work2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Analysis\n",
      "corr:  0.989\n",
      "p:  0.000\n"
     ]
    }
   ],
   "source": [
    "df1 = [1, 2, 3, 4, 4, 5, 6, 6, 7, 8]\n",
    "df2 = [23, 29, 49, 64, 74, 87, 96, 97, 109, 119]\n",
    "\n",
    "corr, pval = stats.pearsonr(df1, df2)\n",
    "print(\"Correlation Analysis\")\n",
    "print(\"corr: {0: .3f}\".format(corr))\n",
    "print(\"p: {0: .3f}\".format(pval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>a</th>\n",
       "      <td>0.471435</td>\n",
       "      <td>-1.190976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.432707</td>\n",
       "      <td>-0.312652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-0.720589</td>\n",
       "      <td>0.887163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.859588</td>\n",
       "      <td>-0.636524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.015696</td>\n",
       "      <td>-2.242685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.150036</td>\n",
       "      <td>0.991946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.953324</td>\n",
       "      <td>-2.021255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>-0.334077</td>\n",
       "      <td>0.002118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.405453</td>\n",
       "      <td>0.289092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>1.321158</td>\n",
       "      <td>-1.546906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          A         B\n",
       "a  0.471435 -1.190976\n",
       "b  1.432707 -0.312652\n",
       "c -0.720589  0.887163\n",
       "d  0.859588 -0.636524\n",
       "e  0.015696 -2.242685\n",
       "f  1.150036  0.991946\n",
       "g  0.953324 -2.021255\n",
       "h -0.334077  0.002118\n",
       "i  0.405453  0.289092\n",
       "j  1.321158 -1.546906"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "df=pd.DataFrame(randn(10, 2), index = ['a', 'b', 'c', 'd', 'e','f','g','h','i','j'] ,columns=['A', 'B'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47143516373249306"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['a', 'A'] # 행, 열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
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       "      <th>a</th>\n",
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      "text/plain": [
       "          A\n",
       "a  0.471435\n",
       "b  1.432707\n",
       "c -0.720589\n",
       "d  0.859588\n",
       "e  0.015696\n",
       "f  1.150036\n",
       "g  0.953324\n",
       "h -0.334077\n",
       "i  0.405453\n",
       "j  1.321158"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A']] # A만 뽑아서 데이터 프레임을 만든 것."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.471435\n",
       "b    1.432707\n",
       "c   -0.720589\n",
       "d    0.859588\n",
       "e    0.015696\n",
       "f    1.150036\n",
       "g    0.953324\n",
       "h   -0.334077\n",
       "i    0.405453\n",
       "j    1.321158\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'] # A의 데이터만 뽑은 것 df.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>h</th>\n",
       "      <td>-0.334077</td>\n",
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       "      <th>j</th>\n",
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       "      <td>-1.546906</td>\n",
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      ],
      "text/plain": [
       "          A         B\n",
       "a  0.471435 -1.190976\n",
       "b  1.432707 -0.312652\n",
       "c -0.720589  0.887163\n",
       "d  0.859588 -0.636524\n",
       "e  0.015696 -2.242685\n",
       "f  1.150036  0.991946\n",
       "g  0.953324 -2.021255\n",
       "h -0.334077  0.002118\n",
       "i  0.405453  0.289092\n",
       "j  1.321158 -1.546906"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5554732275349152"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.A.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>-0.312652</td>\n",
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       "      <th>c</th>\n",
       "      <td>-0.720589</td>\n",
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       "      <th>d</th>\n",
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      "text/plain": [
       "          A         B\n",
       "a  0.471435 -1.190976\n",
       "b  1.432707 -0.312652\n",
       "c -0.720589  0.887163\n",
       "d  0.859588 -0.636524\n",
       "e  0.015696 -2.242685"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "          A         B\n",
       "c -0.720589  0.887163\n",
       "d  0.859588 -0.636524\n",
       "e  0.015696 -2.242685\n",
       "f  1.150036  0.991946\n",
       "g  0.953324 -2.021255\n",
       "h -0.334077  0.002118\n",
       "i  0.405453  0.289092"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "df[2:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "          A         B\n",
       "f  1.150036  0.991946\n",
       "g  0.953324 -2.021255\n",
       "h -0.334077  0.002118\n",
       "i  0.405453  0.289092\n",
       "j  1.321158 -1.546906"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['f':, ['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.859588</td>\n",
       "      <td>-0.636524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.015696</td>\n",
       "      <td>-2.242685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B\n",
       "c -0.720589  0.887163\n",
       "d  0.859588 -0.636524\n",
       "e  0.015696 -2.242685"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:5, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8595884137174165"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['d', 'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8595884137174165"
      ]
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.iloc[3, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "          A         B\n",
       "a  0.471435 -1.190976\n",
       "b  1.432707 -0.312652\n",
       "d  0.859588 -0.636524\n",
       "e  0.015696 -2.242685\n",
       "f  1.150036  0.991946\n",
       "g  0.953324 -2.021255\n",
       "i  0.405453  0.289092\n",
       "j  1.321158 -1.546906"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A >0] # df[df['A']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "          A         B\n",
       "f  1.150036  0.991946"
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     "execution_count": 18,
     "metadata": {},
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   "source": [
    "df[(df.A > .5) & (df.B > .5)]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "          A         B\n",
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       "b  1.432707 -0.312652\n",
       "d  0.859588 -0.636524\n",
       "e  0.015696 -2.242685\n",
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       "g  0.953324 -2.021255\n",
       "i  0.405453  0.289092\n",
       "j  1.321158 -1.546906"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df[df.A > df.B]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "          A         B\n",
       "a  0.471435 -1.190976\n",
       "b  1.432707 -0.312652"
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     "metadata": {},
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   "source": [
    "df.loc[['a','b'], ['A','B']]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['A+B']= df.A + df.B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.225747</td>\n",
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      "text/plain": [
       "          A         B       A+B\n",
       "a  0.471435 -1.190976 -0.719541\n",
       "b  1.432707 -0.312652  1.120055\n",
       "c -0.720589  0.887163  0.166574\n",
       "d  0.859588 -0.636524  0.223065\n",
       "e  0.015696 -2.242685 -2.226989\n",
       "f  1.150036  0.991946  2.141982\n",
       "g  0.953324 -2.021255 -1.067931\n",
       "h -0.334077  0.002118 -0.331959\n",
       "i  0.405453  0.289092  0.694545\n",
       "j  1.321158 -1.546906 -0.225747"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "          A         B\n",
       "a  0.471435 -1.190976\n",
       "b  1.432707 -0.312652\n",
       "c -0.720589  0.887163\n",
       "d  0.859588 -0.636524\n",
       "e  0.015696 -2.242685\n",
       "f  1.150036  0.991946\n",
       "g  0.953324 -2.021255\n",
       "h -0.334077  0.002118\n",
       "i  0.405453  0.289092\n",
       "j  1.321158 -1.546906"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('A+B', axis =1)"
   ]
  },
  {
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   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "      <td>0.694545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>1.321158</td>\n",
       "      <td>-1.546906</td>\n",
       "      <td>-0.225747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B       A+B\n",
       "b  1.432707 -0.312652  1.120055\n",
       "c -0.720589  0.887163  0.166574\n",
       "d  0.859588 -0.636524  0.223065\n",
       "e  0.015696 -2.242685 -2.226989\n",
       "f  1.150036  0.991946  2.141982\n",
       "g  0.953324 -2.021255 -1.067931\n",
       "h -0.334077  0.002118 -0.331959\n",
       "i  0.405453  0.289092  0.694545\n",
       "j  1.321158 -1.546906 -0.225747"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('a', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.719541</td>\n",
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       "    <tr>\n",
       "      <th>i</th>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-0.720589</td>\n",
       "      <td>0.887163</td>\n",
       "      <td>0.166574</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "          A         B       A+B\n",
       "b  1.432707 -0.312652  1.120055\n",
       "j  1.321158 -1.546906 -0.225747\n",
       "f  1.150036  0.991946  2.141982\n",
       "g  0.953324 -2.021255 -1.067931\n",
       "d  0.859588 -0.636524  0.223065\n",
       "a  0.471435 -1.190976 -0.719541\n",
       "i  0.405453  0.289092  0.694545\n",
       "e  0.015696 -2.242685 -2.226989\n",
       "h -0.334077  0.002118 -0.331959\n",
       "c -0.720589  0.887163  0.166574"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='A', ascending = False) # ascending default = True # 오름차순 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.193421</td>\n",
       "      <td>0.553439</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.318152</td>\n",
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       "      <td>0.675554</td>\n",
       "      <td>-1.817027</td>\n",
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       "      <td>-0.183109</td>\n",
       "      <td>1.058969</td>\n",
       "      <td>-0.397840</td>\n",
       "      <td>0.337438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.047579</td>\n",
       "      <td>1.045938</td>\n",
       "      <td>0.863717</td>\n",
       "      <td>-0.122092</td>\n",
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       "      <th>9</th>\n",
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       "      <td>2.390961</td>\n",
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      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.471435 -1.190976  1.432707 -0.312652\n",
       "1 -0.720589  0.887163  0.859588 -0.636524\n",
       "2  0.015696 -2.242685  1.150036  0.991946\n",
       "3  0.953324 -2.021255 -0.334077  0.002118\n",
       "4  0.405453  0.289092  1.321158 -1.546906\n",
       "5 -0.202646 -0.655969  0.193421  0.553439\n",
       "6  1.318152 -0.469305  0.675554 -1.817027\n",
       "7 -0.183109  1.058969 -0.397840  0.337438\n",
       "8  1.047579  1.045938  0.863717 -0.122092\n",
       "9  0.124713 -0.322795  0.841675  2.390961"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "df1=pd.DataFrame(randn(10, 4), columns = ['A','B','C','D'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          A         B         C         D\n",
       "1 -0.720589  0.887163  0.859588 -0.636524\n",
       "5 -0.202646 -0.655969  0.193421  0.553439\n",
       "7 -0.183109  1.058969 -0.397840  0.337438\n",
       "2  0.015696 -2.242685  1.150036  0.991946\n",
       "9  0.124713 -0.322795  0.841675  2.390961\n",
       "4  0.405453  0.289092  1.321158 -1.546906\n",
       "0  0.471435 -1.190976  1.432707 -0.312652\n",
       "3  0.953324 -2.021255 -0.334077  0.002118\n",
       "8  1.047579  1.045938  0.863717 -0.122092\n",
       "6  1.318152 -0.469305  0.675554 -1.817027"
      ]
     },
     "execution_count": 27,
     "metadata": {},
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   ],
   "source": [
    "df1.sort_values(by='A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.337438</td>\n",
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       "      <th>i</th>\n",
       "      <td>1.047579</td>\n",
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       "      <td>2.390961</td>\n",
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      ],
      "text/plain": [
       "          A         B         C         D\n",
       "a  0.471435 -1.190976  1.432707 -0.312652\n",
       "c -0.720589  0.887163  0.859588 -0.636524\n",
       "b  0.015696 -2.242685  1.150036  0.991946\n",
       "d  0.953324 -2.021255 -0.334077  0.002118\n",
       "e  0.405453  0.289092  1.321158 -1.546906\n",
       "f -0.202646 -0.655969  0.193421  0.553439\n",
       "g  1.318152 -0.469305  0.675554 -1.817027\n",
       "h -0.183109  1.058969 -0.397840  0.337438\n",
       "i  1.047579  1.045938  0.863717 -0.122092\n",
       "j  0.124713 -0.322795  0.841675  2.390961"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "df1=pd.DataFrame(randn(10,4), index = ['a', 'c', 'b', 'd', 'e','f','g','h','i','j'],columns = ['A','B','C','D'] )\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-2.242685</td>\n",
       "      <td>1.150036</td>\n",
       "      <td>0.991946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-0.720589</td>\n",
       "      <td>0.887163</td>\n",
       "      <td>0.859588</td>\n",
       "      <td>-0.636524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.953324</td>\n",
       "      <td>-2.021255</td>\n",
       "      <td>-0.334077</td>\n",
       "      <td>0.002118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.405453</td>\n",
       "      <td>0.289092</td>\n",
       "      <td>1.321158</td>\n",
       "      <td>-1.546906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>-0.202646</td>\n",
       "      <td>-0.655969</td>\n",
       "      <td>0.193421</td>\n",
       "      <td>0.553439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>1.318152</td>\n",
       "      <td>-0.469305</td>\n",
       "      <td>0.675554</td>\n",
       "      <td>-1.817027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>-0.183109</td>\n",
       "      <td>1.058969</td>\n",
       "      <td>-0.397840</td>\n",
       "      <td>0.337438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>1.047579</td>\n",
       "      <td>1.045938</td>\n",
       "      <td>0.863717</td>\n",
       "      <td>-0.122092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>0.124713</td>\n",
       "      <td>-0.322795</td>\n",
       "      <td>0.841675</td>\n",
       "      <td>2.390961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "a  0.471435 -1.190976  1.432707 -0.312652\n",
       "b  0.015696 -2.242685  1.150036  0.991946\n",
       "c -0.720589  0.887163  0.859588 -0.636524\n",
       "d  0.953324 -2.021255 -0.334077  0.002118\n",
       "e  0.405453  0.289092  1.321158 -1.546906\n",
       "f -0.202646 -0.655969  0.193421  0.553439\n",
       "g  1.318152 -0.469305  0.675554 -1.817027\n",
       "h -0.183109  1.058969 -0.397840  0.337438\n",
       "i  1.047579  1.045938  0.863717 -0.122092\n",
       "j  0.124713 -0.322795  0.841675  2.390961"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1.817027</td>\n",
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       "      <td>-0.397840</td>\n",
       "      <td>0.337438</td>\n",
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       "      <th>i</th>\n",
       "      <td>1.045938</td>\n",
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       "      <td>0.863717</td>\n",
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      "text/plain": [
       "          B         A         C         D\n",
       "a -1.190976  0.471435  1.432707 -0.312652\n",
       "c  0.887163 -0.720589  0.859588 -0.636524\n",
       "b -2.242685  0.015696  1.150036  0.991946\n",
       "d -2.021255  0.953324 -0.334077  0.002118\n",
       "e  0.289092  0.405453  1.321158 -1.546906\n",
       "f -0.655969 -0.202646  0.193421  0.553439\n",
       "g -0.469305  1.318152  0.675554 -1.817027\n",
       "h  1.058969 -0.183109 -0.397840  0.337438\n",
       "i  1.045938  1.047579  0.863717 -0.122092\n",
       "j -0.322795  0.124713  0.841675  2.390961"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.reindex(columns = ['B','A','C','D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-1.817027</td>\n",
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       "      <td>0.337438</td>\n",
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      "text/plain": [
       "         A+         B         C         D\n",
       "a  0.471435 -1.190976  1.432707 -0.312652\n",
       "c -0.720589  0.887163  0.859588 -0.636524\n",
       "b  0.015696 -2.242685  1.150036  0.991946\n",
       "d  0.953324 -2.021255 -0.334077  0.002118\n",
       "e  0.405453  0.289092  1.321158 -1.546906\n",
       "f -0.202646 -0.655969  0.193421  0.553439\n",
       "g  1.318152 -0.469305  0.675554 -1.817027\n",
       "h -0.183109  1.058969 -0.397840  0.337438\n",
       "i  1.047579  1.045938  0.863717 -0.122092\n",
       "j  0.124713 -0.322795  0.841675  2.390961"
      ]
     },
     "execution_count": 82,
     "metadata": {},
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   ],
   "source": [
    "df1.rename(columns={'A':'A+'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1234)\n",
    "df1=pd.DataFrame({'A':['A0','A1','A2','A3'],\n",
    "                  'B':['B0','B1','B2','B3'],\n",
    "                  'C':['C0','C1','C2','C3'],\n",
    "                  'D':['D0','D1','D2','D3']},index = [0,1,2,3])\n",
    "df2=pd.DataFrame({'A':['A4','A5','A6','A7'],\n",
    "                  'B':['B4','B5','B6','B7'],\n",
    "                  'C':['C4','C5','C6','C7'],\n",
    "                  'D':['D4','D5','D6','D7']},index = [4,5,6,7])\n",
    "df3=pd.DataFrame({'A':['A8','A9','A10','A11'],\n",
    "                  'B':['B8','B9','B10','B11'],\n",
    "                  'C':['C8','C9','C10','C11'],\n",
    "                  'D':['D8','D9','D10','D11']},index = [8,9,10,11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
       "      <td>A8</td>\n",
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       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
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       "      <th>9</th>\n",
       "      <td>A9</td>\n",
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       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
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       "      <td>D10</td>\n",
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       "    <tr>\n",
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       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
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      ],
      "text/plain": [
       "      A    B    C    D\n",
       "0    A0   B0   C0   D0\n",
       "1    A1   B1   C1   D1\n",
       "2    A2   B2   C2   D2\n",
       "3    A3   B3   C3   D3\n",
       "4    A4   B4   C4   D4\n",
       "5    A5   B5   C5   D5\n",
       "6    A6   B6   C6   D6\n",
       "7    A7   B7   C7   D7\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2,df3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>D2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D    A    B    C    D    A    B    C    D\n",
       "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN\n",
       "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN\n",
       "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN\n",
       "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN\n",
       "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8\n",
       "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9\n",
       "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10\n",
       "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2,df3], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A12</td>\n",
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       "      <td>E12</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A13</td>\n",
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       "      <th>14</th>\n",
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      "text/plain": [
       "      A    B    C    E\n",
       "12  A12  B12  C12  E12\n",
       "13  A13  B13  C13  E13\n",
       "14  A14  B14  C14  E14\n",
       "15  A15  B15  C15  E15"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=pd.DataFrame({'A':['A12','A13','A14','A15'],\n",
    "                  'B':['B12','B13','B14','B15'],\n",
    "                  'C':['C12','C13','C14','C15'],\n",
    "                  'E':['E12','E13','E14','E15']},index = [12,13,14,15])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>E12</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>A13</td>\n",
       "      <td>B13</td>\n",
       "      <td>C13</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A14</td>\n",
       "      <td>B14</td>\n",
       "      <td>C14</td>\n",
       "      <td>E14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A15</td>\n",
       "      <td>B15</td>\n",
       "      <td>C15</td>\n",
       "      <td>E15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D    A    B    C    D    A    B    C    D    A    B    C  \\\n",
       "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8  NaN  NaN  NaN   \n",
       "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9  NaN  NaN  NaN   \n",
       "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10  NaN  NaN  NaN   \n",
       "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11  NaN  NaN  NaN   \n",
       "12  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A12  B12  C12   \n",
       "13  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A13  B13  C13   \n",
       "14  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A14  B14  C14   \n",
       "15  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A15  B15  C15   \n",
       "\n",
       "      E  \n",
       "0   NaN  \n",
       "1   NaN  \n",
       "2   NaN  \n",
       "3   NaN  \n",
       "4   NaN  \n",
       "5   NaN  \n",
       "6   NaN  \n",
       "7   NaN  \n",
       "8   NaN  \n",
       "9   NaN  \n",
       "10  NaN  \n",
       "11  NaN  \n",
       "12  E12  \n",
       "13  E13  \n",
       "14  E14  \n",
       "15  E15  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2,df3,df4], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>C2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
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       "      <th>5</th>\n",
       "      <td>A5</td>\n",
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       "      <td>C5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A12</td>\n",
       "      <td>B12</td>\n",
       "      <td>C12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A13</td>\n",
       "      <td>B13</td>\n",
       "      <td>C13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A14</td>\n",
       "      <td>B14</td>\n",
       "      <td>C14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A15</td>\n",
       "      <td>B15</td>\n",
       "      <td>C15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C\n",
       "0    A0   B0   C0\n",
       "1    A1   B1   C1\n",
       "2    A2   B2   C2\n",
       "3    A3   B3   C3\n",
       "4    A4   B4   C4\n",
       "5    A5   B5   C5\n",
       "6    A6   B6   C6\n",
       "7    A7   B7   C7\n",
       "8    A8   B8   C8\n",
       "9    A9   B9   C9\n",
       "10  A10  B10  C10\n",
       "11  A11  B11  C11\n",
       "12  A12  B12  C12\n",
       "13  A13  B13  C13\n",
       "14  A14  B14  C14\n",
       "15  A15  B15  C15"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2,df3,df4], join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
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       "      <th>H</th>\n",
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       "      <th>0</th>\n",
       "      <td>A0</td>\n",
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       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "      <td>E0</td>\n",
       "      <td>F0</td>\n",
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       "      <td>H0</td>\n",
       "      <td>G0</td>\n",
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       "      <td>K0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>G1</td>\n",
       "      <td>I1</td>\n",
       "      <td>K1</td>\n",
       "      <td>L1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>G2</td>\n",
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       "      <td>I2</td>\n",
       "      <td>K2</td>\n",
       "      <td>L2</td>\n",
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       "      <th>3</th>\n",
       "      <td>A3</td>\n",
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       "      <td>E3</td>\n",
       "      <td>F3</td>\n",
       "      <td>G3</td>\n",
       "      <td>H3</td>\n",
       "      <td>G3</td>\n",
       "      <td>I3</td>\n",
       "      <td>K3</td>\n",
       "      <td>L3</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    A   B   C   D   E   F   G   H   G   I   K   L\n",
       "0  A0  B0  C0  D0  E0  F0  G0  H0  G0  I0  K0  L0\n",
       "1  A1  B1  C1  D1  E1  F1  G1  H1  G1  I1  K1  L1\n",
       "2  A2  B2  C2  D2  E2  F2  G2  H2  G2  I2  K2  L2\n",
       "3  A3  B3  C3  D3  E3  F3  G3  H3  G3  I3  K3  L3"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat 활용\n",
    "df4 = pd.DataFrame({'A':['A0','A1','A2','A3'],\n",
    "                    'B':['B0','B1','B2','B3'],\n",
    "                    'C':['C0','C1','C2','C3'],\n",
    "                    'D':['D0','D1','D2','D3']},index = [0,1,2,3])\n",
    "df5 = pd.DataFrame({'E':['E0','E1','E2','E3'],\n",
    "                    'F':['F0','F1','F2','F3'],\n",
    "                    'G':['G0','G1','G2','G3'],\n",
    "                    'H':['H0','H1','H2','H3']},index = [0,1,2,3])\n",
    "df6 = pd.DataFrame({'I':['I0','I1','I2','I3'],\n",
    "                    'G':['G0','G1','G2','G3'],\n",
    "                    'K':['K0','K1','K2','K3'],\n",
    "                    'L':['L0','L1','L2','L3']},index = [0,1,2,3])\n",
    "result = pd.concat([df4,df5,df6],axis=1)\n",
    "result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'data1' : [0,1,2,3], 'key' : ['b','b','a','c']})\n",
    "df2 = pd.DataFrame({'data2' : [0,1,2], 'key' : ['a','a','b']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   data1 key\n",
      "0      0   b\n",
      "1      1   b\n",
      "2      2   a\n",
      "3      3   c\n"
     ]
    }
   ],
   "source": [
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   data2 key\n",
      "0      0   a\n",
      "1      1   a\n",
      "2      2   b\n"
     ]
    }
   ],
   "source": [
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0      0   b      2\n",
       "1      1   b      2\n",
       "2      2   a      0\n",
       "3      2   a      1"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2) # default = inner"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\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",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0      0   b    2.0\n",
       "1      1   b    2.0\n",
       "2      2   a    0.0\n",
       "3      2   a    1.0\n",
       "4      3   c    NaN"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2,on='key' ,how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0      0   b    2.0\n",
       "1      1   b    2.0\n",
       "2      2   a    0.0\n",
       "3      2   a    1.0\n",
       "4      3   c    NaN"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2,on='key' ,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>2</td>\n",
       "      <td>a</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 lkey\n",
       "0      0    b\n",
       "1      1    b\n",
       "2      2    a\n",
       "3      3    c"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 =df1.rename(columns={'key':'lkey'})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>key</th>\n",
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       "      <td>b</td>\n",
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       "      <td>2</td>\n",
       "      <td>b</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   data1 lkey  data2 key\n",
       "0      0    b      2   b\n",
       "1      1    b      2   b\n",
       "2      2    a      0   a\n",
       "3      2    a      1   a"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2, left_on='lkey', right_on = 'key', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key1':['K0','K1','K2','K3'],\n",
    "                     'key2':['K0','K1','K0','K1'],\n",
    "                     'A'   :['A0','A1','A2','A3'],\n",
    "                     'B'   :['B0','B1','B2','B3']})\n",
    "\n",
    "right = pd.DataFrame({'key1':['K0','K1','K2','K3'],\n",
    "                      'key2':['K0','K0','K0','K0'],\n",
    "                      'C'   :['C0','C1','C2','C3'],\n",
    "                      'D'   :['D0','D1','D2','D3']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>B</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>K0</td>\n",
       "      <td>K0</td>\n",
       "      <td>C0</td>\n",
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       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K2</td>\n",
       "      <td>K0</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B key1 key2   C   D\n",
       "0  A0  B0   K0   K0  C0  D0\n",
       "1  A2  B2   K2   K0  C2  D2"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,right, on=['key1', 'key2'], how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>K1</td>\n",
       "      <td>K1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K2</td>\n",
       "      <td>K0</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>K3</td>\n",
       "      <td>K1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>K3</td>\n",
       "      <td>K0</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B key1 key2    C    D\n",
       "0   A0   B0   K0   K0   C0   D0\n",
       "1   A1   B1   K1   K1  NaN  NaN\n",
       "2   A2   B2   K2   K0   C2   D2\n",
       "3   A3   B3   K3   K1  NaN  NaN\n",
       "4  NaN  NaN   K1   K0   C1   D1\n",
       "5  NaN  NaN   K3   K0   C3   D3"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,right, on=['key1', 'key2'], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.read_csv('실습파일/FITNESS_1.csv', engine = 'python', encoding ='EUC-KR')\n",
    "\n",
    "right = pd.read_csv('실습파일/FITNESS_2.csv', engine = 'python', encoding ='EUC-KR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>AGEGROUP</th>\n",
       "      <th>WEIGHT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donna</td>\n",
       "      <td>여성</td>\n",
       "      <td>42</td>\n",
       "      <td>40대</td>\n",
       "      <td>68.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gracie</td>\n",
       "      <td>여성</td>\n",
       "      <td>38</td>\n",
       "      <td>30대</td>\n",
       "      <td>81.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Luanne</td>\n",
       "      <td>여성</td>\n",
       "      <td>43</td>\n",
       "      <td>40대</td>\n",
       "      <td>85.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mimi</td>\n",
       "      <td>여성</td>\n",
       "      <td>50</td>\n",
       "      <td>50대</td>\n",
       "      <td>70.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chris</td>\n",
       "      <td>남성</td>\n",
       "      <td>49</td>\n",
       "      <td>40대</td>\n",
       "      <td>81.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Allen</td>\n",
       "      <td>남성</td>\n",
       "      <td>38</td>\n",
       "      <td>30대</td>\n",
       "      <td>89.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>40대</td>\n",
       "      <td>76.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Patty</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>50대</td>\n",
       "      <td>76.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Suzanne</td>\n",
       "      <td>여성</td>\n",
       "      <td>57</td>\n",
       "      <td>50대</td>\n",
       "      <td>59.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Teresa</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>50대</td>\n",
       "      <td>77.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bob</td>\n",
       "      <td>남성</td>\n",
       "      <td>39</td>\n",
       "      <td>30대</td>\n",
       "      <td>75.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Harriett</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>40대</td>\n",
       "      <td>73.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jane</td>\n",
       "      <td>여성</td>\n",
       "      <td>44</td>\n",
       "      <td>40대</td>\n",
       "      <td>73.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Harold</td>\n",
       "      <td>남성</td>\n",
       "      <td>48</td>\n",
       "      <td>40대</td>\n",
       "      <td>91.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sammy</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>50대</td>\n",
       "      <td>83.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Buffy</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>50대</td>\n",
       "      <td>73.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Trent</td>\n",
       "      <td>남성</td>\n",
       "      <td>38</td>\n",
       "      <td>30대</td>\n",
       "      <td>82.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jackie</td>\n",
       "      <td>여성</td>\n",
       "      <td>47</td>\n",
       "      <td>40대</td>\n",
       "      <td>79.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ralph</td>\n",
       "      <td>남성</td>\n",
       "      <td>43</td>\n",
       "      <td>40대</td>\n",
       "      <td>81.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jack</td>\n",
       "      <td>남성</td>\n",
       "      <td>51</td>\n",
       "      <td>50대</td>\n",
       "      <td>69.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Annie</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>50대</td>\n",
       "      <td>67.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kate</td>\n",
       "      <td>여성</td>\n",
       "      <td>45</td>\n",
       "      <td>40대</td>\n",
       "      <td>66.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Carl</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>50대</td>\n",
       "      <td>79.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Don</td>\n",
       "      <td>남성</td>\n",
       "      <td>44</td>\n",
       "      <td>40대</td>\n",
       "      <td>89.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Effie</td>\n",
       "      <td>여성</td>\n",
       "      <td>48</td>\n",
       "      <td>40대</td>\n",
       "      <td>61.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>George</td>\n",
       "      <td>남성</td>\n",
       "      <td>37</td>\n",
       "      <td>30대</td>\n",
       "      <td>77.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Iris</td>\n",
       "      <td>여성</td>\n",
       "      <td>39</td>\n",
       "      <td>30대</td>\n",
       "      <td>75.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mark</td>\n",
       "      <td>남성</td>\n",
       "      <td>57</td>\n",
       "      <td>50대</td>\n",
       "      <td>73.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Steve</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>50대</td>\n",
       "      <td>91.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Vaughn</td>\n",
       "      <td>남성</td>\n",
       "      <td>44</td>\n",
       "      <td>40대</td>\n",
       "      <td>81.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>William</td>\n",
       "      <td>남성</td>\n",
       "      <td>45</td>\n",
       "      <td>40대</td>\n",
       "      <td>87.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        NAME GENDER  AGE AGEGROUP  WEIGHT\n",
       "0      Donna     여성   42      40대   68.15\n",
       "1     Gracie     여성   38      30대   81.87\n",
       "2     Luanne     여성   43      40대   85.84\n",
       "3       Mimi     여성   50      50대   70.87\n",
       "4      Chris     남성   49      40대   81.42\n",
       "5      Allen     남성   38      30대   89.02\n",
       "6      Nancy     여성   49      40대   76.32\n",
       "7      Patty     여성   52      50대   76.32\n",
       "8    Suzanne     여성   57      50대   59.08\n",
       "9     Teresa     여성   51      50대   77.91\n",
       "10       Bob     남성   39      30대   75.07\n",
       "11  Harriett     여성   49      40대   73.37\n",
       "12      Jane     여성   44      40대   73.03\n",
       "13    Harold     남성   48      40대   91.63\n",
       "14     Sammy     남성   54      50대   83.12\n",
       "15     Buffy     여성   52      50대   73.71\n",
       "16     Trent     남성   38      30대   82.78\n",
       "17    Jackie     여성   47      40대   79.15\n",
       "18     Ralph     남성   43      40대   81.19\n",
       "19      Jack     남성   51      50대   69.63\n",
       "20     Annie     여성   51      50대   67.25\n",
       "21      Kate     여성   45      40대   66.45\n",
       "22      Carl     남성   54      50대   79.38\n",
       "23       Don     남성   44      40대   89.47\n",
       "24     Effie     여성   48      40대   61.24\n",
       "25    George     남성   37      30대   77.45\n",
       "26      Iris     여성   39      30대   75.98\n",
       "27      Mark     남성   57      50대   73.37\n",
       "28     Steve     남성   54      50대   91.63\n",
       "29    Vaughn     남성   44      40대   81.42\n",
       "30   William     남성   45      40대   87.66"
      ]
     },
     "execution_count": 134,
     "metadata": {},
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    }
   ],
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    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>OXY</th>\n",
       "      <th>RUNTIME</th>\n",
       "      <th>RUNPULSE</th>\n",
       "      <th>RSTPULSE</th>\n",
       "      <th>MAXPULSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donna</td>\n",
       "      <td>여성</td>\n",
       "      <td>42</td>\n",
       "      <td>59.571</td>\n",
       "      <td>8.17</td>\n",
       "      <td>166</td>\n",
       "      <td>40</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gracie</td>\n",
       "      <td>여성</td>\n",
       "      <td>38</td>\n",
       "      <td>60.055</td>\n",
       "      <td>8.63</td>\n",
       "      <td>170</td>\n",
       "      <td>48</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Luanne</td>\n",
       "      <td>여성</td>\n",
       "      <td>43</td>\n",
       "      <td>54.297</td>\n",
       "      <td>8.65</td>\n",
       "      <td>156</td>\n",
       "      <td>45</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mimi</td>\n",
       "      <td>여성</td>\n",
       "      <td>50</td>\n",
       "      <td>54.625</td>\n",
       "      <td>8.92</td>\n",
       "      <td>146</td>\n",
       "      <td>48</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chris</td>\n",
       "      <td>남성</td>\n",
       "      <td>49</td>\n",
       "      <td>49.156</td>\n",
       "      <td>8.95</td>\n",
       "      <td>180</td>\n",
       "      <td>44</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Allen</td>\n",
       "      <td>남성</td>\n",
       "      <td>38</td>\n",
       "      <td>49.874</td>\n",
       "      <td>9.22</td>\n",
       "      <td>178</td>\n",
       "      <td>55</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>48.673</td>\n",
       "      <td>9.40</td>\n",
       "      <td>186</td>\n",
       "      <td>56</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Patty</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>45.441</td>\n",
       "      <td>9.63</td>\n",
       "      <td>164</td>\n",
       "      <td>48</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Suzanne</td>\n",
       "      <td>여성</td>\n",
       "      <td>57</td>\n",
       "      <td>50.545</td>\n",
       "      <td>9.93</td>\n",
       "      <td>148</td>\n",
       "      <td>49</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Teresa</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>46.672</td>\n",
       "      <td>10.00</td>\n",
       "      <td>162</td>\n",
       "      <td>48</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bob</td>\n",
       "      <td>남성</td>\n",
       "      <td>39</td>\n",
       "      <td>45.313</td>\n",
       "      <td>10.07</td>\n",
       "      <td>185</td>\n",
       "      <td>62</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Harriett</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>50.388</td>\n",
       "      <td>10.08</td>\n",
       "      <td>168</td>\n",
       "      <td>67</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jane</td>\n",
       "      <td>여성</td>\n",
       "      <td>44</td>\n",
       "      <td>50.541</td>\n",
       "      <td>10.13</td>\n",
       "      <td>168</td>\n",
       "      <td>45</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Harold</td>\n",
       "      <td>남성</td>\n",
       "      <td>48</td>\n",
       "      <td>46.774</td>\n",
       "      <td>10.25</td>\n",
       "      <td>162</td>\n",
       "      <td>48</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sammy</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>51.855</td>\n",
       "      <td>10.33</td>\n",
       "      <td>166</td>\n",
       "      <td>50</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Buffy</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>45.790</td>\n",
       "      <td>10.47</td>\n",
       "      <td>186</td>\n",
       "      <td>59</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Trent</td>\n",
       "      <td>남성</td>\n",
       "      <td>38</td>\n",
       "      <td>47.467</td>\n",
       "      <td>10.50</td>\n",
       "      <td>170</td>\n",
       "      <td>53</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jackie</td>\n",
       "      <td>여성</td>\n",
       "      <td>47</td>\n",
       "      <td>47.273</td>\n",
       "      <td>10.60</td>\n",
       "      <td>162</td>\n",
       "      <td>47</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ralph</td>\n",
       "      <td>남성</td>\n",
       "      <td>43</td>\n",
       "      <td>49.091</td>\n",
       "      <td>10.85</td>\n",
       "      <td>162</td>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jack</td>\n",
       "      <td>남성</td>\n",
       "      <td>51</td>\n",
       "      <td>40.836</td>\n",
       "      <td>10.95</td>\n",
       "      <td>168</td>\n",
       "      <td>57</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Annie</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>45.118</td>\n",
       "      <td>11.08</td>\n",
       "      <td>172</td>\n",
       "      <td>48</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kate</td>\n",
       "      <td>여성</td>\n",
       "      <td>45</td>\n",
       "      <td>44.754</td>\n",
       "      <td>11.12</td>\n",
       "      <td>176</td>\n",
       "      <td>51</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Carl</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>46.080</td>\n",
       "      <td>11.17</td>\n",
       "      <td>156</td>\n",
       "      <td>62</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Don</td>\n",
       "      <td>남성</td>\n",
       "      <td>44</td>\n",
       "      <td>44.609</td>\n",
       "      <td>11.37</td>\n",
       "      <td>178</td>\n",
       "      <td>62</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Effie</td>\n",
       "      <td>여성</td>\n",
       "      <td>48</td>\n",
       "      <td>47.920</td>\n",
       "      <td>11.50</td>\n",
       "      <td>170</td>\n",
       "      <td>52</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>George</td>\n",
       "      <td>남성</td>\n",
       "      <td>37</td>\n",
       "      <td>44.811</td>\n",
       "      <td>11.63</td>\n",
       "      <td>176</td>\n",
       "      <td>58</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Iris</td>\n",
       "      <td>여성</td>\n",
       "      <td>39</td>\n",
       "      <td>45.681</td>\n",
       "      <td>11.95</td>\n",
       "      <td>176</td>\n",
       "      <td>70</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mark</td>\n",
       "      <td>남성</td>\n",
       "      <td>57</td>\n",
       "      <td>39.407</td>\n",
       "      <td>12.63</td>\n",
       "      <td>174</td>\n",
       "      <td>58</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Steve</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>39.203</td>\n",
       "      <td>12.88</td>\n",
       "      <td>168</td>\n",
       "      <td>44</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Vaughn</td>\n",
       "      <td>남성</td>\n",
       "      <td>44</td>\n",
       "      <td>39.442</td>\n",
       "      <td>13.08</td>\n",
       "      <td>174</td>\n",
       "      <td>63</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>William</td>\n",
       "      <td>남성</td>\n",
       "      <td>45</td>\n",
       "      <td>37.388</td>\n",
       "      <td>14.03</td>\n",
       "      <td>186</td>\n",
       "      <td>56</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        NAME GENDER  AGE     OXY  RUNTIME  RUNPULSE  RSTPULSE  MAXPULSE\n",
       "0      Donna     여성   42  59.571     8.17       166        40       172\n",
       "1     Gracie     여성   38  60.055     8.63       170        48       186\n",
       "2     Luanne     여성   43  54.297     8.65       156        45       168\n",
       "3       Mimi     여성   50  54.625     8.92       146        48       155\n",
       "4      Chris     남성   49  49.156     8.95       180        44       185\n",
       "5      Allen     남성   38  49.874     9.22       178        55       180\n",
       "6      Nancy     여성   49  48.673     9.40       186        56       188\n",
       "7      Patty     여성   52  45.441     9.63       164        48       166\n",
       "8    Suzanne     여성   57  50.545     9.93       148        49       155\n",
       "9     Teresa     여성   51  46.672    10.00       162        48       168\n",
       "10       Bob     남성   39  45.313    10.07       185        62       185\n",
       "11  Harriett     여성   49  50.388    10.08       168        67       168\n",
       "12      Jane     여성   44  50.541    10.13       168        45       168\n",
       "13    Harold     남성   48  46.774    10.25       162        48       164\n",
       "14     Sammy     남성   54  51.855    10.33       166        50       170\n",
       "15     Buffy     여성   52  45.790    10.47       186        59       188\n",
       "16     Trent     남성   38  47.467    10.50       170        53       172\n",
       "17    Jackie     여성   47  47.273    10.60       162        47       164\n",
       "18     Ralph     남성   43  49.091    10.85       162        64       170\n",
       "19      Jack     남성   51  40.836    10.95       168        57       172\n",
       "20     Annie     여성   51  45.118    11.08       172        48       172\n",
       "21      Kate     여성   45  44.754    11.12       176        51       176\n",
       "22      Carl     남성   54  46.080    11.17       156        62       165\n",
       "23       Don     남성   44  44.609    11.37       178        62       182\n",
       "24     Effie     여성   48  47.920    11.50       170        52       176\n",
       "25    George     남성   37  44.811    11.63       176        58       176\n",
       "26      Iris     여성   39  45.681    11.95       176        70       180\n",
       "27      Mark     남성   57  39.407    12.63       174        58       176\n",
       "28     Steve     남성   54  39.203    12.88       168        44       172\n",
       "29    Vaughn     남성   44  39.442    13.08       174        63       176\n",
       "30   William     남성   45  37.388    14.03       186        56       192"
      ]
     },
     "execution_count": 135,
     "metadata": {},
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   "execution_count": 137,
   "metadata": {},
   "outputs": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>GENDER_x</th>\n",
       "      <th>AGE_x</th>\n",
       "      <th>AGEGROUP</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>GENDER_y</th>\n",
       "      <th>AGE_y</th>\n",
       "      <th>OXY</th>\n",
       "      <th>RUNTIME</th>\n",
       "      <th>RUNPULSE</th>\n",
       "      <th>RSTPULSE</th>\n",
       "      <th>MAXPULSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donna</td>\n",
       "      <td>여성</td>\n",
       "      <td>42</td>\n",
       "      <td>40대</td>\n",
       "      <td>68.15</td>\n",
       "      <td>여성</td>\n",
       "      <td>42</td>\n",
       "      <td>59.571</td>\n",
       "      <td>8.17</td>\n",
       "      <td>166</td>\n",
       "      <td>40</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gracie</td>\n",
       "      <td>여성</td>\n",
       "      <td>38</td>\n",
       "      <td>30대</td>\n",
       "      <td>81.87</td>\n",
       "      <td>여성</td>\n",
       "      <td>38</td>\n",
       "      <td>60.055</td>\n",
       "      <td>8.63</td>\n",
       "      <td>170</td>\n",
       "      <td>48</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Luanne</td>\n",
       "      <td>여성</td>\n",
       "      <td>43</td>\n",
       "      <td>40대</td>\n",
       "      <td>85.84</td>\n",
       "      <td>여성</td>\n",
       "      <td>43</td>\n",
       "      <td>54.297</td>\n",
       "      <td>8.65</td>\n",
       "      <td>156</td>\n",
       "      <td>45</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mimi</td>\n",
       "      <td>여성</td>\n",
       "      <td>50</td>\n",
       "      <td>50대</td>\n",
       "      <td>70.87</td>\n",
       "      <td>여성</td>\n",
       "      <td>50</td>\n",
       "      <td>54.625</td>\n",
       "      <td>8.92</td>\n",
       "      <td>146</td>\n",
       "      <td>48</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chris</td>\n",
       "      <td>남성</td>\n",
       "      <td>49</td>\n",
       "      <td>40대</td>\n",
       "      <td>81.42</td>\n",
       "      <td>남성</td>\n",
       "      <td>49</td>\n",
       "      <td>49.156</td>\n",
       "      <td>8.95</td>\n",
       "      <td>180</td>\n",
       "      <td>44</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Allen</td>\n",
       "      <td>남성</td>\n",
       "      <td>38</td>\n",
       "      <td>30대</td>\n",
       "      <td>89.02</td>\n",
       "      <td>남성</td>\n",
       "      <td>38</td>\n",
       "      <td>49.874</td>\n",
       "      <td>9.22</td>\n",
       "      <td>178</td>\n",
       "      <td>55</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>40대</td>\n",
       "      <td>76.32</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>48.673</td>\n",
       "      <td>9.40</td>\n",
       "      <td>186</td>\n",
       "      <td>56</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Patty</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>50대</td>\n",
       "      <td>76.32</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>45.441</td>\n",
       "      <td>9.63</td>\n",
       "      <td>164</td>\n",
       "      <td>48</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Suzanne</td>\n",
       "      <td>여성</td>\n",
       "      <td>57</td>\n",
       "      <td>50대</td>\n",
       "      <td>59.08</td>\n",
       "      <td>여성</td>\n",
       "      <td>57</td>\n",
       "      <td>50.545</td>\n",
       "      <td>9.93</td>\n",
       "      <td>148</td>\n",
       "      <td>49</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Teresa</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>50대</td>\n",
       "      <td>77.91</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>46.672</td>\n",
       "      <td>10.00</td>\n",
       "      <td>162</td>\n",
       "      <td>48</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bob</td>\n",
       "      <td>남성</td>\n",
       "      <td>39</td>\n",
       "      <td>30대</td>\n",
       "      <td>75.07</td>\n",
       "      <td>남성</td>\n",
       "      <td>39</td>\n",
       "      <td>45.313</td>\n",
       "      <td>10.07</td>\n",
       "      <td>185</td>\n",
       "      <td>62</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Harriett</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>40대</td>\n",
       "      <td>73.37</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>50.388</td>\n",
       "      <td>10.08</td>\n",
       "      <td>168</td>\n",
       "      <td>67</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jane</td>\n",
       "      <td>여성</td>\n",
       "      <td>44</td>\n",
       "      <td>40대</td>\n",
       "      <td>73.03</td>\n",
       "      <td>여성</td>\n",
       "      <td>44</td>\n",
       "      <td>50.541</td>\n",
       "      <td>10.13</td>\n",
       "      <td>168</td>\n",
       "      <td>45</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Harold</td>\n",
       "      <td>남성</td>\n",
       "      <td>48</td>\n",
       "      <td>40대</td>\n",
       "      <td>91.63</td>\n",
       "      <td>남성</td>\n",
       "      <td>48</td>\n",
       "      <td>46.774</td>\n",
       "      <td>10.25</td>\n",
       "      <td>162</td>\n",
       "      <td>48</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sammy</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>50대</td>\n",
       "      <td>83.12</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>51.855</td>\n",
       "      <td>10.33</td>\n",
       "      <td>166</td>\n",
       "      <td>50</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Buffy</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>50대</td>\n",
       "      <td>73.71</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>45.790</td>\n",
       "      <td>10.47</td>\n",
       "      <td>186</td>\n",
       "      <td>59</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Trent</td>\n",
       "      <td>남성</td>\n",
       "      <td>38</td>\n",
       "      <td>30대</td>\n",
       "      <td>82.78</td>\n",
       "      <td>남성</td>\n",
       "      <td>38</td>\n",
       "      <td>47.467</td>\n",
       "      <td>10.50</td>\n",
       "      <td>170</td>\n",
       "      <td>53</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jackie</td>\n",
       "      <td>여성</td>\n",
       "      <td>47</td>\n",
       "      <td>40대</td>\n",
       "      <td>79.15</td>\n",
       "      <td>여성</td>\n",
       "      <td>47</td>\n",
       "      <td>47.273</td>\n",
       "      <td>10.60</td>\n",
       "      <td>162</td>\n",
       "      <td>47</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ralph</td>\n",
       "      <td>남성</td>\n",
       "      <td>43</td>\n",
       "      <td>40대</td>\n",
       "      <td>81.19</td>\n",
       "      <td>남성</td>\n",
       "      <td>43</td>\n",
       "      <td>49.091</td>\n",
       "      <td>10.85</td>\n",
       "      <td>162</td>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jack</td>\n",
       "      <td>남성</td>\n",
       "      <td>51</td>\n",
       "      <td>50대</td>\n",
       "      <td>69.63</td>\n",
       "      <td>남성</td>\n",
       "      <td>51</td>\n",
       "      <td>40.836</td>\n",
       "      <td>10.95</td>\n",
       "      <td>168</td>\n",
       "      <td>57</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Annie</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>50대</td>\n",
       "      <td>67.25</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>45.118</td>\n",
       "      <td>11.08</td>\n",
       "      <td>172</td>\n",
       "      <td>48</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kate</td>\n",
       "      <td>여성</td>\n",
       "      <td>45</td>\n",
       "      <td>40대</td>\n",
       "      <td>66.45</td>\n",
       "      <td>여성</td>\n",
       "      <td>45</td>\n",
       "      <td>44.754</td>\n",
       "      <td>11.12</td>\n",
       "      <td>176</td>\n",
       "      <td>51</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Carl</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>50대</td>\n",
       "      <td>79.38</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>46.080</td>\n",
       "      <td>11.17</td>\n",
       "      <td>156</td>\n",
       "      <td>62</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Don</td>\n",
       "      <td>남성</td>\n",
       "      <td>44</td>\n",
       "      <td>40대</td>\n",
       "      <td>89.47</td>\n",
       "      <td>남성</td>\n",
       "      <td>44</td>\n",
       "      <td>44.609</td>\n",
       "      <td>11.37</td>\n",
       "      <td>178</td>\n",
       "      <td>62</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Effie</td>\n",
       "      <td>여성</td>\n",
       "      <td>48</td>\n",
       "      <td>40대</td>\n",
       "      <td>61.24</td>\n",
       "      <td>여성</td>\n",
       "      <td>48</td>\n",
       "      <td>47.920</td>\n",
       "      <td>11.50</td>\n",
       "      <td>170</td>\n",
       "      <td>52</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>George</td>\n",
       "      <td>남성</td>\n",
       "      <td>37</td>\n",
       "      <td>30대</td>\n",
       "      <td>77.45</td>\n",
       "      <td>남성</td>\n",
       "      <td>37</td>\n",
       "      <td>44.811</td>\n",
       "      <td>11.63</td>\n",
       "      <td>176</td>\n",
       "      <td>58</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Iris</td>\n",
       "      <td>여성</td>\n",
       "      <td>39</td>\n",
       "      <td>30대</td>\n",
       "      <td>75.98</td>\n",
       "      <td>여성</td>\n",
       "      <td>39</td>\n",
       "      <td>45.681</td>\n",
       "      <td>11.95</td>\n",
       "      <td>176</td>\n",
       "      <td>70</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mark</td>\n",
       "      <td>남성</td>\n",
       "      <td>57</td>\n",
       "      <td>50대</td>\n",
       "      <td>73.37</td>\n",
       "      <td>남성</td>\n",
       "      <td>57</td>\n",
       "      <td>39.407</td>\n",
       "      <td>12.63</td>\n",
       "      <td>174</td>\n",
       "      <td>58</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Steve</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>50대</td>\n",
       "      <td>91.63</td>\n",
       "      <td>남성</td>\n",
       "      <td>54</td>\n",
       "      <td>39.203</td>\n",
       "      <td>12.88</td>\n",
       "      <td>168</td>\n",
       "      <td>44</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Vaughn</td>\n",
       "      <td>남성</td>\n",
       "      <td>44</td>\n",
       "      <td>40대</td>\n",
       "      <td>81.42</td>\n",
       "      <td>남성</td>\n",
       "      <td>44</td>\n",
       "      <td>39.442</td>\n",
       "      <td>13.08</td>\n",
       "      <td>174</td>\n",
       "      <td>63</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>William</td>\n",
       "      <td>남성</td>\n",
       "      <td>45</td>\n",
       "      <td>40대</td>\n",
       "      <td>87.66</td>\n",
       "      <td>남성</td>\n",
       "      <td>45</td>\n",
       "      <td>37.388</td>\n",
       "      <td>14.03</td>\n",
       "      <td>186</td>\n",
       "      <td>56</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        NAME GENDER_x  AGE_x AGEGROUP  WEIGHT GENDER_y  AGE_y     OXY  \\\n",
       "0      Donna       여성     42      40대   68.15       여성     42  59.571   \n",
       "1     Gracie       여성     38      30대   81.87       여성     38  60.055   \n",
       "2     Luanne       여성     43      40대   85.84       여성     43  54.297   \n",
       "3       Mimi       여성     50      50대   70.87       여성     50  54.625   \n",
       "4      Chris       남성     49      40대   81.42       남성     49  49.156   \n",
       "5      Allen       남성     38      30대   89.02       남성     38  49.874   \n",
       "6      Nancy       여성     49      40대   76.32       여성     49  48.673   \n",
       "7      Patty       여성     52      50대   76.32       여성     52  45.441   \n",
       "8    Suzanne       여성     57      50대   59.08       여성     57  50.545   \n",
       "9     Teresa       여성     51      50대   77.91       여성     51  46.672   \n",
       "10       Bob       남성     39      30대   75.07       남성     39  45.313   \n",
       "11  Harriett       여성     49      40대   73.37       여성     49  50.388   \n",
       "12      Jane       여성     44      40대   73.03       여성     44  50.541   \n",
       "13    Harold       남성     48      40대   91.63       남성     48  46.774   \n",
       "14     Sammy       남성     54      50대   83.12       남성     54  51.855   \n",
       "15     Buffy       여성     52      50대   73.71       여성     52  45.790   \n",
       "16     Trent       남성     38      30대   82.78       남성     38  47.467   \n",
       "17    Jackie       여성     47      40대   79.15       여성     47  47.273   \n",
       "18     Ralph       남성     43      40대   81.19       남성     43  49.091   \n",
       "19      Jack       남성     51      50대   69.63       남성     51  40.836   \n",
       "20     Annie       여성     51      50대   67.25       여성     51  45.118   \n",
       "21      Kate       여성     45      40대   66.45       여성     45  44.754   \n",
       "22      Carl       남성     54      50대   79.38       남성     54  46.080   \n",
       "23       Don       남성     44      40대   89.47       남성     44  44.609   \n",
       "24     Effie       여성     48      40대   61.24       여성     48  47.920   \n",
       "25    George       남성     37      30대   77.45       남성     37  44.811   \n",
       "26      Iris       여성     39      30대   75.98       여성     39  45.681   \n",
       "27      Mark       남성     57      50대   73.37       남성     57  39.407   \n",
       "28     Steve       남성     54      50대   91.63       남성     54  39.203   \n",
       "29    Vaughn       남성     44      40대   81.42       남성     44  39.442   \n",
       "30   William       남성     45      40대   87.66       남성     45  37.388   \n",
       "\n",
       "    RUNTIME  RUNPULSE  RSTPULSE  MAXPULSE  \n",
       "0      8.17       166        40       172  \n",
       "1      8.63       170        48       186  \n",
       "2      8.65       156        45       168  \n",
       "3      8.92       146        48       155  \n",
       "4      8.95       180        44       185  \n",
       "5      9.22       178        55       180  \n",
       "6      9.40       186        56       188  \n",
       "7      9.63       164        48       166  \n",
       "8      9.93       148        49       155  \n",
       "9     10.00       162        48       168  \n",
       "10    10.07       185        62       185  \n",
       "11    10.08       168        67       168  \n",
       "12    10.13       168        45       168  \n",
       "13    10.25       162        48       164  \n",
       "14    10.33       166        50       170  \n",
       "15    10.47       186        59       188  \n",
       "16    10.50       170        53       172  \n",
       "17    10.60       162        47       164  \n",
       "18    10.85       162        64       170  \n",
       "19    10.95       168        57       172  \n",
       "20    11.08       172        48       172  \n",
       "21    11.12       176        51       176  \n",
       "22    11.17       156        62       165  \n",
       "23    11.37       178        62       182  \n",
       "24    11.50       170        52       176  \n",
       "25    11.63       176        58       176  \n",
       "26    11.95       176        70       180  \n",
       "27    12.63       174        58       176  \n",
       "28    12.88       168        44       172  \n",
       "29    13.08       174        63       176  \n",
       "30    14.03       186        56       192  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='NAME',how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merge =pd.merge(left, right, on=['NAME','GENDER','AGE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>NAME</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>AGEGROUP</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>OXY</th>\n",
       "      <th>RUNTIME</th>\n",
       "      <th>RUNPULSE</th>\n",
       "      <th>RSTPULSE</th>\n",
       "      <th>MAXPULSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donna</td>\n",
       "      <td>여성</td>\n",
       "      <td>42</td>\n",
       "      <td>40대</td>\n",
       "      <td>68.15</td>\n",
       "      <td>59.571</td>\n",
       "      <td>8.17</td>\n",
       "      <td>166</td>\n",
       "      <td>40</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gracie</td>\n",
       "      <td>여성</td>\n",
       "      <td>38</td>\n",
       "      <td>30대</td>\n",
       "      <td>81.87</td>\n",
       "      <td>60.055</td>\n",
       "      <td>8.63</td>\n",
       "      <td>170</td>\n",
       "      <td>48</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Luanne</td>\n",
       "      <td>여성</td>\n",
       "      <td>43</td>\n",
       "      <td>40대</td>\n",
       "      <td>85.84</td>\n",
       "      <td>54.297</td>\n",
       "      <td>8.65</td>\n",
       "      <td>156</td>\n",
       "      <td>45</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mimi</td>\n",
       "      <td>여성</td>\n",
       "      <td>50</td>\n",
       "      <td>50대</td>\n",
       "      <td>70.87</td>\n",
       "      <td>54.625</td>\n",
       "      <td>8.92</td>\n",
       "      <td>146</td>\n",
       "      <td>48</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>40대</td>\n",
       "      <td>76.32</td>\n",
       "      <td>48.673</td>\n",
       "      <td>9.40</td>\n",
       "      <td>186</td>\n",
       "      <td>56</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Patty</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>50대</td>\n",
       "      <td>76.32</td>\n",
       "      <td>45.441</td>\n",
       "      <td>9.63</td>\n",
       "      <td>164</td>\n",
       "      <td>48</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Suzanne</td>\n",
       "      <td>여성</td>\n",
       "      <td>57</td>\n",
       "      <td>50대</td>\n",
       "      <td>59.08</td>\n",
       "      <td>50.545</td>\n",
       "      <td>9.93</td>\n",
       "      <td>148</td>\n",
       "      <td>49</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Teresa</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>50대</td>\n",
       "      <td>77.91</td>\n",
       "      <td>46.672</td>\n",
       "      <td>10.00</td>\n",
       "      <td>162</td>\n",
       "      <td>48</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Harriett</td>\n",
       "      <td>여성</td>\n",
       "      <td>49</td>\n",
       "      <td>40대</td>\n",
       "      <td>73.37</td>\n",
       "      <td>50.388</td>\n",
       "      <td>10.08</td>\n",
       "      <td>168</td>\n",
       "      <td>67</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jane</td>\n",
       "      <td>여성</td>\n",
       "      <td>44</td>\n",
       "      <td>40대</td>\n",
       "      <td>73.03</td>\n",
       "      <td>50.541</td>\n",
       "      <td>10.13</td>\n",
       "      <td>168</td>\n",
       "      <td>45</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Buffy</td>\n",
       "      <td>여성</td>\n",
       "      <td>52</td>\n",
       "      <td>50대</td>\n",
       "      <td>73.71</td>\n",
       "      <td>45.790</td>\n",
       "      <td>10.47</td>\n",
       "      <td>186</td>\n",
       "      <td>59</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jackie</td>\n",
       "      <td>여성</td>\n",
       "      <td>47</td>\n",
       "      <td>40대</td>\n",
       "      <td>79.15</td>\n",
       "      <td>47.273</td>\n",
       "      <td>10.60</td>\n",
       "      <td>162</td>\n",
       "      <td>47</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Annie</td>\n",
       "      <td>여성</td>\n",
       "      <td>51</td>\n",
       "      <td>50대</td>\n",
       "      <td>67.25</td>\n",
       "      <td>45.118</td>\n",
       "      <td>11.08</td>\n",
       "      <td>172</td>\n",
       "      <td>48</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kate</td>\n",
       "      <td>여성</td>\n",
       "      <td>45</td>\n",
       "      <td>40대</td>\n",
       "      <td>66.45</td>\n",
       "      <td>44.754</td>\n",
       "      <td>11.12</td>\n",
       "      <td>176</td>\n",
       "      <td>51</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Effie</td>\n",
       "      <td>여성</td>\n",
       "      <td>48</td>\n",
       "      <td>40대</td>\n",
       "      <td>61.24</td>\n",
       "      <td>47.920</td>\n",
       "      <td>11.50</td>\n",
       "      <td>170</td>\n",
       "      <td>52</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Iris</td>\n",
       "      <td>여성</td>\n",
       "      <td>39</td>\n",
       "      <td>30대</td>\n",
       "      <td>75.98</td>\n",
       "      <td>45.681</td>\n",
       "      <td>11.95</td>\n",
       "      <td>176</td>\n",
       "      <td>70</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        NAME GENDER  AGE AGEGROUP  WEIGHT     OXY  RUNTIME  RUNPULSE  \\\n",
       "0      Donna     여성   42      40대   68.15  59.571     8.17       166   \n",
       "1     Gracie     여성   38      30대   81.87  60.055     8.63       170   \n",
       "2     Luanne     여성   43      40대   85.84  54.297     8.65       156   \n",
       "3       Mimi     여성   50      50대   70.87  54.625     8.92       146   \n",
       "6      Nancy     여성   49      40대   76.32  48.673     9.40       186   \n",
       "7      Patty     여성   52      50대   76.32  45.441     9.63       164   \n",
       "8    Suzanne     여성   57      50대   59.08  50.545     9.93       148   \n",
       "9     Teresa     여성   51      50대   77.91  46.672    10.00       162   \n",
       "11  Harriett     여성   49      40대   73.37  50.388    10.08       168   \n",
       "12      Jane     여성   44      40대   73.03  50.541    10.13       168   \n",
       "15     Buffy     여성   52      50대   73.71  45.790    10.47       186   \n",
       "17    Jackie     여성   47      40대   79.15  47.273    10.60       162   \n",
       "20     Annie     여성   51      50대   67.25  45.118    11.08       172   \n",
       "21      Kate     여성   45      40대   66.45  44.754    11.12       176   \n",
       "24     Effie     여성   48      40대   61.24  47.920    11.50       170   \n",
       "26      Iris     여성   39      30대   75.98  45.681    11.95       176   \n",
       "\n",
       "    RSTPULSE  MAXPULSE  \n",
       "0         40       172  \n",
       "1         48       186  \n",
       "2         45       168  \n",
       "3         48       155  \n",
       "6         56       188  \n",
       "7         48       166  \n",
       "8         49       155  \n",
       "9         48       168  \n",
       "11        67       168  \n",
       "12        45       168  \n",
       "15        59       188  \n",
       "17        47       164  \n",
       "20        48       172  \n",
       "21        51       176  \n",
       "24        52       176  \n",
       "26        70       180  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge[df_merge['GENDER']=='여성']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>남성</th>\n",
       "      <td>46.333333</td>\n",
       "      <td>82.282667</td>\n",
       "      <td>44.753733</td>\n",
       "      <td>11.19400</td>\n",
       "      <td>172.20</td>\n",
       "      <td>55.733333</td>\n",
       "      <td>175.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>여성</th>\n",
       "      <td>47.312500</td>\n",
       "      <td>72.908750</td>\n",
       "      <td>49.834000</td>\n",
       "      <td>10.01625</td>\n",
       "      <td>167.25</td>\n",
       "      <td>51.312500</td>\n",
       "      <td>171.875</td>\n",
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      "text/plain": [
       "              AGE     WEIGHT        OXY   RUNTIME  RUNPULSE   RSTPULSE  \\\n",
       "GENDER                                                                   \n",
       "남성      46.333333  82.282667  44.753733  11.19400    172.20  55.733333   \n",
       "여성      47.312500  72.908750  49.834000  10.01625    167.25  51.312500   \n",
       "\n",
       "        MAXPULSE  \n",
       "GENDER            \n",
       "남성       175.800  \n",
       "여성       171.875  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.groupby(\"GENDER\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>GENDER</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">남성</th>\n",
       "      <th>30대</th>\n",
       "      <td>81.080000</td>\n",
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       "    <tr>\n",
       "      <th>40대</th>\n",
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       "      <th>50대</th>\n",
       "      <td>79.426000</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">여성</th>\n",
       "      <th>30대</th>\n",
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       "    <tr>\n",
       "      <th>40대</th>\n",
       "      <td>72.943750</td>\n",
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       "    <tr>\n",
       "      <th>50대</th>\n",
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      "text/plain": [
       "                    WEIGHT\n",
       "GENDER AGEGROUP           \n",
       "남성     30대       81.080000\n",
       "       40대       85.465000\n",
       "       50대       79.426000\n",
       "여성     30대       78.925000\n",
       "       40대       72.943750\n",
       "       50대       70.856667"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.groupby([\"GENDER\",\"AGEGROUP\"]).mean()[['WEIGHT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">남성</th>\n",
       "      <th>30대</th>\n",
       "      <td>81.080000</td>\n",
       "      <td>6.197596</td>\n",
       "      <td>80.115</td>\n",
       "      <td>75.07</td>\n",
       "      <td>89.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40대</th>\n",
       "      <td>85.465000</td>\n",
       "      <td>4.687536</td>\n",
       "      <td>84.540</td>\n",
       "      <td>81.19</td>\n",
       "      <td>91.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50대</th>\n",
       "      <td>79.426000</td>\n",
       "      <td>8.591014</td>\n",
       "      <td>79.380</td>\n",
       "      <td>69.63</td>\n",
       "      <td>91.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">여성</th>\n",
       "      <th>30대</th>\n",
       "      <td>78.925000</td>\n",
       "      <td>4.164859</td>\n",
       "      <td>78.925</td>\n",
       "      <td>75.98</td>\n",
       "      <td>81.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40대</th>\n",
       "      <td>72.943750</td>\n",
       "      <td>7.732543</td>\n",
       "      <td>73.200</td>\n",
       "      <td>61.24</td>\n",
       "      <td>85.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50대</th>\n",
       "      <td>70.856667</td>\n",
       "      <td>6.920074</td>\n",
       "      <td>72.290</td>\n",
       "      <td>59.08</td>\n",
       "      <td>77.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean       std  median    min    max\n",
       "GENDER AGEGROUP                                           \n",
       "남성     30대       81.080000  6.197596  80.115  75.07  89.02\n",
       "       40대       85.465000  4.687536  84.540  81.19  91.63\n",
       "       50대       79.426000  8.591014  79.380  69.63  91.63\n",
       "여성     30대       78.925000  4.164859  78.925  75.98  81.87\n",
       "       40대       72.943750  7.732543  73.200  61.24  85.84\n",
       "       50대       70.856667  6.920074  72.290  59.08  77.91"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.groupby([\"GENDER\",\"AGEGROUP\"])['WEIGHT'].agg(['mean', 'std', 'median', 'min','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>OXY</th>\n",
       "      <th>RUNTIME</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGEGROUP</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">남성</th>\n",
       "      <th>30대</th>\n",
       "      <td>81.080000</td>\n",
       "      <td>2.312581</td>\n",
       "      <td>11.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40대</th>\n",
       "      <td>85.465000</td>\n",
       "      <td>4.981042</td>\n",
       "      <td>14.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50대</th>\n",
       "      <td>79.426000</td>\n",
       "      <td>5.449159</td>\n",
       "      <td>12.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">여성</th>\n",
       "      <th>30대</th>\n",
       "      <td>78.925000</td>\n",
       "      <td>10.163953</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40대</th>\n",
       "      <td>72.943750</td>\n",
       "      <td>4.630587</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50대</th>\n",
       "      <td>70.856667</td>\n",
       "      <td>3.791139</td>\n",
       "      <td>11.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    WEIGHT        OXY  RUNTIME\n",
       "GENDER AGEGROUP                               \n",
       "남성     30대       81.080000   2.312581    11.63\n",
       "       40대       85.465000   4.981042    14.03\n",
       "       50대       79.426000   5.449159    12.88\n",
       "여성     30대       78.925000  10.163953    11.95\n",
       "       40대       72.943750   4.630587    11.50\n",
       "       50대       70.856667   3.791139    11.08"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.groupby(['GENDER', 'AGEGROUP'])['WEIGHT', 'OXY', 'RUNTIME'].agg({'WEIGHT': 'mean', 'OXY':'std','RUNTIME':'max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>6</td>\n",
       "      <td>two</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bar  baz  foo zoo\n",
       "0   A    1  one   x\n",
       "1   B    2  one   y\n",
       "2   C    3  one   z\n",
       "3   A    4  two   q\n",
       "4   B    5  two   w\n",
       "5   C    6  two   t"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'foo' : ['one','one','one','two','two','two'],\n",
    "                   'bar' : ['A','B','C','A','B','C'],\n",
    "                   'baz' : [1,2,3,4,5,6],\n",
    "                   'zoo' : ['x','y','z','q','w','t']})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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      ],
      "text/plain": [
       "bar  A  B  C\n",
       "foo         \n",
       "one  1  2  3\n",
       "two  4  5  6"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index = 'foo', columns = 'bar', values = 'baz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>6</td>\n",
       "      <td>two</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bar  baz  foo zoo\n",
       "0   A    1  one   x\n",
       "1   A    2  one   y\n",
       "2   C    3  one   z\n",
       "3   A    4  two   q\n",
       "4   B    5  two   w\n",
       "5   C    6  two   t"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'foo' : ['one','one','one','two','two','two'],\n",
    "                   'bar' : ['A','A','C','A','B','C'],\n",
    "                   'baz' : [1,2,3,4,5,6],\n",
    "                   'zoo' : ['x','y','z','q','w','t']})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <tbody>\n",
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       "      <th>one</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>two</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bar    A    B    C\n",
       "foo               \n",
       "one  3.0  NaN  3.0\n",
       "two  4.0  5.0  6.0"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index = 'foo', columns = 'bar', values = 'baz', aggfunc = sum) # aggfunc = 중복 처리함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
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       "      <td>6</td>\n",
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       "      <td>x</td>\n",
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       "      <th>7</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>zoo</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>zoo</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>zoo</td>\n",
       "      <td>q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>zoo</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>zoo</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    foo bar variable value\n",
       "0   one   A      baz     1\n",
       "1   one   A      baz     2\n",
       "2   one   C      baz     3\n",
       "3   two   A      baz     4\n",
       "4   two   B      baz     5\n",
       "5   two   C      baz     6\n",
       "6   one   A      zoo     x\n",
       "7   one   A      zoo     y\n",
       "8   one   C      zoo     z\n",
       "9   two   A      zoo     q\n",
       "10  two   B      zoo     w\n",
       "11  two   C      zoo     t"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.melt(id_vars = ['foo','bar'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>foo</th>\n",
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      "text/plain": [
       "   foo bar variable value\n",
       "0  one   A      zoo     x\n",
       "1  one   A      zoo     y\n",
       "2  one   C      zoo     z\n",
       "3  two   A      zoo     q\n",
       "4  two   B      zoo     w\n",
       "5  two   C      zoo     t"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.melt(id_vars = ['foo','bar'], value_vars = ['zoo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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