",
       "     Oceania    71.910000  16417.333380\n",
       "1977 Africa     49.580423   2585.938508\n",
       "     Americas   64.391560   7352.007126\n",
       "     Asia       59.610556   7791.314020\n",
       "     Europe     71.937767  14283.979110\n",
       "     Oceania    72.855000  17283.957605\n",
       "1982 Africa     51.592865   2481.592960\n",
       "     Americas   66.228840   7506.737088\n",
       "     Asia       62.617939   7434.135157\n",
       "     Europe     72.806400  15617.896551\n",
       "     Oceania    74.290000  18554.709840\n",
       "1987 Africa     53.344788   2282.668991\n",
       "     Americas   68.090720   7793.400261\n",
       "     Asia       64.851182   7608.226508\n",
       "     Europe     73.642167  17214.310727\n",
       "     Oceania    75.320000  20448.040160\n",
       "1992 Africa     53.629577   2281.810333\n",
       "     Americas   69.568360   8044.934406\n",
       "     Asia       66.537212   8639.690248\n",
       "     Europe     74.440100  17061.568084\n",
       "     Oceania    76.945000  20894.045885\n",
       "1997 Africa     53.598269   2378.759555\n",
       "     Americas   71.150480   8889.300863\n",
       "     Asia       68.020515   9834.093295\n",
       "     Europe     75.505167  19076.781802\n",
       "     Oceania    78.190000  24024.175170\n",
       "2002 Africa     53.325231   2599.385159\n",
       "     Americas   72.422040   9287.677107\n",
       "     Asia       69.233879  10174.090397\n",
       "     Europe     76.700600  21711.732422\n",
       "     Oceania    79.740000  26938.778040\n",
       "2007 Africa     54.806038   3089.032605\n",
       "     Americas   73.608120  11003.031625\n",
       "     Asia       70.728485  12473.026870\n",
       "     Europe     77.648600  25054.481636\n",
       "     Oceania    80.719500  29810.188275"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 연도와 대륙별로 데이처 그룹화\n",
    "# 2. 연도에 따른 대륙별 기대수명과 GDP 평균 계산\n",
    "\n",
    "multi_group_var = df.groupby(['year','continent'])[['lifeExp','gdpPercap']].mean()\n",
    "multi_group_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2bd4e19e-8f26-4be6-848b-507c6f6c97b5",
   "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></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1952</th>\n",
       "      <th>Africa</th>\n",
       "      <td>39.135500</td>\n",
       "      <td>1252.572466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>53.279840</td>\n",
       "      <td>4079.062552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>46.314394</td>\n",
       "      <td>5195.484004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>64.408500</td>\n",
       "      <td>5661.057435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>69.255000</td>\n",
       "      <td>10298.085650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lifeExp     gdpPercap\n",
       "year continent                         \n",
       "1952 Africa     39.135500   1252.572466\n",
       "     Americas   53.279840   4079.062552\n",
       "     Asia       46.314394   5195.484004\n",
       "     Europe     64.408500   5661.057435\n",
       "     Oceania    69.255000  10298.085650"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 처음 10개 행 보기\n",
    "multi_group_var.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de99717c-6b35-4b8d-9df2-72e93be601f4",
   "metadata": {},
   "source": [
    "### <멀티 계층구조을 없애고 데이터프레임을 평탄화하기>\n",
    "* df.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "031685bc-9ab6-4b34-964b-f2130c45df87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.135500</td>\n",
       "      <td>1252.572466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1952</td>\n",
       "      <td>Americas</td>\n",
       "      <td>53.279840</td>\n",
       "      <td>4079.062552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1952</td>\n",
       "      <td>Asia</td>\n",
       "      <td>46.314394</td>\n",
       "      <td>5195.484004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1952</td>\n",
       "      <td>Europe</td>\n",
       "      <td>64.408500</td>\n",
       "      <td>5661.057435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1952</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>69.255000</td>\n",
       "      <td>10298.085650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1957</td>\n",
       "      <td>Africa</td>\n",
       "      <td>41.266346</td>\n",
       "      <td>1385.236062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1957</td>\n",
       "      <td>Americas</td>\n",
       "      <td>55.960280</td>\n",
       "      <td>4616.043733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1957</td>\n",
       "      <td>Asia</td>\n",
       "      <td>49.318544</td>\n",
       "      <td>5787.732940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1957</td>\n",
       "      <td>Europe</td>\n",
       "      <td>66.703067</td>\n",
       "      <td>6963.012816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1957</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>70.295000</td>\n",
       "      <td>11598.522455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1962</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.319442</td>\n",
       "      <td>1598.078825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1962</td>\n",
       "      <td>Americas</td>\n",
       "      <td>58.398760</td>\n",
       "      <td>4901.541870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1962</td>\n",
       "      <td>Asia</td>\n",
       "      <td>51.563223</td>\n",
       "      <td>5729.369625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1962</td>\n",
       "      <td>Europe</td>\n",
       "      <td>68.539233</td>\n",
       "      <td>8365.486814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1962</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>71.085000</td>\n",
       "      <td>12696.452430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1967</td>\n",
       "      <td>Africa</td>\n",
       "      <td>45.334538</td>\n",
       "      <td>2050.363801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1967</td>\n",
       "      <td>Americas</td>\n",
       "      <td>60.410920</td>\n",
       "      <td>5668.253496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1967</td>\n",
       "      <td>Asia</td>\n",
       "      <td>54.663640</td>\n",
       "      <td>5971.173374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1967</td>\n",
       "      <td>Europe</td>\n",
       "      <td>69.737600</td>\n",
       "      <td>10143.823757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1967</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>71.310000</td>\n",
       "      <td>14495.021790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1972</td>\n",
       "      <td>Africa</td>\n",
       "      <td>47.450942</td>\n",
       "      <td>2339.615674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1972</td>\n",
       "      <td>Americas</td>\n",
       "      <td>62.394920</td>\n",
       "      <td>6491.334139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1972</td>\n",
       "      <td>Asia</td>\n",
       "      <td>57.319269</td>\n",
       "      <td>8187.468699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1972</td>\n",
       "      <td>Europe</td>\n",
       "      <td>70.775033</td>\n",
       "      <td>12479.575246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1972</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>71.910000</td>\n",
       "      <td>16417.333380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1977</td>\n",
       "      <td>Africa</td>\n",
       "      <td>49.580423</td>\n",
       "      <td>2585.938508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1977</td>\n",
       "      <td>Americas</td>\n",
       "      <td>64.391560</td>\n",
       "      <td>7352.007126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1977</td>\n",
       "      <td>Asia</td>\n",
       "      <td>59.610556</td>\n",
       "      <td>7791.314020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1977</td>\n",
       "      <td>Europe</td>\n",
       "      <td>71.937767</td>\n",
       "      <td>14283.979110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1977</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>72.855000</td>\n",
       "      <td>17283.957605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1982</td>\n",
       "      <td>Africa</td>\n",
       "      <td>51.592865</td>\n",
       "      <td>2481.592960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1982</td>\n",
       "      <td>Americas</td>\n",
       "      <td>66.228840</td>\n",
       "      <td>7506.737088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1982</td>\n",
       "      <td>Asia</td>\n",
       "      <td>62.617939</td>\n",
       "      <td>7434.135157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1982</td>\n",
       "      <td>Europe</td>\n",
       "      <td>72.806400</td>\n",
       "      <td>15617.896551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1982</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>74.290000</td>\n",
       "      <td>18554.709840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>53.344788</td>\n",
       "      <td>2282.668991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>68.090720</td>\n",
       "      <td>7793.400261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1987</td>\n",
       "      <td>Asia</td>\n",
       "      <td>64.851182</td>\n",
       "      <td>7608.226508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1987</td>\n",
       "      <td>Europe</td>\n",
       "      <td>73.642167</td>\n",
       "      <td>17214.310727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1987</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>75.320000</td>\n",
       "      <td>20448.040160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1992</td>\n",
       "      <td>Africa</td>\n",
       "      <td>53.629577</td>\n",
       "      <td>2281.810333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1992</td>\n",
       "      <td>Americas</td>\n",
       "      <td>69.568360</td>\n",
       "      <td>8044.934406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1992</td>\n",
       "      <td>Asia</td>\n",
       "      <td>66.537212</td>\n",
       "      <td>8639.690248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1992</td>\n",
       "      <td>Europe</td>\n",
       "      <td>74.440100</td>\n",
       "      <td>17061.568084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1992</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>76.945000</td>\n",
       "      <td>20894.045885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1997</td>\n",
       "      <td>Africa</td>\n",
       "      <td>53.598269</td>\n",
       "      <td>2378.759555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1997</td>\n",
       "      <td>Americas</td>\n",
       "      <td>71.150480</td>\n",
       "      <td>8889.300863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1997</td>\n",
       "      <td>Asia</td>\n",
       "      <td>68.020515</td>\n",
       "      <td>9834.093295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1997</td>\n",
       "      <td>Europe</td>\n",
       "      <td>75.505167</td>\n",
       "      <td>19076.781802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1997</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>78.190000</td>\n",
       "      <td>24024.175170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2002</td>\n",
       "      <td>Africa</td>\n",
       "      <td>53.325231</td>\n",
       "      <td>2599.385159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2002</td>\n",
       "      <td>Americas</td>\n",
       "      <td>72.422040</td>\n",
       "      <td>9287.677107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2002</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.233879</td>\n",
       "      <td>10174.090397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2002</td>\n",
       "      <td>Europe</td>\n",
       "      <td>76.700600</td>\n",
       "      <td>21711.732422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2002</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>79.740000</td>\n",
       "      <td>26938.778040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2007</td>\n",
       "      <td>Africa</td>\n",
       "      <td>54.806038</td>\n",
       "      <td>3089.032605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2007</td>\n",
       "      <td>Americas</td>\n",
       "      <td>73.608120</td>\n",
       "      <td>11003.031625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2007</td>\n",
       "      <td>Asia</td>\n",
       "      <td>70.728485</td>\n",
       "      <td>12473.026870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2007</td>\n",
       "      <td>Europe</td>\n",
       "      <td>77.648600</td>\n",
       "      <td>25054.481636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2007</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>80.719500</td>\n",
       "      <td>29810.188275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year continent    lifeExp     gdpPercap\n",
       "0   1952    Africa  39.135500   1252.572466\n",
       "1   1952  Americas  53.279840   4079.062552\n",
       "2   1952      Asia  46.314394   5195.484004\n",
       "3   1952    Europe  64.408500   5661.057435\n",
       "4   1952   Oceania  69.255000  10298.085650\n",
       "5   1957    Africa  41.266346   1385.236062\n",
       "6   1957  Americas  55.960280   4616.043733\n",
       "7   1957      Asia  49.318544   5787.732940\n",
       "8   1957    Europe  66.703067   6963.012816\n",
       "9   1957   Oceania  70.295000  11598.522455\n",
       "10  1962    Africa  43.319442   1598.078825\n",
       "11  1962  Americas  58.398760   4901.541870\n",
       "12  1962      Asia  51.563223   5729.369625\n",
       "13  1962    Europe  68.539233   8365.486814\n",
       "14  1962   Oceania  71.085000  12696.452430\n",
       "15  1967    Africa  45.334538   2050.363801\n",
       "16  1967  Americas  60.410920   5668.253496\n",
       "17  1967      Asia  54.663640   5971.173374\n",
       "18  1967    Europe  69.737600  10143.823757\n",
       "19  1967   Oceania  71.310000  14495.021790\n",
       "20  1972    Africa  47.450942   2339.615674\n",
       "21  1972  Americas  62.394920   6491.334139\n",
       "22  1972      Asia  57.319269   8187.468699\n",
       "23  1972    Europe  70.775033  12479.575246\n",
       "24  1972   Oceania  71.910000  16417.333380\n",
       "25  1977    Africa  49.580423   2585.938508\n",
       "26  1977  Americas  64.391560   7352.007126\n",
       "27  1977      Asia  59.610556   7791.314020\n",
       "28  1977    Europe  71.937767  14283.979110\n",
       "29  1977   Oceania  72.855000  17283.957605\n",
       "30  1982    Africa  51.592865   2481.592960\n",
       "31  1982  Americas  66.228840   7506.737088\n",
       "32  1982      Asia  62.617939   7434.135157\n",
       "33  1982    Europe  72.806400  15617.896551\n",
       "34  1982   Oceania  74.290000  18554.709840\n",
       "35  1987    Africa  53.344788   2282.668991\n",
       "36  1987  Americas  68.090720   7793.400261\n",
       "37  1987      Asia  64.851182   7608.226508\n",
       "38  1987    Europe  73.642167  17214.310727\n",
       "39  1987   Oceania  75.320000  20448.040160\n",
       "40  1992    Africa  53.629577   2281.810333\n",
       "41  1992  Americas  69.568360   8044.934406\n",
       "42  1992      Asia  66.537212   8639.690248\n",
       "43  1992    Europe  74.440100  17061.568084\n",
       "44  1992   Oceania  76.945000  20894.045885\n",
       "45  1997    Africa  53.598269   2378.759555\n",
       "46  1997  Americas  71.150480   8889.300863\n",
       "47  1997      Asia  68.020515   9834.093295\n",
       "48  1997    Europe  75.505167  19076.781802\n",
       "49  1997   Oceania  78.190000  24024.175170\n",
       "50  2002    Africa  53.325231   2599.385159\n",
       "51  2002  Americas  72.422040   9287.677107\n",
       "52  2002      Asia  69.233879  10174.090397\n",
       "53  2002    Europe  76.700600  21711.732422\n",
       "54  2002   Oceania  79.740000  26938.778040\n",
       "55  2007    Africa  54.806038   3089.032605\n",
       "56  2007  Americas  73.608120  11003.031625\n",
       "57  2007      Asia  70.728485  12473.026870\n",
       "58  2007    Europe  77.648600  25054.481636\n",
       "59  2007   Oceania  80.719500  29810.188275"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flatten = multi_group_var.reset_index()\n",
    "flatten"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c575b1-0ae9-4040-93eb-f973b9c95cb1",
   "metadata": {},
   "source": [
    "#### < 그룹화된 데이터 개수 구하기>\n",
    "* series.nunique() : 중복을 제외한 개수\n",
    "* series.value_counts() : 지정한 열 또는 행의 개수(빈도수)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9febb08-0e99-4edb-b58d-4ec1d8899bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa      52\n",
       "Americas    25\n",
       "Asia        33\n",
       "Europe      30\n",
       "Oceania      2\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('continent')['country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2496cd3d-b057-47b6-8467-78b7b9ef1cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent  country       \n",
       "Africa     Algeria           12\n",
       "           Angola            12\n",
       "           Benin             12\n",
       "           Botswana          12\n",
       "           Burkina Faso      12\n",
       "                             ..\n",
       "Europe     Switzerland       12\n",
       "           Turkey            12\n",
       "           United Kingdom    12\n",
       "Oceania    Australia         12\n",
       "           New Zealand       12\n",
       "Name: count, Length: 142, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('continent')['country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb57a3e4-ea8e-41a7-a8f7-4585d022b404",
   "metadata": {},
   "source": [
    "## 6. 데이터 시각화\n",
    "* 데이터프레임으로 시각화 : df.plot()\n",
    "  *  matplotlib 사용하므로 plt.show() 호출할 필요가 없다.\n",
    "* %matplotlib inline 지원"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2c1d78b0-7842-481b-8e49-cd6627234f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1952    49.057620\n",
       "1957    51.507401\n",
       "1962    53.609249\n",
       "1967    55.678290\n",
       "1972    57.647386\n",
       "1977    59.570157\n",
       "1982    61.533197\n",
       "1987    63.212613\n",
       "1992    64.160338\n",
       "1997    65.014676\n",
       "2002    65.694923\n",
       "2007    67.007423\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. 연도별 평균 기대수명 구하기\n",
    "year_by_lifeExp = df.groupby('year')['lifeExp'].mean()\n",
    "year_by_lifeExp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fe658be7-a506-4caf-bbe9-3120c565f64e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year'>"
      ]
     },
     "execution_count": 44,
     "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": [
    "#2. 데이터프레임 대상으로 plot 메서드 호출\n",
    "year_by_lifeExp.plot(color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef982f32-9fa0-4b27-981a-e53da7df99e4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}