Lecture07_Pandas_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# &#50672;&#49549;&#51201;&#51004;&#47196; &#44050;&#51012; &#52636;&#47141;\n",
    "# from IPython.core.interactiveshell import InteractiveShell\n",
    "# InteractiveShell.ast_node_interactivity = \"last_expr\"\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "- NumPy&#47484; &#44592;&#48152;&#51004;&#47196; &#47564;&#46308;&#50612;&#51652; &#49352;&#47196;&#50868; &#54056;&#53412;&#51648;\n",
    "- **DataFrame**&#51060;&#46972;&#45716; &#54952;&#50984;&#51201;&#51064; &#51088;&#47308;&#44396;&#51312;&#47484; &#51228;&#44277;\n",
    " - &#54665;&#44284; &#50676; &#47112;&#51060;&#48660;&#51060; &#48512;&#52265;&#46108; &#45796;&#52264;&#50896; &#48176;&#50676;\n",
    " - &#50668;&#47084; &#44032;&#51648; &#53440;&#51077;&#51032; &#45936;&#51060;&#53552;&#47484; &#44032;&#51656; &#49688; &#51080;&#51004;&#47728; &#45936;&#51060;&#53552; &#45572;&#46973;&#46020; &#54728;&#50857;(NaN)\n",
    "- &#47112;&#51060;&#48660;&#51060; &#48537;&#51008; &#45936;&#51060;&#53552;&#47484; &#50948;&#54620; &#54200;&#47532;&#54620; &#49828;&#53664;&#47532;&#51648; &#51064;&#53552;&#54168;&#51060;&#49828;&#47484; &#51228;&#44277;\n",
    "- &#45936;&#51060;&#53552;&#48288;&#51060;&#49828; &#54532;&#47112;&#51076;&#50892;&#53356;&#50752; &#49828;&#54532;&#47112;&#46300;&#49884;&#53944; &#54532;&#47196;&#44536;&#47016; &#49324;&#50857;&#51088;&#50640;&#44172; &#51061;&#49689;&#54620; &#44053;&#47141;&#54620; &#45936;&#51060;&#53552; &#50672;&#49328;&#51012; &#44396;&#54788;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas &#44061;&#52404;\n",
    "- <U>&#54665;&#44284; &#50676;&#51060; &#45800;&#49692; &#51221;&#49688;&#54805; &#51064;&#45937;&#49828;&#44032; &#50500;&#45772; **&#47112;&#51060;&#48660;**&#47196; &#49885;&#48324;&#46104;&#45716; NumPy&#51032; (&#44396;&#51312;&#54868;&#46108;) &#48176;&#50676;&#51012; &#48372;&#44053;&#54620; &#48260;&#51204;</U>\n",
    "- Pandas&#51032; &#49464; &#44032;&#51648; &#44592;&#48376; &#51088;&#47308;&#44396;&#51312;\n",
    " - Series\n",
    " - DataFrame\n",
    " - Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NumPy&#50752; Pandas&#47484; import &#54620;&#45796;.\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Series &#44061;&#52404;\n",
    " - &#51064;&#45937;&#49905;&#46108; &#45936;&#51060;&#53552;&#51032; 1&#52264;&#50896; &#48176;&#50676;&#51060;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.25\n",
       "1    0.50\n",
       "2    0.75\n",
       "3    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series&#51032; values&#50640; &#51217;&#44540;&#54616;&#44592;\n",
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series&#51032; index&#50640; &#51217;&#44540;&#54616;&#44592;\n",
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index&#47484; &#51060;&#50857;&#54616;&#50668; Series&#51032; value&#50640; &#51217;&#44540;&#54616;&#44592;\n",
    "data[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#51068;&#48152;&#54868;&#46108; NumPy &#48176;&#50676;**\n",
    "> - NumPy &#48176;&#50676;&#50640;&#45716; &#44050;&#50640; &#51217;&#44540;&#54616;&#45716; &#45936; &#49324;&#50857;&#46104;&#45716; &#50516;&#47925;&#51201;&#51004;&#47196; &#51221;&#51032;&#46108; &#51221;&#49688;&#54805; &#51064;&#45937;&#49828;&#44032; &#51316;&#51116;\n",
    "> - Pandas Series&#50640;&#45716; &#44050;&#50640; &#50672;&#44208;&#46108; **&#47749;&#49884;&#51201;**&#51004;&#47196; &#51221;&#51032;&#46108; &#51064;&#45937;&#49828;&#44032; &#51316;&#51116;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0], index=['a', 'b', 'c', 'd'])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#53945;&#49688;&#54620; &#46357;&#49492;&#45320;&#47532;**\n",
    "> - &#46357;&#49492;&#45320;&#47532;&#45716; &#51068;&#47144;&#51032; &#51076;&#51032;&#51032; &#44050;&#50640; &#51076;&#51032;&#51032; &#53412;&#47484; &#47588;&#54609;&#54616;&#45716; &#44396;&#51312;\n",
    "> - Series&#45716; &#53440;&#51077;&#51060; &#51648;&#51221;&#46108; &#53412;&#47484; &#51068;&#47144;&#51032; &#53440;&#51077;&#51060; &#51648;&#51221;&#46108; &#44050;&#50640; &#47588;&#54609;&#54616;&#45716; &#44396;&#51312; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "New York      19651127\n",
       "Texas         26448193\n",
       "california    38332521\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {'california': 38332521,\n",
    "                  'Texas': 26448193,\n",
    "                  'New York': 19651127,\n",
    "                  'Florida': 19552860,\n",
    "                  'Illinois': 12882135}\n",
    "\n",
    "# Series&#51032; &#49373;&#49457;&#51088;&#47196; &#46357;&#49492;&#45320;&#47532;&#47484; &#45347;&#45716;&#45796;.\n",
    "population = pd.Series(population_dict)\n",
    "\n",
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Series &#44061;&#52404; &#44396;&#49457;&#54616;&#44592;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data&#44032; &#47532;&#49828;&#53944;&#45208; NumPy &#48176;&#50676;&#51068; &#49688; &#51080;&#44256;, &#44536;&#47088; &#44221;&#50864; index&#45716; &#51221;&#49688;&#44032; &#44592;&#48376;\n",
    "pd.Series([2, 4, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    5\n",
       "200    5\n",
       "300    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data&#45716; &#51648;&#51221;&#46108; &#51064;&#45937;&#49828;&#47484; &#52292;&#50864;&#44592; &#50948;&#49352; &#48152;&#48373;&#46104;&#45716; &#49828;&#52860;&#46972;&#44050;&#51068; &#49688; &#51080;&#45796;.\n",
    "pd.Series(5, index=[100, 200, 300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    b\n",
       "2    a\n",
       "3    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data&#44032; &#46357;&#49492;&#45320;&#47532;&#51068;&#46412;, &#51060; &#44221;&#50864; index&#45716; &#44592;&#48376;&#51201;&#51004;&#47196; &#46357;&#49492;&#45320;&#47532; &#53412;&#47484; &#51221;&#47148;&#54644;&#49436; &#52712;&#54620;&#45796;.\n",
    "pd.Series({2:'a', 1:'b', 3:'c'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    c\n",
       "2    a\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data&#44032; &#46357;&#49492;&#45320;&#47532;&#51068;&#46412;, index&#47484; &#47749;&#49884;&#51201;&#51004;&#47196; &#49444;&#51221;&#54624; &#49688; &#51080;&#45796;.\n",
    "pd.Series({2:'a', 1:'b', 3:'c'}, index=[3, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=red>Pandas DataFrame &#44061;&#52404;</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#51068;&#48152;&#54868;&#46108; NumPy &#48176;&#50676;**\n",
    "> - <U>&#50976;&#50672;&#54620; **&#54665; &#51064;&#45937;&#49828;**&#50752; &#50976;&#50672;&#54620; **&#50676; &#51060;&#47492;**&#51012; &#44032;&#51652; 2&#52264;&#50896; &#48176;&#50676;</U>\n",
    "> - &#51221;&#47148;&#46108; Series &#44061;&#52404;&#51032; &#50672;&#49549;&#51004;&#47196; &#48380; &#49688; &#51080;&#45796;. (&#51221;&#47148; : &#44057;&#51008; &#51064;&#45937;&#49828;&#47484; &#44277;&#50976;&#54620;&#45796;!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "california    423967\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_dict = {'california': 423967,\n",
    "            'Texas': 695662,\n",
    "            'New York': 141297,\n",
    "            'Florida': 170312,\n",
    "            'Illinois': 149995}\n",
    "\n",
    "# &#46357;&#49492;&#45320;&#47532;&#51032; &#53412;&#47484; &#44592;&#51456;&#51004;&#47196; &#51221;&#47148;&#54644;&#49436; &#48372;&#50668;&#51456;&#45796;.\n",
    "area = pd.Series(area_dict)\n",
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "New York      19651127\n",
       "Texas         26448193\n",
       "california    38332521\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {'california': 38332521,\n",
    "                  'Texas': 26448193,\n",
    "                  'New York': 19651127,\n",
    "                  'Florida': 19552860,\n",
    "                  'Illinois': 12882135}\n",
    "\n",
    "population = pd.Series(population_dict)\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>california</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  population\n",
       "Florida     170312    19552860\n",
       "Illinois    149995    12882135\n",
       "New York    141297    19651127\n",
       "Texas       695662    26448193\n",
       "california  423967    38332521"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series &#44061;&#52404; 2&#44060;&#47484; &#51060;&#50857;&#54616;&#50668; DataFrame&#51012; &#49373;&#49457;&#54620;&#45796;. key&#45716; 'population', 'area'&#44032; &#46104;&#47728; &#50676; &#51064;&#45937;&#49828;&#44032; &#46108;&#45796;.\n",
    "# &#46160; &#44060;&#51032; Series&#44032; &#44277;&#53685;&#51004;&#47196; &#44032;&#51648;&#44256; &#51080;&#45912; key &#51060;&#47492;&#51060; &#54665; &#51064;&#45937;&#49828;&#44032; &#46108;&#45796;.\n",
    "states = pd.DataFrame({'population': population,\n",
    "                      'area':area})\n",
    "\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Florida', 'Illinois', 'New York', 'Texas', 'california'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#51064;&#45937;&#49828; &#47112;&#51060;&#48660;&#50640; &#51217;&#44540;\n",
    "states.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area', 'population'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#50676; &#47112;&#51060;&#48660;&#50640; &#51217;&#44540;\n",
    "states.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#53945;&#49688;&#54620; &#46357;&#49492;&#45320;&#47532;**\n",
    "> - &#50676; &#51060;&#47492;&#51012; &#50676; &#45936;&#51060;&#53552;&#47196; &#51060;&#47364;&#51652; Series&#50640; &#47588;&#54609;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "california    423967\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states['area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **DataFrame &#44061;&#52404; &#44396;&#49457;&#54616;&#44592;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>california</th>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population\n",
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "New York      19651127\n",
       "Texas         26448193\n",
       "california    38332521"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"&#45800;&#51068; Series &#44061;&#52404;&#50640;&#49436; &#44396;&#49457;&#54616;&#44592;\"\"\"\n",
    "\n",
    "pd.DataFrame(population, columns=['population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'a': 0, 'b': 0}, {'a': 1, 'b': 2}, {'a': 2, 'b': 4}]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  0  0\n",
       "1  1  2\n",
       "2  2  4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"&#46357;&#49492;&#45320;&#47532;&#51032; &#47532;&#49828;&#53944;&#50640;&#49436; &#44396;&#49457;&#54616;&#44592;\"\"\"\n",
    "\n",
    "data = [{'a': i, 'b': 2*i} for i in range(3)]\n",
    "display(data)\n",
    "\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': [1, 2, 3], 'b': [4, 5, 6]}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"&#46357;&#49492;&#45320;&#47532;&#47196; &#44396;&#49457;&#54616;&#44592;\"\"\"\n",
    "\n",
    "data = {'a': [1, 2, 3], 'b': [4, 5, 6]}\n",
    "display(data)\n",
    "\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "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>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>california</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  population\n",
       "Florida     170312    19552860\n",
       "Illinois    149995    12882135\n",
       "New York    141297    19651127\n",
       "Texas       695662    26448193\n",
       "california  423967    38332521"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Series &#44061;&#52404;&#51032; &#46357;&#49492;&#45320;&#47532;&#50640; &#44396;&#49457;&#54616;&#44592;\"\"\"\n",
    "\n",
    "print(type(population))\n",
    "print(type(area))\n",
    "\n",
    "pd.DataFrame({'population': population, 'area': area})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(0, 0.), (0, 0.), (0, 0.)], dtype=[('A', '<i8'), ('B', '<f8')])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B\n",
       "0  0  0.0\n",
       "1  0  0.0\n",
       "2  0  0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"NumPy&#51032; &#44396;&#51312;&#54868;&#46108; &#48176;&#50676;&#50640;&#49436; &#44396;&#49457;&#54616;&#44592;\"\"\"\n",
    "\n",
    "a = np.zeros(3, dtype = [('A', 'i8'), ('B','f8')])\n",
    "display(a)\n",
    "\n",
    "pd.DataFrame(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a  b    c\n",
       "0  1.0  2  NaN\n",
       "1  NaN  2  3.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#46357;&#49492;&#45320;&#47532;&#51032; &#51068;&#48512; &#53412;&#44032; &#45572;&#46973;&#46104;&#45908;&#46972;&#46020; Padnas&#45716; NaN &#44050;&#51004;&#47196; &#52292;&#50868;&#45796;. (Not A Number)\n",
    "pd.DataFrame([{'a': 1, 'b': 2}, {'b': 2, 'c': 3}])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Index &#44061;&#52404;\n",
    "- &#48520;&#48320;&#51032; &#48176;&#50676;&#51060;&#45208; &#51221;&#47148;&#46108; &#51665;&#54633;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#48520;&#48320;&#51032; &#48176;&#50676;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 3, 5, 7, 11], dtype='int64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.Index([2, 3, 5, 7, 11])\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 5, 11], dtype='int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(idx[1])\n",
    "display(idx[::2])  # Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NumPy &#48176;&#50676;&#50640;&#49436; &#51061;&#49689;&#54620; &#49549;&#49457;&#46308;\n",
    "display(idx.size)\n",
    "display(idx.shape)\n",
    "display(idx.ndim)\n",
    "display(idx.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Index does not support mutable operations",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-d43dc682b34d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# &#51068;&#48152;&#51201;&#51064; &#48169;&#48277;&#51004;&#47196;&#45716; &#48320;&#44221;&#46112; &#49688; &#50630;&#45796;. &#51593; Immutable &#54616;&#45796;.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1723\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1724\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1726\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Index does not support mutable operations"
     ]
    }
   ],
   "source": [
    "# &#51068;&#48152;&#51201;&#51064; &#48169;&#48277;&#51004;&#47196;&#45716; &#48320;&#44221;&#46112; &#49688; &#50630;&#45796;. &#51593; Immutable &#54616;&#45796;.\n",
    "idx[1] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#51221;&#47148;&#46108; &#51665;&#54633;**\n",
    ">- &#54028;&#51060;&#50028;&#50640; &#45236;&#51109;&#46108; set &#45936;&#51060;&#53552; &#44396;&#51312;&#50640;&#49436; &#49324;&#50857;&#54616;&#45716; &#54364;&#44592;&#48277;&#51012; &#46384;&#47480;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 5, 7], dtype='int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 3, 5, 7, 9, 11], dtype='int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 9, 11], dtype='int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_A = pd.Index([1, 3, 5, 7, 9])\n",
    "idx_B = pd.Index([2, 3, 5, 7, 11])\n",
    "\n",
    "# &#44368;&#51665;&#54633;\n",
    "display(idx_A & idx_B)\n",
    "\n",
    "# &#54633;&#51665;&#54633;\n",
    "display(idx_A | idx_B)\n",
    "\n",
    "# &#50668;&#51665;&#54633;\n",
    "display(idx_A ^ idx_B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#45936;&#51060;&#53552; &#51064;&#45937;&#49905;&#44284; &#49440;&#53469;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series&#50640;&#49436; &#45936;&#51060;&#53552; &#49440;&#53469;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Series: &#46357;&#49492;&#45320;&#47532;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0.25\n",
      "b    0.50\n",
      "c    0.75\n",
      "d    1.00\n",
      "dtype: float64\n",
      "0.5\n"
     ]
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0], index = ['a', 'b', 'c', 'd'])\n",
    "\n",
    "print(data)\n",
    "print(data['b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#53412;&#51032; &#54252;&#54632; &#50976;&#47924;&#47484; &#52404;&#53356;&#54644;&#48372;&#51088;\n",
    "'a' in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#53412;&#51665;&#54633;\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "e    1.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#49352;&#47196;&#50868; &#44050; &#52628;&#44032;\n",
    "data['e'] = 1.25\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Series: 1&#52264;&#50896; &#48176;&#50676;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "e    1.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"&#47749;&#49884;&#51201;&#51064; &#51064;&#45937;&#49828;&#47196; &#49836;&#46972;&#51060;&#49905;&#54616;&#44592;\"\"\"\n",
    "\n",
    "# Python &#52972;&#47113;&#49496;, NumPy &#48176;&#50676;&#50640;&#49436;&#50752;&#45716; &#45804;&#47532; &#47560;&#51648;&#47561; &#51064;&#45937;&#49828;&#44032; &#54252;&#54632;&#46108;&#45796;.\n",
    "data['a':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"&#50516;&#47925;&#51201; &#51221;&#49688; &#51064;&#45937;&#49828;&#47196; &#49836;&#46972;&#51060;&#49905;&#54616;&#44592;\"\"\"\n",
    "\n",
    "# &#47560;&#51648;&#47561; &#51064;&#45937;&#49828;&#45716; &#54252;&#54632;&#46104;&#51648; &#50506;&#45716;&#45796;.\n",
    "data[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Masking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "c     True\n",
       "d     True\n",
       "e     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data > 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     True\n",
       "b     True\n",
       "c     True\n",
       "d    False\n",
       "e    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data < 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "c     True\n",
       "d    False\n",
       "e    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data > 0.3) & (data < 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0.50\n",
       "c    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data > 0.3) & (data < 0.8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "e    1.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Fancy &#51064;&#45937;&#49905;\"\"\"\n",
    "\n",
    "data[['a', 'e']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame&#50640;&#49436; &#45936;&#51060;&#53552; &#49440;&#53469;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "New York      19651127\n",
       "Texas         26448193\n",
       "california    38332521\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# &#48120;&#44397;&#51032; population\n",
    "display(population)\n",
    "display(type(population))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "california    423967\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# &#48120;&#44397;&#51032; area\n",
    "display(area)\n",
    "display(type(area))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>california</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  population\n",
       "Florida     170312    19552860\n",
       "Illinois    149995    12882135\n",
       "New York    141297    19651127\n",
       "Texas       695662    26448193\n",
       "california  423967    38332521"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'population': population, 'area': area})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "california    423967\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#50676; &#51060;&#47492;&#51004;&#47196; &#46108; &#46357;&#49492;&#45320;&#47532; &#49828;&#53440;&#51068;&#51032; &#51064;&#45937;&#49905;&#51012; &#53685;&#54644; &#51217;&#44540;\n",
    "data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "california    423967\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#50676; &#51060;&#47492;&#51012; &#51060;&#50857;&#54644; &#49549;&#49457; &#49828;&#53440;&#51068;&#47196; &#51217;&#44540;\n",
    "data.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "      <td>85.883763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>california</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  population     density\n",
       "Florida     170312    19552860  114.806121\n",
       "Illinois    149995    12882135   85.883763\n",
       "New York    141297    19651127  139.076746\n",
       "Texas       695662    26448193   38.018740\n",
       "california  423967    38332521   90.413926"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#49352;&#47196;&#50868; &#50676;&#51012; &#52628;&#44032; (NumPy&#51032; ufunc&#51032; &#48289;&#53552;&#54868; &#50672;&#49328;)\n",
    "data['density'] = data['population'] / data['area']\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **DataFrame: 2&#52264;&#50896; &#48176;&#50676;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.70312000e+05, 1.95528600e+07, 1.14806121e+02],\n",
       "       [1.49995000e+05, 1.28821350e+07, 8.58837628e+01],\n",
       "       [1.41297000e+05, 1.96511270e+07, 1.39076746e+02],\n",
       "       [6.95662000e+05, 2.64481930e+07, 3.80187404e+01],\n",
       "       [4.23967000e+05, 3.83325210e+07, 9.04139261e+01]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#45936;&#51060;&#53552; &#48176;&#50676;\n",
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Florida</th>\n",
       "      <th>Illinois</th>\n",
       "      <th>New York</th>\n",
       "      <th>Texas</th>\n",
       "      <th>california</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>1.703120e+05</td>\n",
       "      <td>1.499950e+05</td>\n",
       "      <td>1.412970e+05</td>\n",
       "      <td>6.956620e+05</td>\n",
       "      <td>4.239670e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>1.955286e+07</td>\n",
       "      <td>1.288214e+07</td>\n",
       "      <td>1.965113e+07</td>\n",
       "      <td>2.644819e+07</td>\n",
       "      <td>3.833252e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>1.148061e+02</td>\n",
       "      <td>8.588376e+01</td>\n",
       "      <td>1.390767e+02</td>\n",
       "      <td>3.801874e+01</td>\n",
       "      <td>9.041393e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Florida      Illinois      New York         Texas  \\\n",
       "area        1.703120e+05  1.499950e+05  1.412970e+05  6.956620e+05   \n",
       "population  1.955286e+07  1.288214e+07  1.965113e+07  2.644819e+07   \n",
       "density     1.148061e+02  8.588376e+01  1.390767e+02  3.801874e+01   \n",
       "\n",
       "              california  \n",
       "area        4.239670e+05  \n",
       "population  3.833252e+07  \n",
       "density     9.041393e+01  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#54665;&#44284; &#50676;&#51012; &#48148;&#44984;&#44592; (Transpose)\n",
    "data.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#50516;&#47925;&#51201; &#51221;&#49688; &#51064;&#45937;&#49828; (area&#45716; 0, population&#51008; 1, density&#45716; 2)&#47484; &#51060;&#50857;&#54644;&#49436; &#54665;&#51004;&#47196; &#51217;&#44540; &#44032;&#45733;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.70312000e+05, 1.95528600e+07, 1.14806121e+02])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#52395;&#48264;&#51704; &#54665;&#50640; &#51217;&#44540;. values&#47484; &#51060;&#50857;&#54616;&#50688;&#51020;&#51012; &#44592;&#50613;&#54616;&#46972;. &#51593; data[0]&#51060; &#50500;&#45768;&#45796;. Florida &#45936;&#51060;&#53552;&#50640; &#51217;&#44540;.\n",
    "data.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "california    423967\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#50676;&#50640; &#51217;&#44540;\n",
    "data['area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#52628;&#44032;&#51201;&#51064; &#51064;&#45937;&#49905; &#44508;&#52825;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "california    423967\n",
       "Name: area, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# &#51064;&#45937;&#49905;&#51008; &#50676;&#51012; &#52280;&#51312;\n",
    "display(data['area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Florida'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2524\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Florida'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-790265778ff2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# &#54665;&#51012; &#52280;&#51312;&#54644;&#48372;&#51088;. &#54665;&#50640; &#51217;&#44540;&#54616;&#44592; &#50948;&#54644;&#49436;&#45716; &#50516;&#47925;&#51201; &#51221;&#49688; index&#47484; &#49324;&#50857;&#54620;&#45796;.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Florida'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2137\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2144\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2148\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1840\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1841\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1843\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1844\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3842\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3844\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3845\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2525\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2529\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Florida'"
     ]
    }
   ],
   "source": [
    "# &#54665;&#51012; &#52280;&#51312;&#54644;&#48372;&#51088;. &#54665;&#50640; &#51217;&#44540;&#54616;&#44592; &#50948;&#54644;&#49436;&#45716; &#50516;&#47925;&#51201; &#51221;&#49688; index&#47484; &#49324;&#50857;&#54620;&#45796;.\n",
    "display(data['Florida'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "      <td>85.883763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area  population     density\n",
       "Florida   170312    19552860  114.806121\n",
       "Illinois  149995    12882135   85.883763"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#49836;&#46972;&#51060;&#49905;&#51008; &#54665;&#51012; &#52280;&#51312; (&#54775;&#44040;&#47532;&#51648; &#47568;&#51088;.)\n",
    "data['Florida': 'Illinois']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>california</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  population    density\n",
       "california  423967    38332521  90.413926"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#49836;&#46972;&#51060;&#49905;&#51060; &#50676;&#51012; &#52280;&#51312;&#54616;&#46020;&#47197; &#54644;&#48372;&#51088;.\n",
    "# &#44592;&#45824;&#54664;&#45912; &#44163;&#44284;&#45716; &#45796;&#47476;&#44172; &#47560;&#51648;&#47561; &#54665;&#51060; &#52636;&#47141;&#46108;&#45796;.\n",
    "data['area':'population'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "      <td>85.883763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area  population     density\n",
       "Illinois  149995    12882135   85.883763\n",
       "New York  141297    19651127  139.076746"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#49836;&#46972;&#51060;&#49905;&#51008; &#49707;&#51088;&#47196; &#48320;&#44221; &#44032;&#45733;\n",
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida        True\n",
       "Illinois      False\n",
       "New York       True\n",
       "Texas         False\n",
       "california    False\n",
       "Name: density, dtype: bool"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#47560;&#49828;&#53433; &#50672;&#49328;&#51008; &#54665; &#45800;&#50948;&#47196; &#54644;&#49437;\n",
    "data.density > 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area  population     density\n",
       "Florida   170312    19552860  114.806121\n",
       "New York  141297    19651127  139.076746"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.density > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas&#50640;&#49436; &#45936;&#51060;&#53552; &#50672;&#49328;&#54616;&#44592;\n",
    "&#44592;&#48376; &#49328;&#49696; &#50672;&#49328;(&#45927;&#49480;, &#48772;&#49480;, &#44273;&#49480; &#46321;)&#44284; &#48373;&#51105;&#54620; &#50672;&#49328;(&#49340;&#44033;&#54632;&#49688;, &#51648;&#49688;&#50752; &#47196;&#44536; &#54632;&#49688; &#46321;) &#47784;&#46160;&#50640;&#49436; &#50836;&#49548; &#45800;&#50948;&#51032; &#50672;&#49328;&#51012; &#48736;&#47476;&#44172; &#49688;&#54665;\n",
    " - &#50976;&#45768;&#48260;&#49444; &#54632;&#49688; : &#51064;&#45937;&#49828; &#48372;&#51316;\n",
    " - &#50976;&#45768;&#48260;&#49444; &#54632;&#49688; : &#51064;&#45937;&#49828; &#51221;&#47148;\n",
    " - &#50976;&#45768;&#48260;&#49444; &#54632;&#49688; : DataFrame&#44284; Series&#44036;&#51032; &#50672;&#49328;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#50976;&#45768;&#48260;&#49444; &#54632;&#49688; : &#51064;&#45937;&#49828; &#48372;&#51316;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- Pandas&#45716; NumPy&#50752; &#54632;&#44760; &#51089;&#50629;&#54616;&#46020;&#47197; &#49444;&#44228;&#46096;&#44592; &#46412;&#47928;&#50640; NumPy&#51032; &#50976;&#45768;&#48260;&#49444; &#54632;&#49688;&#44032; Pandas Series&#50752; DataFrame &#44061;&#52404;&#50640; &#46041;&#51089;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    3\n",
       "2    7\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "\n",
    "# 0~10&#44620;&#51648; &#47004;&#45924;&#54616;&#44172; 4&#44060;&#51032; &#49688;&#47484; &#48977;&#50500;&#49436; value&#47196; &#47564;&#46304;&#45796;.\n",
    "ser = pd.Series(rng.randint(0, 10, 4))\n",
    "\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     403.428793\n",
       "1      20.085537\n",
       "2    1096.633158\n",
       "3      54.598150\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index&#45716; &#48372;&#51316;&#46104;&#44256;, value&#50640;&#47564; &#51648;&#49688; &#44050;&#51060; &#51201;&#50857; &#46108;&#45796;.\n",
    "np.exp(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C  D\n",
       "0  6  9  2  6\n",
       "1  7  4  3  7\n",
       "2  7  2  5  4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0~10&#44620;&#51648; &#47004;&#45924;&#54616;&#44172; &#49688;&#47484; &#48977;&#50500;&#49436; 3 by 4 &#54665;&#47148;&#51012; &#47564;&#46304;&#45796;.\n",
    "df = pd.DataFrame(rng.randint(0, 10, (3 ,4)),\n",
    "                 columns=['A', 'B', 'C', 'D'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.224647e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A             B         C             D\n",
       "0 -1.000000  7.071068e-01  1.000000 -1.000000e+00\n",
       "1 -0.707107  1.224647e-16  0.707107 -7.071068e-01\n",
       "2 -0.707107  1.000000e+00 -0.707107  1.224647e-16"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#47560;&#52268;&#44032;&#51648;&#47196; data frame &#45236;&#51032; value&#47564; &#51201;&#50857;&#46108;&#45796;.\n",
    "np.sin(df * np.pi / 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#50976;&#45768;&#48260;&#49444; &#54632;&#49688; : &#51064;&#45937;&#49828; &#51221;&#47148;\n",
    "- &#46160; &#44060;&#51032; Series &#46608;&#45716; DataFrame &#44061;&#52404;&#50640; &#51060;&#54637; &#50672;&#49328;&#51012; &#51201;&#50857;&#54616;&#45716; &#44221;&#50864;, Pandas&#45716; &#50672;&#49328;&#51012; &#49688;&#54665;&#54616;&#45716; &#44284;&#51221;&#50640;&#49436; &#51064;&#45937;&#49828;&#47484; &#51221;&#47148;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Series&#50640;&#49436; &#51064;&#45937;&#49828; &#51221;&#47148;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alaska              NaN\n",
       "California    90.413926\n",
       "New York            NaN\n",
       "Texas         38.018740\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#46160; &#44060;&#51032; &#45796;&#47480; &#45936;&#51060;&#53552; &#49548;&#49828;&#47484; &#44208;&#54633;&#54644; &#48120;&#44397; &#51452;&#50640;&#49436; &#47732;&#51201; &#44592;&#51456; &#49345;&#50948; &#49464; &#44060;&#51032; &#51452;&#50752; &#51064;&#44396; &#44592;&#51456; &#49345;&#50948; &#49464; &#44060;&#51032; &#51452;&#47484; &#52286;&#45716;&#45796;&#44256; &#44032;&#51221;&#54620;&#45796;.\n",
    "area = pd.Series({'Alaska': 1723337, 'Texas': 695662,\n",
    "                 'California': 423967}, name='area')\n",
    "\n",
    "population = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
    "                       'New York': 19651127}, name='population')\n",
    "\n",
    "# &#44208;&#44284; &#48176;&#50676;&#51008; &#46160; &#51077;&#47141; &#48176;&#50676;&#51032; &#51064;&#45937;&#49828;&#51032; &#54633;&#51665;&#54633;&#51012; &#45812;&#44256; &#51080;&#45796;.\n",
    "population / area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- &#46168; &#51473; &#54616;&#45208;&#46972;&#46020; &#44050;&#51060; &#50630;&#45716; &#54637;&#47785;&#51008; Pandas&#44032; &#45572;&#46973;&#46108; &#45936;&#51060;&#53552;&#47484; &#54364;&#49884;&#54616;&#45716; &#48169;&#49885;&#50640; &#46384;&#46972; NaN &#51593;, &#49707;&#51088;&#44032; &#50500;&#45784;(Not a Number)&#51004;&#47196; &#54364;&#49884;&#46108;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    5.0\n",
       "2    9.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = pd.Series([2, 4, 6], index=[0, 1, 2])\n",
    "B = pd.Series([1, 3, 5], index=[1, 2, 3])\n",
    "\n",
    "A + B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- NaN &#44050; &#49324;&#50857;&#51012; &#50896;&#52824; &#50506;&#51012; &#44221;&#50864;, &#50672;&#49328;&#51088; &#45824;&#49888;&#50640; &#51201;&#51208;&#54620; &#44061;&#52404; &#47700;&#49436;&#46300;&#47484; &#49324;&#50857;&#54644; &#52292;&#50864;&#44592; &#44050;&#51012; &#49688;&#51221;&#54624; &#49688; &#51080;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    5.0\n",
       "2    9.0\n",
       "3    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#44061;&#52404; &#47700;&#49436;&#46300; add &#49324;&#50857;, &#48708;&#50612;&#51080;&#45716; &#45936;&#51060;&#53552;&#50640; 0&#51012; &#52292;&#50892;&#46972;.\n",
    "A.add(B, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> DataFrame&#50640;&#49436; &#51064;&#45937;&#49828; &#51221;&#47148;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b\n",
       "0  1  11\n",
       "1  5   1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = pd.DataFrame(rng.randint(0, 20, (2, 2)), columns = list('ab'))\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>b</th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   b  a  c\n",
       "0  4  0  9\n",
       "1  5  8  0\n",
       "2  9  2  6"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = pd.DataFrame(rng.randint(0, 10, (3, 3)), columns = list('bac'))\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a     b   c\n",
       "0   1.0  15.0 NaN\n",
       "1  13.0   6.0 NaN\n",
       "2   NaN   NaN NaN"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#50630;&#45716; &#49688;&#51032; &#44221;&#50864; NaN&#51060; &#52292;&#50892;&#51652;&#45796;.\n",
    "A + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a     b   c\n",
       "0   1.0  15.0 NaN\n",
       "1  13.0   6.0 NaN\n",
       "2   NaN   NaN NaN"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add method&#47484; &#49324;&#50857;&#54616;&#50668; NaN&#51012; &#52292;&#50892; &#45347;&#51012; &#49688; &#51080;&#45796;.\n",
    "A.add(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  a     1\n",
      "   b    11\n",
      "1  a     5\n",
      "   b     1\n",
      "dtype: int64\n",
      "4.5\n"
     ]
    }
   ],
   "source": [
    "print(A.stack())\n",
    "print(A.stack().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a     b     c\n",
       "0   1.0  15.0  13.5\n",
       "1  13.0   6.0   4.5\n",
       "2   6.5  13.5  10.5"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill = A.stack().mean()\n",
    "A.add(B, fill_value = fill)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#50976;&#45768;&#48260;&#49444; &#54632;&#49688; : DataFrame&#44284; Series &#44036;&#51032; &#50672;&#49328;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 8, 2, 4],\n",
       "       [2, 6, 4, 8],\n",
       "       [6, 1, 3, 8]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = rng.randint(10, size = (3, 4))\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0],\n",
       "       [-1, -2,  2,  4],\n",
       "       [ 3, -7,  1,  4]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(A))\n",
    "print(type(A[0]))\n",
    "\n",
    "A - A[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  R  S  T\n",
       "0  3  8  2  4\n",
       "1  2  6  4  8\n",
       "2  6  1  3  8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Q    3\n",
       "R    8\n",
       "S    2\n",
       "T    4\n",
       "Name: 0, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  R  S  T\n",
       "0  0  0  0  0\n",
       "1 -1 -2  2  4\n",
       "2  3 -7  1  4"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(A, columns = list('QRST'))\n",
    "\n",
    "display(df)\n",
    "\n",
    "# df[0] : &#50640;&#47084; &#48156;&#49373;. &#51649;&#51217; &#54644;&#48372;&#49884;&#50724;. df.keys()&#47484; &#52432;&#48372;&#51088;.\n",
    "display(df.iloc[0])\n",
    "\n",
    "# data frame&#50640;&#49436; &#54665;&#51004;&#47196; &#51217;&#44540;&#54616;&#44592; &#50948;&#54644;&#49436;&#45716; iloc&#51012; &#49324;&#50857;&#54620;&#45796;.\n",
    "df - df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q    3\n",
       "S    2\n",
       "Name: 0, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q   R    S   T\n",
       "0  0.0 NaN  0.0 NaN\n",
       "1 -1.0 NaN  2.0 NaN\n",
       "2  3.0 NaN  1.0 NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0&#48264; row&#47484; &#44032;&#51648;&#44256; &#50724;&#46104;, &#49828;&#53597; &#49324;&#51060;&#51592;&#44032; 2&#51060;&#45796;.\n",
    "halfrow = df.iloc[0, ::2]\n",
    "display(halfrow)\n",
    "\n",
    "display(df - halfrow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#45572;&#46973;&#46108; &#45936;&#51060;&#53552; &#52376;&#47532;&#54616;&#44592;\n",
    "### Pandas&#50640;&#49436; &#45572;&#46973;&#46108; &#45936;&#51060;&#53552;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **None : &#54028;&#51060;&#50028;&#51032; &#45572;&#46973;&#46108; &#45936;&#51060;&#53552;**\n",
    "> - None&#51008; &#54028;&#51060;&#50028; &#44061;&#52404;&#51060;&#48064;&#47196; &#47784;&#46304; NumPy / Pandas &#48176;&#50676;&#50640;&#49436; &#49324;&#50857;&#54624; &#49688; &#50630;&#44256; &#45936;&#51060;&#53552; &#53440;&#51077;&#51060; 'object'&#51064; &#48176;&#50676;&#50640;&#49436;&#47564; &#49324;&#50857;&#54624; &#49688; &#51080;&#45796;. (&#51593; &#54028;&#51060;&#50028; &#44061;&#52404;&#51032; &#48176;&#50676;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, None, 3, 4], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value1 = np.array([1, None, 3, 4])\n",
    "\n",
    "# value1&#51032; dtype&#51008; object&#51060;&#45796;.\n",
    "value1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-6bec7f8a793b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# &#48176;&#50676;&#50640;&#49436; &#54028;&#51060;&#50028; &#44061;&#52404;&#47484; &#49324;&#50857;&#54620;&#45796;&#45716; &#44163;&#51008; None &#44050;&#51012; &#44032;&#51652; &#48176;&#50676;&#50640;&#49436; `sum()` &#51060;&#45208; `min()` &#44057;&#51008; &#51665;&#44228; &#50672;&#49328;&#51012; &#54616;&#47732; &#51068;&#48152;&#51201;&#51004;&#47196; &#50724;&#47448;&#44032; &#48156;&#49373;&#54624; &#44163;&#51060;&#45796;.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mvalue1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
     ]
    }
   ],
   "source": [
    "# &#48176;&#50676;&#50640;&#49436; &#54028;&#51060;&#50028; &#44061;&#52404;&#47484; &#49324;&#50857;&#54620;&#45796;&#45716; &#44163;&#51008; None &#44050;&#51012; &#44032;&#51652; &#48176;&#50676;&#50640;&#49436; `sum()` &#51060;&#45208; `min()` &#44057;&#51008; &#51665;&#44228; &#50672;&#49328;&#51012; &#54616;&#47732; &#51068;&#48152;&#51201;&#51004;&#47196; &#50724;&#47448;&#44032; &#48156;&#49373;&#54624; &#44163;&#51060;&#45796;.\n",
    "value1.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **NaN : &#45572;&#46973;&#46108; &#49707;&#51088; &#45936;&#51060;&#53552;**\n",
    "> - Not a Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = np.array([1, np.nan, 3, 4])\n",
    "value2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n",
      "nan\n"
     ]
    }
   ],
   "source": [
    "# NaN &#45927;&#49480;\n",
    "print(1 + np.NaN)\n",
    "\n",
    "# NaN &#44273;&#49480;\n",
    "print(0 * np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n",
      "nan\n"
     ]
    }
   ],
   "source": [
    "# NaN&#51060; &#51316;&#51116; &#54624; &#46412;, &#50672;&#49328;&#50640;&#49436; &#50640;&#47084;&#47484; &#45236;&#51648;&#45716; &#50506;&#51648;&#47564; &#51221;&#54869;&#54620; &#44050;&#51012; &#52636;&#47141;&#54616;&#51648; &#50506;&#45716;&#45796;.\n",
    "print(value2.sum())\n",
    "print(value2.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# &#45572;&#46973;&#46108; &#44050;&#51012; &#47924;&#49884;&#54616;&#45716; &#53945;&#48324;&#54620; &#51665;&#44228; &#50672;&#49328;\n",
    "print(np.nansum(value2))\n",
    "print(np.nanmin(value2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Pandas&#50640;&#49436; NaN&#44284; None**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas&#45716; nan&#44284; none&#51012; &#54840;&#54872;&#49457; &#51080;&#44172; &#52376;&#47532;&#54616;&#44256;, &#51201;&#51208;&#54620; &#44221;&#50864; &#49436;&#47196; &#48320;&#54872;&#54624; &#49688; &#51080;&#44172; &#54664;&#45796;.\n",
    "pd.Series([1, np.nan, 2, None])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#45328; &#44050; &#50672;&#49328;&#54616;&#44592;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#45328; &#44050; &#53456;&#51648;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#48512;&#50872; &#47560;&#49828;&#53356;&#47484; &#48152;&#54872;\n",
    "data = pd.Series([1, np.nan, 'hello', None])\n",
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.notnull())\n",
    "\n",
    "# &#48512;&#50872; &#47560;&#49828;&#53356;&#45716; Series&#45208; DataFrame &#51064;&#45937;&#49828;&#47196; &#51649;&#51217; &#49324;&#50857;&#44032;&#45733;\n",
    "display(data[data.notnull()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#45328; &#44050; &#51228;&#44144;&#54616;&#44592;**\n",
    "> - `dropna()` : nan &#44050; &#51228;&#44144;&#54616;&#44592;\n",
    "> - `fillna()` : nan &#44050; &#52292;&#50864;&#44592;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series &#51032; &#44221;&#50864;\n",
    "data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- DataFrame &#50640;&#49436;&#45716; &#45800;&#51068; &#44050;&#51012; &#51648;&#50872; &#49688;&#45716; &#50630;&#44256; &#51204;&#52404; &#54665;&#51060;&#45208; &#51204;&#52404; &#50676;&#51012; &#49325;&#51228;&#54616;&#45716; &#44163;&#47564; &#44032;&#45733;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1  2\n",
       "0  1.0  NaN  2\n",
       "1  2.0  3.0  5\n",
       "2  NaN  4.0  6"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame &#51032; &#44221;&#50864;\n",
    "df = pd.DataFrame([[1, np.nan, 2], \n",
    "                  [2, 3, 5],\n",
    "                  [np.nan, 4, 6]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1  2\n",
       "1  2.0  3.0  5"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2\n",
       "0  2\n",
       "1  5\n",
       "2  6"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#45328; &#44050;&#51012; &#54252;&#54632;&#54616;&#45716; &#47784;&#46304; &#50676;&#51012; &#49325;&#51228;&#54616;&#44592; - data frame&#50640;&#49436;&#45716; numpy&#50752; &#45804;&#47532; &#50676;&#51012; &#49325;&#51228;&#54616;&#44592; &#50948;&#54644;&#49436; axis = 1&#47484; &#49324;&#50857;&#54616;&#50688;&#51020;&#50640; &#51452;&#51032;&#54616;&#46972;.\n",
    "df.dropna(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- &#50676; &#46608;&#45716; &#54665;&#51012; &#49325;&#51228;&#54624; &#46412;, &#50676;&#44284; &#54665;&#51060; &#44032;&#51648;&#44256; &#51080;&#45716; &#45328;&#44050;&#51032; &#44079;&#49688;&#50640; &#46384;&#46972;&#49436; &#51648;&#50872;&#51648; &#47568;&#51648;&#47484; &#44208;&#51221;&#54624; &#49688; &#51080;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     0    1  2   3\n",
       "0  1.0  NaN  2 NaN\n",
       "1  2.0  3.0  5 NaN\n",
       "2  NaN  4.0  6 NaN"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NaN&#51004;&#47196;&#47564; &#51060;&#47336;&#50612;&#51652; &#52972;&#47100;&#51012; &#52628;&#44032;&#54620;&#45796;.\n",
    "df[3] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     0    1  2\n",
       "0  1.0  NaN  2\n",
       "1  2.0  3.0  5\n",
       "2  NaN  4.0  6"
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     "execution_count": 90,
     "metadata": {},
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   "source": [
    "# column&#51032; &#47784;&#46304; &#50896;&#49548;&#44032; &#45328; &#44050;&#51068; &#46412;&#47564; &#49325;&#51228;\n",
    "df.dropna(axis = 1, how = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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",
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       "\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",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "     0    1  2   3\n",
       "1  2.0  3.0  5 NaN"
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     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row&#51032; &#50896;&#49548; &#51473;&#50640;&#49436; &#45328;&#51060; &#50500;&#45772; &#44050;&#51060; &#52572;&#49548; 3&#44060;&#44032; &#51080;&#50612;&#50556; &#50676;&#51012; &#49325;&#51228; &#54616;&#51648; &#50506;&#51020;\n",
    "df.dropna(axis = 0, thresh = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#45328; &#44050; &#52292;&#50864;&#44592;**\n",
    "> - &#45328; &#44050;&#51012; &#49325;&#51228;&#54616;&#51648; &#50506;&#44256;, &#50976;&#54952;&#54620; &#44050;&#51004;&#47196; &#45824;&#52404;&#54644;&#50556; &#54624; &#46412; `fillna()`&#47700;&#49548;&#46300;&#47484; &#49324;&#50857;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    NaN\n",
       "c    2.0\n",
       "d    NaN\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series &#51068; &#46412;&#45716; key&#47484; &#49444;&#51221;&#54616;&#44592; &#50948;&#54644;&#49436; index, data frame &#51068; &#46412;&#45716; key&#47484; &#49444;&#51221;&#54616;&#44592; &#50948;&#54644;&#49436; columns\n",
    "data = pd.Series([1, np.nan, 2, None, 3], index = list('abcde'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    0.0\n",
       "c    2.0\n",
       "d    0.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#45800;&#51068; &#44050; 0&#51004;&#47196; &#52292;&#50864;&#44592;\n",
    "data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    2.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#51060;&#51204;&#51032; &#44050;(index - 1)&#51004;&#47196; &#52292;&#50864;&#44592;\n",
    "data.fillna(method = 'ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#46244;&#51032; &#44050;(index + 1)&#51004;&#47196; &#52292;&#50864;&#44592;\n",
    "data.fillna(method = 'bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4.0</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3\n",
       "0  1.0  1.0  2.0  2.0\n",
       "1  2.0  3.0  5.0  5.0\n",
       "2  NaN  4.0  6.0  6.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DafaFrame&#51032; &#44221;&#50864; &#52629;&#51012; &#51648;&#51221;&#54616;&#50668; &#52292;&#50872; &#49688; &#51080;&#45796;.\n",
    "display(df)\n",
    "\n",
    "df.fillna(method = 'ffill', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#44228;&#52789;&#51201; &#51064;&#45937;&#49905;\n",
    "- Pandas&#45716; &#44592;&#48376;&#51201;&#51004;&#47196; 3&#52264;&#50896;&#44284; 4&#52264;&#50896; &#45936;&#51060;&#53552;&#47484; &#52376;&#47532;&#54624; &#49688; &#51080;&#45716; Panel&#44284; Panel4D &#44060;&#52404;&#47484; &#51228;&#44277;&#54620;&#45796;.\n",
    "- &#45908; &#51068;&#48152;&#51201;&#51004;&#47196; &#49324;&#50857;&#46104;&#45716; &#54056;&#53556;&#51008; &#45800;&#51068; &#51064;&#45937;&#49828; &#45236;&#50640; &#50668;&#47084; &#51064;&#45937;&#49828; &#47112;&#48296;&#51012; &#54252;&#54632;&#54616;&#45716; **&#44228;&#52789;&#51201; &#51064;&#45937;&#49905;**, **&#45796;&#51473; &#51064;&#45937;&#49905;**&#51060;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=red>&#45936;&#51060;&#53552;&#49464;&#53944; &#44208;&#54633; : Concat&#44284; Append</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#46160; &#44060;&#51032; &#45796;&#47480; &#45936;&#51060;&#53552;&#47484; &#47588;&#50864; &#44036;&#45800;&#54616;&#44172; &#50672;&#44208;&#54616;&#45716; &#44163;&#48512;&#53552;, &#53580;&#51060;&#53552; &#44036; &#44217;&#52824;&#45716; &#48512;&#48516;&#51012; &#51228;&#45824;&#47196; &#52376;&#47532;&#54616;&#45716; &#48373;&#51105;&#54620; &#45936;&#51060;&#53552;&#48288;&#51060;&#49828; &#49828;&#53440;&#51068;&#51012; &#51312;&#51064;&#54616;&#44256; &#48337;&#54633;&#54616;&#45716; &#44163;&#44620;&#51648; &#45796;&#50577;&#54616;&#44172; &#49324;&#50857;&#46112; &#49688; &#51080;&#45796;. Series, DataFrame&#51008; &#51060; &#50976;&#54805;&#51032; &#50672;&#49328;&#51012; &#50684;&#46160;&#50640; &#46160;&#44256; &#47564;&#46308;&#50612;&#51652; &#44163;&#51060;&#45796;.\n",
    "- &#45936;&#51060;&#53552; &#47021;&#44544;&#47553;(Data Wrangling), &#45936;&#51060;&#53552; &#47676;&#51669;(Data Munging) : &#50896;&#51088;&#47308;(raw data)&#47484; &#46608; &#45796;&#47480; &#54805;&#53468;&#47196; &#51204;&#54872;&#54616;&#44144;&#45208; &#47588;&#54609;&#54616;&#45716; &#44284;&#51221;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    A   B   C\n",
       "0  A0  B0  C0\n",
       "1  A1  B1  C1\n",
       "2  A2  B2  C2"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# &#49324;&#50857;&#54624; &#47700;&#49548;&#46300;&#47484; &#48120;&#47532; &#47564;&#46308;&#51088;.\n",
    "def make_df(cols, rows):\n",
    "    \"\"\"&#48736;&#47476;&#44172; DataFrame&#51012; &#49373;&#49457;\"\"\"\n",
    "    data = {c : [str(c) + str(i) for i in rows] for c in cols}\n",
    "    return pd.DataFrame(data, rows)\n",
    "    \n",
    "# &#50696;&#51228;\n",
    "make_df('ABC', range(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pd.concat&#51012; &#51060;&#50857;&#54620; &#44036;&#45800;&#54620; &#50672;&#44208;\n",
    "> **Series &#44061;&#52404;&#47484; &#44036;&#45800;&#54616;&#44172; &#50672;&#44208;&#54624; &#46412; &#49324;&#50857;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    A\n",
       "2    B\n",
       "3    C\n",
       "dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series(['A', 'B', 'C'], index = [1, 2, 3])\n",
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    D\n",
       "5    E\n",
       "6    F\n",
       "dtype: object"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2 = pd.Series(['D', 'E', 'F'], index = [4, 5, 6])\n",
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    A\n",
      "2    B\n",
      "3    C\n",
      "4    D\n",
      "5    E\n",
      "6    F\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Concat&#51032; &#44592;&#48376;&#44050;&#51008; axis=0&#51060;&#45796;. &#51593; row&#50640; concat &#46108;&#45796;.\n",
    "print(pd.concat([ser1, ser2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **DataFrame &#44061;&#52404;&#47484; &#44036;&#45800;&#54616;&#44172; &#50672;&#44208;&#54624; &#46412; &#49324;&#50857; 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
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       "    A   B\n",
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       "4  A4  B4"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "df1 = make_df('AB', [1, 2])\n",
    "df2 = make_df('AB', [3, 4])\n",
    "\n",
    "display(df1)\n",
    "display(df2)\n",
    "\n",
    "# &#44592;&#48376;&#51201;&#51004;&#47196; row &#50500;&#47000;&#47196; &#50672;&#44208;&#46108;&#45796;.\n",
    "display(pd.concat([df1, df2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **DataFrame &#44061;&#52404;&#47484; &#44036;&#45800;&#54616;&#44172; &#50672;&#44208;&#54624; &#46412; &#49324;&#50857; 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "df3 = make_df('AB', [0, 1])\n",
    "df4 = make_df('CD', [0, 1])\n",
    "\n",
    "display(df3)\n",
    "display(df4)\n",
    "\n",
    "# row &#50500;&#47000;&#47196; &#48537;&#51068; &#46412;\n",
    "display(pd.concat([df3, df4], axis = 0))\n",
    "\n",
    "# column &#46244;&#50640; &#48537;&#51068; &#46412;\n",
    "display(pd.concat([df3, df4], axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#51064;&#45937;&#49828; &#48373;&#51228;**\n",
    "> - `np.concatenate`, `pd.concat`&#51032; &#51473;&#50836;&#54620; &#52264;&#51060;&#45716; Pandas&#50640;&#49436;&#51032; &#50672;&#44208;&#51008; &#44536; &#44208;&#44284;&#44032; &#48373;&#51228;&#46108; &#51064;&#45937;&#49828;&#47484; &#44032;&#51648;&#45908;&#46972;&#46020; &#51064;&#45937;&#49828;&#47484; &#50976;&#51648;&#54616;&#45716; &#44163;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>A</th>\n",
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       "  <tbody>\n",
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       "</table>\n",
       "</div>"
      ],
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       "    A   B\n",
       "0  A0  B0\n",
       "1  A1  B1"
      ]
     },
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     "output_type": "display_data"
    },
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     "data": {
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       "<div>\n",
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       "\n",
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       "    A   B\n",
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    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1], dtype='int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1], dtype='int64')"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = make_df('AB', [0, 1])\n",
    "y = make_df('AB', [2, 3])\n",
    "\n",
    "# &#48373;&#51228; &#51064;&#45937;&#49828; &#49373;&#49457;\n",
    "y.index = x.index\n",
    "\n",
    "display(x)\n",
    "display(y)\n",
    "\n",
    "display(x.index)\n",
    "display(y.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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",
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       "\n",
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       "      <th>A</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
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       "    <tr>\n",
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       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
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       "</table>\n",
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       "    A   B\n",
       "0  A0  B0\n",
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       "0  A2  B2\n",
       "1  A3  B3"
      ]
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   "source": [
    "# &#51064;&#45937;&#49828;&#44032; &#48373;&#51228;&#46104;&#50612;&#49436; &#44217;&#52432;&#51652;&#45796;.\n",
    "concated_df = pd.concat([x, y])\n",
    "display(concated_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    A0\n",
       "B    B0\n",
       "Name: 0, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "A    A2\n",
       "B    B2\n",
       "Name: 0, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# &#44592;&#51316;&#51032; &#51064;&#45937;&#49828;&#44032; &#50976;&#51648;&#46108;&#45796;.\n",
    "# Index&#47484; &#44217;&#52432;&#51648;&#51648; &#50506;&#44172; &#52376;&#47532;&#54624; &#49688; &#51080;&#45716; &#48169;&#48277;&#51008;?\n",
    "display(concated_df.iloc[0])\n",
    "display(concated_df.iloc[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValueError: Indexes have overlapping values: [0, 1]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"&#52376;&#47532; &#48169;&#48277; 1 : &#48152;&#48373;&#51012; &#50640;&#47084;&#47196; &#51105;&#50500;&#45240;&#45796;.\"\"\"\n",
    "\n",
    "try:\n",
    "    pd.concat([x, y], verify_integrity = True)\n",
    "except ValueError as e:\n",
    "     print(\"ValueError:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
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    "\"\"\"&#52376;&#47532; &#48169;&#48277; 2 : &#51064;&#45937;&#49828;&#47484; &#47924;&#49884;&#54620;&#45796;.\"\"\"\n",
    "display(x)\n",
    "display(y)\n",
    "\n",
    "display(pd.concat([x, y], ignore_index = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
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       "      A   B\n",
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    "\"\"\"&#52376;&#47532; &#48169;&#48277; 3 : &#45796;&#51473; &#51064;&#45937;&#49828; &#53412;&#47484; &#52628;&#44032;&#54620;&#45796;.\"\"\"\n",
    "display(x)\n",
    "display(y)\n",
    "\n",
    "display(pd.concat([x, y], keys = ['a', 'b']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  </thead>\n",
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       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>0</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "      A   B\n",
       "a 0  A0  B0\n",
       "  1  A1  B1\n",
       "b 0  A2  B2\n",
       "  1  A3  B3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"&#49352;&#47196;&#50868; &#53412;&#44032; row&#50640; &#54805;&#49457;&#46108;&#45796;.\"\"\"\n",
    "new_row_key = pd.concat([x, y], axis=0, keys = ['a', 'b'])\n",
    "display(new_row_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A0'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "A    A0\n",
       "B    B0\n",
       "Name: (a, 0), dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'B3'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#45796;&#51473; &#51064;&#45937;&#49828;&#47484; &#51060;&#50857;&#54616;&#50668; &#54616;&#45208;&#51032; &#50896;&#49548;&#50640; &#51217;&#44540;&#54616;&#47140;&#47732; tuple &#54805;&#53468;&#47196; &#51217;&#44540;&#54644;&#50556; &#54620;&#45796;.\n",
    "display(new_row_key.loc[(\"a\", 0), \"A\"])\n",
    "\n",
    "# &#45796;&#51473; &#51064;&#45937;&#49828;&#47484; &#51060;&#50857;&#54616;&#50668; row&#50640; &#51217;&#44540;&#54616;&#47140;&#47732; &#50500;&#47000;&#50752; &#44057;&#51060; &#51217;&#44540;&#54644;&#50556; &#54620;&#45796;.\n",
    "display(new_row_key.loc[(\"a\", 0)])\n",
    "\n",
    "# &#45796;&#51473; &#51064;&#45937;&#49828;&#50752; iloc&#51012; &#54632;&#44760; &#49324;&#50857;&#54616;&#50668; &#50896;&#49548; &#48143; row&#50640; &#51217;&#44540;&#54624; &#49688;&#45716; &#50630;&#45796;. \n",
    "new_row_key.iloc[3, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">a</th>\n",
       "      <th colspan=\"2\" halign=\"left\">b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a       b    \n",
       "    A   B   A   B\n",
       "0  A0  B0  A2  B2\n",
       "1  A1  B1  A3  B3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"&#49352;&#47196;&#50868; &#53412;&#44032; column&#50640; &#54805;&#49457;&#46108;&#45796;.\"\"\"\n",
    "new_col_key = pd.concat([x, y], axis=1, keys = ['a', 'b'])\n",
    "display(new_col_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A0'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    A0\n",
       "1    A1\n",
       "Name: A, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# &#45796;&#51473; &#51064;&#45937;&#49828;&#47484; &#51060;&#50857;&#54616;&#50668; &#54616;&#45208;&#51032; &#50896;&#49548;&#50640; &#51217;&#44540;&#54616;&#45716; &#48277;&#51008; &#50948;&#50752; &#48708;&#49847;&#54616;&#45796;.\n",
    "display(new_col_key.loc[0, ('a', 'A')])\n",
    "\n",
    "# &#45796;&#51473; &#51064;&#45937;&#49828;&#47484; &#51060;&#50857;&#54616;&#50668; column&#50640; &#51217;&#44540;\n",
    "display(new_col_key['a']['A'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Concat &#48143; Join&#51012; &#51060;&#50857;&#54620; &#50672;&#44208;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C\n",
       "1  A1  B1  C1\n",
       "2  A2  B2  C2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    B   C   D\n",
       "1  B1  C1  D1\n",
       "2  B2  C2  D2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"&#52972;&#47100;&#51032; &#51060;&#47492;&#51060; &#44057;&#51008; &#44163;&#45180;&#47532; &#51312;&#51064;&#46108;&#45796;. defualt&#45716; outer &#51312;&#51064;&#51060;&#45796;.\"\"\"\n",
    "\n",
    "df5 = make_df('ABC', [1, 2])\n",
    "df6 = make_df('BCD', [1, 2])\n",
    "\n",
    "display(df5)\n",
    "display(df6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C    D\n",
       "1   A1  B1  C1  NaN\n",
       "2   A2  B2  C2  NaN\n",
       "1  NaN  B1  C1   D1\n",
       "2  NaN  B2  C2   D2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outer &#51312;&#51064;&#51060;&#44592; &#46412;&#47928;&#50640; &#50630;&#45716; &#44050;&#51008; NaN&#51004;&#47196; &#52292;&#50892;&#51652;&#45796;.\n",
    "display(pd.concat([df5, df6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    B   C\n",
       "1  B1  C1\n",
       "2  B2  C2\n",
       "1  B1  C1\n",
       "2  B2  C2"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"inner &#51312;&#51064; &#49324;&#50857;&#54616;&#47732; &#44368;&#51665;&#54633;&#51004;&#47196; &#48320;&#44221;&#46108;&#45796;.\"\"\"\n",
    "\n",
    "pd.concat([df5, df6], join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C\n",
       "1   A1  B1  C1\n",
       "2   A2  B2  C2\n",
       "1  NaN  B1  C1\n",
       "2  NaN  B2  C2"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"&#45224;&#44592;&#44256; &#49910;&#51008; &#50676;&#51012; &#51648;&#51221;&#54624; &#49688; &#51080;&#45796;.\"\"\"\n",
    "\n",
    "# A, B, C &#50676;&#47564; &#45224;&#44592;&#44256; &#49910;&#51008; &#44221;&#50864;.\n",
    "pd.concat([df5, df6], join_axes=[df5.columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **append &#47700;&#49436;&#46300;**\n",
    "> - &#50896;&#47000;&#51032; &#44061;&#52404;&#47484; &#48320;&#44221;&#54616;&#51648; &#50506;&#44256;, &#44208;&#54633;&#46108; &#45936;&#51060;&#53552;&#47484; &#44032;&#51648;&#45716; &#49352;&#47196;&#50868; &#44061;&#52404;&#47484; &#47564;&#46304;&#45796;.\n",
    "> - &#49352; &#51064;&#45937;&#49828;&#50752; &#45936;&#51060;&#53552; &#48260;&#54140;&#47484; &#49373;&#49457;&#54616;&#44592; &#46412;&#47928;&#50640; &#47588;&#50864; &#54952;&#50984;&#51201;&#51064; &#48169;&#49885;&#51060;&#46972;&#44256; &#48372;&#44592;&#45716; &#50612;&#47157;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B\n",
       "1  A1  B1\n",
       "2  A2  B2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B\n",
       "3  A3  B3\n",
       "4  A4  B4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df1)\n",
    "display(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B\n",
       "1  A1  B1\n",
       "2  A2  B2\n",
       "3  A3  B3\n",
       "4  A4  B4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df1.append(df2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=red>&#45936;&#51060;&#53552;&#49464;&#53944; &#44208;&#54633;&#54616;&#44592; : &#48337;&#54633;&#44284; &#51312;&#51064;</font>\n",
    " - Pandas&#44032; &#51228;&#44277;&#54616;&#45716; &#44592;&#48376; &#44592;&#45733;&#51032; &#54616;&#45208;&#45716; &#44256;&#49457;&#45733; &#51064;&#47700;&#47784;&#47532; &#51312;&#51064;&#44284; &#48337;&#54633; &#50672;&#49328;&#51060;&#45796;.\n",
    " - `pd.merge` &#54632;&#49688;&#51060;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### &#44288;&#44228; &#45824;&#49688;\n",
    "- &#44288;&#44228; &#45936;&#51060;&#53552;(Relation data)&#47484; &#51312;&#51089;&#54616;&#45716; &#44508;&#52825;&#51032; &#51221;&#54805; &#51665;&#54633;&#51060;&#51088; &#45824;&#48512;&#48516;&#51032; &#45936;&#51060;&#53552;&#48288;&#51060;&#49828;&#50640;&#49436; &#49324;&#50857;&#54624; &#49688; &#51080;&#45716; &#50672;&#49328;&#51032; &#44060;&#45392;&#51201; &#44592;&#48152;&#51012; &#54805;&#49457;&#54616;&#45716; &#44288;&#44228; &#45824;&#49688;(Relation algebra)&#51032; &#54616;&#50948; &#51665;&#54633;&#50640; &#54644;&#45817;&#54616;&#45716; &#54665;&#50948;&#44032; &#44396;&#54788;&#46120;\n",
    "- `pd.merge()` &#54632;&#49688;&#45716; &#51068;&#45824;&#51068;, &#45796;&#45824;&#51068;, &#45796;&#45824;&#45796; &#51312;&#51064; &#44057;&#51008; &#50668;&#47084; &#44032;&#51648; &#51312;&#51064; &#50976;&#54805;&#51012; &#44396;&#54788;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#51068;&#45824;&#51068; &#51312;&#51064;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#54924;&#49324;&#51032; &#51649;&#50896; &#47751; &#47749;&#50640; &#45824;&#54620; &#51221;&#48372;&#47484; &#54252;&#54632;&#54616;&#45716; &#46160; &#44060;&#51032; DataFrame&#51012; &#49373;&#44033;&#54644;&#48372;&#51088;.\n",
    "import pandas as pd\n",
    "\n",
    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                   'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>employee</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee  hire_date\n",
       "0     Lisa       2004\n",
       "1      Bob       2008\n",
       "2     Jake       2012\n",
       "3      Sue       2014"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
    "                   'hire_date': [2004, 2008, 2012, 2014]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#51060; &#51221;&#48372;&#47484; &#54616;&#45208;&#51032; DataFrame&#51004;&#47196; &#44208;&#54633;&#54616;&#44256; &#49910;&#45796;.\n",
    "df3 = pd.merge(df1, df2)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pd.merge()` &#54632;&#49688;&#45716; DataFrame&#51060; employee &#50676;&#51012; &#44032;&#51648;&#44256; &#51080;&#45796;&#45716; &#44163;&#51012; &#50508;&#44256; &#51088;&#46041;&#51004;&#47196; &#51060; &#50676;&#51012; **&#53412;**&#47196; &#49324;&#50857;&#54644; &#51312;&#51064;&#54620;&#45796;. &#51060; &#46412; &#44033; &#50676;&#51032; &#54637;&#47785; &#49692;&#49436;&#44032; &#48152;&#46300;&#49884; &#50976;&#51648;&#46104;&#45716; &#44163;&#51008; &#50500;&#45768;&#45796;. &#44033; &#50676;&#51032; &#54637;&#47785; &#49692;&#49436;&#47484; `pd.merge()` &#54632;&#49688;&#44032; &#51221;&#54869;&#54616;&#44172; &#47582;&#52628;&#50612; &#50672;&#49328;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#45796;&#45824;&#51068;(many-to-one) &#51312;&#51064;**\n",
    "- &#46160; &#44060;&#51032; &#53412; &#50676; &#51473; &#54616;&#45208;&#44032; &#51473;&#48373;&#46108; &#54637;&#47785;&#51012; &#54252;&#54632;&#54616;&#45716; &#44221;&#50864;&#51032; &#51312;&#51064;&#51012; &#51032;&#48120;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>group</th>\n",
       "      <th>supervisor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>Carly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HR</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group supervisor\n",
       "0   Accounting      Carly\n",
       "1  Engineering      Guido\n",
       "2           HR      Steve"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'],\n",
    "                   'supervisor': ['Carly', 'Guido', 'Steve']})\n",
    "\n",
    "display(df3)\n",
    "display(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>supervisor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "      <td>Carly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "      <td>Guido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date supervisor\n",
       "0      Bob   Accounting       2008      Carly\n",
       "1     Jake  Engineering       2012      Guido\n",
       "2     Lisa  Engineering       2004      Guido\n",
       "3      Sue           HR       2014      Steve"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.merge(df3, df4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **&#45796;&#45824;&#45796;(Many-to-many) &#51312;&#51064;**\n",
    "- &#50812;&#51901;&#44284; &#50724;&#47480;&#51901; &#48176;&#50676;&#51032; &#53412; &#50676;&#50640; &#47784;&#46160; &#51473;&#48373; &#54637;&#47785;&#51060; &#51316;&#51116;&#54616;&#47732; &#44208;&#44284;&#45716; &#45796;&#45824;&#45796; &#48337;&#54633;&#51060; &#46108;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HR</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HR</td>\n",
       "      <td>organization</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group     hire_date\n",
       "0   Accounting          math\n",
       "1   Accounting  spreadsheets\n",
       "2  Engineering        coding\n",
       "3  Engineering         linux\n",
       "4           HR  spreadsheets\n",
       "5           HR  organization"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df5 = pd.DataFrame({'group': ['Accounting', 'Accounting',\n",
    "                              'Engineering', 'Engineering',\n",
    "                              'HR', 'HR'],\n",
    "                   'hire_date': ['math', 'spreadsheets', \n",
    "                                 'coding', 'linux', \n",
    "                                 'spreadsheets', 'organization']})\n",
    "display(df1)\n",
    "display(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>spreadsheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>organization</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group     hire_date\n",
       "0      Bob   Accounting          math\n",
       "1      Bob   Accounting  spreadsheets\n",
       "2     Jake  Engineering        coding\n",
       "3     Jake  Engineering         linux\n",
       "4     Lisa  Engineering        coding\n",
       "5     Lisa  Engineering         linux\n",
       "6      Sue           HR  spreadsheets\n",
       "7      Sue           HR  organization"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.merge(df1, df5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#48337;&#54633; &#53412; &#51648;&#51221;\n",
    "- `pd.merge()`&#45716; &#46160; &#44060;&#51032; &#51077;&#47141;&#44050; &#49324;&#51060;&#50640; &#51068;&#52824;&#54616;&#45716; &#54616;&#45208; &#51060;&#49345;&#51032; &#50676; &#51060;&#47492;&#51012; &#52286;&#50500; &#44536;&#44163;&#51012; &#53412;&#47196; &#49324;&#50857;\n",
    "- &#50676; &#51060;&#47492;&#51060; &#51096; &#51068;&#52824;&#54616;&#45716; &#44221;&#50864;&#45716; &#55124;&#54616;&#51648; &#50506;&#51004;&#47728;, pd.merge()&#44032; &#51060; &#47928;&#51228;&#47484; &#52376;&#47532;&#54616;&#44592; &#50948;&#54620; &#45796;&#50577;&#54620; &#50741;&#49496;&#51012; &#51228;&#44277;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **on &#53412;&#50892;&#46300;**\n",
    "- &#44032;&#51109; &#44036;&#45800;&#54620; &#48169;&#48333;&#51008; &#50676; &#51060;&#47492;&#51060;&#45208; &#50676;&#51060;&#47492;&#51032; &#47532;&#49828;&#53944;&#47484; &#52712;&#54616;&#45716; on &#53412;&#50892;&#46300;&#47484; &#49324;&#50857;&#54644; &#53412; &#50676;&#51032; &#51060;&#47492;&#51012; &#47749;&#49884;&#51201;&#51004;&#47196; &#51648;&#51221;&#54616;&#45716; &#44163;&#51060;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>employee</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee  hire_date\n",
       "0     Lisa       2004\n",
       "1      Bob       2008\n",
       "2     Jake       2012\n",
       "3      Sue       2014"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df1)\n",
    "display(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='employee')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#51060; &#50741;&#49496;&#51008; &#50812;&#51901;&#44284; &#50724;&#47480;&#51901; DataFrame&#51060; &#47784;&#46160; &#51648;&#51221;&#46108; &#50676; &#51060;&#47492;&#51012; &#44032;&#51652; &#44221;&#50864;&#50640;&#47564; &#46041;&#51089;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **left_on&#44284; right_on &#53412;&#50892;&#46300;**\n",
    "- &#45796;&#47480; &#50676; &#51060;&#47492;&#51012; &#44032;&#51652; &#46160; &#45936;&#51060;&#53552; &#49464;&#53944;&#47484; &#48337;&#54633;&#54616;&#44256; &#49910;&#51012; &#46412; &#49324;&#50857;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  salary\n",
       "0   Bob   70000\n",
       "1  Jake   80000\n",
       "2  Lisa  120000\n",
       "3   Sue   90000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                   'salary': [70000, 80000, 120000, 90000]})\n",
    "\n",
    "display(df1)\n",
    "display(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Bob</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Jake</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>Sue</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  name  salary\n",
       "0      Bob   Accounting   Bob   70000\n",
       "1     Jake  Engineering  Jake   80000\n",
       "2     Lisa  Engineering  Lisa  120000\n",
       "3      Sue           HR   Sue   90000"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  salary\n",
       "0      Bob   Accounting   70000\n",
       "1     Jake  Engineering   80000\n",
       "2     Lisa  Engineering  120000\n",
       "3      Sue           HR   90000"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#51473;&#48373;&#46108; &#50676;&#51008; &#49325;&#51228;&#54616;&#46020;&#47197; &#54620;&#45796;.\n",
    "pd.merge(df1, df3, left_on=\"employee\", right_on=\"name\").drop(\"name\", axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **left_index&#50752; right_index &#53412;&#50892;&#46300;**\n",
    "- &#50676;&#51012; &#48337;&#54633;&#54616;&#45716; &#45824;&#49888; &#51064;&#45937;&#49828;&#47196; &#48337;&#54633;&#54644;&#50556; &#54616;&#45716; &#44221;&#50864;&#46020; &#51080;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>employee</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee  hire_date\n",
       "0     Lisa       2004\n",
       "1      Bob       2008\n",
       "2     Jake       2012\n",
       "3      Sue       2014"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# &#47676;&#51200; df1, df2&#47484; &#48372;&#51088;.\n",
    "display(df1)\n",
    "display(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jake</th>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lisa</th>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sue</th>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                group\n",
       "employee             \n",
       "Bob        Accounting\n",
       "Jake      Engineering\n",
       "Lisa      Engineering\n",
       "Sue                HR"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>hire_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lisa</th>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jake</th>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sue</th>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          hire_date\n",
       "employee           \n",
       "Lisa           2004\n",
       "Bob            2008\n",
       "Jake           2012\n",
       "Sue            2014"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# &#52972;&#47100; &#51060;&#47492;&#51060;&#50632;&#45912; &#44163;&#51012; &#51064;&#45937;&#49828;&#47484; &#49444;&#51221;\n",
    "df1a = df1.set_index('employee')\n",
    "df2a = df2.set_index('employee')\n",
    "\n",
    "display(df1a)\n",
    "display(df2a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jake</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lisa</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sue</th>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                group  hire_date\n",
       "employee                        \n",
       "Bob        Accounting       2008\n",
       "Jake      Engineering       2012\n",
       "Lisa      Engineering       2004\n",
       "Sue                HR       2014"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#51060; &#46412;&#45716; left_index&#45208; right_index&#47484; &#51648;&#51221;&#54616;&#50668; &#51312;&#51064;\n",
    "pd.merge(df1a, df2a, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### &#54200;&#51032;&#47484; &#50948;&#54644; DataFrame&#51008; &#44592;&#48376;&#51201;&#51004;&#47196; &#51064;&#45937;&#49828; &#44592;&#48152;&#51004;&#47196; &#51312;&#51064;&#54616;&#45716; &#48337;&#54633;&#51012; &#49688;&#54665;&#54616;&#45716; `join()` &#47700;&#49436;&#46300;&#47484; &#44396;&#54788;&#54620;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jake</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lisa</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sue</th>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                group  hire_date\n",
       "employee                        \n",
       "Bob        Accounting       2008\n",
       "Jake      Engineering       2012\n",
       "Lisa      Engineering       2004\n",
       "Sue                HR       2014"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1a.join(df2a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#51064;&#45937;&#49828;&#50752; &#50676;&#51012; &#49438;&#44256;&#51088; &#54620;&#45796;&#47732; `left_index`&#47484; `right_on`&#44284; &#44208;&#54633;&#54616;&#44144;&#45208; `left_on`&#51012; `right_index`&#50752; &#44208;&#54633;&#54644; &#50896;&#54616;&#45716; &#44208;&#44284;&#47484; &#50619;&#51012; &#49688; &#51080;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jake</th>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lisa</th>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sue</th>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                group\n",
       "employee             \n",
       "Bob        Accounting\n",
       "Jake      Engineering\n",
       "Lisa      Engineering\n",
       "Sue                HR"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  salary\n",
       "0   Bob   70000\n",
       "1  Jake   80000\n",
       "2  Lisa  120000\n",
       "3   Sue   90000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df1a)\n",
    "display(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>Bob</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Jake</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Engineering</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HR</td>\n",
       "      <td>Sue</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group  name  salary\n",
       "0   Accounting   Bob   70000\n",
       "1  Engineering  Jake   80000\n",
       "2  Engineering  Lisa  120000\n",
       "3           HR   Sue   90000"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1a, df3, left_index=True, right_on=\"name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### &#51312;&#51064;&#51012; &#50948;&#54620; &#51665;&#54633; &#50672;&#49328; &#51648;&#51221;&#54616;&#44592;\n",
    " - inner join\n",
    " - outer join\n",
    " - left join\n",
    " - right join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **inner join**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>name</th>\n",
       "      <th>food</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
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       "      <th>2</th>\n",
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      "text/plain": [
       "    name   food\n",
       "0  Peter   fish\n",
       "1   Paul  beans\n",
       "2   Mary  bread"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>wine</td>\n",
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       "      <th>1</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name drink\n",
       "0    Mary  wine\n",
       "1  Joseph  beer"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df6 = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'],\n",
    "                   'food': ['fish', 'beans', 'bread']},\n",
    "                  columns=['name', 'food'])\n",
    "\n",
    "df7 = pd.DataFrame({'name': ['Mary', 'Joseph'],\n",
    "                   'drink': ['wine', 'beer']},\n",
    "                  columns=['name', 'drink'])\n",
    "\n",
    "display(df6)\n",
    "display(df7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name   food drink\n",
       "0  Mary  bread  wine"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#44592;&#48376;&#51201;&#51004;&#47196; &#44208;&#44284;&#50640;&#45716; &#51077;&#47141;&#44050;&#51032; &#46160; &#51665;&#54633;&#50640; &#45824;&#54620; &#44368;&#51665;&#54633;&#51060; &#46308;&#50612;&#44036;&#45796;.\n",
    "pd.merge(df6, df7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name   food drink\n",
       "0  Mary  bread  wine"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#47749;&#49884;&#51201;&#51004;&#47196;&#45716; &#45796;&#51020;&#44284; &#44057;&#51060; &#54620;&#45796;.\n",
    "pd.merge(df6, df7, how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **outer join**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter</td>\n",
       "      <td>fish</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>beans</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name   food drink\n",
       "0   Peter   fish   NaN\n",
       "1    Paul  beans   NaN\n",
       "2    Mary  bread  wine\n",
       "3  Joseph    NaN  beer"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df6, df7, how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **left join**\n",
    "- &#50812;&#51901; &#54637;&#47785;&#51012; &#44592;&#51456;&#51004;&#47196; &#54620;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter</td>\n",
       "      <td>fish</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul</td>\n",
       "      <td>beans</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name   food drink\n",
       "0  Peter   fish   NaN\n",
       "1   Paul  beans   NaN\n",
       "2   Mary  bread  wine"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df6, df7, how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **right join**\n",
    "- &#50724;&#47480;&#51901; &#54637;&#47785;&#51012; &#44592;&#51456;&#51004;&#47196; &#54620;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>name</th>\n",
       "      <th>food</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>bread</td>\n",
       "      <td>wine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>NaN</td>\n",
       "      <td>beer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name   food drink\n",
       "0    Mary  bread  wine\n",
       "1  Joseph    NaN  beer"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df6, df7, how='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#50676; &#51060;&#47492;&#51060; &#44217;&#52824;&#45716; &#44221;&#50864; :  Suffixes &#53412;&#50892;&#46300;\n",
    "- &#52649;&#46028;&#54616;&#45716; &#51060;&#47492;&#51012; &#44032;&#51652; &#44221;&#50864;&#47484; &#49332;&#54196;&#48372;&#51088;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>name</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  rank\n",
       "0   Bob     1\n",
       "1  Jake     2\n",
       "2  Lisa     3\n",
       "3   Sue     4"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                   'rank': [1, 2, 3, 4]})\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>name</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  rank\n",
       "0   Bob     3\n",
       "1  Jake     1\n",
       "2  Lisa     4\n",
       "3   Sue     2"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                   'rank': [3, 1, 4, 2]})\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>name</th>\n",
       "      <th>rank_x</th>\n",
       "      <th>rank_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  rank_x  rank_y\n",
       "0   Bob       1       3\n",
       "1  Jake       2       1\n",
       "2  Lisa       3       4\n",
       "3   Sue       4       2"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# name&#51012; key&#47196; &#54616;&#47732; rank&#44032; &#44217;&#52432;&#51652;&#45796;. &#51060; &#44221;&#50864;&#45716; &#51088;&#46041;&#51004;&#47196; &#51060;&#47492;&#51060; &#48320;&#44221;&#46108;&#45796;.\n",
    "pd.merge(df8, df9, on='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>name</th>\n",
       "      <th>rank_L</th>\n",
       "      <th>rank_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  rank_L  rank_R\n",
       "0   Bob       1       3\n",
       "1  Jake       2       1\n",
       "2  Lisa       3       4\n",
       "3   Sue       4       2"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# suffixes &#53412;&#50892;&#46300;&#47484; &#49324;&#50857;&#54644;&#49436; &#51217;&#48120;&#49324;&#47484; &#48324;&#46020;&#47196; &#51460; &#49688; &#51080;&#45796;.\n",
    "pd.merge(df8, df9, on='name', suffixes=[\"_L\", \"_R\"])"
   ]
  }
 ],
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  "anaconda-cloud": {},
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   "display_name": "Python 3",
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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