4장_판다스_데이터_분석_(4.5).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c428604",
   "metadata": {},
   "source": [
    "# 데이터프레임 인덱스 조작"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ded4882",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  C1    C2    C3    C4\n",
       "0  A  0.55  0.65  0.79\n",
       "1  B  0.72  0.44  0.53\n",
       "2  C   0.6  0.89  0.57\n",
       "3  D  0.54  0.96  0.93\n",
       "4  E  0.42  0.38  0.07"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(0)\n",
    "df1 = pd.DataFrame(np.vstack([list('ABCDE'),\n",
    "                              np.round(np.random.rand(3, 5), 2)]).T,\n",
    "                   columns=[\"C1\", \"C2\", \"C3\", \"C4\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b933f0be",
   "metadata": {},
   "outputs": [
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       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      C2    C3    C4\n",
       "C1                  \n",
       "A   0.55  0.65  0.79\n",
       "B   0.72  0.44  0.53\n",
       "C    0.6  0.89  0.57\n",
       "D   0.54  0.96  0.93\n",
       "E   0.42  0.38  0.07"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.set_index(\"C1\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f493254",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
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       "    <tr>\n",
       "      <th>C2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.55</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.72</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.54</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
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       "    <tr>\n",
       "      <th>0.42</th>\n",
       "      <td>0.38</td>\n",
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      ],
      "text/plain": [
       "        C3    C4\n",
       "C2              \n",
       "0.55  0.65  0.79\n",
       "0.72  0.44  0.53\n",
       "0.6   0.89  0.57\n",
       "0.54  0.96  0.93\n",
       "0.42  0.38  0.07"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.set_index(\"C2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63199bc2",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.53</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.07</td>\n",
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      "text/plain": [
       "  C1    C2    C3    C4\n",
       "0  A  0.55  0.65  0.79\n",
       "1  B  0.72  0.44  0.53\n",
       "2  C   0.6  0.89  0.57\n",
       "3  D  0.54  0.96  0.93\n",
       "4  E  0.42  0.38  0.07"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22bff77c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
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       "  <tbody>\n",
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       "      <td>0.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.79</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.07</td>\n",
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      ],
      "text/plain": [
       "     C2    C3    C4\n",
       "0  0.55  0.65  0.79\n",
       "1  0.72  0.44  0.53\n",
       "2   0.6  0.89  0.57\n",
       "3  0.54  0.96  0.93\n",
       "4  0.42  0.38  0.07"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc263dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>C1</th>\n",
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       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>정우</td>\n",
       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>기훈</td>\n",
       "      <td>100</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>태경</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>기건</td>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>도영</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
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      "text/plain": [
       "   C1   C2   C3   C4\n",
       "0  정우   98  100   85\n",
       "1  기훈  100   78   84\n",
       "2  태경   59   34   27\n",
       "3  기건   95   96   97\n",
       "4  도영   78   74  100"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score1 = pd.DataFrame([[\"정우\", 98, 100, 85], \n",
    "                    [\"기훈\", 100, 78, 84], \n",
    "                    [\"태경\", 59, 34, 27], \n",
    "                    [\"기건\", 95, 96, 97],\n",
    "                    [\"도영\", 78, 74,100]], \n",
    "                    columns = [\"C1\", \"C2\", \"C3\", \"C4\"])\n",
    "df_score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e0124a4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
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       "    <tr>\n",
       "      <th>기훈</th>\n",
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       "      <td>84</td>\n",
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       "    <tr>\n",
       "      <th>태경</th>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>기건</th>\n",
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       "      <td>97</td>\n",
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       "    <tr>\n",
       "      <th>도영</th>\n",
       "      <td>78</td>\n",
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       "      <td>100</td>\n",
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      ],
      "text/plain": [
       "     C2   C3   C4\n",
       "C1               \n",
       "정우   98  100   85\n",
       "기훈  100   78   84\n",
       "태경   59   34   27\n",
       "기건   95   96   97\n",
       "도영   78   74  100"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score1.set_index(\"C1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8fb4b3a6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>정우</td>\n",
       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>기훈</td>\n",
       "      <td>100</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>태경</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>기건</td>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>도영</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  C1   C2   C3   C4\n",
       "0      0  정우   98  100   85\n",
       "1      1  기훈  100   78   84\n",
       "2      2  태경   59   34   27\n",
       "3      3  기건   95   96   97\n",
       "4      4  도영   78   74  100"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score2 = df_score1.reset_index()\n",
    "df_score2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dfb8261e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
       "      <th>C2</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
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       "      <th>정우</th>\n",
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       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>기훈</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
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       "      <td>84</td>\n",
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       "    <tr>\n",
       "      <th>태경</th>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>기건</th>\n",
       "      <td>3</td>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>도영</th>\n",
       "      <td>4</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index   C2   C3   C4\n",
       "C1                      \n",
       "정우      0   98  100   85\n",
       "기훈      1  100   78   84\n",
       "태경      2   59   34   27\n",
       "기건      3   95   96   97\n",
       "도영      4   78   74  100"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score2.set_index(\"C1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "089a66e1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>1.76</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.87</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.15</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.49</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.85</td>\n",
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      ],
      "text/plain": [
       "      A           B      \n",
       "     C1    C2    C1    C2\n",
       "0  1.76  0.40  0.98  2.24\n",
       "1  1.87 -0.98  0.95 -0.15\n",
       "2 -0.10  0.41  0.14  1.45\n",
       "3  0.76  0.12  0.44  0.33\n",
       "4  1.49 -0.21  0.31 -0.85"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "df3 = pd.DataFrame(np.round(np.random.randn(5, 4), 2),\n",
    "                   columns=[[\"A\", \"A\", \"B\", \"B\"],\n",
    "                            [\"C1\", \"C2\", \"C1\", \"C2\"]])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8c935cc",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Cidx1</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.87</td>\n",
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       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.49</td>\n",
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       "      <td>0.31</td>\n",
       "      <td>-0.85</td>\n",
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      ],
      "text/plain": [
       "Cidx1     A           B      \n",
       "Cidx2    C1    C2    C1    C2\n",
       "0      1.76  0.40  0.98  2.24\n",
       "1      1.87 -0.98  0.95 -0.15\n",
       "2     -0.10  0.41  0.14  1.45\n",
       "3      0.76  0.12  0.44  0.33\n",
       "4      1.49 -0.21  0.31 -0.85"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.columns.names = [\"Cidx1\", \"Cidx2\"]\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f9e3142a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>D</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">M</th>\n",
       "      <th>id_1</th>\n",
       "      <td>1.76</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.98</td>\n",
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       "      <th>id_2</th>\n",
       "      <td>1.87</td>\n",
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       "      <td>0.95</td>\n",
       "      <td>-0.15</td>\n",
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       "    <tr>\n",
       "      <th>id_3</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.45</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>id_1</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_2</th>\n",
       "      <td>1.49</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_3</th>\n",
       "      <td>-2.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cidx1           A           B      \n",
       "Cidx2           C     D     C     D\n",
       "Ridx1 Ridx2                        \n",
       "M     id_1   1.76  0.40  0.98  2.24\n",
       "      id_2   1.87 -0.98  0.95 -0.15\n",
       "      id_3  -0.10  0.41  0.14  1.45\n",
       "F     id_1   0.76  0.12  0.44  0.33\n",
       "      id_2   1.49 -0.21  0.31 -0.85\n",
       "      id_3  -2.55  0.65  0.86 -0.74"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "df4 = pd.DataFrame(np.round(np.random.randn(6, 4), 2),\n",
    "                   columns=[[\"A\", \"A\", \"B\", \"B\"],\n",
    "                            [\"C\", \"D\", \"C\", \"D\"]],\n",
    "                   index=[[\"M\", \"M\", \"M\", \"F\", \"F\", \"F\"],\n",
    "                          [\"id_\" + str(i + 1) for i in range(3)] * 2])\n",
    "df4.columns.names = [\"Cidx1\", \"Cidx2\"]\n",
    "df4.index.names = [\"Ridx1\", \"Ridx2\"]\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4630482a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.76,  0.4 ,  0.98,  2.24],\n",
       "       [ 1.87, -0.98,  0.95, -0.15],\n",
       "       [-0.1 ,  0.41,  0.14,  1.45],\n",
       "       [ 0.76,  0.12,  0.44,  0.33],\n",
       "       [ 1.49, -0.21,  0.31, -0.85],\n",
       "       [-2.55,  0.65,  0.86, -0.74]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "np.round(np.random.randn(6, 4), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6968e1cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"2\" valign=\"top\">id_2</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">id_3</th>\n",
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       "      <th>B</th>\n",
       "      <td>0.14</td>\n",
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       "      <th rowspan=\"6\" valign=\"top\">F</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">id_1</th>\n",
       "      <th>A</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">id_2</th>\n",
       "      <th>A</th>\n",
       "      <td>1.49</td>\n",
       "      <td>-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">id_3</th>\n",
       "      <th>A</th>\n",
       "      <td>-2.55</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cidx2                 C     D\n",
       "Ridx1 Ridx2 Cidx1            \n",
       "M     id_1  A      1.76  0.40\n",
       "            B      0.98  2.24\n",
       "      id_2  A      1.87 -0.98\n",
       "            B      0.95 -0.15\n",
       "      id_3  A     -0.10  0.41\n",
       "            B      0.14  1.45\n",
       "F     id_1  A      0.76  0.12\n",
       "            B      0.44  0.33\n",
       "      id_2  A      1.49 -0.21\n",
       "            B      0.31 -0.85\n",
       "      id_3  A     -2.55  0.65\n",
       "            B      0.86 -0.74"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.stack(\"Cidx1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e77212cd",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Cidx1</th>\n",
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       "      <th>C</th>\n",
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      "text/plain": [
       "Cidx1                 A     B\n",
       "Ridx1 Ridx2 Cidx2            \n",
       "M     id_1  C      1.76  0.98\n",
       "            D      0.40  2.24\n",
       "      id_2  C      1.87  0.95\n",
       "            D     -0.98 -0.15\n",
       "      id_3  C     -0.10  0.14\n",
       "            D      0.41  1.45\n",
       "F     id_1  C      0.76  0.44\n",
       "            D      0.12  0.33\n",
       "      id_2  C      1.49  0.31\n",
       "            D     -0.21 -0.85\n",
       "      id_3  C     -2.55  0.86\n",
       "            D      0.65 -0.74"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df4.stack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c30905c",
   "metadata": {},
   "outputs": [
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       "Cidx1     A                                   B                              \n",
       "Cidx2     C                 D                 C                 D            \n",
       "Ridx2  id_1  id_2  id_3  id_1  id_2  id_3  id_1  id_2  id_3  id_1  id_2  id_3\n",
       "Ridx1                                                                        \n",
       "F      0.76  1.49 -2.55  0.12 -0.21  0.65  0.44  0.31  0.86  0.33 -0.85 -0.74\n",
       "M      1.76  1.87 -0.10  0.40 -0.98  0.41  0.98  0.95  0.14  2.24 -0.15  1.45"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.unstack(\"Ridx2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "020dccc2",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "Cidx1     A                       B                  \n",
       "Cidx2     C           D           C           D      \n",
       "Ridx1     F     M     F     M     F     M     F     M\n",
       "Ridx2                                                \n",
       "id_1   0.76  1.76  0.12  0.40  0.44  0.98  0.33  2.24\n",
       "id_2   1.49  1.87 -0.21 -0.98  0.31  0.95 -0.85 -0.15\n",
       "id_3  -2.55 -0.10  0.65  0.41  0.86  0.14 -0.74  1.45"
      ]
     },
     "execution_count": 34,
     "metadata": {},
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    }
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   "source": [
    "df4.unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "974e5d0f",
   "metadata": {},
   "outputs": [
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       "Cidx1     A           B      \n",
       "Cidx2    C1    C2    C1    C2\n",
       "0      1.76  0.40  0.98  2.24\n",
       "1      1.87 -0.98  0.95 -0.15\n",
       "2     -0.10  0.41  0.14  1.45\n",
       "3      0.76  0.12  0.44  0.33\n",
       "4      1.49 -0.21  0.31 -0.85"
      ]
     },
     "execution_count": 35,
     "metadata": {},
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    }
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   "source": [
    "df3"
   ]
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2a3a18d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.98\n",
       "1    0.95\n",
       "2    0.14\n",
       "3    0.44\n",
       "4    0.31\n",
       "Name: (B, C1), dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[(\"B\",\"C1\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "73892b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[0, (\"B\", \"C1\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e56551c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.iloc[0, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dec3b84d",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "Cidx2    C1    C2\n",
       "0      1.76  0.40\n",
       "1      1.87 -0.98\n",
       "2     -0.10  0.41\n",
       "3      0.76  0.12\n",
       "4      1.49 -0.21"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1351f96d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Cidx1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">A</th>\n",
       "      <th colspan=\"2\" halign=\"left\">B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Cidx2</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridx1</th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"3\" valign=\"top\">M</th>\n",
       "      <th>id_1</th>\n",
       "      <td>1.76</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_2</th>\n",
       "      <td>1.87</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_3</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>id_1</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_2</th>\n",
       "      <td>1.49</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.85</td>\n",
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       "    <tr>\n",
       "      <th>id_3</th>\n",
       "      <td>-2.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cidx1           A           B      \n",
       "Cidx2           C     D     C     D\n",
       "Ridx1 Ridx2                        \n",
       "M     id_1   1.76  0.40  0.98  2.24\n",
       "      id_2   1.87 -0.98  0.95 -0.15\n",
       "      id_3  -0.10  0.41  0.14  1.45\n",
       "F     id_1   0.76  0.12  0.44  0.33\n",
       "      id_2   1.49 -0.21  0.31 -0.85\n",
       "      id_3  -2.55  0.65  0.86 -0.74"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2ef266be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.76"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[(\"M\", \"id_1\"), (\"A\", \"C\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "385b7de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridx1  Ridx2\n",
       "M      id_1     1.76\n",
       "       id_2     1.87\n",
       "       id_3    -0.10\n",
       "F      id_1     0.76\n",
       "       id_2     1.49\n",
       "       id_3    -2.55\n",
       "Name: (A, C), dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[:, (\"A\", \"C\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d32b522b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cidx1  Cidx2\n",
       "A      C        1.76\n",
       "       D        0.40\n",
       "B      C        0.98\n",
       "       D        2.24\n",
       "Name: (M, id_1), dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df4.loc[(\"M\", \"id_1\"), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "120a8c23",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.44</td>\n",
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       "      <th>All</th>\n",
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       "      <td>3.23</td>\n",
       "      <td>0.39</td>\n",
       "      <td>3.68</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Cidx1           A           B      \n",
       "Cidx2           C     D     C     D\n",
       "Ridx1 Ridx2                        \n",
       "M     id_1   1.76  0.40  0.98  2.24\n",
       "      id_2   1.87 -0.98  0.95 -0.15\n",
       "      id_3  -0.10  0.41  0.14  1.45\n",
       "F     id_1   0.76  0.12  0.44  0.33\n",
       "      id_2   1.49 -0.21  0.31 -0.85\n",
       "      id_3  -2.55  0.65  0.86 -0.74\n",
       "All   All    3.23  0.39  3.68  2.28"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[(\"All\",\"All\"), :] = df4.sum()\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "182c058b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_14072\\1821575397.py:1: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "  df4 = df4.drop(index = \"All\")\n"
     ]
    }
   ],
   "source": [
    "df4 = df4.drop(index = \"All\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7f73dea4",
   "metadata": {},
   "outputs": [
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       "Cidx1           A           B      \n",
       "Cidx2           C     D     C     D\n",
       "Ridx1 Ridx2                        \n",
       "M     id_1   1.76  0.40  0.98  2.24\n",
       "      id_2   1.87 -0.98  0.95 -0.15\n",
       "      id_3  -0.10  0.41  0.14  1.45"
      ]
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     "execution_count": 93,
     "metadata": {},
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   "source": [
    "df4.loc[(\"M\", slice(None)), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "728444b3",
   "metadata": {},
   "outputs": [
    {
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       "Cidx1           A           B      \n",
       "Cidx2           C     D     C     D\n",
       "Ridx1 Ridx2                        \n",
       "M     id_1   1.76  0.40  0.98  2.24\n",
       "F     id_1   0.76  0.12  0.44  0.33"
      ]
     },
     "execution_count": 94,
     "metadata": {},
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    "df4.loc[(slice(None), \"id_1\"), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5a9cf3ea",
   "metadata": {},
   "outputs": [
    {
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       "      <th>id_1</th>\n",
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       "      <td>0.76</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
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       "      <th>id_2</th>\n",
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       "      <td>1.49</td>\n",
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       "      <td>-0.85</td>\n",
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       "      <th>id_3</th>\n",
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       "      <td>0.86</td>\n",
       "      <td>-0.74</td>\n",
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       "      <th>All</th>\n",
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      "text/plain": [
       "Cidx1           A           B      \n",
       "Cidx2           C     D     C     D\n",
       "Ridx2 Ridx1                        \n",
       "id_1  M      1.76  0.40  0.98  2.24\n",
       "id_2  M      1.87 -0.98  0.95 -0.15\n",
       "id_3  M     -0.10  0.41  0.14  1.45\n",
       "id_1  F      0.76  0.12  0.44  0.33\n",
       "id_2  F      1.49 -0.21  0.31 -0.85\n",
       "id_3  F     -2.55  0.65  0.86 -0.74\n",
       "All   All    3.23  0.39  3.68  2.28"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.swaplevel(\"Ridx1\", \"Ridx2\")\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ddc9150c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Cidx2</th>\n",
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       "      <th>D</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Cidx1</th>\n",
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       "      <th>B</th>\n",
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       "      <th>Ridx1</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">M</th>\n",
       "      <th>id_1</th>\n",
       "      <td>1.76</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_2</th>\n",
       "      <td>1.87</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.15</td>\n",
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       "    <tr>\n",
       "      <th>id_3</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>id_1</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.44</td>\n",
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       "    <tr>\n",
       "      <th>id_2</th>\n",
       "      <td>1.49</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.85</td>\n",
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       "    <tr>\n",
       "      <th>id_3</th>\n",
       "      <td>-2.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.74</td>\n",
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       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th>All</th>\n",
       "      <td>3.23</td>\n",
       "      <td>0.39</td>\n",
       "      <td>3.68</td>\n",
       "      <td>2.28</td>\n",
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      ],
      "text/plain": [
       "Cidx2           C     D     C     D\n",
       "Cidx1           A     A     B     B\n",
       "Ridx1 Ridx2                        \n",
       "M     id_1   1.76  0.40  0.98  2.24\n",
       "      id_2   1.87 -0.98  0.95 -0.15\n",
       "      id_3  -0.10  0.41  0.14  1.45\n",
       "F     id_1   0.76  0.12  0.44  0.33\n",
       "      id_2   1.49 -0.21  0.31 -0.85\n",
       "      id_3  -2.55  0.65  0.86 -0.74\n",
       "All   All    3.23  0.39  3.68  2.28"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df4.swaplevel(\"Cidx1\", \"Cidx2\", 1)\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9a5ebcde",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.14</td>\n",
       "      <td>1.45</td>\n",
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      ],
      "text/plain": [
       "Cidx1           A           B      \n",
       "Cidx2           C     D     C     D\n",
       "Ridx2 Ridx1                        \n",
       "All   All    3.23  0.39  3.68  2.28\n",
       "id_1  F      0.76  0.12  0.44  0.33\n",
       "      M      1.76  0.40  0.98  2.24\n",
       "id_2  F      1.49 -0.21  0.31 -0.85\n",
       "      M      1.87 -0.98  0.95 -0.15\n",
       "id_3  F     -2.55  0.65  0.86 -0.74\n",
       "      M     -0.10  0.41  0.14  1.45"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.sort_index(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "09cf16be",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.28</td>\n",
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      ],
      "text/plain": [
       "Cidx2           C           D      \n",
       "Cidx1           A     B     A     B\n",
       "Ridx1 Ridx2                        \n",
       "M     id_1   1.76  0.98  0.40  2.24\n",
       "      id_2   1.87  0.95 -0.98 -0.15\n",
       "      id_3  -0.10  0.14  0.41  1.45\n",
       "F     id_1   0.76  0.44  0.12  0.33\n",
       "      id_2   1.49  0.31 -0.21 -0.85\n",
       "      id_3  -2.55  0.86  0.65 -0.74\n",
       "All   All    3.23  3.68  0.39  2.28"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.sort_index(axis=1, level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9ba8e486",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>67</td>\n",
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       "      <td>81</td>\n",
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      ],
      "text/plain": [
       "   반  번호   국어   영어   수학\n",
       "0  A  01  100   98  100\n",
       "1  A  02   92   87   93\n",
       "2  A  03   88  100   95\n",
       "3  A  04   91   79  100\n",
       "4  A  05   88   99   88\n",
       "5  B  01   90   78  100\n",
       "6  B  02   92   67   83\n",
       "7  B  03   90  100   67\n",
       "8  B  04   98   79   80\n",
       "9  B  05   88   79   81"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [\"반\", \"번호\", \"국어\", \"영어\", \"수학\"]\n",
    "\n",
    "df_score3 = pd.DataFrame([[\"A\", \"01\", 100, 98, 100], \n",
    "                         [\"A\", \"02\", 92, 87, 93], \n",
    "                         [\"A\", \"03\", 88, 100, 95],\n",
    "                         [\"A\", \"04\", 91, 79, 100], \n",
    "                         [\"A\", \"05\", 88, 99, 88], \n",
    "                         [\"B\", \"01\", 90, 78, 100], \n",
    "                         [\"B\", \"02\", 92, 67, 83], \n",
    "                         [\"B\", \"03\", 90, 100, 67],\n",
    "                         [\"B\", \"04\", 98, 79, 80], \n",
    "                         [\"B\", \"05\", 88, 79, 81]],\n",
    "                        columns = columns)\n",
    "\n",
    "df_score3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f6d884bc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>90</td>\n",
       "      <td>78</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>92</td>\n",
       "      <td>67</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>98</td>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       국어   영어   수학\n",
       "반 번호               \n",
       "A 01  100   98  100\n",
       "  02   92   87   93\n",
       "  03   88  100   95\n",
       "  04   91   79  100\n",
       "  05   88   99   88\n",
       "B 01   90   78  100\n",
       "  02   92   67   83\n",
       "  03   90  100   67\n",
       "  04   98   79   80\n",
       "  05   88   79   81"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score4 = df_score3.set_index([\"반\",\"번호\"])\n",
    "df_score4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "ab51b126",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>99.33</td>\n",
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       "      <td>89.33</td>\n",
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       "      <th>02</th>\n",
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       "      <td>67</td>\n",
       "      <td>83</td>\n",
       "      <td>80.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>67</td>\n",
       "      <td>85.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>98</td>\n",
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       "      <td>80</td>\n",
       "      <td>85.67</td>\n",
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       "      <td>81</td>\n",
       "      <td>82.67</td>\n",
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      ],
      "text/plain": [
       "       국어   영어   수학     평균\n",
       "반 번호                      \n",
       "A 01  100   98  100  99.33\n",
       "  02   92   87   93  90.67\n",
       "  03   88  100   95  94.33\n",
       "  04   91   79  100  90.00\n",
       "  05   88   99   88  91.67\n",
       "B 01   90   78  100  89.33\n",
       "  02   92   67   83  80.67\n",
       "  03   90  100   67  85.67\n",
       "  04   98   79   80  85.67\n",
       "  05   88   79   81  82.67"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score4[\"평균\"] = round(df_score4.mean(axis=1),2)\n",
    "df_score4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "813cb617",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     국어       영어        수학     \n",
       "반     A   B    A    B    A    B\n",
       "번호                             \n",
       "01  100  90   98   78  100  100\n",
       "02   92  92   87   67   93   83\n",
       "03   88  90  100  100   95   67\n",
       "04   91  98   79   79  100   80\n",
       "05   88  88   99   79   88   81"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df_score3.set_index([\"번호\", \"반\"])\n",
    "df_score5 = temp.unstack(\"반\").sort_index(level = 0)\n",
    "df_score5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "0684eab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>100.0</td>\n",
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       "      <td>93.0</td>\n",
       "      <td>83.0</td>\n",
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       "      <th>05</th>\n",
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       "      <td>79.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>81.0</td>\n",
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       "    <tr>\n",
       "      <th>평균</th>\n",
       "      <td>91.8</td>\n",
       "      <td>91.6</td>\n",
       "      <td>92.6</td>\n",
       "      <td>80.6</td>\n",
       "      <td>95.2</td>\n",
       "      <td>82.2</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "       국어           영어            수학       \n",
       "반       A     B      A      B      A      B\n",
       "번호                                         \n",
       "01  100.0  90.0   98.0   78.0  100.0  100.0\n",
       "02   92.0  92.0   87.0   67.0   93.0   83.0\n",
       "03   88.0  90.0  100.0  100.0   95.0   67.0\n",
       "04   91.0  98.0   79.0   79.0  100.0   80.0\n",
       "05   88.0  88.0   99.0   79.0   88.0   81.0\n",
       "평균   91.8  91.6   92.6   80.6   95.2   82.2"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score5.loc[(\"평균\", slice(None))] = df_score5.mean()\n",
    "df_score5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd9a2c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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