Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aca27c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import string\n",
    "np.random.seed(56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25f1dfbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def id():\n",
    "    return ''.join(random.choice(string.ascii_letters) for i in range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81ad33ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score():\n",
    "    return np.random.randint(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27211f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({\"국어\":[score()for i in range(10)],\"영어\":[score()for i in range(10)],\"수학\":[score()for i in range(10)],\"사회\":[score()for i in range(10)],}, index=[id() for i in range(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8eb45627",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-35-fbd51146eddf>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-35-fbd51146eddf>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    df.loc[['JDnJh':'feXxM']]\u001b[0m\n\u001b[1;37m                   ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "df.loc[['JDnJh''feXxM']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "08996550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>사회</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JDnJh</th>\n",
       "      <td>78</td>\n",
       "      <td>94</td>\n",
       "      <td>56</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       국어  영어  수학  사회\n",
       "JDnJh  78  94  56   8"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7238729d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13, 23, 22, 48, 83, 52, 39, 50, 20, 5]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4c335a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({\"국어\":[score()for i in range(10)],\"영어\":[score()for i in range(10)],\"수학\":[score()for i in range(10)],\"사회\":[score()for i in range(10)],}, index=[id() for i in range(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "12893391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>사회</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OBDZS</th>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sCoTy</th>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQHd</th>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>51</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pLiwC</th>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POUaQ</th>\n",
       "      <td>24</td>\n",
       "      <td>83</td>\n",
       "      <td>39</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gHYyR</th>\n",
       "      <td>32</td>\n",
       "      <td>66</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bDrAx</th>\n",
       "      <td>90</td>\n",
       "      <td>69</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctVyA</th>\n",
       "      <td>26</td>\n",
       "      <td>69</td>\n",
       "      <td>87</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RXwiF</th>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feXxM</th>\n",
       "      <td>79</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       국어  영어  수학  사회\n",
       "OBDZS  72  31  78  84\n",
       "sCoTy  15  65  20  62\n",
       "UBQHd  37  19  51  72\n",
       "pLiwC  51  47  73  25\n",
       "POUaQ  24  83  39  96\n",
       "gHYyR  32  66  86   1\n",
       "bDrAx  90  69  23  75\n",
       "ctVyA  26  69  87  65\n",
       "RXwiF  91  97  46  67\n",
       "feXxM  79  27  48  22"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "29fe3ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'과학']=pd.Series([score() for i in range(10)], index = df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2ede0c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>사회</th>\n",
       "      <th>과학</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OBDZS</th>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sCoTy</th>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQHd</th>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>51</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pLiwC</th>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>25</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POUaQ</th>\n",
       "      <td>24</td>\n",
       "      <td>83</td>\n",
       "      <td>39</td>\n",
       "      <td>96</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gHYyR</th>\n",
       "      <td>32</td>\n",
       "      <td>66</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bDrAx</th>\n",
       "      <td>90</td>\n",
       "      <td>69</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctVyA</th>\n",
       "      <td>26</td>\n",
       "      <td>69</td>\n",
       "      <td>87</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RXwiF</th>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>67</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feXxM</th>\n",
       "      <td>79</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       국어  영어  수학  사회  과학\n",
       "OBDZS  72  31  78  84  32\n",
       "sCoTy  15  65  20  62  63\n",
       "UBQHd  37  19  51  72  29\n",
       "pLiwC  51  47  73  25  67\n",
       "POUaQ  24  83  39  96  53\n",
       "gHYyR  32  66  86   1  60\n",
       "bDrAx  90  69  23  75  70\n",
       "ctVyA  26  69  87  65  14\n",
       "RXwiF  91  97  46  67  66\n",
       "feXxM  79  27  48  22  36"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "56754eda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OBDZS    297\n",
       "sCoTy    225\n",
       "UBQHd    208\n",
       "pLiwC    263\n",
       "POUaQ    295\n",
       "gHYyR    245\n",
       "bDrAx    327\n",
       "ctVyA    261\n",
       "RXwiF    367\n",
       "feXxM    212\n",
       "Name: 총합, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#각 학생들의 점수의 총합을 표현하는 컬럼을 추가하세요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0b53d2fc",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'과학'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '과학'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-5cc1049a618d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"과학\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2997\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2998\u001b[0m         \u001b[1;31m# Do we have a slicer (on rows)?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2999\u001b[1;33m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_to_index_sliceable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3000\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3001\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36mconvert_to_index_sliceable\u001b[1;34m(obj, key)\u001b[0m\n\u001b[0;32m   2208\u001b[0m     \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2209\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2210\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2212\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_convert_slice_indexer\u001b[1;34m(self, key, kind)\u001b[0m\n\u001b[0;32m   3388\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3389\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3390\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3392\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   5275\u001b[0m         \u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5276\u001b[0m         \"\"\"\n\u001b[1;32m-> 5277\u001b[1;33m         \u001b[0mstart_slice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5278\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5279\u001b[0m         \u001b[1;31m# return a slice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   5474\u001b[0m         \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5475\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5476\u001b[1;33m             \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5477\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5478\u001b[0m             \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   5394\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5395\u001b[0m                 \u001b[1;31m# raise the original KeyError\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5396\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   5388\u001b[0m         \u001b[1;31m# we need to look up the label\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5389\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5390\u001b[1;33m             \u001b[0mslc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5391\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5392\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3080\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3082\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3084\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '과학'"
     ]
    }
   ],
   "source": [
    "df[\"과학\":[score() for i in range(10)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3f471de6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>사회</th>\n",
       "      <th>과학</th>\n",
       "      <th>총합</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OBDZS</th>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>32</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sCoTy</th>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBQHd</th>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>51</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pLiwC</th>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>25</td>\n",
       "      <td>67</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POUaQ</th>\n",
       "      <td>24</td>\n",
       "      <td>83</td>\n",
       "      <td>39</td>\n",
       "      <td>96</td>\n",
       "      <td>53</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gHYyR</th>\n",
       "      <td>32</td>\n",
       "      <td>66</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bDrAx</th>\n",
       "      <td>90</td>\n",
       "      <td>69</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctVyA</th>\n",
       "      <td>26</td>\n",
       "      <td>69</td>\n",
       "      <td>87</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RXwiF</th>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>67</td>\n",
       "      <td>66</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feXxM</th>\n",
       "      <td>79</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       국어  영어  수학  사회  과학   총합\n",
       "OBDZS  72  31  78  84  32  297\n",
       "sCoTy  15  65  20  62  63  225\n",
       "UBQHd  37  19  51  72  29  208\n",
       "pLiwC  51  47  73  25  67  263\n",
       "POUaQ  24  83  39  96  53  295\n",
       "gHYyR  32  66  86   1  60  245\n",
       "bDrAx  90  69  23  75  70  327\n",
       "ctVyA  26  69  87  65  14  261\n",
       "RXwiF  91  97  46  67  66  367\n",
       "feXxM  79  27  48  22  36  212"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'총합']=df.sum(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4dd93005",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
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       "      <th>과학</th>\n",
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       "      <th>OBDZS</th>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
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       "      <td>99.000000</td>\n",
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       "    <tr>\n",
       "      <th>sCoTy</th>\n",
       "      <td>15</td>\n",
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       "      <th>pLiwC</th>\n",
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       "      <th>POUaQ</th>\n",
       "      <td>24</td>\n",
       "      <td>83</td>\n",
       "      <td>39</td>\n",
       "      <td>96</td>\n",
       "      <td>53</td>\n",
       "      <td>98.333333</td>\n",
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       "    <tr>\n",
       "      <th>gHYyR</th>\n",
       "      <td>32</td>\n",
       "      <td>66</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>81.666667</td>\n",
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       "    <tr>\n",
       "      <th>bDrAx</th>\n",
       "      <td>90</td>\n",
       "      <td>69</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>109.000000</td>\n",
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       "    <tr>\n",
       "      <th>ctVyA</th>\n",
       "      <td>26</td>\n",
       "      <td>69</td>\n",
       "      <td>87</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RXwiF</th>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>67</td>\n",
       "      <td>66</td>\n",
       "      <td>122.333333</td>\n",
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       "    <tr>\n",
       "      <th>feXxM</th>\n",
       "      <td>79</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>70.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       국어  영어  수학  사회  과학          평균\n",
       "OBDZS  72  31  78  84  32   99.000000\n",
       "sCoTy  15  65  20  62  63   75.000000\n",
       "UBQHd  37  19  51  72  29   69.333333\n",
       "pLiwC  51  47  73  25  67   87.666667\n",
       "POUaQ  24  83  39  96  53   98.333333\n",
       "gHYyR  32  66  86   1  60   81.666667\n",
       "bDrAx  90  69  23  75  70  109.000000\n",
       "ctVyA  26  69  87  65  14   87.000000\n",
       "RXwiF  91  97  46  67  66  122.333333\n",
       "feXxM  79  27  48  22  36   70.666667"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[:,df.columns !=\"총합\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4c010fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>국어</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>사회</th>\n",
       "      <th>과학</th>\n",
       "      <th>평균</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>OBDZS</th>\n",
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       "      <td>66.000000</td>\n",
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       "      <th>sCoTy</th>\n",
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       "      <td>32</td>\n",
       "      <td>66</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>54.444444</td>\n",
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       "    <tr>\n",
       "      <th>bDrAx</th>\n",
       "      <td>90</td>\n",
       "      <td>69</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctVyA</th>\n",
       "      <td>26</td>\n",
       "      <td>69</td>\n",
       "      <td>87</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RXwiF</th>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>67</td>\n",
       "      <td>66</td>\n",
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       "      <th>feXxM</th>\n",
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       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>47.111111</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       국어  영어  수학  사회  과학         평균\n",
       "OBDZS  72  31  78  84  32  66.000000\n",
       "sCoTy  15  65  20  62  63  50.000000\n",
       "UBQHd  37  19  51  72  29  46.222222\n",
       "pLiwC  51  47  73  25  67  58.444444\n",
       "POUaQ  24  83  39  96  53  65.555556\n",
       "gHYyR  32  66  86   1  60  54.444444\n",
       "bDrAx  90  69  23  75  70  72.666667\n",
       "ctVyA  26  69  87  65  14  58.000000\n",
       "RXwiF  91  97  46  67  66  81.555556\n",
       "feXxM  79  27  48  22  36  47.111111"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'평균']=df.mean(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a4d90651",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot set a row with mismatched columns",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-dda01a3b1496>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"아랍어\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    690\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    691\u001b[0m         \u001b[0miloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 692\u001b[1;33m         \u001b[0miloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    694\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1628\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1629\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer_missing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1630\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1631\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer_missing\u001b[1;34m(self, indexer, value)\u001b[0m\n\u001b[0;32m   1916\u001b[0m                     \u001b[1;31m# must have conforming columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1917\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1918\u001b[1;33m                         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot set a row with mismatched columns\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1920\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot set a row with mismatched columns"
     ]
    }
   ],
   "source": [
    "df.loc[len(df)]=[\"아랍어\",[score() for i in range(10)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6eae8c31",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       국어  영어  수학  사회  과학         평균\n",
       "OBDZS  72  31  78  84  32  66.000000\n",
       "sCoTy  15  65  20  62  63  50.000000\n",
       "UBQHd  37  19  51  72  29  46.222222\n",
       "pLiwC  51  47  73  25  67  58.444444\n",
       "POUaQ  24  83  39  96  53  65.555556\n",
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       "ctVyA  26  69  87  65  14  58.000000\n",
       "RXwiF  91  97  46  67  66  81.555556\n",
       "feXxM  79  27  48  22  36  47.111111"
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     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "71d2649e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('평균', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e85bb435",
   "metadata": {},
   "outputs": [
    {
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       "       국어  영어  수학  사회  과학\n",
       "OBDZS  72  31  78  84  32\n",
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       "ctVyA  26  69  87  65  14\n",
       "RXwiF  91  97  46  67  66\n",
       "feXxM  79  27  48  22  36"
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8bbbb960",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['총합']=df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "369c5f2e",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>feXxM</th>\n",
       "      <td>79.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>총합</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         국어    영어    수학    사회    과학\n",
       "OBDZS  72.0  31.0  78.0  84.0  32.0\n",
       "sCoTy  15.0  65.0  20.0  62.0  63.0\n",
       "UBQHd  37.0  19.0  51.0  72.0  29.0\n",
       "pLiwC  51.0  47.0  73.0  25.0  67.0\n",
       "POUaQ  24.0  83.0  39.0  96.0  53.0\n",
       "gHYyR  32.0  66.0  86.0   1.0  60.0\n",
       "bDrAx  90.0  69.0  23.0  75.0  70.0\n",
       "ctVyA  26.0  69.0  87.0  65.0  14.0\n",
       "RXwiF  91.0  97.0  46.0  67.0  66.0\n",
       "feXxM  79.0  27.0  48.0  22.0  36.0\n",
       "총합      NaN   NaN   NaN   NaN   NaN"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "116a0b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>국어</th>\n",
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       "      <th>OBDZS</th>\n",
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       "      <td>78.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>sCoTy</th>\n",
       "      <td>15.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
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       "    <tr>\n",
       "      <th>UBQHd</th>\n",
       "      <td>37.0</td>\n",
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       "      <td>72.0</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
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       "      <th>POUaQ</th>\n",
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       "      <td>96.0</td>\n",
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       "      <th>gHYyR</th>\n",
       "      <td>32.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <th>bDrAx</th>\n",
       "      <td>90.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
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       "    <tr>\n",
       "      <th>ctVyA</th>\n",
       "      <td>26.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>RXwiF</th>\n",
       "      <td>91.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feXxM</th>\n",
       "      <td>79.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>36.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         국어    영어    수학    사회    과학\n",
       "OBDZS  72.0  31.0  78.0  84.0  32.0\n",
       "sCoTy  15.0  65.0  20.0  62.0  63.0\n",
       "UBQHd  37.0  19.0  51.0  72.0  29.0\n",
       "pLiwC  51.0  47.0  73.0  25.0  67.0\n",
       "POUaQ  24.0  83.0  39.0  96.0  53.0\n",
       "gHYyR  32.0  66.0  86.0   1.0  60.0\n",
       "bDrAx  90.0  69.0  23.0  75.0  70.0\n",
       "ctVyA  26.0  69.0  87.0  65.0  14.0\n",
       "RXwiF  91.0  97.0  46.0  67.0  66.0\n",
       "feXxM  79.0  27.0  48.0  22.0  36.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('총합')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ab5128e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>OBDZS</th>\n",
       "      <td>72.0</td>\n",
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       "      <td>78.0</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>29.0</td>\n",
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       "      <th>pLiwC</th>\n",
       "      <td>51.0</td>\n",
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       "      <th>gHYyR</th>\n",
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       "      <td>86.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>245.0</td>\n",
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       "    <tr>\n",
       "      <th>bDrAx</th>\n",
       "      <td>90.0</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>327.0</td>\n",
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       "    <tr>\n",
       "      <th>ctVyA</th>\n",
       "      <td>26.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>261.0</td>\n",
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       "    <tr>\n",
       "      <th>RXwiF</th>\n",
       "      <td>91.0</td>\n",
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       "      <td>46.0</td>\n",
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       "    <tr>\n",
       "      <th>feXxM</th>\n",
       "      <td>79.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>총합</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         국어    영어    수학    사회    과학     총합\n",
       "OBDZS  72.0  31.0  78.0  84.0  32.0  297.0\n",
       "sCoTy  15.0  65.0  20.0  62.0  63.0  225.0\n",
       "UBQHd  37.0  19.0  51.0  72.0  29.0  208.0\n",
       "pLiwC  51.0  47.0  73.0  25.0  67.0  263.0\n",
       "POUaQ  24.0  83.0  39.0  96.0  53.0  295.0\n",
       "gHYyR  32.0  66.0  86.0   1.0  60.0  245.0\n",
       "bDrAx  90.0  69.0  23.0  75.0  70.0  327.0\n",
       "ctVyA  26.0  69.0  87.0  65.0  14.0  261.0\n",
       "RXwiF  91.0  97.0  46.0  67.0  66.0  367.0\n",
       "feXxM  79.0  27.0  48.0  22.0  36.0  212.0\n",
       "총합      NaN   NaN   NaN   NaN   NaN    0.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['총합']=df.sum(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "04264beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('총합')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1d0b8fcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>53.0</td>\n",
       "      <td>295.0</td>\n",
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       "      <th>gHYyR</th>\n",
       "      <td>32.0</td>\n",
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       "      <td>86.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>245.0</td>\n",
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       "    <tr>\n",
       "      <th>bDrAx</th>\n",
       "      <td>90.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>327.0</td>\n",
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       "    <tr>\n",
       "      <th>ctVyA</th>\n",
       "      <td>26.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>261.0</td>\n",
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       "    <tr>\n",
       "      <th>RXwiF</th>\n",
       "      <td>91.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feXxM</th>\n",
       "      <td>79.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>212.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         국어    영어    수학    사회    과학     총합\n",
       "OBDZS  72.0  31.0  78.0  84.0  32.0  297.0\n",
       "sCoTy  15.0  65.0  20.0  62.0  63.0  225.0\n",
       "UBQHd  37.0  19.0  51.0  72.0  29.0  208.0\n",
       "pLiwC  51.0  47.0  73.0  25.0  67.0  263.0\n",
       "POUaQ  24.0  83.0  39.0  96.0  53.0  295.0\n",
       "gHYyR  32.0  66.0  86.0   1.0  60.0  245.0\n",
       "bDrAx  90.0  69.0  23.0  75.0  70.0  327.0\n",
       "ctVyA  26.0  69.0  87.0  65.0  14.0  261.0\n",
       "RXwiF  91.0  97.0  46.0  67.0  66.0  367.0\n",
       "feXxM  79.0  27.0  48.0  22.0  36.0  212.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dbf30dd0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-dde89be1d30f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'과목총합'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df=df['과목총합']=df.sum()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de7aa978",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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