4장_판다스_데이터_분석_(4.7).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6fee5aee",
   "metadata": {},
   "source": [
    "# 피벗테이블과 그룹분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54b8ce89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>도시</th>\n",
       "      <th>연도</th>\n",
       "      <th>인구</th>\n",
       "      <th>지역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "      <td>9904312</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>2010</td>\n",
       "      <td>9631482</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>2005</td>\n",
       "      <td>9762546</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>부산</td>\n",
       "      <td>2015</td>\n",
       "      <td>3448737</td>\n",
       "      <td>경상권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>부산</td>\n",
       "      <td>2010</td>\n",
       "      <td>3393191</td>\n",
       "      <td>경상권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>부산</td>\n",
       "      <td>2005</td>\n",
       "      <td>3512547</td>\n",
       "      <td>경상권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "      <td>2890451</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>2010</td>\n",
       "      <td>263203</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   도시    연도       인구   지역\n",
       "0  서울  2015  9904312  수도권\n",
       "1  서울  2010  9631482  수도권\n",
       "2  서울  2005  9762546  수도권\n",
       "3  부산  2015  3448737  경상권\n",
       "4  부산  2010  3393191  경상권\n",
       "5  부산  2005  3512547  경상권\n",
       "6  인천  2015  2890451  수도권\n",
       "7  인천  2010   263203  수도권"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    \"도시\": [\"서울\", \"서울\", \"서울\", \"부산\", \"부산\", \"부산\", \"인천\", \"인천\"],\n",
    "    \"연도\": [\"2015\", \"2010\", \"2005\", \"2015\", \"2010\", \"2005\", \"2015\", \"2010\"],\n",
    "    \"인구\": [9904312, 9631482, 9762546, 3448737, 3393191, 3512547, 2890451, 263203],\n",
    "    \"지역\": [\"수도권\", \"수도권\", \"수도권\", \"경상권\", \"경상권\", \"경상권\", \"수도권\", \"수도권\"]\n",
    "}\n",
    "columns = [\"도시\", \"연도\", \"인구\", \"지역\"]\n",
    "df1 = pd.DataFrame(data, columns=columns)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ed76c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>연도</th>\n",
       "      <th>2005</th>\n",
       "      <th>2010</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>도시</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>3512547.0</td>\n",
       "      <td>3393191.0</td>\n",
       "      <td>3448737.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>9762546.0</td>\n",
       "      <td>9631482.0</td>\n",
       "      <td>9904312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>NaN</td>\n",
       "      <td>263203.0</td>\n",
       "      <td>2890451.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도       2005       2010       2015\n",
       "도시                                 \n",
       "부산  3512547.0  3393191.0  3448737.0\n",
       "서울  9762546.0  9631482.0  9904312.0\n",
       "인천        NaN   263203.0  2890451.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.pivot(\"도시\", \"연도\", \"인구\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98d654c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>연도</th>\n",
       "      <th>2005</th>\n",
       "      <th>2010</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역</th>\n",
       "      <th>도시</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>경상권</th>\n",
       "      <th>부산</th>\n",
       "      <td>3512547.0</td>\n",
       "      <td>3393191.0</td>\n",
       "      <td>3448737.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">수도권</th>\n",
       "      <th>서울</th>\n",
       "      <td>9762546.0</td>\n",
       "      <td>9631482.0</td>\n",
       "      <td>9904312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>NaN</td>\n",
       "      <td>263203.0</td>\n",
       "      <td>2890451.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도           2005       2010       2015\n",
       "지역  도시                                 \n",
       "경상권 부산  3512547.0  3393191.0  3448737.0\n",
       "수도권 서울  9762546.0  9631482.0  9904312.0\n",
       "    인천        NaN   263203.0  2890451.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.pivot([\"지역\", \"도시\"], \"연도\", \"인구\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc5cd0ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10</td>\n",
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       "      <th>1</th>\n",
       "      <td>A</td>\n",
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       "      <td>20</td>\n",
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       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
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       "      <th>3</th>\n",
       "      <td>B</td>\n",
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       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2  data1  data2\n",
       "0    A  one      1     10\n",
       "1    A  two      2     20\n",
       "2    B  one      3     30\n",
       "3    B  two      4     40\n",
       "4    A  one      5     50"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "df2 = pd.DataFrame({\n",
    "    'key1': ['A', 'A', 'B', 'B', 'A'],\n",
    "    'key2': ['one', 'two', 'one', 'two', 'one'],\n",
    "    'data1': [1, 2, 3, 4, 5],\n",
    "    'data2': [10, 20, 30, 40, 50]\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b25fbf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001AC7FB11700>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = df2.groupby(df2.key1)\n",
    "groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "73840a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': [0, 1, 4], 'B': [2, 3]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dedcc06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>key1</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
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      "text/plain": [
       "      data1  data2\n",
       "key1              \n",
       "A         8     80\n",
       "B         7     70"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "435649e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "A    8\n",
       "B    7\n",
       "Name: data1, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.data1.groupby(df2.key1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "71dfaa82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "A    8\n",
       "B    7\n",
       "Name: data1, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(df2.key1)[\"data1\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "916e1aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "A    8\n",
       "B    7\n",
       "Name: data1, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(df2.key1).sum()[\"data1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ea67a0d5",
   "metadata": {},
   "outputs": [
    {
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       "      data1\n",
       "key1       \n",
       "A         8\n",
       "B         7"
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     "execution_count": 27,
     "metadata": {},
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    }
   ],
   "source": [
    "df3 = pd.DataFrame(df2.groupby(df2.key1).sum()[\"data1\"])\n",
    "type(df3)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4ea8b9c5",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2  data1  data2\n",
       "0    A  one      1     10\n",
       "1    A  two      2     20\n",
       "2    B  one      3     30\n",
       "3    B  two      4     40\n",
       "4    A  one      5     50"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f499484c",
   "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>data1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>one</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>one</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           data1\n",
       "key1 key2       \n",
       "A    one       6\n",
       "     two       2\n",
       "B    one       3\n",
       "     two       4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df2.groupby([df2.key1, df2.key2]).sum()[\"data1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "56e1598c",
   "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>도시</th>\n",
       "      <th>연도</th>\n",
       "      <th>인구</th>\n",
       "      <th>지역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "      <td>9904312</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>2010</td>\n",
       "      <td>9631482</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>2005</td>\n",
       "      <td>9762546</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>부산</td>\n",
       "      <td>2015</td>\n",
       "      <td>3448737</td>\n",
       "      <td>경상권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>부산</td>\n",
       "      <td>2010</td>\n",
       "      <td>3393191</td>\n",
       "      <td>경상권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>부산</td>\n",
       "      <td>2005</td>\n",
       "      <td>3512547</td>\n",
       "      <td>경상권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "      <td>2890451</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>2010</td>\n",
       "      <td>263203</td>\n",
       "      <td>수도권</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   도시    연도       인구   지역\n",
       "0  서울  2015  9904312  수도권\n",
       "1  서울  2010  9631482  수도권\n",
       "2  서울  2005  9762546  수도권\n",
       "3  부산  2015  3448737  경상권\n",
       "4  부산  2010  3393191  경상권\n",
       "5  부산  2005  3512547  경상권\n",
       "6  인천  2015  2890451  수도권\n",
       "7  인천  2010   263203  수도권"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9dc532e1",
   "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>연도</th>\n",
       "      <th>2005</th>\n",
       "      <th>2010</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>경상권</th>\n",
       "      <td>3512547</td>\n",
       "      <td>3393191</td>\n",
       "      <td>3448737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>수도권</th>\n",
       "      <td>9762546</td>\n",
       "      <td>9894685</td>\n",
       "      <td>12794763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도      2005     2010      2015\n",
       "지역                             \n",
       "경상권  3512547  3393191   3448737\n",
       "수도권  9762546  9894685  12794763"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"인구\"].groupby([df1[\"지역\"], df1[\"연도\"]]).sum().unstack(\"연도\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e94624c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "77e5d7b3",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "98448647",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>1.348837</td>\n",
       "      <td>1.913043</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>1.428571</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>1.612245</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>1.785714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa          1.348837     1.913043      1.900000     6.000000\n",
       "versicolor      1.428571     1.700000      1.700000     1.800000\n",
       "virginica       1.612245     1.727273      1.533333     1.785714"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def peak_to_peak(x):\n",
    "    return x.max() / x.min()\n",
    "\n",
    "iris.groupby(iris.species).agg(peak_to_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "62e2a317",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.428</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa             5.006        3.428         1.462        0.246\n",
       "versicolor         5.936        2.770         4.260        1.326\n",
       "virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "261fda80",
   "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>species</th>\n",
       "      <th>setosa</th>\n",
       "      <th>versicolor</th>\n",
       "      <th>virginica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">sepal_length</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.006000</td>\n",
       "      <td>5.936000</td>\n",
       "      <td>6.588000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.352490</td>\n",
       "      <td>0.516171</td>\n",
       "      <td>0.635880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>6.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.200000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">sepal_width</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.428000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>2.974000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.379064</td>\n",
       "      <td>0.313798</td>\n",
       "      <td>0.322497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>2.525000</td>\n",
       "      <td>2.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.400000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.675000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.400000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">petal_length</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.462000</td>\n",
       "      <td>4.260000</td>\n",
       "      <td>5.552000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.173664</td>\n",
       "      <td>0.469911</td>\n",
       "      <td>0.551895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>5.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.575000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>5.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>6.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">petal_width</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.246000</td>\n",
       "      <td>1.326000</td>\n",
       "      <td>2.026000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.105386</td>\n",
       "      <td>0.197753</td>\n",
       "      <td>0.274650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "species                setosa  versicolor  virginica\n",
       "sepal_length count  50.000000   50.000000  50.000000\n",
       "             mean    5.006000    5.936000   6.588000\n",
       "             std     0.352490    0.516171   0.635880\n",
       "             min     4.300000    4.900000   4.900000\n",
       "             25%     4.800000    5.600000   6.225000\n",
       "             50%     5.000000    5.900000   6.500000\n",
       "             75%     5.200000    6.300000   6.900000\n",
       "             max     5.800000    7.000000   7.900000\n",
       "sepal_width  count  50.000000   50.000000  50.000000\n",
       "             mean    3.428000    2.770000   2.974000\n",
       "             std     0.379064    0.313798   0.322497\n",
       "             min     2.300000    2.000000   2.200000\n",
       "             25%     3.200000    2.525000   2.800000\n",
       "             50%     3.400000    2.800000   3.000000\n",
       "             75%     3.675000    3.000000   3.175000\n",
       "             max     4.400000    3.400000   3.800000\n",
       "petal_length count  50.000000   50.000000  50.000000\n",
       "             mean    1.462000    4.260000   5.552000\n",
       "             std     0.173664    0.469911   0.551895\n",
       "             min     1.000000    3.000000   4.500000\n",
       "             25%     1.400000    4.000000   5.100000\n",
       "             50%     1.500000    4.350000   5.550000\n",
       "             75%     1.575000    4.600000   5.875000\n",
       "             max     1.900000    5.100000   6.900000\n",
       "petal_width  count  50.000000   50.000000  50.000000\n",
       "             mean    0.246000    1.326000   2.026000\n",
       "             std     0.105386    0.197753   0.274650\n",
       "             min     0.100000    1.000000   1.400000\n",
       "             25%     0.200000    1.200000   1.800000\n",
       "             50%     0.200000    1.300000   2.000000\n",
       "             75%     0.300000    1.500000   2.300000\n",
       "             max     0.600000    1.800000   2.500000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species).describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7bd1ef9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
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       "      <th>species</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">setosa</th>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">versicolor</th>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">virginica</th>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sepal_length  sepal_width  petal_length  petal_width  \\\n",
       "species                                                                \n",
       "setosa     24            4.8          3.4           1.9          0.2   \n",
       "           44            5.1          3.8           1.9          0.4   \n",
       "           23            5.1          3.3           1.7          0.5   \n",
       "versicolor 83            6.0          2.7           5.1          1.6   \n",
       "           77            6.7          3.0           5.0          1.7   \n",
       "           72            6.3          2.5           4.9          1.5   \n",
       "virginica  118           7.7          2.6           6.9          2.3   \n",
       "           117           7.7          3.8           6.7          2.2   \n",
       "           122           7.7          2.8           6.7          2.0   \n",
       "\n",
       "                   species  \n",
       "species                     \n",
       "setosa     24       setosa  \n",
       "           44       setosa  \n",
       "           23       setosa  \n",
       "versicolor 83   versicolor  \n",
       "           77   versicolor  \n",
       "           72   versicolor  \n",
       "virginica  118   virginica  \n",
       "           117   virginica  \n",
       "           122   virginica  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def top3_petal_length(df):\n",
    "    return df.sort_values(by=\"petal_length\", ascending=False)[:3]\n",
    "\n",
    "iris.groupby(iris.species).apply(top3_petal_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bc48d84c",
   "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></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">setosa</th>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">versicolor</th>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">virginica</th>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sepal_length  sepal_width  petal_length  petal_width  \\\n",
       "species                                                                \n",
       "setosa     43            5.0          3.5           1.6          0.6   \n",
       "           23            5.1          3.3           1.7          0.5   \n",
       "           16            5.4          3.9           1.3          0.4   \n",
       "           31            5.4          3.4           1.5          0.4   \n",
       "           26            5.0          3.4           1.6          0.4   \n",
       "versicolor 70            5.9          3.2           4.8          1.8   \n",
       "           77            6.7          3.0           5.0          1.7   \n",
       "           56            6.3          3.3           4.7          1.6   \n",
       "           85            6.0          3.4           4.5          1.6   \n",
       "           83            6.0          2.7           5.1          1.6   \n",
       "virginica  100           6.3          3.3           6.0          2.5   \n",
       "           144           6.7          3.3           5.7          2.5   \n",
       "           109           7.2          3.6           6.1          2.5   \n",
       "           114           5.8          2.8           5.1          2.4   \n",
       "           140           6.7          3.1           5.6          2.4   \n",
       "\n",
       "                   species  \n",
       "species                     \n",
       "setosa     43       setosa  \n",
       "           23       setosa  \n",
       "           16       setosa  \n",
       "           31       setosa  \n",
       "           26       setosa  \n",
       "versicolor 70   versicolor  \n",
       "           77   versicolor  \n",
       "           56   versicolor  \n",
       "           85   versicolor  \n",
       "           83   versicolor  \n",
       "virginica  100   virginica  \n",
       "           144   virginica  \n",
       "           109   virginica  \n",
       "           114   virginica  \n",
       "           140   virginica  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def top5_petal_width(df):\n",
    "    return df.sort_values(by = \"petal_width\", ascending = False)[:5]\n",
    "\n",
    "iris.groupby(iris.species).apply(top5_petal_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "29b419c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_length_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>5.6</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>5.1</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.1</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.9</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>5.7</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>small</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length petal_length_class\n",
       "140           5.6             medium\n",
       "141           5.1              small\n",
       "142           5.1              small\n",
       "143           5.9              large\n",
       "144           5.7             medium\n",
       "145           5.2              small\n",
       "146           5.0              small\n",
       "147           5.2              small\n",
       "148           5.4             medium\n",
       "149           5.1              small"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def q3cut(s):\n",
    "    return pd.qcut(s, 3, labels = [\"small\", \"medium\", \"large\"]).astype(str)\n",
    "\n",
    "iris[\"petal_length_class\"] = iris.groupby(iris.species).petal_length.transform(q3cut)\n",
    "iris[[\"petal_length\", \"petal_length_class\"]].tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2d9da345",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>petal_length_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>small</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>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>small</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n",
       "0             5.1          3.5           1.4          0.2     setosa   \n",
       "1             4.9          3.0           1.4          0.2     setosa   \n",
       "2             4.7          3.2           1.3          0.2     setosa   \n",
       "3             4.6          3.1           1.5          0.2     setosa   \n",
       "4             5.0          3.6           1.4          0.2     setosa   \n",
       "..            ...          ...           ...          ...        ...   \n",
       "145           6.7          3.0           5.2          2.3  virginica   \n",
       "146           6.3          2.5           5.0          1.9  virginica   \n",
       "147           6.5          3.0           5.2          2.0  virginica   \n",
       "148           6.2          3.4           5.4          2.3  virginica   \n",
       "149           5.9          3.0           5.1          1.8  virginica   \n",
       "\n",
       "    petal_length_class  \n",
       "0                small  \n",
       "1                small  \n",
       "2                small  \n",
       "3               medium  \n",
       "4                small  \n",
       "..                 ...  \n",
       "145              small  \n",
       "146              small  \n",
       "147              small  \n",
       "148             medium  \n",
       "149              small  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4296793b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.428</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa             5.006        3.428         1.462        0.246\n",
       "versicolor         5.936        2.770         4.260        1.326\n",
       "virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f62eaecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>연도</th>\n",
       "      <th>2005</th>\n",
       "      <th>2010</th>\n",
       "      <th>2015</th>\n",
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       "    <tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>3512547.0</td>\n",
       "      <td>3393191.0</td>\n",
       "      <td>3448737.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>9762546.0</td>\n",
       "      <td>9631482.0</td>\n",
       "      <td>9904312.0</td>\n",
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       "    <tr>\n",
       "      <th>인천</th>\n",
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      "text/plain": [
       "연도       2005       2010       2015\n",
       "도시                                 \n",
       "부산  3512547.0  3393191.0  3448737.0\n",
       "서울  9762546.0  9631482.0  9904312.0\n",
       "인천        NaN   263203.0  2890451.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.pivot_table(\"인구\", \"도시\", \"연도\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "538502ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>서울</th>\n",
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       "      <td>9904312.0</td>\n",
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       "      <th>인천</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도       2005       2010       2015\n",
       "도시                                 \n",
       "부산  3512547.0  3393191.0  3448737.0\n",
       "서울  9762546.0  9631482.0  9904312.0\n",
       "인천        NaN   263203.0  2890451.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.pivot(\"도시\", \"연도\", \"인구\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "15f51a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>연도</th>\n",
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       "      <th>평균</th>\n",
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       "  <tbody>\n",
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       "      <td>3512547.0</td>\n",
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       "      <td>3.451492e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>9762546.0</td>\n",
       "      <td>9631482.0</td>\n",
       "      <td>9904312.0</td>\n",
       "      <td>9.766113e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>NaN</td>\n",
       "      <td>263203.0</td>\n",
       "      <td>2890451.0</td>\n",
       "      <td>1.576827e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>평균</th>\n",
       "      <td>6637546.5</td>\n",
       "      <td>4429292.0</td>\n",
       "      <td>5414500.0</td>\n",
       "      <td>5.350809e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도       2005       2010       2015            평균\n",
       "도시                                               \n",
       "부산  3512547.0  3393191.0  3448737.0  3.451492e+06\n",
       "서울  9762546.0  9631482.0  9904312.0  9.766113e+06\n",
       "인천        NaN   263203.0  2890451.0  1.576827e+06\n",
       "평균  6637546.5  4429292.0  5414500.0  5.350809e+06"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.pivot_table(\"인구\", \"도시\", \"연도\", margins=True, margins_name = \"평균\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "44d4944a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>인구</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>연도</th>\n",
       "      <th>도시</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2005</th>\n",
       "      <th>부산</th>\n",
       "      <td>3512547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>9762546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010</th>\n",
       "      <th>부산</th>\n",
       "      <td>3393191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>9631482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>263203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
       "      <th>부산</th>\n",
       "      <td>3448737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>9904312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>2890451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              인구\n",
       "연도   도시         \n",
       "2005 부산  3512547\n",
       "     서울  9762546\n",
       "2010 부산  3393191\n",
       "     서울  9631482\n",
       "     인천   263203\n",
       "2015 부산  3448737\n",
       "     서울  9904312\n",
       "     인천  2890451"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.pivot_table(\"인구\", index = [\"연도\", \"도시\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ea0d4575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3c2d6ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.203927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.073584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.088222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.098204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.159744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size   tip_pct\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3  0.203927\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2  0.073584\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2  0.088222\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2  0.098204\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2  0.159744"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6657e635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.785943</td>\n",
       "      <td>2.998279</td>\n",
       "      <td>2.569672</td>\n",
       "      <td>0.160803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.902412</td>\n",
       "      <td>1.383638</td>\n",
       "      <td>0.951100</td>\n",
       "      <td>0.061072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.347500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.129127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.795000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.154770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.127500</td>\n",
       "      <td>3.562500</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.191475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill         tip        size     tip_pct\n",
       "count  244.000000  244.000000  244.000000  244.000000\n",
       "mean    19.785943    2.998279    2.569672    0.160803\n",
       "std      8.902412    1.383638    0.951100    0.061072\n",
       "min      3.070000    1.000000    1.000000    0.035638\n",
       "25%     13.347500    2.000000    2.000000    0.129127\n",
       "50%     17.795000    2.900000    2.000000    0.154770\n",
       "75%     24.127500    3.562500    3.000000    0.191475\n",
       "max     50.810000   10.000000    6.000000    0.710345"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ebb7bbd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bill  tip  smoker  day  time  size  tip_pct\n",
       "sex                                                      \n",
       "Male           157  157     157  157   157   157      157\n",
       "Female          87   87      87   87    87    87       87"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(\"sex\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e9c9cfed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Male      157\n",
       "Female     87\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(\"sex\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "185b98d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     smoker\n",
       "Male    Yes       60\n",
       "        No        97\n",
       "Female  Yes       33\n",
       "        No        54\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby([\"sex\", \"smoker\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bee82ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>60</td>\n",
       "      <td>97</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>33</td>\n",
       "      <td>54</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>93</td>\n",
       "      <td>151</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker  Yes   No  All\n",
       "sex                  \n",
       "Male     60   97  157\n",
       "Female   33   54   87\n",
       "All      93  151  244"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(\"tip_pct\", \"sex\", \"smoker\", aggfunc = \"count\", margins = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b885a99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.157651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.166491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tip_pct\n",
       "sex             \n",
       "Male    0.157651\n",
       "Female  0.166491"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(\"sex\")[[\"tip_pct\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3a449fba",
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
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       "      <th>No</th>\n",
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      "text/plain": [
       "                tip_pct\n",
       "sex    smoker          \n",
       "Male   Yes     0.152771\n",
       "       No      0.160669\n",
       "Female Yes     0.182150\n",
       "       No      0.156921"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(\"tip_pct\", [\"sex\", \"smoker\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bad4efa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Male</th>\n",
       "      <td>157.0</td>\n",
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       "      <td>0.064778</td>\n",
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       "      <td>0.121389</td>\n",
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       "      <td>0.056433</td>\n",
       "      <td>0.140416</td>\n",
       "      <td>0.155581</td>\n",
       "      <td>0.194266</td>\n",
       "      <td>0.416667</td>\n",
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      "text/plain": [
       "       tip_pct                                                              \\\n",
       "         count      mean       std       min       25%       50%       75%   \n",
       "sex                                                                          \n",
       "Male     157.0  0.157651  0.064778  0.035638  0.121389  0.153492  0.186240   \n",
       "Female    87.0  0.166491  0.053632  0.056433  0.140416  0.155581  0.194266   \n",
       "\n",
       "                  \n",
       "             max  \n",
       "sex               \n",
       "Male    0.710345  \n",
       "Female  0.416667  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(\"sex\")[[\"tip_pct\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0b5d03d8",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       tip_pct                                                              \\\n",
       "         count      mean       std       min       25%       50%       75%   \n",
       "smoker                                                                       \n",
       "Yes       93.0  0.163196  0.085119  0.035638  0.106771  0.153846  0.195059   \n",
       "No       151.0  0.159328  0.039910  0.056797  0.136906  0.155625  0.185014   \n",
       "\n",
       "                  \n",
       "             max  \n",
       "smoker            \n",
       "Yes     0.710345  \n",
       "No      0.291990  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(\"smoker\")[[\"tip_pct\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7e03ac2e",
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.182150</td>\n",
       "      <td>0.071595</td>\n",
       "      <td>0.056433</td>\n",
       "      <td>0.152439</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.198216</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.036421</td>\n",
       "      <td>0.056797</td>\n",
       "      <td>0.139708</td>\n",
       "      <td>0.149691</td>\n",
       "      <td>0.181630</td>\n",
       "      <td>0.252672</td>\n",
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      ],
      "text/plain": [
       "              tip_pct                                                    \\\n",
       "                count      mean       std       min       25%       50%   \n",
       "sex    smoker                                                             \n",
       "Male   Yes       60.0  0.152771  0.090588  0.035638  0.101845  0.141015   \n",
       "       No        97.0  0.160669  0.041849  0.071804  0.131810  0.157604   \n",
       "Female Yes       33.0  0.182150  0.071595  0.056433  0.152439  0.173913   \n",
       "       No        54.0  0.156921  0.036421  0.056797  0.139708  0.149691   \n",
       "\n",
       "                                   \n",
       "                    75%       max  \n",
       "sex    smoker                      \n",
       "Male   Yes     0.191697  0.710345  \n",
       "       No      0.186220  0.291990  \n",
       "Female Yes     0.198216  0.416667  \n",
       "       No      0.181630  0.252672  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby([\"sex\",\"smoker\"])[[\"tip_pct\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6d4ddc11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>27.18</td>\n",
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       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.159744</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size   tip_pct\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3  0.203927\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2  0.073584\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2  0.088222\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2  0.098204\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2  0.159744"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f694862e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.164024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>2</th>\n",
       "      <td>0.162659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>5</th>\n",
       "      <td>0.159839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>2</th>\n",
       "      <td>0.159744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>2</th>\n",
       "      <td>0.155289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Dinner</th>\n",
       "      <th>4</th>\n",
       "      <td>0.153168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.152662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>3</th>\n",
       "      <td>0.151439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
       "      <th>4</th>\n",
       "      <td>0.145515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.144599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>4</th>\n",
       "      <td>0.138289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <th>Lunch</th>\n",
       "      <th>5</th>\n",
       "      <td>0.121389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>4</th>\n",
       "      <td>0.117750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>5</th>\n",
       "      <td>0.106572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <th>Dinner</th>\n",
       "      <th>6</th>\n",
       "      <td>0.103799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   tip_pct\n",
       "day  time   size          \n",
       "Sat  Dinner 1     0.231832\n",
       "Fri  Lunch  1     0.223776\n",
       "            3     0.187735\n",
       "            2     0.181969\n",
       "Thur Lunch  1     0.181728\n",
       "Sun  Dinner 2     0.180870\n",
       "Thur Lunch  6     0.173706\n",
       "            2     0.164024\n",
       "Fri  Dinner 2     0.162659\n",
       "Sun  Dinner 5     0.159839\n",
       "Thur Dinner 2     0.159744\n",
       "Sat  Dinner 2     0.155289\n",
       "Sun  Dinner 4     0.153168\n",
       "            3     0.152662\n",
       "Sat  Dinner 3     0.151439\n",
       "Thur Lunch  4     0.145515\n",
       "            3     0.144599\n",
       "Sat  Dinner 4     0.138289\n",
       "Thur Lunch  5     0.121389\n",
       "Fri  Dinner 4     0.117750\n",
       "Sat  Dinner 5     0.106572\n",
       "Sun  Dinner 6     0.103799"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(\"tip_pct\", \n",
    "                 [\"day\", \"time\", \"size\"]).sort_values(by = \"tip_pct\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1a4904d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
       "      <th>Thur</th>\n",
       "      <td>0.181728</td>\n",
       "      <td>0.164024</td>\n",
       "      <td>0.144599</td>\n",
       "      <td>0.145515</td>\n",
       "      <td>0.121389</td>\n",
       "      <td>0.173706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>0.223776</td>\n",
       "      <td>0.181969</td>\n",
       "      <td>0.187735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
       "      <th>Thur</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.159744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.162659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.231832</td>\n",
       "      <td>0.155289</td>\n",
       "      <td>0.151439</td>\n",
       "      <td>0.138289</td>\n",
       "      <td>0.106572</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.180870</td>\n",
       "      <td>0.152662</td>\n",
       "      <td>0.153168</td>\n",
       "      <td>0.159839</td>\n",
       "      <td>0.103799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size                1         2         3         4         5         6\n",
       "time   day                                                             \n",
       "Lunch  Thur  0.181728  0.164024  0.144599  0.145515  0.121389  0.173706\n",
       "       Fri   0.223776  0.181969  0.187735       NaN       NaN       NaN\n",
       "Dinner Thur       NaN  0.159744       NaN       NaN       NaN       NaN\n",
       "       Fri        NaN  0.162659       NaN  0.117750       NaN       NaN\n",
       "       Sat   0.231832  0.155289  0.151439  0.138289  0.106572       NaN\n",
       "       Sun        NaN  0.180870  0.152662  0.153168  0.159839  0.103799"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips1 = tips.pivot_table('tip_pct', ['time', 'day'], 'size', dropna = True)\n",
    "tips1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6c9f1a39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23183196675277998"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips1.max().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "87289c42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.161276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>0.169913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.153152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.166897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tip_pct\n",
       "day           \n",
       "Thur  0.161276\n",
       "Fri   0.169913\n",
       "Sat   0.153152\n",
       "Sun   0.166897"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(\"day\")[[\"tip_pct\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c70b9f79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lunch</th>\n",
       "      <td>0.164128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dinner</th>\n",
       "      <td>0.159518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tip_pct\n",
       "time            \n",
       "Lunch   0.164128\n",
       "Dinner  0.159518"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(\"time\")[[\"tip_pct\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "41896c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.217292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.165719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.152157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.145949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.141495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.156229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tip_pct\n",
       "size          \n",
       "1     0.217292\n",
       "2     0.165719\n",
       "3     0.152157\n",
       "4     0.145949\n",
       "5     0.141495\n",
       "6     0.156229"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(\"size\")[[\"tip_pct\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9569fb79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.212445</td>\n",
       "      <td>0.167426</td>\n",
       "      <td>0.159109</td>\n",
       "      <td>0.138681</td>\n",
       "      <td>0.129267</td>\n",
       "      <td>0.138752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.026905</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>0.019411</td>\n",
       "      <td>0.015219</td>\n",
       "      <td>0.027493</td>\n",
       "      <td>0.049432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.181728</td>\n",
       "      <td>0.155289</td>\n",
       "      <td>0.144599</td>\n",
       "      <td>0.117750</td>\n",
       "      <td>0.106572</td>\n",
       "      <td>0.103799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.202752</td>\n",
       "      <td>0.160473</td>\n",
       "      <td>0.149729</td>\n",
       "      <td>0.133154</td>\n",
       "      <td>0.113980</td>\n",
       "      <td>0.121276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.223776</td>\n",
       "      <td>0.163341</td>\n",
       "      <td>0.152050</td>\n",
       "      <td>0.141902</td>\n",
       "      <td>0.121389</td>\n",
       "      <td>0.138752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.227804</td>\n",
       "      <td>0.176658</td>\n",
       "      <td>0.161430</td>\n",
       "      <td>0.147428</td>\n",
       "      <td>0.140614</td>\n",
       "      <td>0.156229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.231832</td>\n",
       "      <td>0.181969</td>\n",
       "      <td>0.187735</td>\n",
       "      <td>0.153168</td>\n",
       "      <td>0.159839</td>\n",
       "      <td>0.173706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size          1         2         3         4         5         6\n",
       "count  3.000000  6.000000  4.000000  4.000000  3.000000  2.000000\n",
       "mean   0.212445  0.167426  0.159109  0.138681  0.129267  0.138752\n",
       "std    0.026905  0.011250  0.019411  0.015219  0.027493  0.049432\n",
       "min    0.181728  0.155289  0.144599  0.117750  0.106572  0.103799\n",
       "25%    0.202752  0.160473  0.149729  0.133154  0.113980  0.121276\n",
       "50%    0.223776  0.163341  0.152050  0.141902  0.121389  0.138752\n",
       "75%    0.227804  0.176658  0.161430  0.147428  0.140614  0.156229\n",
       "max    0.231832  0.181969  0.187735  0.153168  0.159839  0.173706"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "6c9c70eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>tip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Yes</th>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tip\n",
       "sex    smoker      \n",
       "Male   Yes     9.00\n",
       "       No      7.75\n",
       "Female Yes     5.50\n",
       "       No      4.20"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def peak_to_peak(x):\n",
    "    return x.max() - x.min()\n",
    "\n",
    "\n",
    "tips.groupby([\"sex\", \"smoker\"])[[\"tip\"]].agg(peak_to_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "b658dad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>tip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Yes</th>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tip\n",
       "sex    smoker      \n",
       "Male   Yes     9.00\n",
       "       No      7.75\n",
       "Female Yes     5.50\n",
       "       No      4.20"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby([\"sex\", \"smoker\"])[[\"tip\"]].apply(peak_to_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "1cbfa90b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>peak_to_peak</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Yes</th>\n",
       "      <td>22.284500</td>\n",
       "      <td>43.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>19.791237</td>\n",
       "      <td>40.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>17.977879</td>\n",
       "      <td>41.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>18.105185</td>\n",
       "      <td>28.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              total_bill             \n",
       "                    mean peak_to_peak\n",
       "sex    smoker                        \n",
       "Male   Yes     22.284500        43.56\n",
       "       No      19.791237        40.82\n",
       "Female Yes     17.977879        41.23\n",
       "       No      18.105185        28.58"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby([\"sex\", \"smoker\"]).agg([\"mean\", peak_to_peak])[[\"total_bill\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a90d825b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>tip_pct</th>\n",
       "      <th>total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Yes</th>\n",
       "      <td>0.152771</td>\n",
       "      <td>43.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.160669</td>\n",
       "      <td>40.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>0.182150</td>\n",
       "      <td>41.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.156921</td>\n",
       "      <td>28.58</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tip_pct  total_bill\n",
       "sex    smoker                      \n",
       "Male   Yes     0.152771       43.56\n",
       "       No      0.160669       40.82\n",
       "Female Yes     0.182150       41.23\n",
       "       No      0.156921       28.58"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby([\"sex\", \"smoker\"]).agg(\n",
    "    {'tip_pct': 'mean', 'total_bill': peak_to_peak})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "35e18fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"2\" halign=\"left\">size</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Male</th>\n",
       "      <th>Thur</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.164417</td>\n",
       "      <td>0.165706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>2.125000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.144730</td>\n",
       "      <td>0.138005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2.629630</td>\n",
       "      <td>2.656250</td>\n",
       "      <td>0.139067</td>\n",
       "      <td>0.162132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.883721</td>\n",
       "      <td>0.173964</td>\n",
       "      <td>0.158291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Female</th>\n",
       "      <th>Thur</th>\n",
       "      <td>2.428571</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>0.163073</td>\n",
       "      <td>0.155971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.209129</td>\n",
       "      <td>0.165296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>0.163817</td>\n",
       "      <td>0.147993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>0.237075</td>\n",
       "      <td>0.165710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 size             tip_pct          \n",
       "smoker            Yes        No       Yes        No\n",
       "sex    day                                         \n",
       "Male   Thur  2.300000  2.500000  0.164417  0.165706\n",
       "       Fri   2.125000  2.000000  0.144730  0.138005\n",
       "       Sat   2.629630  2.656250  0.139067  0.162132\n",
       "       Sun   2.600000  2.883721  0.173964  0.158291\n",
       "Female Thur  2.428571  2.480000  0.163073  0.155971\n",
       "       Fri   2.000000  2.500000  0.209129  0.165296\n",
       "       Sat   2.200000  2.307692  0.163817  0.147993\n",
       "       Sun   2.500000  3.071429  0.237075  0.165710"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'], ['sex', 'day'], 'smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "5155b242",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>No</th>\n",
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       "      <td>0</td>\n",
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       "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
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       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Yes</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "day                   Thur  Fri  Sat  Sun\n",
       "time   sex    smoker                     \n",
       "Lunch  Male   Yes       23    5    0    0\n",
       "              No        50    0    0    0\n",
       "       Female Yes       17    6    0    0\n",
       "              No        60    3    0    0\n",
       "Dinner Male   Yes        0   12   71   39\n",
       "              No         0    4   85  124\n",
       "       Female Yes        0    8   33   10\n",
       "              No         2    2   30   43"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table('size', ['time', 'sex', 'smoker'], 'day',\n",
    "                 aggfunc='sum', fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "da82a90a",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "8b70c2d4",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch   fare embarked   class  \\\n",
       "886         0       2    male  27.0      0      0  13.00        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.00        S   First   \n",
       "888         0       3  female   NaN      1      2  23.45        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.00        C   First   \n",
       "890         0       3    male  32.0      0      0   7.75        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ec899fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone age_class  \n",
       "0    man        True  NaN  Southampton    no  False        성인  \n",
       "1  woman       False    C    Cherbourg   yes  False        성인  \n",
       "2  woman       False  NaN  Southampton   yes   True        성인  \n",
       "3  woman       False    C  Southampton   yes  False        성인  \n",
       "4    man        True  NaN  Southampton    no   True        성인  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ['미성년', '성인', ' 노인']\n",
    "bins = [1, 20, 60, 100]\n",
    "titanic['age_class'] = pd.cut(titanic['age'], bins, labels = labels)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d069cb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>미성년</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.465116</td>\n",
       "      <td>0.671233</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>미성년</th>\n",
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       "      <td>0.180556</td>\n",
       "      <td>0.228261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.426829</td>\n",
       "      <td>0.051948</td>\n",
       "      <td>0.132948</td>\n",
       "      <td>0.186747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>노인</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>0.654054</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.227666</td>\n",
       "      <td>0.397143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class                First    Second     Third       All\n",
       "sex    age_class                                        \n",
       "female 미성년        0.928571  1.000000  0.465116  0.671233\n",
       "       성인         0.971014  0.896552  0.407407  0.779006\n",
       "        노인        1.000000       NaN  1.000000  1.000000\n",
       "male   미성년        0.500000  0.357143  0.180556  0.228261\n",
       "       성인         0.426829  0.051948  0.132948  0.186747\n",
       "        노인        0.083333  0.333333  0.000000  0.105263\n",
       "All               0.654054  0.464286  0.227666  0.397143"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', ['sex', 'age_class'], 'class', margins = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "62ef6d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>class</th>\n",
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       "  <tbody>\n",
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       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
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       "      <td>0.742038</td>\n",
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       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
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      ],
      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', 'sex', 'class', margins = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "73aa3599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>미성년</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.465116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성인</th>\n",
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       "    <tr>\n",
       "      <th>노인</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>미성년</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.180556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성인</th>\n",
       "      <td>0.426829</td>\n",
       "      <td>0.051948</td>\n",
       "      <td>0.132948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>노인</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  survived                    \n",
       "class                First    Second     Third\n",
       "sex    age_class                              \n",
       "female 미성년        0.928571  1.000000  0.465116\n",
       "       성인         0.971014  0.896552  0.407407\n",
       "        노인        1.000000       NaN  1.000000\n",
       "male   미성년        0.500000  0.357143  0.180556\n",
       "       성인         0.426829  0.051948  0.132948\n",
       "        노인        0.083333  0.333333  0.000000"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby([\"sex\", \"age_class\", \"class\"])[[\"survived\"]].mean().unstack(\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f0653eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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