experiments/EDA/EDA.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "b4f3d6ff-6abb-4fb8-a2a3-4b2ddf4e0fc5",
   "metadata": {},
   "source": [
    "# 📚 RECJOON 추천 모델링을 위한 EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5123d1a1-f718-4cd3-9289-96c0f36a7359",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d9e0f4-8ce4-488a-b23a-ed06f7dfe195",
   "metadata": {},
   "source": [
    "## 🖊 분석을 시작하기 전에"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b5e106-7e25-420f-87b6-dffa973b3614",
   "metadata": {},
   "source": [
    "##### 이 자료는 네이버 부스트캠프 AI Tech 3기 RecSys 14조인 RECognizer 팀원들이 분석한 EDA 자료를 취합하여 정리한 결과입니다.\n",
    "##### Baekjoon Online Judge(BOJ)와 solved.ac 이용자들의 전반적인 데이터를 파악하는 데 도움이 되도록 comment와 함께 정리했습니다.\n",
    "##### 분석에 사용한 데이터는 2022년 4월 21일 기준입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7246d2-a52c-4819-be4f-5787c09d1c12",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1683e5a-74d5-4768-82f7-cfb86c64fde5",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5f52a4-3945-4791-8cf0-7331a55a826d",
   "metadata": {},
   "source": [
    "## 📘 전반적인 데이터 훑어보기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588dbe6a-b0b8-4dae-b6f4-7cb47fc7c1f3",
   "metadata": {},
   "source": [
    "### 우선 필요한 라이브러리를 불러올게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fcfaf912-a999-4233-9e7b-988ed037a5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "a6e9e8b9-9106-4f4c-9768-c79a242f1bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "\n",
    "from scipy import stats\n",
    "from sklearn.metrics.pairwise import cosine_similarity as cs\n",
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baffdb38-7251-4abe-83b9-743f8dd750f5",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c31755e-8e56-4ff4-a72b-6d26f79595ac",
   "metadata": {},
   "source": [
    "### 수집한 데이터를 확인해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "930cd616-a42f-4e2c-bdb1-5f8335f6e6b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_users = pd.read_csv('../data/users.csv')  # 유저 데이터\n",
    "df_problems = pd.read_csv('../data/problems.csv')  # 문제 데이터\n",
    "df_problems_solved = pd.read_csv('../data/problems_solved.csv')  # 유저별 푼 문제 데이터\n",
    "df_records_solved = pd.read_csv('../data/records_solved.csv')  # 유저별 최근 60개의 '맞혔습니다' 데이터"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607dfdf7-85e1-4209-8974-0d8403fd820f",
   "metadata": {},
   "source": [
    "#### 유저 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08103580-2b48-4623-8b57-8cd122a06378",
   "metadata": {},
   "outputs": [
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       "          id               handle  solved_count  user_class  tier  rating  \\\n",
       "0          1              koosaga         11075          10    31    3252   \n",
       "1          2             cki86201          6059          10    31    3231   \n",
       "2          3  mitnegativeinfinity          2188          10    31    3180   \n",
       "3          4                ainta          4111          10    31    3135   \n",
       "4          5               yclock          2670          10    31    3116   \n",
       "...      ...                  ...           ...         ...   ...     ...   \n",
       "65168  65185               zpqmdh             0           0     0       0   \n",
       "65169  65186              zumge98             0           0     0       0   \n",
       "65170  65187              zxcv123             0           0     0       0   \n",
       "65171  65188                zxr83             0           0     0       0   \n",
       "65172  65189              zzzqw11             0           0     0       0   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "0                        2802              250                     175   \n",
       "1                        2781              250                     175   \n",
       "2                        2730              250                     175   \n",
       "3                        2685              250                     175   \n",
       "4                        2666              250                     175   \n",
       "...                       ...              ...                     ...   \n",
       "65168                       0                0                       0   \n",
       "65169                       0                0                       0   \n",
       "65170                       0                0                       0   \n",
       "65171                       0                0                       0   \n",
       "65172                       0                0                       0   \n",
       "\n",
       "               exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "0      14225816820            5                   94          24      1   \n",
       "1      10292773001            0                   26           3      2   \n",
       "2       6393679340            0                   12           8      3   \n",
       "3       6940261423            0                   14           2      4   \n",
       "4       4132785157            0                   14           3      5   \n",
       "...            ...          ...                  ...         ...    ...   \n",
       "65168            0            0                    0           0  63901   \n",
       "65169            0            0                    0           0  63901   \n",
       "65170            0            0                    0           0  63901   \n",
       "65171            0            0                    0           0  63901   \n",
       "65172            0            0                    0           0  63901   \n",
       "\n",
       "       organization  \n",
       "0       394,439,753  \n",
       "1               299  \n",
       "2               440  \n",
       "3      299,915,1566  \n",
       "4           299,439  \n",
       "...             ...  \n",
       "65168           NaN  \n",
       "65169           NaN  \n",
       "65170           NaN  \n",
       "65171           NaN  \n",
       "65172           NaN  \n",
       "\n",
       "[65173 rows x 15 columns]"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "display(df_users)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5545ed36-af5b-4415-b2cc-fe6e4c022b50",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423c2974-fc87-4c6b-a1be-5f0147f5f97f",
   "metadata": {},
   "source": [
    "#### 문제 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7f07fa5-6b57-4f93-817e-756a4bb72dc3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22985</th>\n",
       "      <td>22986</td>\n",
       "      <td>25030</td>\n",
       "      <td>Polynomial Quine</td>\n",
       "      <td>gaussian_elimination,linear_algebra,math,numbe...</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22986</th>\n",
       "      <td>22987</td>\n",
       "      <td>25031</td>\n",
       "      <td>Unifying Values</td>\n",
       "      <td>dp,prefix_sum</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22987</th>\n",
       "      <td>22988</td>\n",
       "      <td>25032</td>\n",
       "      <td>Young Zebra</td>\n",
       "      <td>bfs,dfs,graphs,graph_traversal</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22988 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  problem_id             title  \\\n",
       "0          1        1000               A+B   \n",
       "1          2        1001               A-B   \n",
       "2          3        1002                터렛   \n",
       "3          4        1003           피보나치 함수   \n",
       "4          5        1004             어린 왕자   \n",
       "...      ...         ...               ...   \n",
       "22983  22984       25028    Fully Generate   \n",
       "22984  22985       25029        Joyful KMP   \n",
       "22985  22986       25030  Polynomial Quine   \n",
       "22986  22987       25031   Unifying Values   \n",
       "22987  22988       25032       Young Zebra   \n",
       "\n",
       "                                                    tags  is_solvable  \\\n",
       "0                         arithmetic,implementation,math         True   \n",
       "1                         arithmetic,implementation,math         True   \n",
       "2                                          geometry,math         True   \n",
       "3                                                     dp         True   \n",
       "4                                               geometry         True   \n",
       "...                                                  ...          ...   \n",
       "22983                 dp,exponentiation_by_squaring,math         True   \n",
       "22984                                                NaN         True   \n",
       "22985  gaussian_elimination,linear_algebra,math,numbe...         True   \n",
       "22986                                      dp,prefix_sum         True   \n",
       "22987                     bfs,dfs,graphs,graph_traversal         True   \n",
       "\n",
       "       accepted_user_count  level  average_tries  \n",
       "0                   175152      1              2  \n",
       "1                   146736      1              1  \n",
       "2                    25716      7              4  \n",
       "3                    34653      8              3  \n",
       "4                     8438      8              2  \n",
       "...                    ...    ...            ...  \n",
       "22983                    3     14              2  \n",
       "22984                    3     22              1  \n",
       "22985                    2     23              1  \n",
       "22986                    4     14              1  \n",
       "22987                    4     16              1  \n",
       "\n",
       "[22988 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_problems)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5672fe88-469f-4919-9448-5b42ab06ea33",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29769a6d-6605-4e17-926e-4c7e5dd12ab4",
   "metadata": {},
   "source": [
    "#### 유저별 푼 문제 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec2acf5a-3544-4c4f-9c32-af9e82ea3bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>problems</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1006,1007,1008,1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>cki86201</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1006,1007,1008,1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>mitnegativeinfinity</td>\n",
       "      <td>1000,1001,1019,1056,1067,1144,1150,1311,1372,1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ainta</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1007,1008,1009,1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>yclock</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1007,1008,1009,1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>63871</th>\n",
       "      <td>63872</td>\n",
       "      <td>ohjcms1</td>\n",
       "      <td>2557,10718,15596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63872</th>\n",
       "      <td>63873</td>\n",
       "      <td>steven1010</td>\n",
       "      <td>2557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63873</th>\n",
       "      <td>63874</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>1000,1001,1008,1330,2438,2439,2557,2588,2739,2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63874</th>\n",
       "      <td>63875</td>\n",
       "      <td>yijh061016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63875</th>\n",
       "      <td>63876</td>\n",
       "      <td>yoonkoo2001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63876 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id               handle  \\\n",
       "0          1              koosaga   \n",
       "1          2             cki86201   \n",
       "2          3  mitnegativeinfinity   \n",
       "3          4                ainta   \n",
       "4          5               yclock   \n",
       "...      ...                  ...   \n",
       "63871  63872              ohjcms1   \n",
       "63872  63873           steven1010   \n",
       "63873  63874              wwowwww   \n",
       "63874  63875           yijh061016   \n",
       "63875  63876          yoonkoo2001   \n",
       "\n",
       "                                                problems  \n",
       "0      1000,1001,1002,1003,1004,1005,1006,1007,1008,1...  \n",
       "1      1000,1001,1002,1003,1004,1005,1006,1007,1008,1...  \n",
       "2      1000,1001,1019,1056,1067,1144,1150,1311,1372,1...  \n",
       "3      1000,1001,1002,1003,1004,1005,1007,1008,1009,1...  \n",
       "4      1000,1001,1002,1003,1004,1005,1007,1008,1009,1...  \n",
       "...                                                  ...  \n",
       "63871                                   2557,10718,15596  \n",
       "63872                                               2557  \n",
       "63873  1000,1001,1008,1330,2438,2439,2557,2588,2739,2...  \n",
       "63874                                                NaN  \n",
       "63875                                                NaN  \n",
       "\n",
       "[63876 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_problems_solved)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe09f31-98c8-443b-9ca2-fa60b98516fc",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da803c2-451a-4c68-b33a-7d3d634ee372",
   "metadata": {},
   "source": [
    "#### 유저별 최근 60개의 '맞혔습니다' 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77fb5745-c1d7-4a74-955b-1792f1b80c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>problem</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>4139</td>\n",
       "      <td>2022-04-26 20:37:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>24116</td>\n",
       "      <td>2022-04-25 23:47:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>24124</td>\n",
       "      <td>2022-04-25 23:47:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>24125</td>\n",
       "      <td>2022-04-25 23:47:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>24126</td>\n",
       "      <td>2022-04-25 23:47:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000108</th>\n",
       "      <td>3000109</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>1001</td>\n",
       "      <td>2021-02-22 11:14:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000109</th>\n",
       "      <td>3000110</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>1000</td>\n",
       "      <td>2021-02-22 10:57:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000110</th>\n",
       "      <td>3000111</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>10171</td>\n",
       "      <td>2021-02-22 10:28:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000111</th>\n",
       "      <td>3000112</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>10718</td>\n",
       "      <td>2021-02-18 10:44:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000112</th>\n",
       "      <td>3000113</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>2557</td>\n",
       "      <td>2021-02-18 10:42:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000113 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   handle  problem            timestamp\n",
       "0              1  koosaga     4139  2022-04-26 20:37:39\n",
       "1              2  koosaga    24116  2022-04-25 23:47:38\n",
       "2              3  koosaga    24124  2022-04-25 23:47:25\n",
       "3              4  koosaga    24125  2022-04-25 23:47:17\n",
       "4              5  koosaga    24126  2022-04-25 23:47:07\n",
       "...          ...      ...      ...                  ...\n",
       "3000108  3000109  wwowwww     1001  2021-02-22 11:14:45\n",
       "3000109  3000110  wwowwww     1000  2021-02-22 10:57:44\n",
       "3000110  3000111  wwowwww    10171  2021-02-22 10:28:05\n",
       "3000111  3000112  wwowwww    10718  2021-02-18 10:44:08\n",
       "3000112  3000113  wwowwww     2557  2021-02-18 10:42:30\n",
       "\n",
       "[3000113 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_records_solved)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a23b71-28f9-470a-b644-e8022e2f33b2",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3bb536-b6ad-461c-b795-80cedcf66722",
   "metadata": {},
   "source": [
    "### NA 값이 있는지 우선 확인해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bbd5c53b-7475-42a3-ad3c-61f4f60a196b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0.000000\n",
       "problem_id             0.000000\n",
       "title                  0.000044\n",
       "tags                   0.382678\n",
       "is_solvable            0.000000\n",
       "accepted_user_count    0.000000\n",
       "level                  0.000000\n",
       "average_tries          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems.isna().sum(axis=0)/22988"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998137ee-4138-4a91-aa94-f80870058799",
   "metadata": {},
   "source": [
    "태그에 NA 값이 전체 데이터에서 약 38% 존재합니다.<br/>\n",
    "태그를 모델 인풋으로 사용한다면 어떻게 전처리하는게 좋을지 논의가 필요해보이네요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea5ef319-8b9a-446d-a654-cc356e849ed7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>172</th>\n",
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       "      <th>192</th>\n",
       "      <td>193</td>\n",
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       "      <td>3</td>\n",
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       "      <th>203</th>\n",
       "      <td>204</td>\n",
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       "      <td>20</td>\n",
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       "      <th>22965</th>\n",
       "      <td>22966</td>\n",
       "      <td>25010</td>\n",
       "      <td>지름길</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
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       "      <td>26</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22968</th>\n",
       "      <td>22969</td>\n",
       "      <td>25013</td>\n",
       "      <td>하나 둘 셋</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22975</th>\n",
       "      <td>22976</td>\n",
       "      <td>25020</td>\n",
       "      <td>코딩 테스트</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22982</th>\n",
       "      <td>22983</td>\n",
       "      <td>25027</td>\n",
       "      <td>August14 Beginner Contest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22984</th>\n",
       "      <td>22985</td>\n",
       "      <td>25029</td>\n",
       "      <td>Joyful KMP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8797 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  problem_id                      title tags  is_solvable  \\\n",
       "172      173        1172          선인장 Automorphisms  NaN         True   \n",
       "190      191        1190                     골 세레모니  NaN         True   \n",
       "191      192        1191                         게임  NaN         True   \n",
       "192      193        1192                         장갑  NaN         True   \n",
       "203      204        1203                         게임  NaN         True   \n",
       "...      ...         ...                        ...  ...          ...   \n",
       "22965  22966       25010                        지름길  NaN         True   \n",
       "22968  22969       25013                     하나 둘 셋  NaN         True   \n",
       "22975  22976       25020                     코딩 테스트  NaN         True   \n",
       "22982  22983       25027  August14 Beginner Contest  NaN         True   \n",
       "22984  22985       25029                 Joyful KMP  NaN         True   \n",
       "\n",
       "       accepted_user_count  level  average_tries  \n",
       "172                      6     24              3  \n",
       "190                      3     23              9  \n",
       "191                      5      0              6  \n",
       "192                     13      0              3  \n",
       "203                      4      0             20  \n",
       "...                    ...    ...            ...  \n",
       "22965                    1     26              1  \n",
       "22968                    1      0              1  \n",
       "22975                    0      0              0  \n",
       "22982                    5      0             10  \n",
       "22984                    3     22              1  \n",
       "\n",
       "[8797 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems[df_problems.tags.isna()==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6270c35-2acd-4664-9cdc-a99dd937368e",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6faf50b2-4fa2-4556-895e-66a436e43b02",
   "metadata": {},
   "source": [
    "### 문제 데이터의 통계를 살펴볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "feb139ad-1fd5-45eb-9f57-8a9838591e82",
   "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>id</th>\n",
       "      <th>problem_id</th>\n",
       "      <th>accepted_user_count</th>\n",
       "      <th>level</th>\n",
       "      <th>average_tries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22988.00000</td>\n",
       "      <td>22988.000000</td>\n",
       "      <td>22988.000000</td>\n",
       "      <td>22988.000000</td>\n",
       "      <td>22988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11494.50000</td>\n",
       "      <td>13162.811597</td>\n",
       "      <td>575.048895</td>\n",
       "      <td>9.035149</td>\n",
       "      <td>2.144423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6636.20833</td>\n",
       "      <td>6947.628658</td>\n",
       "      <td>4684.972201</td>\n",
       "      <td>8.455286</td>\n",
       "      <td>48.516343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5747.75000</td>\n",
       "      <td>7315.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11494.50000</td>\n",
       "      <td>13333.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17241.25000</td>\n",
       "      <td>19156.250000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22988.00000</td>\n",
       "      <td>25032.000000</td>\n",
       "      <td>196876.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>7340.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id    problem_id  accepted_user_count         level  \\\n",
       "count  22988.00000  22988.000000         22988.000000  22988.000000   \n",
       "mean   11494.50000  13162.811597           575.048895      9.035149   \n",
       "std     6636.20833   6947.628658          4684.972201      8.455286   \n",
       "min        1.00000   1000.000000             0.000000      0.000000   \n",
       "25%     5747.75000   7315.750000             2.000000      0.000000   \n",
       "50%    11494.50000  13333.500000            11.000000      8.000000   \n",
       "75%    17241.25000  19156.250000            58.000000     16.000000   \n",
       "max    22988.00000  25032.000000        196876.000000     30.000000   \n",
       "\n",
       "       average_tries  \n",
       "count   22988.000000  \n",
       "mean        2.144423  \n",
       "std        48.516343  \n",
       "min         0.000000  \n",
       "25%         1.000000  \n",
       "50%         1.000000  \n",
       "75%         2.000000  \n",
       "max      7340.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80cd7367-05eb-4889-aaa8-c7c19af81f91",
   "metadata": {},
   "source": [
    "average_tries의 max값이 7,340인 데이터는 확인이 필요해보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a744a4d-ae9a-4439-b8de-f08a59e3c847",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457f71de-f502-4194-8d67-adff4c694965",
   "metadata": {},
   "source": [
    "### 문제 데이터의 각 컬럼의 고유값 개수를 구해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c3c0d60-d752-4011-9910-4c5297bc5173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "problem_id 고유값: 22988\n",
      "level 고유값: 31\n",
      "title 고유값: 21110\n",
      "tag 고유값:  186\n"
     ]
    }
   ],
   "source": [
    "print('problem_id 고유값:', df_problems.problem_id.nunique())\n",
    "print('level 고유값:', df_problems.level.nunique())\n",
    "print('title 고유값:', df_problems.title.nunique())\n",
    "print('tag 고유값: ', len(set(sum(df_problems.tags.apply(lambda x: str(x).split(',')).to_list(),[]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7237aeba-c779-49ea-8e7d-5aed2a515e0e",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565f2ff0-227b-4138-88db-d059def7f4e6",
   "metadata": {},
   "source": [
    "### 문제 데이터의 연속형 변수 컬럼을 확인해봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ddf3b6d-cec8-491d-a433-d3076c29964b",
   "metadata": {},
   "source": [
    "#### - accepted_user_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3963e2cd-9567-4ea8-b74f-5bfb92ac2b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'zoom to mean')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nOBCksfQbBMoy2Ahoknr27KnS0tKwm1GrkpIS2oeDFnrXboKKFAAQYaEGaUXFvuB8aDUMNgIARES4QbqvIujGTZWcRkUKNC3XXXdd2E2oE+3DoQi5a7eSihQ4grgeBLQPhyL0c6Sq4/IXbhEIAHBd6EGauWuXihRoapYtW6YTTzxRxcXFuu+++0Jpw8cff6yzzz5bvXv31kknnRQ8ZHzr1q0677zz1KNHD5133nnatm2bJMlaq5tvvlnFxcXq27ev3nrrrUZpZzwe14ABA3ThhRdKktavX69TTz1VxcXFuvzyy4Mio6KiQpdffrmKi4t16qmncjlKSJy8sxGjdoGmJR6P68Ybb9TSpUu1evVqLVy4UKtXr270duTm5urBBx/U6tWrtXLlSs2dO1erV6/Wfffdp3PPPVdr167VueeeGwT90qVLtXbtWq1du1bz58/XhAkTGqWds2bNUq9evYKfb7/9dk2ePFllZWVq27atFixYIElasGCB2rZtq7KyMk2ePFm33357o7QP1YUcpHV37VKRAk3D66+/ruLiYnXv3l35+fm64oortGjRokZvx7HHHquBAwdKklq1aqVevXpp06ZNWrRokcaOHStJGjt2rF544QVJ0qJFizRmzBgZY3Taaadp+/bt+vTTTxu0jRs3btTvf/97XXvttZK8qviVV17RpZdemrF9yXZfeumlWr58uay1Ddo+1BRakFprVVVZyZ2NgCPApk2b1LVr1+Dn448/Xps2bQqxRd5ded5++22deuqp2rx5s4499lhJ0jHHHKPNmzdLCqfdt9xyi376058q5hcZW7ZsUZs2bZSbm1ujDanty83NVevWrbVly5YGbR9qCi1I4/G49z8nLn8B0Mh2796tkSNH6uGHH9bRRx9d7T1jjIxJv99a41i8eLE6deqkQYMGhbJ9HJrQbhGYPFmeabARl78ATUuXLl308ccfBz9v3LhRXbp0CaUtlZWVGjlypL7//e/rkksukSR17txZn376qY499lh9+umn6tSpUyjtXrFihV588UUtWbJEe/fu1c6dOzVp0iRt375dVVVVys3NrdaGZPuOP/54VVVVaceOHWrfvn2DtQ+ZhVaRBpe20LULNHmnnHKK1q5dq/Xr12vfvn165plnNGLEiEZvh7VW48ePV69evXTrrbcG00eMGKHHH39ckvT444/roosuCqY/8cQTstZq5cqVat26ddAF3BDuvfdebdy4UeXl5XrmmWd0zjnn6KmnntLZZ5+t559/PmP7ku1+/vnndc4554RWTR/JQq9IMz/Y20gmRkUKNBG5ubn6+c9/rvPPP1/xeFzjxo3TSSed1OjtWLFihZ588kn16dNH/fv3lyTdc889mjJlii677DItWLBA3bp103PPPSdJGj58uJYsWaLi4mIdddRRevTRRxu9zZJ0//3364orrtDUqVM1YMAAjR8/XpI0fvx4jR49WsXFxWrXrp2eeeaZUNp3pDMHM8KrpKTEHq4nD2zcuFFXXXWV9hSdpWbr/yJJ2n3KNcH7R7/1pC679JJGG24OAEA6Y8yb1to6n/0XftduporUn07XLgDAdaEFaTIkMw42kqRYjCAFADgv/Io002AjiXOkAIBICL0iTY7QrcHQtQsAcF+otwiU5I3QzcBSkQIAIiD8IK2FNZwjBQC4jyAFcER64YUXDukJNC1btmyA1rhn+/bt+q//+q+wmxEJTgcpXbsAGsqhBmljC+v3IEFaf+4GqWLaR0UKHDG++93vatCgQTrppJM0f/58Sd7DwAcOHKh+/frp3HPPleTdcP6aa65Rnz591LdvX/3mN7+RJP3hD3/Q6aefroEDB2rUqFHavXu3JKmwsFD//u//rj59+ugb3/iGysrK9Le//U0vvviifvSjH6l///5at26d1q1bp6FDh2rQoEE688wztWbNGkneQ7VPP/109enTR1OnTq1zH1599dXgYdySdNNNN+mxxx6TJE2ZMkW9e/dW3759ddttt0mSPv/8c40cOVKnnHKKTjnlFK1YsUKSNG3aNI0ePVqDBw/W6NGjM24rHo/rtttu08knn6y+fftqzpw5kqTly5drwIAB6tOnj8aNG6eKiorgc/jiiy8kSaWlpRoyZEiwrXHjxmnIkCHq3r27Zs+eHbR33bp16t+/v370ox/V5xAesUK7ReABcR0pcET51a9+pXbt2mnPnj065ZRTdNFFF+kHP/iB/vznP6uoqEhbt26VJP3kJz9R69at9d5770mStm3bpi+++EIzZszQyy+/rBYtWuj+++/XQw89pB//+MeSFMz/xBNP6JZbbtHixYs1YsQIXXjhhcFzPs8991zNmzdPPXr00GuvvaYbbrhBr7zyiiZNmqQJEyZozJgxmjt37iHt25YtW/Tb3/5Wa9askTFG27dvlyRNmjRJkydP1hlnnKGPPvpI559/vv75z39KklavXq2//vWvat68ecZ1zp8/X+Xl5Vq1apVyc3O1detW7d27V1dffbWWL1+uE044QWPGjNEjjzyiW265pc72rVmzRn/605+0a9cunXjiiZowYYLuu+8+vf/++1q1atUh7fORxNkgtSZn/7WmAJq82bNn67e//a0k6eOPP9b8+fN11llnqaioSJLUrl07SdLLL79c7Z6ybdu21eLFi7V69WoNHjxYkned+umnnx7Mc+WVVwZ/T548uca2d+/erb/97W8aNWpUMC1Zya1YsSKoekePHq3bb7/9oPetdevWatasmcaPH68LL7wwqFpffvnlat3LO3fuDCrpESNG1BqiyWWvv/764Dml7dq10zvvvKOioiKdcMIJkryHgM+dO/eAQXrBBReooKBABQUF6tSpU/A8VtSPs0EqE1NlJedIgSPBq6++qpdffll///vfddRRR2nIkCHq379/0L16INZanXfeeVq4cGHG91OfiJLp6SiJREJt2rSptfqq7xNVcnNzlUgkgp/37t0bTH/99de1fPlyPf/88/r5z3+uV155RYlEQitXrlSzZs1qrKtFixb12mZ9pbYt2a6kgoKC4HVOTg7jUw6Ss+dIuSEDcOTYsWOH2rZtq6OOOkpr1qzRypUrtXfvXv35z3/W+vXrJSno2j3vvPOqdbFu27ZNp512mlasWKGysjJJ0pdffqkPP/wwmOfZZ58N/k5Wqq1atdKuXbskSUcffbSKior061//WpIXzO+8844kafDgwUEF/NRTT9W5H926ddPq1atVUVGh7du3a/ny5ZK8infHjh0aPny4Zs6cGaz7O9/5TnBuU9JBdaOed955+sUvfhGE3tatW3XiiSeqvLw8+ByefPJJfetb35LknSN98803JSmosOuS+vmgbu4GaSymqiqCFDgSDB06VFVVVerVq5emTJmi0047TR07dtT8+fN1ySWXqF+/frr88sslSVOnTtW2bdt08sknq1+/fvrTn/6kjh076rHHHtOVV16pvn376vTTT69WzW7btk19+/bVrFmzNHPmTEnSFVdcoQceeEADBgzQunXr9NRTT2nBggXq16+fTjrpJC1atEiSNGvWLM2dO1d9+vTRpk2b6tyPrl276rLLLtPJJ5+syy67TAMGDJAk7dq1SxdeeKH69u2rM844Qw899JAkrzu7tLRUffv2Ve/evTVv3rx6f2bXXnutvva1r6lv377q16+fnn76aTVr1kyPPvqoRo0apT59+igWi+n666+XJN19992aNGmSSkpKlJNTy61ZU7Rv316DBw/WySefzGCjAwjtMWpvvfWWbr31Vn3Vc7iar1kqqfpj1ArKV6jDvs16cdELh2V7AI5MhYWFKi0tVYcOHcJuCiLI6ceoHZDJURVduwAAx7k72CjGDRkAZK+8vPywr/O9996rcX1nQUGBXnvttcO+rZdeeqnGSOGioqJghDPC52yQWhNTVZwgBeCePn36NNr1leeff77OP//8RtkWDo3TXbuJeLzaUHIAAFzjcJB6TaN7FwDgMmeD1Ma84dlcSwoAcJmzQZqsSAlSAIDLnA9SunYBAC5zNkjp2gUARIGzQUpFCgCIAoeDlIoUAOA+Z4PUxhhsBABwn7NBStcuACAKHA5SunYBAO5zOEipSAEA7nM2SLn8BQAQBc4GKXc2AgBEAUEKAEAWnA1SyzlSAEAEOBuk4hwpACAC3A1SKlIAQATkht0Aj5UktXzjVzXeefjhh/Xwww8fti0VFBSooqIi43udOnWStVaff/65JCk3N7dGkHfu3FmbN28Ofs7Ly5O0v3Lu3r27LrroIs2cOVOS1LFjx2B9xx9/vHJzc/XZZ5/JWquWLVtqy5YtysvL0x133KEHHnhAXbt21Q9/+ENNnTpViURCxhjNmDFDTzzxhG6++WY9+OCDqqio0KZNmxSLxTRjxgzNnTtX69ev149//GM999xzisfjysnJ0fjx43X33Xdr1qxZKi4uliRt2bJF//Ef/6Gqqirl5OTo3/7t3zR79mzdfPPNmj17tu6++25t27ZNEydOVNeuXXXvvfeqffv22rJli6ZPn667775b7du3D9Y1depUGWN066236sEHH5QxRj/5yU8kqcb8SanLpc+7bds2TZo0SdOnTw/2OdmuZDuS7U8kEorFYsrJydGMGTOqtWv69Ok1lq2P9P0sKyvTpEmTgs8w0+eQabm61j9lyhRt3LhRc+bMCY5LXW1I7rO1ttp+ph5Pa21wLNOPUXJddR2TTNtPft+Sx6m+n2FTkek41Ofzi7qo7acL7TXW2nrPXFJSYktLSw/Lht966y3deuut+qrncDVfs6TOec1h2WLjMcboYD5XqXpot2zZUrt37w7ea9mypb788kt169ZN5eXl1ZZLnTc9+JPvFRYW6rHHHpMkPfTQQ3rxxReDeQoLC7VhwwZ169ZNGzZs0IgRI/TOO+8E27nooos0efJkPfTQQ/rd736nESNGaPLkyTXWVVhYWG0Za22N+ZNSl0ufN7nt1H1OtivZjtT2JyXbmVz/7373uxrL1kf6fl599dUqLy8PPsNMn0Om5epaf+pnljwudbUh/fNK327q+tL3N3VddR2TTNtP/b6lb/dIkOk41Ofzi7qo7WdDt9cY86a1tqSuecLv2vVD1NTxJ2oONkSl6l3YqSGa/NlaWyNE0+dNr56T75WXl6usrExbtmzRsmXLqs1TXl4erNtaqyVLllTbzpIlS1RWVqZly5bJWqtly5Zpy5Yt2rJli5YuXVptPanLLF26tNr8SenLpc6buu3UfU6uJ9mOTJYuXRq0K9nW1GVT21Cb1GWXLVum0tLSoD3l5eUqLS2t8TlkWq62bWX6zMrKyupsQ/o+J/czdd7U9aUfo+S6li5dWu82pn5+mbZ7JKjtOBzM9ymK6vtddoUr7Q09SJtLat6smS699FI1a9Ys7OY0WTNmzNDjjz9+wMFb6e9XVlZqxowZSiQSkqR4PK4nnnhCjz/+eK3nrysrK4P3kvMnpS+XOm9dbYvH45oxY0at81RWVgbtSrY1ddnUNtQmddl4PK5p06ZVe3/atGk1PodMy9W2rUyf/4wZM+psQ/o+J/eztvWltiF1XZWVlcG8B2pj+ueXvt0jQabjUJ9jHHX1/S67wpX2HrBr1xhznaTrJOlrX/vaoA0bNhyWDSe7dq2kUZdeqptuuklz5szRb37zm8OyfgDAkaekpEQ//elPFYsdnjqxPl27BxxsZK2dL2m+38CD77Osh8WLF8taq9///vcNsXoAwBGiffv2MqZxTwqGPmp3jyTt3Usl2sB++ctfqm3btrryyiu1b9++WufLy8ur1l2Yl5enRx55RDfccIP27dungoICPf3005KkK664ImPXYupI5uT8qaNIU5dLnTd926kKCgo0d+7coB3p8vPztXDhQkmqsY/pbajNli1bgmULCgr0n//5n7rtttuC93/2s5/pzjvvrPY5JEdzpi5X27bS913yjkvqyN30daXvc3I/07eb/lklj1Hy/fz8fEmqVxszrTN1u0eCuo5Dfb9PUVTf77IrXGlv6OdI1XO4JO8CmLr+RMmh/G8oN3f//2latmxZ7b2WLVvKGKPCwsIay6XOm7qO1PcKCwtVXFys9u3ba+jQodXmKSwsDNZtjNHw4cOrbWf48OEqLi7W0KFDZYzR0KFD1b59e7Vv317Dhg2rtp7UZYYNG1Zt/qT05VLnTd126j4n15NsRybDhg0L2pVsa+qy9fnHlbrs0KFDVVJSErSnsLBQJSUlNT6HTMvVtq1Mn1n65S/p60rf5+R+ps6bur70Y5Rc17Bhw+rdxtTPL9N2jwS1HYeD+T5FUX2/y65wpb3hB+kBHO6RuwUFBbW+16lTJ3Xs2DH4OT2YJO860lR5eXlBVSV515Hecsstwc+p6zv++ONVWFioZs2aqaCgIDjoeXl5uvPOO9W8eXOdcMIJmj59upo3b66CggI1a9ZM06ZNU58+fTR16lT16tVL3bt3V0FBgZo3b67p06ere/fuMsborrvuUq9evXTCCSeoV69emjZtmlq0aKGpU6cGbRg7dqx69+4dzDN16tRg3X369NGYMWM0derUoC1jxowJlku+n7quXr16qXfv3kHbevfurTFjxmScP9Ny6fNOnTpVLVq0qLbPqetJbX9xcXGwH+ntyrRsfaS3O9me5GdY237Vtb/p8/Xo0UPNmzevdlzqWldyn9P3M/29TPubuq6DaWPq9y15nI40mY7DwX6foihq++lCex25jtS7JGD3KdfsnyFeqVarntbxXY7THVOm6KSTTjos2wUAoL6icR1pLUyiSkrEdenIkYQoAMBZzgaprHdtUKbuVQAAXOFukCbikhSMNgQAwEXOBqmhIgUARICzQSrrVaSpI2IBAHCNu0GaoCIFALjP2SBNdu1SkQIAXOZskIogBQBEgMNB6p0jpWsXAOAyd4M0QUUKAHCfs0FqqEgBABHgbJByjhQAEAXOBykVKQDAZc4GqeEWgQCACHA2SKlIAQBR4HCQcotAAID7nA1Swy0CAQAR4GyQ0rULAIgCh4M0rpycHMVi7jYRAABnU8okEsrN5fwoAMBtzgapbEI5dOsCABzncJDGOT8KAHCes0FqbEJ5eQQpAMBtzgapEnHl5XFXIwCA29wNUipSAEAEOB6kjNoFALjN2SA1Nk6QAgCc52yQKkFFCgBwn7NBamxC+QQpAMBx7gapqEgBAO5zN0htghsyAACc53SQUpECAFwXWpAaY7wX1mZ+n4oUABABoQVpUG36zx2twcaVn8+djQAAbgs/SBPxzDMkqEgBAO4LLUiT1aaxtQQpN2QAAERA6EFKRQoAiLLQu3Zrq0htgooUAOA+NytSa7lpPQAgEhwNUm8aXbsAANeFHqQZu3b9S2KoSAEArgstSHNycrybMmSoSE3CC1IqUgCA60K9s1FeXj4VKQAg0kK9125uXl6d50gJUgCA60IN0vz8/MxBmqAiBQBEQ8hBmpexa9cwahcAEBGhBmleXi0VKedIAQAREWqQFuTXPdiIihQA4Lpwg7QgPzgfmsokGGwEAIiGkIO0IBihWw1duwCAiAj5HGmeYhmDlMFGAIBoCP3yF2Mzde1SkQIAosGBIGWwEQAgusIPUu5sBACIsNCDNNNgI8NgIwBARIQfpBlvEUhFCgCIhtBH7SrOOVIAQHSFXpHaRFWN6XTtAgCiIvQgzSjBdaQAgGgIv2s3E5tQLBZTTk5O4zYIAICD5GRFamxCOVSjAIAIcDJIZeN06wIAIsGRrl1b/Y1EQrm5DDQCALiPihQAgCw4GaTGJrj0BQAQCU4GqRIEKQAgGtwMUhtXXh5duwAA9zkZpHTtAgCiwskglY0rnyAFAESAI5e/VGdsovaQBQDAIU5WpMYmuPwFABAJjgap5RwpACASHO3a5YYMAIBocLQiZdQuACAanKxIRZACACIi1CA1xig3U2DaOEEKAIiEUINUqqUqTTBqFwAQDQ4EaYbzpFSkAICICD1IM93ByMYZtQsAiIbQgzSvIK0itZbBRgCAyAg9SAvSL4GxCUl1jOgFAMAhoQdpcC1pLMf72w9SunYBAFEQeloV5BdIOfmKH9XOm0BFCgCIkNAr0oKCfO+8qM8k4pKoSAEA0RB6kHpdu/uDlIoUABAloQdpXl5etYpUNr5/OgAAjgs9SNMrUkNFCgCIEEeCNEWCUbsAgOgIPUjp2gUARFnoQVpb1y4VKQAgCpwIUpM6IUFFCgCIDieCVNL+7l0GGwEAIsSdIPXRtQsAiBKHgtSvSOnaBQBESOhBmgxMk9a1S0UKAIiC0IM0vSJNdu3WuL4UAAAHuROkyStgLDetBwBER+hBuv9caPIcKaN2AQDREXqQ1uzapSIFAESHO0EadO1SkQIAoiP0IK3RtWsTMsYoJycntDYBAFBfoQdpjTsbJeLKyc2VMab2hQAAcIQ7QZpy+UtuDudHAQDR4FCQ+mxcuXkEKQAgGpwJ0uDORomEcnMZaAQAiAZngjS1a5cRuwCAqHAnSIN77dK1CwCIjtCDtEb1aRPKo2sXABARoQdpLJZsgt+1m6BrFwAQHaEHaSClazefrl0AQES4E6QpdzbiEWoAgKhwI0iNCXKUUbsAgChxIki9mwF6SRqzCZ78AgCIDCeC1MN1pACA6HEjSI2RST5GTVSkAIDocCJIU7t2ufwFABAlTgSppGqXvxCkAICocCJIvWeP7r/8hSAFAESFE0Hq2f9gb86RAgCiwp0gDQpSunYBANHhRJAGXbvWUpECACLFiSCV5A828spSKlIAQFS4E6SyUiIhiSAFAESHE0FqjPGuJbVxSaJrFwAQGU4EaZJJeEFKRQoAiAongtQbbCQpUSmJihQAEB1OBGmSiXtByvNIAQBR4WSQUpECAKLCiSBNdu2a+D5JnCMFAESHE0Ea8IOUihQAEBVOBWmya5eKFAAQFU4E6f6uXYIUABAtTgRpgMFGAICIcSpITYLBRgCAaHEiSNO7dqlIAQBR4USQBjhHCgCIGKeClMFGAICocSJIuSEDACCqnAjSJM6RAgCixqkg5RwpACBqnAjSoGs3wS0CAQDR4kSQJvFgbwBA1DgVpJKUk5Oz/0HfAAA4zrkgzc2lGgUARIdDQepVoTmcHwUARIgzQZrszWWgEQAgSpwJ0qS8PIIUABAd7gSpX5Lm5eWH3BAAAOrPmSBNjtOlIgUARIkzQZqUx6hdAECEOBOkyWtH8/IJUgBAdDgTpEnc1QgAECXOBWk+QQoAiBBngjTo2iVIAQAR4kyQJnFDBgBAlDgXpFSkAIAocSZIk127VKQAgChxJkiT8vO5sxEAIDqcC1IqUgBAlDgTpIzaBQBEkTNBmkRFCgCIEueClIoUABAlzgQpXbsAgChyJkiT6NoFAESJM0FKRQoAiCJngjSJihQAECXOBSkVKQAgSpwJUrp2AQBR5ESQDh06VIMHD5ZEkAIAosWJE5ITJ05UVVWVjjnmGJWUlITdHAAA6s2JIJW8QUbXXntt2M0AAOCgONG1CwBAVBGkAABkgSAFACALBCkAAFkgSAEAyAJBCgBAFghSAACyQJACAJAFghQAgCwQpAAAZIEgBQAgCwQpAABZIEgBAMgCQQoAQBYIUgAAskCQAgCQBYIUAIAsEKQAAGSBIAUAIAsEKQAAWSBIAQDIAkEKAEAWCFIAALJAkAIAkAWCFACALBCkAABkgSAFACALBCkAAFkgSAEAyAJBCgBAFghSAACyQJACAJAFghQAgCwQpAAAZIEgBQAgCwQpAABZIEgBAMgCQQoAQBYIUgAAskCQAgCQBYIUAIAsGGtt/Wc25nNJGxqoLR0kfdFA625MTWU/JPbFVeyLm9gXN2W7L92stR3rmuGggrQhGWNKrbUlYbcjW01lPyT2xVXsi5vYFzc1xr7QtQsAQBYIUgAAsuBSkM4PuwGHSVPZD4l9cRX74ib2xU0Nvi/OnCMFACCKXKpIAQCInNCD1Bgz1BjzgTGmzBgzJez2SJIxpqsx5k/GmNXGmH8YYyb506cZYzYZY1b5f4anLHOHvw8fGGPOT5mecf+MMUXGmNf86c8aY/IbcH/KjTHv+W0u9ae1M8b80Riz1v+7rT/dGGNm++161xgzMGU9Y/351xpjxqZMH+Svv8xf1jTQfpyY8tmvMsbsNMbcEpXjYoz5lTHmX8aY91OmNfhxqG0bDbAvDxhj1vjt/a0xpo0/vdAYsyfl+Mw71DbX9bkc5n1p8O+UMabA/7nMf7+wgfbl2ZT9KDfGrPKnu35cavs97N6/GWttaH8k5UhaJ6m7pHxJ70jqHWab/HYdK2mg/7qVpA8l9ZY0TdJtGebv7be9QFKRv085de2fpOckXeG/nidpQgPuT7mkDmnTfippiv96iqT7/dfDJS2VZCSdJuk1f3o7Sf/r/93Wf93Wf+91f17jLzuskb47n0nqFpXjIuksSQMlvd+Yx6G2bTTAvnxHUq7/+v6UfSlMnS9tPQfV5to+lwbYlwb/Tkm6QdI8//UVkp5tiH1Je/9BST+OyHGp7fewc/9mGvSXXT0+qNMlvZTy8x2S7gizTbW0c5Gk8+r4x1Wt3ZJe8vct4/75B+0L7f+lU22+Bmh/uWoG6QeSjk35wn7gv/6FpCvT55N0paRfpEz/hT/tWElrUqZXm68B9+k7klb4ryNzXJT2y6sxjkNt2zjc+5L23sWSnqprvkNpc22fSwMclwb/TiWX9V/n+vOZhjoufls+ltQjKsclrV3J38PO/ZsJu2u3i7wDm7TRn+YMv7tlgKTX/Ek3+d0Gv0op92vbj9qmt5e03VpblTa9oVhJfzDGvGmMuc6f1tla+6n/+jNJnf3XB7svXfzX6dMb2hWSFqb8HMXjIjXOcahtGw1pnLz/4ScVGWPeNsb8P2PMmf60Q2lzY/7OaOjvVLCM//4Of/6GcqakzdbatSnTInFc0n4PO/dvJuwgdZoxpqWk30i6xVq7U9Ijkr4uqb+kT+V1k0TBGdbagZKGSbrRGHNW6pvW+29XZIZv++eYRkj6tT8pqselmsY4Do2xDWPMXZKqJD3lT/pU0testQMk3SrpaWPM0fVdX0jfzybxnUpzpar/5zMSxyXD7+FGbUN9thF2kG6S1DXl5+P9aaEzxuTJO3hPWWv/R5KstZuttXFrbULSf0v6hj97bftR2/QtktoYY3LTpjcIa+0m/+9/Sfqt3+7NxphjJcn/+1/+7Ae7L5v81+nTG9IwSW9ZazdL0T0uvsY4DrVt47Azxlwt6UJJ3/d/AclaW2Gt3eK/flPeucQTDrHNjfI7o5G+U8Ey/vut/fkPO3/9l0h6NjktCscl0+/hQ2hDg/+bCTtI35DUwx/Vli+vu+7FkNskf+TWAkn/tNY+lDL92JTZLpaUHBn3oqQr/FF4RZJ6yDuJnXH//F8wf5J0qb/8WHn9/w2xLy2MMa2Sr+WdW3zfb/PYDNt/UdIYfwTcaZJ2+F0cL0n6jjGmrd/N9R1553o+lbTTGHOa/7mNaah9SVHtf9ZRPC4pGuM41LaNw8oYM1TSv0saYa39KmV6R2NMjv+6u7zj8L+H2ObaPpfDvS+N8Z1K3cdLJb2S/M9HA/i2vPOBQVem68eltt/Dh9CGhv83czhPBh/iCeTh8kZjrZN0V9jt8dt0hrxS/l1Jq/w/wyU9Kek9f/qLSjmZLukufx8+UMqo1dr2T97ovtcllcnroixooH3pLm8E4TuS/pFsg7xzMcslrZX0sqR2/nQjaa7f3vcklaSsa5zf3jJJ16RML5H3i2adpJ/rMAyYqGN/Wsj7X3vrlGmROC7ywv9TSZXyzseMb4zjUNs2GmBfyuSdi0r+m0mOSB3pf/dWSXpL0v851DbX9bkc5n1p8O+UpGb+z2X++90bYl/86Y9Juj5tXtePS22/h537N8OdjQAAyELYXbsAAEQaQQoAQBYIUgAAskCQAgCQBYIUAIAsEKQAAGSBIAUOE2PMd40xvQ9hud0N0R7XGGPaGGNuCLsdwOFGkAKHz3flPebJaSm3q2tsbeQ9OgxoUghSNFnGmBeM98Sbfxj/qTfGe/jyW8aYd4wxy/1pLY0xjxrvAb/vGmNG+tO/Y4z5uz//r4138+zkg9J/6s//ujGm2BjzTXk30n/AeA9J/rr/Z5nfhr8YY3r6yxf5633PGDPjAPswxBizOOXnnxvvfrYyxtxnvIcev2uM+Zk/raMx5jfGmDf8P4P96dOMMU8aY1bIu2tPpm3lGGN+Zox531/nRH/6ucZ7Qsh7xnsSSkHK59DBf11ijHk1ZVu/Msa8aoz5X2PMzf4m7pP0df/zeeCgDibgsLD+Zwo0hnHW2q3GmOaS3jDGLJJ3A/KzrLXrjTHt/Pn+Q959OftIkn9Pzg6Spkr6trX2S2PM7fKekPF//WV2WGv7GGPGSHrYWnuhMeZFSYuttc/761ku77Zsa40xp0r6L0nnSJol6RFr7RPGmBsPZceMMe3l3QO2p7XWGmPa+G/NkjTTWvtXY8zX5N1ntJf/Xm95TwLaU8tqr5P3jMr+1toqY0w7Y0wzebeXO9da+6Ex5glJEyQ9fIAm9pR0trwHMn9gjHlE3gOST7bW9j/Y/QVcRpCiKbvZGHOx/7qrvKD4s7V2vSRZa7f6731b3k3G5U/fZoy5UF7wrPDuZ618SX9PWffClL9npm/Yr16/KenX/vKSVOD/PVjefU4lrzq8/xD2bYekvZIW+BVrsmr9tqTeKds8OllJy7uJem0hmlx2nvWfnen/J6SfpPXW2g/9eR6XdKMOHKS/t9ZWSKowxvxLjfMMVCAUBCmaJGPMEHnBcLq19iu/23GVvEqpXquQ9Edr7ZW1vG9reZ0Uk/dA5/71WL4uVap+CqaZ5D0I2hjzDUnnyntyyE3yqt2YpNOstXtTV+IH65f13GZ9pbatWdp7FSmv4+J3DZowzpGiqWotaZsfoj0lnSbvl/1Zxnv8lVK6dv8or8qSP72tpJWSBhtjiv1pLYwxJ6Ss//KUv5OV6i55XZmy3gOI1xtjRvnLG7+6k6QV2l8Bf/8A+7FBXoVZ4Hffnuuvr6W8J+AskTRZUnLdf5A0MWVf+h9g/an+KOmHycFI/ufzgaTC5OcgabSk/+e/Lpc0yH89UgcWfD5AU0KQoqlaJinXGPNPeYNcVkr6XF737v8YY97R/occz5DU1h9k846ks621n0u6WtJCY8y78sIytZpt60+fJC/IJOkZST/yB+Z8XV5IjvfX+Q9JF/nzTZJ0ozHmPUld6toJa+3Hkp6T96in5yS97b/VStJivw1/lXf+VpJullTiDxZaLen6+n1ckqRfSvpI0rt+m7/nV7bXyOuifk9SQtI8f/7pkmYZY0rlVZ11st5DpFf4nzODjdBk8Bg14CAZY8rlPevwi7DbAiB8VKQAAGSBihRwgDGmj2pe31lhrT21AbZ1vmqOFF5vrb040/wA6kaQAgCQBbp2AQDIAkEKAEAWCFIAALJAkAIAkAWCFACALPx/4cmG2sBIiKsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(figsize=[8,8])\n",
    "ax= sns.boxplot(df_problems.accepted_user_count)\n",
    "ax= sns.violinplot(df_problems.accepted_user_count)\n",
    "\n",
    "ax2 = plt.axes([0.6, 0.6, .2, .2], facecolor='w')\n",
    "ax2= sns.boxplot(df_problems.accepted_user_count, ax=ax2)\n",
    "ax2.set_xlim([0,575])\n",
    "ax2.set_title('zoom to mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be45f031-fb4d-4650-a47c-91035878f924",
   "metadata": {},
   "source": [
    "문제별 맞은 사람 수가 평균 값인 575 근처로 쏠려있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78885b6-3574-42e0-9712-1cce67693416",
   "metadata": {},
   "source": [
    "#### - average_tries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4595efb-ba56-47a3-a943-8b657ba4de93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(figsize=[5,5])\n",
    "ax= sns.boxplot(df_problems.average_tries)\n",
    "ax= sns.violinplot(df_problems.average_tries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd8507a7-9986-4e9c-8796-fb8150198098",
   "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>id</th>\n",
       "      <th>problem_id</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "      <th>is_solvable</th>\n",
       "      <th>accepted_user_count</th>\n",
       "      <th>level</th>\n",
       "      <th>average_tries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9158</th>\n",
       "      <td>9159</td>\n",
       "      <td>10944</td>\n",
       "      <td>랜덤 게임~~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>7340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  problem_id    title tags  is_solvable  accepted_user_count  level  \\\n",
       "9158  9159       10944  랜덤 게임~~  NaN         True                   44      0   \n",
       "\n",
       "      average_tries  \n",
       "9158           7340  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems[df_problems.average_tries == 7340]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf665692-6f1b-4730-915d-60c7fae20c46",
   "metadata": {},
   "source": [
    "문제 시도 횟수에서 이전에 보았던 것처럼 7,340인 이상치가 하나 존재합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "174c6b25-f6f5-46dc-9d24-4b4e8db8c110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'zoom to mean')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(figsize=[5,5])\n",
    "ax= sns.boxplot(df_problems[df_problems.average_tries!=7340].average_tries)\n",
    "ax= sns.violinplot(df_problems[df_problems.average_tries!=7340].average_tries)\n",
    "\n",
    "ax2 = plt.axes([0.6, 0.6, .2, .2], facecolor='w')\n",
    "ax2= sns.boxplot(df_problems[df_problems.average_tries!=7340].average_tries, ax=ax2)\n",
    "ax2.set_xlim([0,3])\n",
    "ax2.set_title('zoom to mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e524bf9a-8b7f-4989-b103-4d93b883b0c9",
   "metadata": {},
   "source": [
    "이상치를 제거했을 때 평균값인 2에 평균 시도 횟수가 몰려있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64844469-b95a-4d1d-81e8-3961eeb62eb8",
   "metadata": {},
   "source": [
    "#### average_tries가 7340인 데이터는 제외하는게 좋을것으로 보입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6d5b9e4-8e1c-49fb-83ea-1055cb4fd9f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "제거 전: 22988\n",
      "제거 후: 22987\n"
     ]
    }
   ],
   "source": [
    "print('제거 전:', len(df_problems))\n",
    "df_problems.drop(df_problems[df_problems.average_tries == 7340].index, axis=0, inplace=True)\n",
    "print('제거 후:', len(df_problems))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5cc189-8adf-4dc5-8dd8-1e12c2d80223",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "115f0463-c2ee-4b2b-94ef-e885f436b325",
   "metadata": {},
   "source": [
    "### 유저가 맞힌 문제 수를 분석해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "70e4fec2-f386-4656-bfb2-9fec8dac5fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def num(x):\n",
    "    return len(str(x).split(','))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51bc142c-3afb-4e93-ab5a-391a74a4643c",
   "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>id</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63871</th>\n",
       "      <td>63872</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63872</th>\n",
       "      <td>63873</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63873</th>\n",
       "      <td>63874</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63874</th>\n",
       "      <td>63875</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63875</th>\n",
       "      <td>63876</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63876 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id    num\n",
       "0          1  11226\n",
       "1          2   6138\n",
       "2          3   2333\n",
       "3          4   4280\n",
       "4          5   2765\n",
       "...      ...    ...\n",
       "63871  63872      3\n",
       "63872  63873      1\n",
       "63873  63874     29\n",
       "63874  63875      1\n",
       "63875  63876      1\n",
       "\n",
       "[63876 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems_solved['num']= df_problems_solved.problems.apply(num)\n",
    "df_problems_solved[['id', 'num']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fcc0f7c5-9978-4e4d-b304-18542f9a779f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    63876.000000\n",
       "mean       133.216764\n",
       "std        229.644633\n",
       "min          1.000000\n",
       "25%         33.000000\n",
       "50%         73.000000\n",
       "75%        155.000000\n",
       "max      11226.000000\n",
       "Name: num, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems_solved.num.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3896df09-647a-421c-af84-caf57e202fc4",
   "metadata": {},
   "source": [
    "유저가 푼 문제 수의 평균은 133개입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2fcfcecd-4093-40d1-a749-8cf715692f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'zoom to mean')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(figsize=[5,5])\n",
    "ax= sns.boxplot(df_problems_solved.num)\n",
    "ax= sns.violinplot(df_problems_solved.num)\n",
    "\n",
    "ax2 = plt.axes([0.6, 0.6, .2, .2], facecolor='w')\n",
    "ax2= sns.boxplot(df_problems_solved.num, ax=ax2)\n",
    "ax2.set_xlim([0,363])\n",
    "ax2.set_title('zoom to mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1ee3e0-e538-473e-883a-6dce7551cfbb",
   "metadata": {},
   "source": [
    "유저의 문제 풀이 수는 평균인 133개에 몰려있네요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f78173c-7e90-4e33-aabd-960431c5fc5f",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375136a4-9465-42fc-a24a-daca4fb77440",
   "metadata": {},
   "source": [
    "### 가장 문제를 많이 푼 유저는 누구일까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8f16c31-c4b8-4cc5-85d1-7de99e868e16",
   "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>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>problems</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1006,1007,1008,1...</td>\n",
       "      <td>11226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   handle                                           problems    num\n",
       "0   1  koosaga  1000,1001,1002,1003,1004,1005,1006,1007,1008,1...  11226"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems_solved[df_problems_solved.num== 11226]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1123b057-1ad8-4934-ba2f-6ff2fe276b48",
   "metadata": {},
   "source": [
    "users에 기록된 koosaga의 solved_count인 11075와 값이 다르네요. 😮<br/>\n",
    "이는 BOJ와 solved.ac에서 집계하는 문제 수의 차이, 부분 점수가 들어가는 100% 맞지 못한 문제 등 여러 요인으로 인한 것으로 보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9807837-47a7-4a27-b369-1ea36c534c95",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ef81e6-87bb-4f90-87f3-0a6749f493e1",
   "metadata": {},
   "source": [
    "### 유저 데이터에서 문제를 하나도 풀지 않은 사람의 수는 몇 명일까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b25f1e4a-43b1-4784-84dc-f6e7bb74e943",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "문제를 한 번도 안 푼 사람의 수 : 1297\n"
     ]
    }
   ],
   "source": [
    "print(\"문제를 한 번도 안 푼 사람의 수 :\", len(df_users.loc[df_users['solved_count'] == 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844f1ba4-b984-4141-9cd2-e6a238952ec2",
   "metadata": {},
   "source": [
    "약 2%의 유저가 문제를 하나도 풀지 않았음을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5a49ff-be9d-48f9-a782-2e95e3fe60d9",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aac3699-155f-40a3-86f4-47ca11ada39f",
   "metadata": {},
   "source": [
    "### 문제의 알고리즘을 분류하는 태그의 종류 수를 구해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb39e22d-8cfa-4120-80b8-982fcb2bd828",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_tags = set()\n",
    "for tags in df_problems['tags'].dropna().values:\n",
    "    for tag in tags.split(','):\n",
    "        set_tags.add(tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb55ddb7-676c-4cc5-b5b1-5f8f4f168c5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tag의 종류 수 : 185\n"
     ]
    }
   ],
   "source": [
    "print(\"tag의 종류 수 :\", len(set_tags))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a421605-5d2d-40f6-9928-e04bbabd5ac6",
   "metadata": {},
   "source": [
    "총 185개의 태그가 존재한다는 걸 알 수 있네요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f36fff-0808-47a4-a2bd-5393ce6aaa3a",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b217981d-411a-4991-adab-9a73c3d9186c",
   "metadata": {},
   "source": [
    "### 문제의 ID별로 제목, 풀린 횟수 그리고 레벨(티어)을 빠르게 읽을 수 있도록 dictionary 형태로 저장해 봅시다. 😎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c39e989e-4b73-4a4a-8cb5-5152ab8f45e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22987/22987 [00:02<00:00, 9372.20it/s]\n"
     ]
    }
   ],
   "source": [
    "id2title = dict() # 문제 id와 title을 딕셔너리로 저장\n",
    "id2count = dict() # 문제 id별로 풀린 횟수를 세기 위한 딕셔너리 생성\n",
    "id2level = dict() # 문제 id별로 레벨 딕셔너리\n",
    "for i in tqdm(range(len(df_problems))):\n",
    "    id2title[df_problems.iloc[i, 1]] = df_problems.iloc[i, 2]\n",
    "    id2count[df_problems.iloc[i, 1]] = 0\n",
    "    id2level[df_problems.iloc[i, 1]] = df_problems.iloc[i, 6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb76a1a7-8ebe-4642-8a71-c5b8d38f31a3",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c123e7d-582c-42e4-97c5-99e83e7a9fe5",
   "metadata": {},
   "source": [
    "### 채점을 허용하지 않아서 아예 처음부터 풀 수 없는 문제는 무엇일까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b754952f-7201-4c86-9b85-1bca6c66b994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
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       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>854</td>\n",
       "      <td>1861</td>\n",
       "      <td>경로 찾기</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>1032</td>\n",
       "      <td>2046</td>\n",
       "      <td>이어달리기</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>1181</td>\n",
       "      <td>2199</td>\n",
       "      <td>DNA 해독 2</td>\n",
       "      <td>ad_hoc</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>1596</td>\n",
       "      <td>2627</td>\n",
       "      <td>트리회전</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>1608</td>\n",
       "      <td>2639</td>\n",
       "      <td>주차장</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22038</th>\n",
       "      <td>22039</td>\n",
       "      <td>24021</td>\n",
       "      <td>Zathras</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22039</th>\n",
       "      <td>22040</td>\n",
       "      <td>24022</td>\n",
       "      <td>Seating Chart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22150</th>\n",
       "      <td>22151</td>\n",
       "      <td>24135</td>\n",
       "      <td>ロゴマーク (Logo)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22707</th>\n",
       "      <td>22708</td>\n",
       "      <td>24734</td>\n",
       "      <td>Eerie Shadows</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22708</th>\n",
       "      <td>22709</td>\n",
       "      <td>24735</td>\n",
       "      <td>Getting Square</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  problem_id           title    tags  is_solvable  \\\n",
       "853      854        1861           경로 찾기     NaN        False   \n",
       "1031    1032        2046           이어달리기     NaN        False   \n",
       "1180    1181        2199        DNA 해독 2  ad_hoc        False   \n",
       "1595    1596        2627            트리회전     NaN        False   \n",
       "1607    1608        2639             주차장     NaN        False   \n",
       "...      ...         ...             ...     ...          ...   \n",
       "22038  22039       24021         Zathras     NaN        False   \n",
       "22039  22040       24022   Seating Chart     NaN        False   \n",
       "22150  22151       24135    ロゴマーク (Logo)     NaN        False   \n",
       "22707  22708       24734   Eerie Shadows     NaN        False   \n",
       "22708  22709       24735  Getting Square     NaN        False   \n",
       "\n",
       "       accepted_user_count  level  average_tries  \n",
       "853                      0      0              0  \n",
       "1031                     0      0              0  \n",
       "1180                     0     15              0  \n",
       "1595                     0      0              0  \n",
       "1607                     0      0              0  \n",
       "...                    ...    ...            ...  \n",
       "22038                    0      0              0  \n",
       "22039                    0      0              0  \n",
       "22150                    0      0              0  \n",
       "22707                    0      0              0  \n",
       "22708                    0      0              0  \n",
       "\n",
       "[303 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is_solvable == False인 것들 확인\n",
    "non_solvable = df_problems.loc[df_problems['is_solvable'] == False]\n",
    "non_solvable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288032e4-2464-42df-9f45-6b42c5944eff",
   "metadata": {},
   "source": [
    "303개의 문제가 풀 수 없는 것으로 나오네요. 😮"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a1ecde4-ed7d-4e0b-ba24-8aeede97e83f",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5430986-4cdb-497e-a208-85bdef728668",
   "metadata": {},
   "source": [
    "### 아예 풀 수 없는 문제는 분석에서 제외하는 게 좋겠죠? 🤔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c1ded925-db72-4834-9ee5-2946fbd6c991",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1003</td>\n",
       "      <td>피보나치 함수</td>\n",
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       "      <td>True</td>\n",
       "      <td>34653</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>8</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <th>22679</th>\n",
       "      <td>22984</td>\n",
       "      <td>25028</td>\n",
       "      <td>Fully Generate</td>\n",
       "      <td>dp,exponentiation_by_squaring,math</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22680</th>\n",
       "      <td>22985</td>\n",
       "      <td>25029</td>\n",
       "      <td>Joyful KMP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22681</th>\n",
       "      <td>22986</td>\n",
       "      <td>25030</td>\n",
       "      <td>Polynomial Quine</td>\n",
       "      <td>gaussian_elimination,linear_algebra,math,numbe...</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22682</th>\n",
       "      <td>22987</td>\n",
       "      <td>25031</td>\n",
       "      <td>Unifying Values</td>\n",
       "      <td>dp,prefix_sum</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22683</th>\n",
       "      <td>22988</td>\n",
       "      <td>25032</td>\n",
       "      <td>Young Zebra</td>\n",
       "      <td>bfs,dfs,graphs,graph_traversal</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22684 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  problem_id             title  \\\n",
       "0          1        1000               A+B   \n",
       "1          2        1001               A-B   \n",
       "2          3        1002                터렛   \n",
       "3          4        1003           피보나치 함수   \n",
       "4          5        1004             어린 왕자   \n",
       "...      ...         ...               ...   \n",
       "22679  22984       25028    Fully Generate   \n",
       "22680  22985       25029        Joyful KMP   \n",
       "22681  22986       25030  Polynomial Quine   \n",
       "22682  22987       25031   Unifying Values   \n",
       "22683  22988       25032       Young Zebra   \n",
       "\n",
       "                                                    tags  is_solvable  \\\n",
       "0                         arithmetic,implementation,math         True   \n",
       "1                         arithmetic,implementation,math         True   \n",
       "2                                          geometry,math         True   \n",
       "3                                                     dp         True   \n",
       "4                                               geometry         True   \n",
       "...                                                  ...          ...   \n",
       "22679                 dp,exponentiation_by_squaring,math         True   \n",
       "22680                                                NaN         True   \n",
       "22681  gaussian_elimination,linear_algebra,math,numbe...         True   \n",
       "22682                                      dp,prefix_sum         True   \n",
       "22683                     bfs,dfs,graphs,graph_traversal         True   \n",
       "\n",
       "       accepted_user_count  level  average_tries  \n",
       "0                   175152      1              2  \n",
       "1                   146736      1              1  \n",
       "2                    25716      7              4  \n",
       "3                    34653      8              3  \n",
       "4                     8438      8              2  \n",
       "...                    ...    ...            ...  \n",
       "22679                    3     14              2  \n",
       "22680                    3     22              1  \n",
       "22681                    2     23              1  \n",
       "22682                    4     14              1  \n",
       "22683                    4     16              1  \n",
       "\n",
       "[22684 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is_solvable == False 인 문제는 제외\n",
    "df_problems = df_problems.drop(index=non_solvable.index).reset_index(drop=True)\n",
    "df_problems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4cbce51-271e-414b-90a4-a6d70ba74ec0",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f848385d-7b9d-49a6-9870-7294a69ebd50",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce367e26-fe73-4803-ae3d-44bb1e8e0bfb",
   "metadata": {},
   "source": [
    "## ⛳️ 문제 레벨(티어) 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b41887d-016a-459e-8aa0-8a73814fc2af",
   "metadata": {},
   "source": [
    "### 문제 레벨 종류를 확인해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4a836f0f-50aa-4fc4-9777-41366470afe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]\n"
     ]
    }
   ],
   "source": [
    "# 문제 level 종류 확인\n",
    "print(sorted(df_problems['level'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a62bbe-6cea-44cc-97d4-3be6e971ef8a",
   "metadata": {},
   "source": [
    "0부터 30까지의 정수로 이루어져 있네요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1acc22-3398-4425-a324-2fd106da4f68",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f97fa4-a466-4a40-8219-024cbbd1d4ba",
   "metadata": {},
   "source": [
    "### 문제 레벨 분포를 확인해 볼까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df5abfb4-847d-4d2d-b511-c3f855ff1bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 문제 level 분포 확인\n",
    "level_cnt = df_problems['level'].value_counts().sort_index()\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=level_cnt.index, y=level_cnt.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6370b7c3-02d0-44b5-9eb3-bbeaecfc2538",
   "metadata": {},
   "source": [
    "레벨이 0인 것이 너무 많네요... 😅<br/>\n",
    "레벨이 0인 문제는 난이도가 매겨지지 않은 문제이므로 이를 제외하여 분포를 다시 한 번 그려볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1cc58ec3-b36a-421b-89f9-e6e624d9d3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# level이 0인 것을 제외한 후 다시 분포를 확인.\n",
    "df_problems = df_problems.loc[df_problems['level'] != 0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5355ad3d-d89e-48ff-86b5-240727d8a3d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "level_cnt = df_problems['level'].value_counts().sort_index()\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=level_cnt.index, y=level_cnt.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45999f04-302e-4873-ab6e-c281215f2832",
   "metadata": {},
   "source": [
    "난이도가 매겨진 문제들은 중간 부분의 레벨에 많이 분포해있군요. <br/>\n",
    "가장 많이 분포한 레벨은 브론즈 2이고, 그 외의 골드 4와 골드 3에도 많이 분포한 것을 볼 수 있습니다. <br/>\n",
    "즉, 중간 정도의 난이도를 가진 문제들이 대체로 많이 난이도가 매겨졌다고 볼 수 있겠네요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b66d97c-6dce-4f0b-94bb-299691de5a5f",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec5f101-3b64-489f-a90e-35e93e50ae1e",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd158b78-03e2-4ac1-8e4d-68ec088e2810",
   "metadata": {},
   "source": [
    "## 🧭 유저별 푼 문제 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62aae1e6-ad40-4443-9018-e4dc0ab99ec0",
   "metadata": {},
   "source": [
    "### 유저들이 어떠한 문제를 많이 풀었는지, 어떤 태그 위주의 무제를 주로 풀었는지 분석해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1431c40c-f9a3-4ccc-9d1b-3a23212cc26c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 63876/63876 [00:08<00:00, 7842.83it/s] \n"
     ]
    }
   ],
   "source": [
    "# 문제별로 풀린 횟수 계산\n",
    "for i in tqdm(range(len(df_problems_solved))):\n",
    "    try:\n",
    "        pro = list(df_problems_solved.iloc[i, 2].split(','))\n",
    "        for j in pro:\n",
    "            j = int(j)\n",
    "            id2count[j] += 1\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1f15819b-f625-4158-818a-1689a4d1ef5b",
   "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>problem_id</th>\n",
       "      <th>count</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>2557</td>\n",
       "      <td>52586</td>\n",
       "      <td>Hello World</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>52277</td>\n",
       "      <td>A+B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>47009</td>\n",
       "      <td>A-B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8935</th>\n",
       "      <td>10718</td>\n",
       "      <td>44505</td>\n",
       "      <td>We love kriii</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1008</td>\n",
       "      <td>43819</td>\n",
       "      <td>A/B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9084</th>\n",
       "      <td>10869</td>\n",
       "      <td>41847</td>\n",
       "      <td>사칙연산</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9211</th>\n",
       "      <td>10998</td>\n",
       "      <td>41146</td>\n",
       "      <td>A×B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>2739</td>\n",
       "      <td>40962</td>\n",
       "      <td>구구단</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>1330</td>\n",
       "      <td>40912</td>\n",
       "      <td>두 수 비교하기</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406</th>\n",
       "      <td>2438</td>\n",
       "      <td>40851</td>\n",
       "      <td>별 찍기 - 1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      problem_id  count          title  level\n",
       "1525        2557  52586    Hello World      1\n",
       "0           1000  52277            A+B      1\n",
       "1           1001  47009            A-B      1\n",
       "8935       10718  44505  We love kriii      1\n",
       "8           1008  43819            A/B      2\n",
       "9084       10869  41847           사칙연산      1\n",
       "9211       10998  41146            A×B      1\n",
       "1707        2739  40962            구구단      3\n",
       "327         1330  40912       두 수 비교하기      2\n",
       "1406        2438  40851       별 찍기 - 1      3"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 문제별로 풀린 횟수를 계산한 데이터프레임\n",
    "# count 컬럼이 풀린 횟수.\n",
    "df_problem_count = pd.DataFrame({'problem_id' : id2count.keys(), 'count' : id2count.values()})\n",
    "df_problem_count['title'] = df_problem_count['problem_id'].apply(lambda x : id2title[x])\n",
    "df_problem_count['level'] = df_problem_count['problem_id'].apply(lambda x : id2level[x])\n",
    "df_problem_count.sort_values('count', inplace=True, ascending=False)\n",
    "df_problem_count[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3632261-32fd-4fc7-8f2f-ef9da8774c8e",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2e5eef-dece-4901-aafd-5038c765879d",
   "metadata": {},
   "source": [
    "### 어떤 태그가 많이 풀렸는지도 알아볼까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fc611fed-00ec-4e5d-8a3a-8249b9976fe3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 14947/14947 [00:00<00:00, 41515.41it/s]\n"
     ]
    }
   ],
   "source": [
    "# is_solvable == True, level > 0인 문제들의 태그 분포 확인\n",
    "dict_tag_cnt = {i:int(j) for i, j in zip(set_tags, np.zeros(len(set_tags)))}\n",
    "for i in tqdm(range(len(df_problems))):\n",
    "    tags = df_problems.iloc[i, 3]\n",
    "    try:\n",
    "        for tag in tags.split(','):\n",
    "            dict_tag_cnt[tag] += 1\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd4ef3d4-5174-4f7e-8b8f-f23dcbb0305d",
   "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>tag</th>\n",
       "      <th>tag_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>math</td>\n",
       "      <td>3838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>implementation</td>\n",
       "      <td>3416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dp</td>\n",
       "      <td>2418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>graphs</td>\n",
       "      <td>2262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data_structures</td>\n",
       "      <td>2221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>string</td>\n",
       "      <td>1522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>greedy</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bruteforcing</td>\n",
       "      <td>1304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>graph_traversal</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sorting</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tag  tag_count\n",
       "0             math       3838\n",
       "1   implementation       3416\n",
       "2               dp       2418\n",
       "3           graphs       2262\n",
       "4  data_structures       2221\n",
       "5           string       1522\n",
       "6           greedy       1344\n",
       "7     bruteforcing       1304\n",
       "8  graph_traversal       1239\n",
       "9          sorting       1050"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 많이 풀린 tag 횟수에 관한 데이터프레임\n",
    "# tag_count 컬럼이 각 tag별 풀린 횟수\n",
    "\n",
    "df_tag_count = pd.DataFrame({'tag' : dict_tag_cnt.keys(), 'tag_count' : dict_tag_cnt.values()})\n",
    "df_tag_count.sort_values('tag_count', ascending=False, inplace=True)\n",
    "df_tag_count.reset_index(drop=True, inplace=True)\n",
    "df_tag_count[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ef3c97",
   "metadata": {},
   "source": [
    "추후 대표 태그를 선정해 input으로 사용해도 좋을 것이라고 판단했습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7489f3-23a9-4b0f-9b83-b1c3382b9762",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e659c7a-552a-4e60-b3e7-54e80680d047",
   "metadata": {},
   "source": [
    "### 유저가 최근에 어떠한 문제들을 많이 풀었는지도 알아봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "30b74287-03f0-4ba3-b4f0-7e7a16050890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "모든 유저가 최근에 풀었던 문제 종류 수 : 11825 개\n"
     ]
    }
   ],
   "source": [
    "# 모든 유저가 최근에 풀었던 문제 종류 수\n",
    "print(\"모든 유저가 최근에 풀었던 문제 종류 수 :\", df_records_solved['problem'].nunique(), \"개\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf07679-2cb9-4650-9288-76dc083d889b",
   "metadata": {},
   "source": [
    "#### 유저가 같은 문제에 관해 \"맞았습니다\" 제출을 여러 번 했으면 가장 최근 풀었던 기록만 남겨둘게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3107ca9c-4acc-480d-acb2-244be14cf147",
   "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>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>problem</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>4139</td>\n",
       "      <td>2022-04-26 20:37:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>24116</td>\n",
       "      <td>2022-04-25 23:47:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>24124</td>\n",
       "      <td>2022-04-25 23:47:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>24125</td>\n",
       "      <td>2022-04-25 23:47:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>24126</td>\n",
       "      <td>2022-04-25 23:47:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565017</th>\n",
       "      <td>3000108</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>10869</td>\n",
       "      <td>2021-02-22 11:18:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565018</th>\n",
       "      <td>3000110</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>1000</td>\n",
       "      <td>2021-02-22 10:57:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565019</th>\n",
       "      <td>3000111</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>10171</td>\n",
       "      <td>2021-02-22 10:28:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565020</th>\n",
       "      <td>3000112</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>10718</td>\n",
       "      <td>2021-02-18 10:44:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565021</th>\n",
       "      <td>3000113</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>2557</td>\n",
       "      <td>2021-02-18 10:42:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2565022 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   handle  problem            timestamp\n",
       "0              1  koosaga     4139  2022-04-26 20:37:39\n",
       "1              2  koosaga    24116  2022-04-25 23:47:38\n",
       "2              3  koosaga    24124  2022-04-25 23:47:25\n",
       "3              4  koosaga    24125  2022-04-25 23:47:17\n",
       "4              5  koosaga    24126  2022-04-25 23:47:07\n",
       "...          ...      ...      ...                  ...\n",
       "2565017  3000108  wwowwww    10869  2021-02-22 11:18:39\n",
       "2565018  3000110  wwowwww     1000  2021-02-22 10:57:44\n",
       "2565019  3000111  wwowwww    10171  2021-02-22 10:28:05\n",
       "2565020  3000112  wwowwww    10718  2021-02-18 10:44:08\n",
       "2565021  3000113  wwowwww     2557  2021-02-18 10:42:30\n",
       "\n",
       "[2565022 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 사용자가 똑같은 문제를 풀었다면 최근에 풀었던 기록만 남겨둠.\n",
    "df_records_solved = df_records_solved.drop_duplicates(subset=('handle', 'problem')).reset_index(drop=True)\n",
    "df_records_solved"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5e2fea-e2b6-4a36-99c0-6cf122fd5d82",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "726f5887-d943-43dd-b62e-51ec79c9c3eb",
   "metadata": {},
   "source": [
    "### 최근 \"맞았습니다\" 기록에서 문제를 10개 이상 푼 유저의 수는 몇 명일까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5ca3ff3c-9e39-41b0-94d8-77a23971937c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "문제를 10개 이상 푼 유저의 수 : 57635 명\n"
     ]
    }
   ],
   "source": [
    "group_records_solved = df_records_solved.groupby('handle')['problem'].count()\n",
    "print(\"문제를 10개 이상 푼 유저의 수 :\", len(group_records_solved[group_records_solved >= 10]), '명')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5415d04d-04b0-4246-833c-6f65a05d52dd",
   "metadata": {},
   "source": [
    "만약 \"맞았습니다\" 데이터를 사용할 수 있으면 약 88%의 유저는 최근에 푼 10개 이상의 문제를 기반으로 좀 더 개인화된 문제를 추천할 수 있겠네요. <br/>\n",
    "하지만 최근 채점 데이터는 웹 스크레이핑이 불가하므로 추천 모델링에는 사용하기가 어려워보입니다. 😢 <br/>\n",
    "나중에 기회가 되면 sequential model 제작에 도전해 봐야겠네요. 🧐"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965f9598-2f9e-4a47-8d32-7146b0ba4835",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51725764-a8a8-4ce8-8cb4-f334d89b96c6",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d407ec5-8e17-4bb1-88d7-56bb1dfe4595",
   "metadata": {},
   "source": [
    "## 🔦 비슷한 문제 탐색 방법 실험해 보기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790bd2fa-ce22-4bd7-818e-83101614333f",
   "metadata": {},
   "source": [
    "### 어떤 한 문제와 유사한 문제는 어떻게 찾는 게 좋을까요? 다양한 방법으로 시도해 봅시다!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61159717-2f4f-4090-9d95-a1c176e929fe",
   "metadata": {},
   "source": [
    "#### 예시로 문제 1027번과 유사한 문제를 다양한 방법으로 찾아볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cca38d79-8f3a-434e-982b-9b5ee68eb23a",
   "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>id</th>\n",
       "      <th>problem_id</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "      <th>is_solvable</th>\n",
       "      <th>accepted_user_count</th>\n",
       "      <th>level</th>\n",
       "      <th>average_tries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1027</td>\n",
       "      <td>고층 건물</td>\n",
       "      <td>bruteforcing,geometry,math</td>\n",
       "      <td>True</td>\n",
       "      <td>1444</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  problem_id  title                        tags  is_solvable  \\\n",
       "27  28        1027  고층 건물  bruteforcing,geometry,math         True   \n",
       "\n",
       "    accepted_user_count  level  average_tries  \n",
       "27                 1444     12              2  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems.loc[df_problems['problem_id'] == 1027]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb6097a-eddc-4fc3-b767-327c545ba57d",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e40d0e-fa42-4af7-a6af-c9581fae60d2",
   "metadata": {},
   "source": [
    "### 우선 cosine 유사도부터 사용해 볼까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ddc2898f-5872-4359-b864-adfd926617ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 14947/14947 [00:04<00:00, 3278.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(13399, 1.0), (196, 0.996732097101857), (6516, 0.996732097101857), (990, 0.9966832412776431), (3406, 0.9966832412776431), (14201, 0.9965693281118013), (4852, 0.9965262709260471), (6924, 0.9965262709260471), (7058, 0.9965262709260471), (2696, 0.9963485084679176), (7, 0.9962884370469508), (737, 0.9962884370469508), (5983, 0.9960608976382687), (7572, 0.9959307434419573), (8293, 0.9959307434419573), (9241, 0.9959307434419573), (12497, 0.9959307434419573), (8026, 0.9959063021641593), (995, 0.9954948587295256)]\n"
     ]
    }
   ],
   "source": [
    "# 코사인 유사도 기반\n",
    "df_for_test = df_problems[['problem_id', 'title', 'level', 'average_tries']]\n",
    "for i in list(list(set_tags)):\n",
    "    df_for_test[i] = 0\n",
    "\n",
    "for i in tqdm(range(len(df_problems))):\n",
    "    tags = df_problems.iloc[i, 3]\n",
    "    try:\n",
    "        for tag in tags.split(','):\n",
    "            df_for_test.iloc[i, list(df_for_test.columns).index(tag)] += 1\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "temp = df_for_test.iloc[:, 2:].values\n",
    "sim = cs(temp, temp)\n",
    "\n",
    "sim_scores = list(enumerate(sim[27]))\n",
    "sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "sim_scores = sim_scores[1:20]\n",
    "print(sim_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd900f1f-8684-49a1-900c-1707acb27235",
   "metadata": {},
   "source": [
    "#### 구한 cosine 유사도 값이 가장 높은 문제 정보를 확인해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0d681eb9-ca46-4590-b680-db11f18cae40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>problem_id</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>average_tries</th>\n",
       "      <th>smaller_to_larger</th>\n",
       "      <th>bipartite_graph</th>\n",
       "      <th>link_cut_tree</th>\n",
       "      <th>coordinate_compression</th>\n",
       "      <th>prefix_sum</th>\n",
       "      <th>euler_characteristic</th>\n",
       "      <th>...</th>\n",
       "      <th>rb_tree</th>\n",
       "      <th>graph_traversal</th>\n",
       "      <th>sliding_window</th>\n",
       "      <th>topological_sorting</th>\n",
       "      <th>interpreter</th>\n",
       "      <th>splay_tree</th>\n",
       "      <th>pick</th>\n",
       "      <th>hirschberg</th>\n",
       "      <th>bitset</th>\n",
       "      <th>point_in_non_convex_polygon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13399</th>\n",
       "      <td>22294</td>\n",
       "      <td>SMH</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1198</td>\n",
       "      <td>삼각형으로 자르기</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>12329</td>\n",
       "      <td>Bullseye (Small)</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2137</td>\n",
       "      <td>가장 가까운 분수</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>6199</td>\n",
       "      <td>Big Square</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14201</th>\n",
       "      <td>23890</td>\n",
       "      <td>달팽이팽이</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4852</th>\n",
       "      <td>9706</td>\n",
       "      <td>FIND THE MARBLES</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6924</th>\n",
       "      <td>12924</td>\n",
       "      <td>멋진 쌍</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7058</th>\n",
       "      <td>13140</td>\n",
       "      <td>Hello World!</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>4995</td>\n",
       "      <td>원과 점</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1007</td>\n",
       "      <td>벡터 매칭</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>1827</td>\n",
       "      <td>여행가이드</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5983</th>\n",
       "      <td>11563</td>\n",
       "      <td>연돌이와 고잠녀</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7572</th>\n",
       "      <td>13908</td>\n",
       "      <td>비밀번호</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8293</th>\n",
       "      <td>14931</td>\n",
       "      <td>물수제비 (SUJEBI)</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9241</th>\n",
       "      <td>16155</td>\n",
       "      <td>업힐과 가희</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12497</th>\n",
       "      <td>20529</td>\n",
       "      <td>가장 가까운 세 사람의 심리적 거리</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8026</th>\n",
       "      <td>14604</td>\n",
       "      <td>Over Fitting (Small)</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2142</td>\n",
       "      <td>정돈된 배열</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 189 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       problem_id                 title  level  average_tries  \\\n",
       "13399       22294                   SMH     12              2   \n",
       "196          1198             삼각형으로 자르기      8              1   \n",
       "6516        12329      Bullseye (Small)      8              1   \n",
       "990          2137             가장 가까운 분수     12              2   \n",
       "3406         6199            Big Square     12              2   \n",
       "14201       23890                 달팽이팽이     13              2   \n",
       "4852         9706      FIND THE MARBLES     11              2   \n",
       "6924        12924                  멋진 쌍     11              2   \n",
       "7058        13140          Hello World!     11              2   \n",
       "2696         4995                  원과 점     20              2   \n",
       "7            1007                 벡터 매칭     14              2   \n",
       "737          1827                 여행가이드     14              2   \n",
       "5983        11563              연돌이와 고잠녀     16              3   \n",
       "7572        13908                  비밀번호     10              2   \n",
       "8293        14931         물수제비 (SUJEBI)     10              2   \n",
       "9241        16155                업힐과 가희     10              2   \n",
       "12497       20529   가장 가까운 세 사람의 심리적 거리     10              2   \n",
       "8026        14604  Over Fitting (Small)     15              2   \n",
       "995          2142                정돈된 배열     14              3   \n",
       "\n",
       "       smaller_to_larger  bipartite_graph  link_cut_tree  \\\n",
       "13399                  0                0              0   \n",
       "196                    0                0              0   \n",
       "6516                   0                0              0   \n",
       "990                    0                0              0   \n",
       "3406                   0                0              0   \n",
       "14201                  0                0              0   \n",
       "4852                   0                0              0   \n",
       "6924                   0                0              0   \n",
       "7058                   0                0              0   \n",
       "2696                   0                0              0   \n",
       "7                      0                0              0   \n",
       "737                    0                0              0   \n",
       "5983                   0                0              0   \n",
       "7572                   0                0              0   \n",
       "8293                   0                0              0   \n",
       "9241                   0                0              0   \n",
       "12497                  0                0              0   \n",
       "8026                   0                0              0   \n",
       "995                    0                0              0   \n",
       "\n",
       "       coordinate_compression  prefix_sum  euler_characteristic  ...  rb_tree  \\\n",
       "13399                       0           0                     0  ...        0   \n",
       "196                         0           0                     0  ...        0   \n",
       "6516                        0           0                     0  ...        0   \n",
       "990                         0           0                     0  ...        0   \n",
       "3406                        0           0                     0  ...        0   \n",
       "14201                       0           0                     0  ...        0   \n",
       "4852                        0           0                     0  ...        0   \n",
       "6924                        0           0                     0  ...        0   \n",
       "7058                        0           0                     0  ...        0   \n",
       "2696                        0           0                     0  ...        0   \n",
       "7                           0           0                     0  ...        0   \n",
       "737                         0           0                     0  ...        0   \n",
       "5983                        0           0                     0  ...        0   \n",
       "7572                        0           0                     0  ...        0   \n",
       "8293                        0           0                     0  ...        0   \n",
       "9241                        0           0                     0  ...        0   \n",
       "12497                       0           0                     0  ...        0   \n",
       "8026                        0           0                     0  ...        0   \n",
       "995                         0           0                     0  ...        0   \n",
       "\n",
       "       graph_traversal  sliding_window  topological_sorting  interpreter  \\\n",
       "13399                0               0                    0            0   \n",
       "196                  0               0                    0            0   \n",
       "6516                 0               0                    0            0   \n",
       "990                  0               0                    0            0   \n",
       "3406                 0               0                    0            0   \n",
       "14201                0               0                    0            0   \n",
       "4852                 0               0                    0            0   \n",
       "6924                 0               0                    0            0   \n",
       "7058                 0               0                    0            0   \n",
       "2696                 0               0                    0            0   \n",
       "7                    0               0                    0            0   \n",
       "737                  0               0                    0            0   \n",
       "5983                 0               0                    0            0   \n",
       "7572                 0               0                    0            0   \n",
       "8293                 0               0                    0            0   \n",
       "9241                 0               0                    0            0   \n",
       "12497                0               0                    0            0   \n",
       "8026                 0               0                    0            0   \n",
       "995                  0               0                    0            0   \n",
       "\n",
       "       splay_tree  pick  hirschberg  bitset  point_in_non_convex_polygon  \n",
       "13399           0     0           0       0                            0  \n",
       "196             0     0           0       0                            0  \n",
       "6516            0     0           0       0                            0  \n",
       "990             0     0           0       0                            0  \n",
       "3406            0     0           0       0                            0  \n",
       "14201           0     0           0       0                            0  \n",
       "4852            0     0           0       0                            0  \n",
       "6924            0     0           0       0                            0  \n",
       "7058            0     0           0       0                            0  \n",
       "2696            0     0           0       0                            0  \n",
       "7               0     0           0       0                            0  \n",
       "737             0     0           0       0                            0  \n",
       "5983            0     0           0       0                            0  \n",
       "7572            0     0           0       0                            0  \n",
       "8293            0     0           0       0                            0  \n",
       "9241            0     0           0       0                            0  \n",
       "12497           0     0           0       0                            0  \n",
       "8026            0     0           0       0                            0  \n",
       "995             0     0           0       0                            0  \n",
       "\n",
       "[19 rows x 189 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.array(sim_scores)[:, 0]\n",
    "df_for_cos = df_for_test.iloc[idx]\n",
    "df_for_cos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff91fad-da2a-438a-9aa2-6802854f8494",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b62ce5f-e6f8-4e8a-84ba-66a1722aff62",
   "metadata": {},
   "source": [
    "### 유클리드 거리 기반으로 유사한 문제를 구해봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4319f264-1f7d-48d2-a717-3225e524b7aa",
   "metadata": {},
   "source": [
    "#### 그전에 두 좌표 사이의 거리를 구하는 식을 함수로 정의해 놓을게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8ff81eee-c5b9-430a-99ca-5f868d34d68a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(x,y):\n",
    "    return np.sqrt(np.sum((x-y)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c3ece938-90eb-4446-a22c-b62076500437",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14947it [00:00, 66049.35it/s]\n"
     ]
    }
   ],
   "source": [
    "# 유클리드 거리 기반 유사한 문제 구하기\n",
    "problem_idx = 27\n",
    "list_dist = []\n",
    "for i, j in tqdm(enumerate(temp)):\n",
    "    list_dist.append((i, dist(temp[problem_idx], j)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d35190a7-6238-4cd6-97ba-c71268a3e9c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(27, 0.0), (13399, 0.0), (990, 1.0), (3406, 1.0), (380, 1.4142135623730951), (788, 1.4142135623730951), (1843, 1.4142135623730951), (2517, 1.4142135623730951), (4852, 1.4142135623730951), (5210, 1.4142135623730951), (5278, 1.4142135623730951), (5305, 1.4142135623730951), (5486, 1.4142135623730951), (6121, 1.4142135623730951), (6924, 1.4142135623730951), (7058, 1.4142135623730951), (8007, 1.4142135623730951), (8077, 1.4142135623730951), (8848, 1.4142135623730951), (9040, 1.4142135623730951)]\n"
     ]
    }
   ],
   "source": [
    "list_dist = sorted(list_dist, key = lambda x : x[1])\n",
    "list_dist = list_dist[:20]\n",
    "print(list_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd80b2d7-5e5c-47a0-854f-f2378ec67295",
   "metadata": {},
   "source": [
    "#### 문제간 거리가 가까울수록 유사한 문제입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cfd3a110-70ef-464b-bc5b-23d7a57e0f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem_id</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>average_tries</th>\n",
       "      <th>smaller_to_larger</th>\n",
       "      <th>bipartite_graph</th>\n",
       "      <th>link_cut_tree</th>\n",
       "      <th>coordinate_compression</th>\n",
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       "      <th>euler_characteristic</th>\n",
       "      <th>...</th>\n",
       "      <th>rb_tree</th>\n",
       "      <th>graph_traversal</th>\n",
       "      <th>sliding_window</th>\n",
       "      <th>topological_sorting</th>\n",
       "      <th>interpreter</th>\n",
       "      <th>splay_tree</th>\n",
       "      <th>pick</th>\n",
       "      <th>hirschberg</th>\n",
       "      <th>bitset</th>\n",
       "      <th>point_in_non_convex_polygon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13399</th>\n",
       "      <td>22294</td>\n",
       "      <td>SMH</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2137</td>\n",
       "      <td>가장 가까운 분수</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>6199</td>\n",
       "      <td>Big Square</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>1407</td>\n",
       "      <td>2로 몇 번 나누어질까</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>1891</td>\n",
       "      <td>사분면</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>3164</td>\n",
       "      <td>패턴</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2517</th>\n",
       "      <td>4651</td>\n",
       "      <td>Transmitters</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4852</th>\n",
       "      <td>9706</td>\n",
       "      <td>FIND THE MARBLES</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5210</th>\n",
       "      <td>10381</td>\n",
       "      <td>Joy of Flight</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5278</th>\n",
       "      <td>10477</td>\n",
       "      <td>경운기</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5305</th>\n",
       "      <td>10504</td>\n",
       "      <td>덧셈</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5486</th>\n",
       "      <td>10761</td>\n",
       "      <td>Bessie Goes Moo</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6121</th>\n",
       "      <td>11735</td>\n",
       "      <td>정산소</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6924</th>\n",
       "      <td>12924</td>\n",
       "      <td>멋진 쌍</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7058</th>\n",
       "      <td>13140</td>\n",
       "      <td>Hello World!</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8007</th>\n",
       "      <td>14583</td>\n",
       "      <td>이음줄</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8077</th>\n",
       "      <td>14658</td>\n",
       "      <td>하늘에서 별똥별이 빗발친다</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8848</th>\n",
       "      <td>15668</td>\n",
       "      <td>방 번호</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9040</th>\n",
       "      <td>15883</td>\n",
       "      <td>First In Last Out</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 189 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       problem_id              title  level  average_tries  smaller_to_larger  \\\n",
       "13399       22294                SMH     12              2                  0   \n",
       "990          2137          가장 가까운 분수     12              2                  0   \n",
       "3406         6199         Big Square     12              2                  0   \n",
       "380          1407       2로 몇 번 나누어질까     12              2                  0   \n",
       "788          1891                사분면     12              3                  0   \n",
       "1843         3164                 패턴     12              2                  0   \n",
       "2517         4651       Transmitters     12              1                  0   \n",
       "4852         9706   FIND THE MARBLES     11              2                  0   \n",
       "5210        10381      Joy of Flight     12              2                  0   \n",
       "5278        10477                경운기     12              3                  0   \n",
       "5305        10504                 덧셈     12              2                  0   \n",
       "5486        10761    Bessie Goes Moo     12              2                  0   \n",
       "6121        11735                정산소     12              2                  0   \n",
       "6924        12924               멋진 쌍     11              2                  0   \n",
       "7058        13140       Hello World!     11              2                  0   \n",
       "8007        14583                이음줄     12              1                  0   \n",
       "8077        14658     하늘에서 별똥별이 빗발친다     12              2                  0   \n",
       "8848        15668               방 번호     12              3                  0   \n",
       "9040        15883  First In Last Out     12              2                  0   \n",
       "\n",
       "       bipartite_graph  link_cut_tree  coordinate_compression  prefix_sum  \\\n",
       "13399                0              0                       0           0   \n",
       "990                  0              0                       0           0   \n",
       "3406                 0              0                       0           0   \n",
       "380                  0              0                       0           0   \n",
       "788                  0              0                       0           0   \n",
       "1843                 0              0                       0           0   \n",
       "2517                 0              0                       0           0   \n",
       "4852                 0              0                       0           0   \n",
       "5210                 0              0                       0           0   \n",
       "5278                 0              0                       0           0   \n",
       "5305                 0              0                       0           0   \n",
       "5486                 0              0                       0           0   \n",
       "6121                 0              0                       0           0   \n",
       "6924                 0              0                       0           0   \n",
       "7058                 0              0                       0           0   \n",
       "8007                 0              0                       0           0   \n",
       "8077                 0              0                       0           0   \n",
       "8848                 0              0                       0           0   \n",
       "9040                 0              0                       0           0   \n",
       "\n",
       "       euler_characteristic  ...  rb_tree  graph_traversal  sliding_window  \\\n",
       "13399                     0  ...        0                0               0   \n",
       "990                       0  ...        0                0               0   \n",
       "3406                      0  ...        0                0               0   \n",
       "380                       0  ...        0                0               0   \n",
       "788                       0  ...        0                0               0   \n",
       "1843                      0  ...        0                0               0   \n",
       "2517                      0  ...        0                0               0   \n",
       "4852                      0  ...        0                0               0   \n",
       "5210                      0  ...        0                0               0   \n",
       "5278                      0  ...        0                0               0   \n",
       "5305                      0  ...        0                0               0   \n",
       "5486                      0  ...        0                0               0   \n",
       "6121                      0  ...        0                0               0   \n",
       "6924                      0  ...        0                0               0   \n",
       "7058                      0  ...        0                0               0   \n",
       "8007                      0  ...        0                0               0   \n",
       "8077                      0  ...        0                0               0   \n",
       "8848                      0  ...        0                0               0   \n",
       "9040                      0  ...        0                0               0   \n",
       "\n",
       "       topological_sorting  interpreter  splay_tree  pick  hirschberg  bitset  \\\n",
       "13399                    0            0           0     0           0       0   \n",
       "990                      0            0           0     0           0       0   \n",
       "3406                     0            0           0     0           0       0   \n",
       "380                      0            0           0     0           0       0   \n",
       "788                      0            0           0     0           0       0   \n",
       "1843                     0            0           0     0           0       0   \n",
       "2517                     0            0           0     0           0       0   \n",
       "4852                     0            0           0     0           0       0   \n",
       "5210                     0            0           0     0           0       0   \n",
       "5278                     0            0           0     0           0       0   \n",
       "5305                     0            0           0     0           0       0   \n",
       "5486                     0            0           0     0           0       0   \n",
       "6121                     0            0           0     0           0       0   \n",
       "6924                     0            0           0     0           0       0   \n",
       "7058                     0            0           0     0           0       0   \n",
       "8007                     0            0           0     0           0       0   \n",
       "8077                     0            0           0     0           0       0   \n",
       "8848                     0            0           0     0           0       0   \n",
       "9040                     0            0           0     0           0       0   \n",
       "\n",
       "       point_in_non_convex_polygon  \n",
       "13399                            0  \n",
       "990                              0  \n",
       "3406                             0  \n",
       "380                              0  \n",
       "788                              0  \n",
       "1843                             0  \n",
       "2517                             0  \n",
       "4852                             0  \n",
       "5210                             0  \n",
       "5278                             0  \n",
       "5305                             0  \n",
       "5486                             0  \n",
       "6121                             0  \n",
       "6924                             0  \n",
       "7058                             0  \n",
       "8007                             0  \n",
       "8077                             0  \n",
       "8848                             0  \n",
       "9040                             0  \n",
       "\n",
       "[19 rows x 189 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_euclidean = df_for_test.iloc[np.array(list_dist)[1:,0]]\n",
    "df_for_euclidean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec170cb8-cfcf-49fb-a0a0-2a4e0b0e6b8f",
   "metadata": {},
   "source": [
    "코사인 유사도보다는 유클리드 기반으로 거리가 가까운 문제를 구한 것의 레벨이 전반적으로 비교 대상인 1027번 문제의 레벨과 비슷한 것을 볼 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c3b988c1-fb77-487d-bf7b-e70f71358429",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem_id</th>\n",
       "      <th>title</th>\n",
       "      <th>level</th>\n",
       "      <th>average_tries</th>\n",
       "      <th>smaller_to_larger</th>\n",
       "      <th>bipartite_graph</th>\n",
       "      <th>link_cut_tree</th>\n",
       "      <th>coordinate_compression</th>\n",
       "      <th>prefix_sum</th>\n",
       "      <th>euler_characteristic</th>\n",
       "      <th>...</th>\n",
       "      <th>rb_tree</th>\n",
       "      <th>graph_traversal</th>\n",
       "      <th>sliding_window</th>\n",
       "      <th>topological_sorting</th>\n",
       "      <th>interpreter</th>\n",
       "      <th>splay_tree</th>\n",
       "      <th>pick</th>\n",
       "      <th>hirschberg</th>\n",
       "      <th>bitset</th>\n",
       "      <th>point_in_non_convex_polygon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13399</th>\n",
       "      <td>22294</td>\n",
       "      <td>SMH</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1198</td>\n",
       "      <td>삼각형으로 자르기</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>12329</td>\n",
       "      <td>Bullseye (Small)</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>4995</td>\n",
       "      <td>원과 점</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 189 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       problem_id             title  level  average_tries  smaller_to_larger  \\\n",
       "13399       22294               SMH     12              2                  0   \n",
       "196          1198         삼각형으로 자르기      8              1                  0   \n",
       "6516        12329  Bullseye (Small)      8              1                  0   \n",
       "2696         4995              원과 점     20              2                  0   \n",
       "\n",
       "       bipartite_graph  link_cut_tree  coordinate_compression  prefix_sum  \\\n",
       "13399                0              0                       0           0   \n",
       "196                  0              0                       0           0   \n",
       "6516                 0              0                       0           0   \n",
       "2696                 0              0                       0           0   \n",
       "\n",
       "       euler_characteristic  ...  rb_tree  graph_traversal  sliding_window  \\\n",
       "13399                     0  ...        0                0               0   \n",
       "196                       0  ...        0                0               0   \n",
       "6516                      0  ...        0                0               0   \n",
       "2696                      0  ...        0                0               0   \n",
       "\n",
       "       topological_sorting  interpreter  splay_tree  pick  hirschberg  bitset  \\\n",
       "13399                    0            0           0     0           0       0   \n",
       "196                      0            0           0     0           0       0   \n",
       "6516                     0            0           0     0           0       0   \n",
       "2696                     0            0           0     0           0       0   \n",
       "\n",
       "       point_in_non_convex_polygon  \n",
       "13399                            0  \n",
       "196                              0  \n",
       "6516                             0  \n",
       "2696                             0  \n",
       "\n",
       "[4 rows x 189 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_cos[(df_for_cos.bruteforcing == 1) & (df_for_cos.geometry == 1) & (df_for_cos.math == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "331a39d9-65cc-4399-bb3f-4806403e88f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>13399</th>\n",
       "      <td>22294</td>\n",
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       "       problem_id title  level  average_tries  smaller_to_larger  \\\n",
       "13399       22294   SMH     12              2                  0   \n",
       "\n",
       "       bipartite_graph  link_cut_tree  coordinate_compression  prefix_sum  \\\n",
       "13399                0              0                       0           0   \n",
       "\n",
       "       euler_characteristic  ...  rb_tree  graph_traversal  sliding_window  \\\n",
       "13399                     0  ...        0                0               0   \n",
       "\n",
       "       topological_sorting  interpreter  splay_tree  pick  hirschberg  bitset  \\\n",
       "13399                    0            0           0     0           0       0   \n",
       "\n",
       "       point_in_non_convex_polygon  \n",
       "13399                            0  \n",
       "\n",
       "[1 rows x 189 columns]"
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     "execution_count": 47,
     "metadata": {},
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   ],
   "source": [
    "df_for_euclidean[(df_for_euclidean.bruteforcing == 1) & (df_for_euclidean.geometry == 1) & (df_for_euclidean.math == 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db74f8dc-ea62-4093-8779-38699480c4b3",
   "metadata": {},
   "source": [
    "그러나 문제 1027번의 태그와 가장 유사하게 추천된 방법은 코사인 유사도였습니다. <br/>\n",
    "두 방법을 적절히 조화해서 추천을 하든가, 아니면 레벨과 태그에 관해 필터링 할 때 각 경우에 관해 좀 더 나은 성능을 보이는 방법을 고려해볼 수도 있겠네요. 🙂"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1db59d-e516-430d-aa2b-a944227ef5ef",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdeff7fd-a32c-4801-829a-6d8005c8e39c",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939a36be-6efe-40b8-b55e-7585aaaf5222",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 🔍 푼 문제가 없지만 경험치 또는 티어가 존재하는 유저"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c93e0b-eff6-40d4-8303-214d43c1e058",
   "metadata": {},
   "source": [
    "### 푼 문제 수가 0인 유저를 다시 한 번 확인해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "01a5da63-6b28-4165-bd75-d269e0cca853",
   "metadata": {},
   "outputs": [
    {
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       "<p>1297 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "          id     handle  solved_count  user_class  tier  rating  \\\n",
       "58499  58514   cdmy0417             0           0     0      25   \n",
       "58549  58564   jj964254             0           0     0      25   \n",
       "58576  58591    mulijoa             0           0     0      25   \n",
       "58646  58661  chsun0303             0           0     0      24   \n",
       "58967  58982   b0n0b0n0             0           0     0      22   \n",
       "...      ...        ...           ...         ...   ...     ...   \n",
       "65168  65185     zpqmdh             0           0     0       0   \n",
       "65169  65186    zumge98             0           0     0       0   \n",
       "65170  65187    zxcv123             0           0     0       0   \n",
       "65171  65188      zxr83             0           0     0       0   \n",
       "65172  65189    zzzqw11             0           0     0       0   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "58499                       0                0                       0   \n",
       "58549                       0                0                       0   \n",
       "58576                       0                0                       0   \n",
       "58646                       0                0                       0   \n",
       "58967                       0                0                       0   \n",
       "...                       ...              ...                     ...   \n",
       "65168                       0                0                       0   \n",
       "65169                       0                0                       0   \n",
       "65170                       0                0                       0   \n",
       "65171                       0                0                       0   \n",
       "65172                       0                0                       0   \n",
       "\n",
       "          exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "58499  510000            4                    4           0  58497   \n",
       "58549  450000            1                    1           4  58497   \n",
       "58576       0            0                    0           0  58497   \n",
       "58646       0            0                    0           0  58645   \n",
       "58967       0            0                    0           0  58969   \n",
       "...       ...          ...                  ...         ...    ...   \n",
       "65168       0            0                    0           0  63901   \n",
       "65169       0            0                    0           0  63901   \n",
       "65170       0            0                    0           0  63901   \n",
       "65171       0            0                    0           0  63901   \n",
       "65172       0            0                    0           0  63901   \n",
       "\n",
       "      organization  \n",
       "58499          214  \n",
       "58549          NaN  \n",
       "58576          NaN  \n",
       "58646          NaN  \n",
       "58967          NaN  \n",
       "...            ...  \n",
       "65168          NaN  \n",
       "65169          NaN  \n",
       "65170          NaN  \n",
       "65171          NaN  \n",
       "65172          NaN  \n",
       "\n",
       "[1297 rows x 15 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users[df_users.solved_count == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "68367257-84e5-48b3-976d-123efbd49d3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "푼 문제 수가 0인 유저(1,297명) 중 rating이나 exp가 0이 아닌 유저들: 24\n"
     ]
    }
   ],
   "source": [
    "print('푼 문제 수가 0인 유저(1,297명) 중 rating이나 exp가 0이 아닌 유저들:',len(df_users[(df_users.solved_count == 0) & ((df_users.rating !=0)|(df_users.exp !=0))]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336d904a-d774-48ce-ac7c-7aa480aa7acc",
   "metadata": {},
   "source": [
    "1,297명 중 rating이나 exp가 0이 아닌 유저들은 24명 입니다.\n",
    "- 해당 유저들은 데이터를 받아오는 solved.ac에서 제재당한 유저일 가능성이 큽니다.\n",
    "    - 대표적인 solved.ac에서 사용자를 제재하는 경우 : 부정행위로 문제를 맞춘 경우\n",
    "    - 이와 비슷한 경우로 발생한 데이터로는 푼 문제 수가 0개가 아니지만 rating과 exp가 모두 0인 유저들이 데이터로 남아있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93187daa-5d8e-4148-9c4c-28041adf81f0",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "078fa93c-9c14-4fa7-bad7-00db384ee036",
   "metadata": {},
   "source": [
    "### 1,297명은 푼 문제에 대한 정보가 존재하지 않으므로 해당 유저들은 df_users에서 제거합시다. 🧐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "508ed1ee-70ea-40a7-b66f-e216f5b1afb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solved_count가 0인 유저들 제거 전: 65173\n",
      "solved_count가 0인 유저들 제거 전: 63876\n"
     ]
    }
   ],
   "source": [
    "print('solved_count가 0인 유저들 제거 전:', len(df_users))\n",
    "df_users.drop(df_users[df_users.solved_count == 0].index.to_list(), inplace=True)\n",
    "print('solved_count가 0인 유저들 제거 전:', len(df_users))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83c4d82-b3e7-4ff0-b929-d00dce58bc42",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905fa18a-2f4d-4f41-b6ca-8eb493bb422c",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa0140c2-d85a-4555-a81b-32f917c98e67",
   "metadata": {},
   "source": [
    "## 🔭 유저 클래스 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81a3713-6141-4b27-b34c-0e67d3a7d084",
   "metadata": {},
   "source": [
    "### 유저 클래스 통계를 확인해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "71e4cb66-0b95-4797-b208-f696942081eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    63876.000000\n",
       "mean         1.278117\n",
       "std          1.507171\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          1.000000\n",
       "75%          2.000000\n",
       "max         10.000000\n",
       "Name: user_class, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.user_class.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84d38ba-ca90-45bf-8297-0c1c012d4b55",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ac960d-c487-4d3b-ad51-26d46465ffcc",
   "metadata": {},
   "source": [
    "### class가 0인 유저를 살펴볼까요? 🤔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "13369055-b223-4211-a880-5f5ee4ba2048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>solved_count</th>\n",
       "      <th>user_class</th>\n",
       "      <th>tier</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_by_problems_sum</th>\n",
       "      <th>rating_by_class</th>\n",
       "      <th>rating_by_solved_count</th>\n",
       "      <th>exp</th>\n",
       "      <th>rival_count</th>\n",
       "      <th>reverse_rival_count</th>\n",
       "      <th>max_streak</th>\n",
       "      <th>rank</th>\n",
       "      <th>organization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>moonrabbit2</td>\n",
       "      <td>562</td>\n",
       "      <td>0</td>\n",
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       "      <td>394,439</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>300iq</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2740</td>\n",
       "      <td>2588</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>211</th>\n",
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       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2499</td>\n",
       "      <td>2349</td>\n",
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       "      <td>148</td>\n",
       "      <td>808995492</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>212</td>\n",
       "      <td>323,449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>324</td>\n",
       "      <td>admathnoob</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2376</td>\n",
       "      <td>2248</td>\n",
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       "      <td>6</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>353</td>\n",
       "      <td>snowflake</td>\n",
       "      <td>436</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2350</td>\n",
       "      <td>2170</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>367079679</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>352</td>\n",
       "      <td>331,461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>64712</th>\n",
       "      <td>64728</td>\n",
       "      <td>ohjcms1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>64954</th>\n",
       "      <td>64971</td>\n",
       "      <td>steven1010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65099</th>\n",
       "      <td>65116</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
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       "      <td>63901</td>\n",
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       "    <tr>\n",
       "      <th>65128</th>\n",
       "      <td>65145</td>\n",
       "      <td>yijh061016</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>65143</th>\n",
       "      <td>65160</td>\n",
       "      <td>yoonkoo2001</td>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24942 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id       handle  solved_count  user_class  tier  rating  \\\n",
       "56        57  moonrabbit2           562           0    26    2767   \n",
       "66        67        300iq           262           0    26    2740   \n",
       "211      212        aeren           372           0    23    2499   \n",
       "323      324   admathnoob           262           0    22    2376   \n",
       "352      353    snowflake           436           0    22    2350   \n",
       "...      ...          ...           ...         ...   ...     ...   \n",
       "64712  64728      ohjcms1             2           0     0       0   \n",
       "64954  64971   steven1010             1           0     0       0   \n",
       "65099  65116      wwowwww            23           0     0       0   \n",
       "65128  65145   yijh061016           127           0     0       0   \n",
       "65143  65160  yoonkoo2001           306           0     0       0   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "56                       2577                0                     165   \n",
       "66                       2588                0                     128   \n",
       "211                      2349                0                     148   \n",
       "323                      2248                0                     128   \n",
       "352                      2170                0                     155   \n",
       "...                       ...              ...                     ...   \n",
       "64712                       0                0                       0   \n",
       "64954                       0                0                       0   \n",
       "65099                       0                0                       0   \n",
       "65128                       0                0                       0   \n",
       "65143                       0                0                       0   \n",
       "\n",
       "              exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "56     2134660724            1                    0           3     57   \n",
       "66     1884622368            0                    0           0     67   \n",
       "211     808995492            0                    2           6    212   \n",
       "323     400634682            0                    1           6    324   \n",
       "352     367079679           16                   19           7    352   \n",
       "...           ...          ...                  ...         ...    ...   \n",
       "64712           0            0                    0           0  63900   \n",
       "64954           0            0                    0           0  63901   \n",
       "65099           0            0                    0           0  63901   \n",
       "65128           0            0                    0           0  63901   \n",
       "65143           0            0                    3           0  63901   \n",
       "\n",
       "      organization  \n",
       "56         394,439  \n",
       "66             NaN  \n",
       "211        323,449  \n",
       "323            NaN  \n",
       "352        331,461  \n",
       "...            ...  \n",
       "64712          NaN  \n",
       "64954          NaN  \n",
       "65099          NaN  \n",
       "65128          NaN  \n",
       "65143          NaN  \n",
       "\n",
       "[24942 rows x 15 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users[df_users.user_class==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c966b9d-8787-46a6-8466-14abebb8d9f1",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8376a7a-e89e-4661-9241-97b3e0a47b33",
   "metadata": {},
   "source": [
    "### 푼 문제 수가 0이 아니지만 rating과 exp(경험치)가 모두 0인 유저들"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "76514091-5b48-451a-9b13-6975a5fc08e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class가 0인 유저들 중 rating과 exp가 모두 0인 유저: 11\n"
     ]
    }
   ],
   "source": [
    "print('class가 0인 유저들 중 rating과 exp가 모두 0인 유저:',len(df_users[(df_users.user_class==0) & (df_users.rating ==0) & (df_users.exp ==0)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "efccbadb-4c71-44bd-8938-e479e1707643",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          id          handle  \\\n",
       "63864  63865         alalala   \n",
       "63865  63866  bojtestaccount   \n",
       "63866  63867           dic05   \n",
       "63867  63868          gg8682   \n",
       "63869  63870        kvvvvvv3   \n",
       "63870  63871          number   \n",
       "63871  63872         ohjcms1   \n",
       "63872  63873      steven1010   \n",
       "63873  63874         wwowwww   \n",
       "63874  63875      yijh061016   \n",
       "63875  63876     yoonkoo2001   \n",
       "\n",
       "                                                problems  num  \n",
       "63864                                                NaN    1  \n",
       "63865                                                NaN    1  \n",
       "63866                                               1000    1  \n",
       "63867                                               1000    1  \n",
       "63869                                                NaN    1  \n",
       "63870  1220,1533,1616,1699,1912,2239,2580,3090,5820,5...   30  \n",
       "63871                                   2557,10718,15596    3  \n",
       "63872                                               2557    1  \n",
       "63873  1000,1001,1008,1330,2438,2439,2557,2588,2739,2...   29  \n",
       "63874                                                NaN    1  \n",
       "63875                                                NaN    1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems_solved[df_problems_solved.handle.isin(list(df_users[(df_users.user_class==0) & (df_users.rating ==0) & (df_users.exp ==0)]['handle']))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9ae1a6-5d35-4dcd-9bcd-871a7b53b5f4",
   "metadata": {},
   "source": [
    "이 11명의 유저들 중엔 problems_solved에 기록된 유저(6명)도 있고 아닌 유저도 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706621a6-0a6f-4a6d-b83e-50f20e4c4da6",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298730f5-26fa-4311-834e-35461967a9a9",
   "metadata": {},
   "source": [
    "### 유저의 클래스 분포를 확인해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e07cd39e-4c12-443d-a831-6aa9a4d8a545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(7,7))\n",
    "df_users.user_class.astype('str')\n",
    "ax=sns.barplot(df_users.user_class.astype('str').value_counts().index,df_users.user_class.astype('str').value_counts())\n",
    "\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x() + p.get_width() / 2., height + 3.5, height, ha = 'center', size = 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3cf15fa-53ca-4071-a1ac-cc970b9593d8",
   "metadata": {},
   "source": [
    "class가 높아질수록 그에 해당하는 유저 수도 감소합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b6ed929-6973-4208-bd37-c87a7a79ff56",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e806f9f-b291-4252-bf23-3155d27fc03c",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641c1508-8331-46e1-a31e-3878b2117817",
   "metadata": {},
   "source": [
    "## 🛡 유저 티어 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7363f24e-1a9a-4be9-9ab6-30e7aa9911f1",
   "metadata": {},
   "source": [
    "### 유저 티어 통계를 살펴봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "293a94ef-dce5-4503-ac95-f755780664fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    63876.000000\n",
       "mean         7.585008\n",
       "std          5.018799\n",
       "min          0.000000\n",
       "25%          4.000000\n",
       "50%          7.000000\n",
       "75%         11.000000\n",
       "max         31.000000\n",
       "Name: tier, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.tier.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "09ec3f4a-26a9-41a3-ac12-7095519679dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,7))\n",
    "df_users.tier.astype('str')\n",
    "ax=sns.barplot(df_users.tier.astype('str').value_counts().index,df_users.tier.astype('str').value_counts(), order= df_users.tier.astype('str').unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38eb7c76-3796-4d2d-98fd-0203398d5850",
   "metadata": {},
   "source": [
    "user_class와 달리 tier는 0과 6-7(실버 1~2) 티어에 유저들이 많이 분포해있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b9604e-cdb5-4dfd-9f9d-097036f6f5e3",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5349c3c9-7e6d-4553-ba85-71c0719ef53a",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c67c8a78-f512-42a6-8281-058f408ff6e2",
   "metadata": {},
   "source": [
    "## 🚀 문제 레벨별 데이터 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc26392-d5ed-4e8c-b3b1-7834f30d8720",
   "metadata": {},
   "source": [
    "### 문제 레벨별 문제 평균 시도 횟수를 구해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "56dcfa84-1bf6-4071-beb8-365780435778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "graph = sns.barplot(x=df_problems.level, y=df_problems.average_tries)\n",
    "graph.axhline(np.mean(df_problems.average_tries))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "048ac5ff-af66-4a7a-b870-c45a77887d57",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78fbe68-178d-4f5f-8644-141360d8ac9f",
   "metadata": {},
   "source": [
    "### 문제 레벨별 맞힌 사람 수의 분포를 구해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6b0145a2-b6e4-4db9-8321-aa3c8edb584d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "graph = sns.barplot(x=df_problems.level, y=df_problems.accepted_user_count)\n",
    "graph.axhline(np.mean(df_problems.average_tries))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6542369f-4989-4ee2-a10b-32b0fbe110b9",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ad9cd62-620e-4593-902e-29b49a19809a",
   "metadata": {},
   "source": [
    "### 레벨이 1에서 3인 것을 제외하고 살펴볼까요? 🤔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "094be763-927e-47d2-b795-1035c835d87b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df_problems.groupby('level')['accepted_user_count'].mean()[df_problems.groupby('level')['accepted_user_count'].mean() < 4000]\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "graph = sns.barplot(x=tmp.index, y=tmp.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd929eb",
   "metadata": {},
   "source": [
    "대체로 입문 단계인 하위권 문제가 많이 풀렸고, 하위권 문제를 제외하여도 문제의 난이도가 높아질 수록 맞힌 유저의 수는 급격히 줄어듭니다. 😯"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9186e355-dca5-4f96-a2f8-0e9a355576e8",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e47f3b6-052b-451c-be9f-6afc56e2eb57",
   "metadata": {},
   "source": [
    "### 문제 레벨별 태그를 분석해봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0eef776-316c-40c3-8e32-d33b6e1bfb32",
   "metadata": {},
   "source": [
    "#### 각 문제 레벨별 태그 종류의 수를 집계해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1d50755c-49ac-454a-b59e-808ec3c5ba7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tag에 nan 값이 있는 행들 삭제\n",
    "new_df_problems = df_problems.dropna()\n",
    "\n",
    "# 각 레벨별 태그 유형별 개수 저장\n",
    "tags_per_level = {}\n",
    "\n",
    "for x in df_problems.level.unique():\n",
    "    levelx_tags = ','.join(new_df_problems[new_df_problems.level == x].tags.values).split(',')\n",
    "    levelx_tags = pd.DataFrame(pd.Series(levelx_tags).value_counts())\n",
    "    levelx_tags.reset_index(inplace=True, drop=False)\n",
    "    levelx_tags.columns = ['tag', 'level'+str(x)]\n",
    "    \n",
    "    tags_per_level[\"level\" + str(x)] = levelx_tags"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c547a3d-b06d-4719-82b8-e2faa7a87fa6",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb8fdd6-d696-4858-ab03-0b0ed9d58d54",
   "metadata": {},
   "source": [
    "#### 레벨 1의 태그별 분포는 어떻게 될까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e9c490de-d172-4eb7-b517-d8a4726d26ab",
   "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>tag</th>\n",
       "      <th>level1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>math</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arithmetic</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>implementation</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arbitrary_precision</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>combinatorics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>simulation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   tag  level1\n",
       "0                 math      47\n",
       "1           arithmetic      45\n",
       "2       implementation      41\n",
       "3  arbitrary_precision       9\n",
       "4        combinatorics       1\n",
       "5           simulation       1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# level1인 경우\n",
    "tags_per_level['level1']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cf0b3a9-215a-4435-a4b5-adfe48fdb293",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba4962f-dab2-4553-9d79-5c4c64ce4da3",
   "metadata": {},
   "source": [
    "#### 문제의 레벨별로 몇 종류의 태그가 속해 있을까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e9815437-e545-48fd-9145-e02a4beb1857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bKZIkSZIkqU79JH4XR8QHgQ0j4mjgSuCMZoolSZIkSarLKu/qOSYz3x0RzwLuArYDTszMK6YaOCI2BM4EHk11FPElwPXARcA84GfAYZn5q6nGkGaqc87ds7F5H3nE5Y3NW5IkSc3o5+YurwZ+kJnHZ+ZrB0n6ivcCn8vM7YHHAD8ETgA+n5kLgM+XYUmSJEnSAPo51XN94PKI+EpEvDwi5k41aERsADwNOAsgM+/JzF8Di4Bzy2TnAgdNNYYkSZIkqTLpxC8z35KZOwLHAlsAX46IK6cYdz6wHDgnIq6NiDPLzWLmZuYvyzS3AVNOLiVJkiRJlb4e4F4so0rK/hfYbIpxZwOPBz6QmY+jekTEg07r7L2D6HgRsTgiro6Iq5cvXz7FIkiSJEnSzNDPNX7HRMSXqK692wQ4OjN3mmLcW4BbMvMbZXgJVSJ4e0RsUeJtQZVk/oXMPD0zF2bmwjlz5kyxCJIkSZI0M0z6rp7A1sCrMvPbgwbNzNsi4uaI2C4zrwf2AH5QXkcA7yx/Lx00liRJkiTNdP08zuENEfGUiDgyM8+JiDnAepl54xRjvwI4PyLWAG4AjqQ6AnlxRBwF3AQcNsV5S9PKknP2bmzehx75ucbmLUmSpG6YdOIXEScBC6me4XcOsDrwEWC3qQQuRw4XTvDRHlOZnyRJkiRpYv3c3OVg4ECqG7GQmb+gesSDJEmSJGka6yfxu6f3Tpvl8QuSJEmSpGmun8Tv4oj4ILBhRBwNXAmc0UyxJEmSJEl16efmLu+OiGcBd1Fd53diZl7RWMkkSZIkSbXo53EOlETPZE+SJEmSRsgqE7+IuJtyXd/4j4DMzIfUXipJkiRJUm1WmfhlpnfulCRJkqQR1s/NXSRJkiRJI8jET5IkSZI6rq+bu0hdcdlZ+zY2772O+mxj85YkSZKmwiN+kiRJktRxJn6SJEmS1HEmfpIkSZLUcSZ+kiRJktRxJn6SJEmS1HEmfpIkSZLUcSZ+kiRJktRxJn6SJEmS1HEmfpIkSZLUcSZ+kiRJktRxJn6SJEmS1HGzh10ACeBrp+/f2Lx3XfzpxuYtSZIkjQKP+EmSJElSx3nET5IkSdKMcfupX2pkvnOP272R+dbFI36SJEmS1HEmfpIkSZLUcSZ+kiRJktRxQ038ImJWRFwbEZ8uw/Mj4hsRsTQiLoqINYZZPkmSJEnqgmEf8Xsl8MOe4XcBJ2fmtsCvgKOGUipJkiRJ6pChJX4RsRWwH3BmGQ7gGcCSMsm5wEFDKZwkSZIkdcgwj/idArwO+HMZ3gT4dWbeW4ZvAbac6B8jYnFEXB0RVy9fvrzxgkqSJEnSKBtK4hcR+wPLMvOaqfx/Zp6emQszc+GcOXNqLp0kSZIkdcuwHuC+G3BgROwLrAU8BHgvsGFEzC5H/bYCbh1S+SRJkiSpM4ZyxC8z35CZW2XmPOC5wBcy83Dgi8ChZbIjgEuHUT5JkiRJ6pJh39VzvNcDr46IpVTX/J015PJIkiRJ0sgb1qme98vMLwFfKu9vAJ44zPJIkiRJUtdMtyN+kiRJkqSamfhJkiRJUseZ+EmSJElSx5n4SZIkSVLHmfhJkiRJUseZ+EmSJElSx5n4SZIkSVLHDf05fpq+fnTaokbmu/2xlzYyX0mSJGm6WXbafzYy382OPaCv6T3iJ0mSJEkdZ+InSZIkSR1n4idJkiRJHWfiJ0mSJEkdZ+InSZIkSR1n4idJkiRJHWfiJ0mSJEkdZ+InSZIkSR1n4idJkiRJHWfiJ0mSJEkdZ+InSZIkSR1n4idJkiRJHWfiJ0mSJEkdZ+InSZIkSR1n4idJkiRJHWfiJ0mSJEkdZ+InSZIkSR03e9gF0OT94rRXNzLfhx77b43MV5IkSdL0MJQjfhGxdUR8MSJ+EBHXRcQry/iNI+KKiPhJ+bvRMMonSZIkSV0yrFM97wVek5k7ALsAx0bEDsAJwOczcwHw+TIsSZIkSRrAUBK/zPxlZn6rvL8b+CGwJbAIOLdMdi5w0DDKJ0mSJEldMvSbu0TEPOBxwDeAuZn5y/LRbcDcYZVLkiRJkrpiqIlfRKwHXAK8KjPv6v0sMxPIFfzf4oi4OiKuXr58eQsllSRJkqTRNbTELyJWp0r6zs/Mj5fRt0fEFuXzLYBlE/1vZp6emQszc+GcOXPaKbAkSZIkjahh3dUzgLOAH2Zm77MEPgUcUd4fAVzadtkkSZIkqWuG9Ry/3YAXAt+LiG+XcW8E3glcHBFHATcBhw2neJIkSZLUHUNJ/DLzq0Cs4OM92iyLJEmSJHXd0O/qKUmSJElqlomfJEmSJHWciZ8kSZIkdZyJnyRJkiR1nImfJEmSJHWciZ8kSZIkdZyJnyRJkiR1nImfJEmSJHXcUB7g3hXL/uPURua72cuOa2S+kiRJkmYmj/hJkiRJUseZ+EmSJElSx5n4SZIkSVLHmfhJkiRJUseZ+EmSJElSx5n4SZIkSVLHmfhJkiRJUseZ+EmSJElSx5n4SZIkSVLHmfhJkiRJUseZ+EmSJElSx80edgHqtPwDH2lkvnP+7gWNzFeSJEmS2uARP0mSJEnqOBM/SZIkSeo4Ez9JkiRJ6jgTP0mSJEnqOBM/SZIkSeo4Ez9JkiRJ6rhpl/hFxN4RcX1ELI2IE4ZdHkmSJEkaddMq8YuIWcBpwD7ADsDzImKH4ZZKkiRJkkbbtEr8gCcCSzPzhsy8B7gQWDTkMkmSJEnSSJtuid+WwM09w7eUcZIkSZKkKYrMHHYZ7hcRhwJ7Z+ZLy/ALgSdl5svHTbcYWFwGtwOun0K4TYE7Biiu8YzXhVjGM57xZk68Li+b8YxnvOHF6/KyjVK8h2XmnJVNMHtq5WnMrcDWPcNblXEPkpmnA6cPEigirs7MhYPMw3jGG/VYxjOe8WZOvC4vm/GMZ7zhxevysnUt3nQ71fObwIKImB8RawDPBT415DJJkiRJ0kibVkf8MvPeiHg5cBkwCzg7M68bcrEkSZIkaaRNq8QPIDM/C3y2hVADnSpqPON1JJbxjGe8mROvy8tmPOMZb3jxurxsnYo3rW7uIkmSJEmq33S7xk+SJEmSVDMTP3VGRMSwy6DRYpup17DWp/Wo6cY2WS/7FqkyaJs08RsnIlpfJ13tWCJidvnb1vKt1VIcACJipc9KaSDeI9qOOQwtfx82KTFntRizNUPoWzYrcddoI1hEbA+QmdnhfrTV5Rrrt1uKNT8i5kTEei3Eart92LfUy76lZvYttcVqu32sP8g/m/gVEbEVQGb+uY3kLyJ2jogDI+Kx2cKFlhGxS0S8KCJ2aTpWifdM4K0RMbeNjjMi9gbOiIi12vgSRsSBwGciYus2OrOI2Ae4FnhqGW56ff51RPxzRBzaUse5sHwftgda6UQjYj/gkxFxGvCPETG3jbgt2xDa2fgs38ElEfHvwGsiotEdMRGxHfCDUn+tbKBFxGMjYutVT1lbvN2A57S1wRQRzwBOjYh1Woi1F9Xjmt4BnNJCzNYSMfuWetm3NBLPvqU+bfct50XEqRHxgojYuN95mPgBEXEA8MWIOAmaT/7KRvyFwDOokof9y/hGOpbyJTgX2A74QkQ8rcl4xWJgT+DvI2Kr0nE28qUoPwonAedm5h+bTqRL8vwO4E2ZeXNm3ttwvL2Bf6BqM6+PiIc2uYwl3lnA74G3ALs2FavE2wtYAuwM/DtwfEQ8quGYC4DTgBOBT1A9PuaSiNiywZh7jX3X21Dq8cKI+CBwXJMJfNnR8y9U7eWrwHzgnqbiFX8CrgAWRcT5UG2gNRWstNOPARs0FWNcvP2AjwLLgD/3jG/yd+JDwPbAxg3H2gk4FXglcArwh8z8fVMboeW78NGIeDvwxojYrIk4JZZ9S72x7Fvqj2ffUl+8NvuWHYEzgfcB1wM7Au+Nfs8Ey8wZ+6I6srAN8EPgzWWFntjz+WoNxNwRuA746zL8nFKBGza0jH8FLAWeXob/ATgK2LqheKuVv4upNuJfA7wHWBuY3UD9bU/Vce1dxm0BPB54dN3xeuLuAZxQ3m8DHAscBDyhgVgLgZ8ATyvDHwQOKO9nNbA+1wcuAPYp414FvBF4AvDQhuJ9FNizjHtqaa8nA49pov5KnLnAB8v71ag2zt4KXAVs1kC83Uo7vXus/pp8Ac8q/cozgBdR/RAubCjWmqW+nlmGF5Q+7iTgFcCcBpfztVQbS98E3g88sol4wNPL93D3MrxW+Vvrd7DMM4CHUG0I7lHGrUd1Kvu6ZbjW3yZgP+AbwO5Uv4MfaqrOSrxdgVPK+4cDPy9t9EJgy5pj/RXw07JsTyht9XJgbkPLtrl9Sy1xwr6lkXU6E/qW95b3XetbHgV8YKx9lOV7B3AesNFk5zOjj/hl5efAMVR76M4BHhURJ5bP/7yy/5+i24F/zMwvR8SszLyI6kvfVF38APibzPxiRGxDlfg9DbgoIl4REQOdKzxezzr7H2ANqi/8b4CzgXMjYp26jvyV+vsRcDFwQtnTeiHVD8IS4JiIWLeOWOPMBfYop++cTXUk9UBg8djR1Br9iqr+rirDtwN/B5CZ99UZqKzPu4HbgBdFxGOpdohsC7wTeHk5BabueL+mStbJzK8A/031Q7Qr1H/dbZnffcDCiPjbzPxzWZdvBb4CHFlnzDKvbYG9gYOpTndZVD6rfa9nVNfAHAK8LTO/kJnnUf24N3JEIDP/BLwhM6+MiA2BDwAXUW2gPRR4S0SsXle8cXWzgGon2hOo1u+PqH4Ma1u3peyHUPVpX43qsoCTI+I9wEl9721dhfK9uAu4A/jf0m9/hmpH2pcj4mFZnZUy8PJFZR2qPuWNmfklqiMrmzfQl/W6jep79l6qDevTqHZqLQXOqvnUrHWByzLzS5n5TeBrVBv050XEpjXGGZNUfcvLWuhbZtFS31LayprAs2mhbynfgz9RnVlzZURsRMN9Czyof2mzb/km9i11WQa8OCJOpfm+ZT3a7VvuBp4VEc/PzPsy8waqs7Nuo9oRMykzNvGLiF0j4hKAzPxiZi4Hvk71Bbg/+YuI7SJikxriPTkiLszMO4DPRkT0bLivQ5VMjN3AY82a4l1cGse15TD3I4DXZuYRwPHAkVRHxgZW1udFPaN+TXUU86vArcABVHvvepd70HgfB8jM55YY1wMXZ+aRwEuBF1Pv8l1c4n0UuIHqAZuXZ+ZxVHsgfwXMqynekyPio5n508z89libyMwTgXUj4m/riNMTb9eIWFIGT6PqYE4E3p+ZL6E6bWJBedUd7z+Bh0XEGWVDcG2qZP7lEbF5XTtgouc63vI9XAy8NiJeVMbfS7WjYos6d/qUeS0BvpWZV1IdRT05Ig7OsusuarxhQWbeA7wJ+FLPRszlVHt6KfEG3vkytj6Le0rsXwPHZ+ZbM3MJ1elSkZn/V1e8smEytrF3GbBOVKfXBNWP+7FlulpOzSplfxdwJ9Ue3Sup9vJ+h2rn1ptqTmxXL/313cAzgUOpvg/HUH1XLouIh9SxfGVD8PfAoZn5+RL3N1R96U6lPHVt5N7fXjLzRqpTuy+i6rPflZnXUf3+/qKOeD2WAYdExEvK8GOADwPfolwzPah44Jr9x2fm7bTXt9xHdTrptU33LT2J2IlUfctYH9JE3zK2Ph+Xmb8r8X9Fc31L7z0Xxuqnrb7lX4H/pYW+Be5vD232Lc9poW8Zq7+dM/OnVH3LhTTft9xB833L2P05dsvMW6ja4NFRXaIG1bbocuBJk55pNnjIdTq/qL7IPwY+Mm78msCTqTbqv0h1xGzgw/sl3k+A83rGrVPGf53qvPXnUp0OskGNy/fhnnGzxk1zGjWdGjLR+gTeDpxQGubrgLcB/0QNp2D2xLuwZ9wB46b5D2Dfmpfvo2X4sVR7yr4OrF7G/RPwzrHpG1ifa5a/i8fi1PXqaZ8f6hl3NNWP0tjpuydT/fDWGe/MMvy40kaO71mfp1PT6aVUOx5+Apw0bvzTS/s8pgwfSbUhs14NdbjVSj47CLix9DWHUB1ZHegUm1XEOxD4ZHl/WGlDU16+idbnWL2Nm+4Iqg2KdeuOV8ZvV8bfyQOng/03sGXd9QdsVdrkMT3jnkk59aaGNjo+3qOAm6j2Is/vGX82sGmd8RjXJ1OdzrcceGxNy7ai+lsP+AJwRBl+AVWfunHN63J34GaqjbIrgdWBV48vzxRj7VOW7RSqDcuDetpGU33LzuU7/bgJPmuibxmL9/gJPqu7b+ldn7cC+69gurr6lgnjle9fU33L2Prceay9Amc02Lc8qL200Lf8RfscW2cN9C3jv3+LyviHNNS3jK+73YFbaKZv2YsqUX478AceuETscODLwIvK8FHA+VRnSq2ybQ680kfxRfmRo9r4vJae5KFnmneXytypgXgXjfv8fVQbFFc1HY+S/JUvwfeBeQ3EW1KGj6PaS7ZvGX4SNVzjMEG8S1awfNcBD6s53rcpyTTV0cRzgc9RHRH7MfDIptsn1ak9y1jBD2INy3dxGd6mtM03UyWB1wPb1hzvuzx458TYZy8pn20+YKyVXsdbpnkc1Q/6WWUZ/6qGZZwoMVqtlGcskd6F6pTTXwA7NhWvvN+D6jSp/an2Ji9oYn32LNvs8p24ZpBlW0G8f+z5fBbwcso1zGP/00T9lfHrAWv0DL+YZhPbR5f2cQLVneMOL9/RgTbOVtRexk3zphL3LxL6utpLmWZ34C6qDZcfATs0VHebUN1YYuw34liqDaopX0vFxNfs/5iycUkzfcuKEpVZNNO3rDBe+VtL37KS9fmgeyBQ9S3HMWDfsop4G5e220TfsqJEZQOa6VtWtGNiJ5rpW1bUXlbrmWbgvmUV9bdJGd6devuW8evywDJ+DvX3LSu6P8dDy3fgaWWZzqbaqfXoSc970EY8Sq/eLw89R9WoTsG4uGf4MWWFDtRJ9xHvE1R7lQZKGiYTrzSY51D9EA/6JVhRvP8Bzi7v5w2h/g4qX/5BfxRWtnwf7Rk+CnghsH0by1fGHdpwe7mgvD+A6sjwBQ2uz29QdhaU4T2pLsiedEc2idhPL53zbmVZxm98bkC1h3CgHRP0ccMoqnPy7xjkeziJeGM/RAuA31JdZzRQPU5yfW4CfLKuOlxZPMrNEMav34bWZ28bPobqlJ66E9vx63JHqov330OVRDQWb1z7XEx1KuaaLbSXh1HtGJzyTcf6XLaXUB2NG7RP2xQ4pLwf+659mp6jClSPPdiAenZ6TurmcHX0LauKxwM7lRZQnTo4cN8yyfW5CXBpHX3LquLx4ESsjqRvpTsKeqYbuG9ZRbw5PZ+fR3WgY6C+pc/2WUvfMsn2Mo+qb1nhGTEDrstNxk1XV98yiweO0G5DdZf180o9HVfGzy2fbdHXvAdtyKP4Ko3uP6j2OCwo464Bzu+Zpra7N60k3oXl/S7Aw1uIN3akaltgm4bjfZvq8Qpj0wzcaU5i+T5S3m/ZwvJ9i5IcDaN9thDvWuCsnmnWbnP5GPBIX5nHrvQcDS7jZlGdAnX/xifVaS+bDBpvXJyVJSpBtQNm4J0Fk4lXPt+C6tSeQZKGftZnMPje3FXFO6mM267N+iufb0B1bUzjiW35fOxOfwOdpjTZ9tnzfsrfwz7qbwdquLyhj3W5DvAGYLsBYjyZB36/1xq3zr4APKq8X0ANiXPPvCezobs61d02B+5bJhnvoVQ76gbpWya9PseWccDlWlW8Hcr7bYdQfxvW1bdMMt7a5e/Afctk4vVMO0jfMtn28khqujvqJNdlXX1L74GM2VR92tgp47tRbZ89Zcox6lgho/SiOnz+Vars/Wc8+Afvp5RkhZoSlVXEu4Fy2+eWlu8G4IwW4y2lHPlrcfnObLm9nNPheDe0/H24//tXU6y2r+OdbGK0PeX63pbijW3ErDFgvGFcFz0d628ssR3oWuU+20uby7cd9Rydaq3++lyXa1LPkeE2r9mf7IbutmPlayneI8fqtKX1+RVqePTVNK6/scR20L6lnx0TA+/U7SPeI4B1Rqn+plB3dfQtjd6fY6CVP4ovqhuM7ER1PvMVVNn0rJ7P57ccr7YjfdM0XtfXZ9vLZ7zJxWn1Ot6eWNMxMfohg5/COuzromdy/Y1cvCG0l2EuW6PX7PfEmuyG7oYtxhs4EWt7fU7z+hvVxLbNRKzt9jItvgvUeH+OgRv0qLyo9ghuQXU91rXAFT2fvRo42njGM95ox6Pl63h75tfJxKjt9Wn9jXa8YdTfNFy2T1DDNfsrWb5OJEZtr0/rb7TjDaP+ptOyUef9OQZdMaPwAjajuvPNE6julPNfVKecrV1W5Lcph2uNZzzjjXa8ErOV63iZOYnRdLku2vqbxvHarL9puGy1XrPfx/KNemI0Xe6BYP1N43ht1t80XLZa788xcEFH5UX1gO0rqG5x/hzgVKrzc6+kph8E4xnPeMOPR8vX8ZZ5dTIxGsb6tP5GO94Q2st0Wbbar9lfxfKNdGI0jPVp/Y12vCG0l+m0bLXdn6PWBj7dXlRHGPbuGX4/DzzDZC2qO389xHjGM97ox+uJ0/Z1iyvrsEc+MRrC+rT+Rjtea/U3DZet7mvMV7Z8I58YDWF9Wn+jHa+1+uvyss2mQyIisqyhiFgLOBhYEBHPp3r44a1UD8T9ZGb+Efij8YxnvNGMN0H8Xak66BuBc4E7MvNZ5bNXR8TdmXlGZt5YZ1yq0y+OAR5LdRH4OyJiVmbel5mPiIj5AGPrZlTitb0+rb/Rjjek+ptuy3bDIHEmsLLle3hEPHwU47W9Pq2/0Y43pPrr7rLVlUEO+8WDz8ndjeqW21tTHVk4E3g71TM3/gzsbzzjGW90400QfxjXEXbyBjnDWJ/W32jHG0J76eyy2VbsW4w31PbS2WW7P27dM2z7xbhTOIDjqW7b/GHg45TTSqiy9yOB7zDAqSbGM57xhhdvFWU5ifauI5wJGy+trU/rb7TjtVl/XV4224p9i/GGV39dXrYHxWxipm2+eOB2q7OoDsleWob/GfgUf3me7LrGM57xRjPeBPGHch1hmX/nNl7aXp/W32jHG1b9dXnZbCu1LZP1N8Lx7FuaaZuZWe2tH1URsSlwNfD4zLwzInYAjgYS2J5qRd4TEXsBX8nM3/deh2Q84xlvdOKVmOOvIzwRWAD8geo6whdRHYV821RjrCL+E4BNMvNzZfj9wOWZ+clSnvWBP2XmXaMQr+31af2Ndrxh1l+Xl63EtK0MwPob7Xj2Lc21zfFWayNIUzLzDuAVwNciYqPM/AHVodonAEeWjc6jqe6Ws3b5nylvdBrPeMYbXrxxneduwHzgA8ALgXuAl1Gdn//WiNh/qnHGx+x5P3bDmqMi4ryI2JoHblhDZv4xM5cP8kPbZry216f1N9rxhtFeet53atnGYva8t60MwPob7Xj2LfW2zVWWaYDtsGkjIvahOhz7OOCJwPOpDpfeBBwEHJaZ1xnPeMYbvXi9HWcZPh7YF7gFWBd4TWbeGBHbAk8FXkV1tPHGqcYcH7d02HcCvwWWA+8Dbqd6OPS+wIGZ+elRiNf2+rT+RjveMOqvy8s2Pq5txb5lpsazb6m/bU5KNnQOaduvskJ/CKwDbEl1I4nFwLbGM57xRjce7V+32Okb5AxhfVp/ox2vtfrr8rLZVuxbjDe8+uvysvVdtrYCtbIwsB9wHbCx8YxnvNGPB2xK9YybjcvwDsDJwL8BnwXWKOP3AtYp72PAmJ3deGl7fVp/ox1vCO2ls8tmW7FvMd5Q20tnl63v8rUVqLUFgkVUz95YrY0VaTzjGa/ZeMABwPXARmX4fOArwNwyfDTwTaqLsweNNRM2Xlpbn9bfaMdrs/66vGy2FfsW4w2v/rq8bFMqW9sBW1koWM94xjNed+IB+wA/AdYDnkH1UPiPUD0Y/jpgxxpjdXbjZRjr0/ob7Xht1l+Xl822Yt9ivOHVX5eXrd9XlMJJ0rQWEfsC7wF2BjYC9gRWB76QmUtrjtWpG+SsIGZr67PteF2vvy63ly4vW4llW6mZ9Te68UpM+5YWmfhJGhkRsR/wL8BTM/POhmN1duOlJ2Zr67PteF2vvy63ly4vW4llWxnheF2vvy63ly4v26QN4zCjL1++fE31RYvXLdKBG+RMp/Vp/Y12vDbrr8vLZlsZ/Xhdr78ut5cuL9tkXh7xkzRyImK9zPxtS7EWAW+m2kOY2XCn2Xa8ErO19dl2vK7XX5fbS5eXrcSyrYxwvK7XX5fbS5eXbZXlMPGTpJXr8sbLTND1+utye+nysoFtZdR1vf663F66vGwrY+InSZIkSR232rALIEmSJElqlomfJEmSJHWciZ8kSZIkdZyJnyRJE4iI2i/8b2KekiRNhomfJEmSJHWciZ8kSasQEcdHxDcj4rsR8ZYy7p0RcWzPNG+OiNeuaHpJkobJxE+SpJWIiD2BBcATgccCO0fE04CLgMN6Jj0MuGgl00uSNDSzh10ASZKmuT3L69oyvB6wIDPPiojNIuKhwBzgV5l5c0S8cqLpgataLrckSfcz8ZMkaeUC+OfM/OAEn30MOBTYnOoI4KqmlyRpKDzVU5KklbsMeElErAcQEVtGxGbls4uA51Ilfx+bxPSSJA2FR/wkSVqJzLw8Ih4FfC0iAH4LvABYlpnXRcT6wK2Z+ctVTT+UBZAkCYjMHHYZJEmSJEkN8lRPSZIkSeo4Ez9JkiRJ6jgTP0mSJEnqOBM/SZIkSeo4Ez9JkiRJ6jgTP0mSJEnqOBM/SZIkSeo4Ez9JkiRJ6rj/D1TzeLL2SFR3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "level_tags = pd.DataFrame(columns={'level', 'level_tag_count'})\n",
    "\n",
    "for i in range(1, 31):\n",
    "    level_tags = level_tags.append({'level': 'level'+str(i), 'level_tag_count': len(tags_per_level['level'+str(i)])}, ignore_index=True)\n",
    "    \n",
    "plt.figure(figsize=(15,5))\n",
    "graph = sns.barplot(x=level_tags.level, y=level_tags.level_tag_count)\n",
    "\n",
    "for item in graph.get_xticklabels():\n",
    "    item.set_rotation(45)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18c310f-dc0a-4de4-804c-bf9f4fdcff93",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39360876-eca2-4358-b171-290c6859e352",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28cf43b3-308a-41f4-94f3-6af0036ed876",
   "metadata": {},
   "source": [
    "## 🔑 태그별 문제 레벨 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6054aa7-c576-47b4-83db-f52f5e35d946",
   "metadata": {},
   "source": [
    "### 각 태그별로 각 레벨에 몇 번 등장하는지를 구해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "917e0751-9491-4d58-a8d7-18dc6a76b29e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 각 태그별 모든 레벨 값을 merge한다.\n",
    "tags_all_level = pd.merge(tags_per_level['level1'], tags_per_level['level2'], on='tag', how='outer')\n",
    "\n",
    "for i in range(3, 31):\n",
    "    tags_all_level = pd.merge(tags_all_level, tags_per_level['level'+str(i)], on='tag', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "84aca0e3-37c7-48e3-9388-424b2b6328a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tag</th>\n",
       "      <th>level1</th>\n",
       "      <th>level2</th>\n",
       "      <th>level3</th>\n",
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       "      <th>level8</th>\n",
       "      <th>level9</th>\n",
       "      <th>...</th>\n",
       "      <th>level21</th>\n",
       "      <th>level22</th>\n",
       "      <th>level23</th>\n",
       "      <th>level24</th>\n",
       "      <th>level25</th>\n",
       "      <th>level26</th>\n",
       "      <th>level27</th>\n",
       "      <th>level28</th>\n",
       "      <th>level29</th>\n",
       "      <th>level30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>math</td>\n",
       "      <td>47.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>...</td>\n",
       "      <td>140.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arithmetic</td>\n",
       "      <td>45.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>implementation</td>\n",
       "      <td>41.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arbitrary_precision</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>combinatorics</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   tag  level1  level2  level3  level4  level5  level6  \\\n",
       "0                 math    47.0    91.0   340.0   280.0   179.0   156.0   \n",
       "1           arithmetic    45.0    65.0   224.0   129.0    48.0    17.0   \n",
       "2       implementation    41.0    66.0   277.0   608.0   418.0   286.0   \n",
       "3  arbitrary_precision     9.0     2.0     7.0    13.0     4.0    14.0   \n",
       "4        combinatorics     1.0     1.0     4.0     7.0    10.0     6.0   \n",
       "\n",
       "   level7  level8  level9  ...  level21  level22  level23  level24  level25  \\\n",
       "0   175.0   170.0   175.0  ...    140.0    122.0     90.0     67.0     64.0   \n",
       "1    13.0    13.0    11.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "2   242.0   206.0   163.0  ...     34.0     31.0     21.0     29.0     20.0   \n",
       "3    12.0    10.0     5.0  ...      7.0      5.0      3.0      1.0      1.0   \n",
       "4    14.0    22.0    13.0  ...     27.0     19.0     24.0     13.0     17.0   \n",
       "\n",
       "   level26  level27  level28  level29  level30  \n",
       "0     45.0     29.0     10.0      6.0      4.0  \n",
       "1      0.0      0.0      0.0      0.0      0.0  \n",
       "2     12.0      4.0      6.0      5.0      2.0  \n",
       "3      3.0      1.0      1.0      0.0      1.0  \n",
       "4     12.0      8.0      2.0      2.0      1.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_all_level.fillna(0, inplace=True)\n",
    "tags_all_level.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "187d6e27-260a-4e0c-88d8-5ddd1fdb1495",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b87e68d-4930-4ea3-ad0d-6c1027450b98",
   "metadata": {},
   "source": [
    "### 각 태그별로 각 레벨에 어떻게 분포되어 있는지를 확인해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "27874fb8-ef94-4b22-9109-96e6fba1c92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 각 레별별 태그의 비율 분포를 확인하기 (상대비교를 위해 각 레벨의 태그 개수를 문제 수 만큼 나누기)\n",
    "tags_all_level_ratio = tags_all_level.copy()\n",
    "\n",
    "# 각 레벨별 문제 수 정리\n",
    "level_ques = pd.DataFrame(new_df_problems.level.value_counts())\n",
    "level_ques.reset_index(inplace=True, drop=False)\n",
    "level_ques.columns = ['level', 'nums']\n",
    "\n",
    "# 각 레벨의 태그 개수에서 문제 수 나누기\n",
    "for i in range(1, 31):\n",
    "    tags_all_level_ratio['level'+str(i)] = tags_all_level['level'+str(i)]/level_ques[level_ques['level'] == i].nums.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ced50222-434a-4df8-8eb7-a174be8f9b38",
   "metadata": {},
   "outputs": [
    {
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       "      <th>level28</th>\n",
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       "      <th>0</th>\n",
       "      <td>math</td>\n",
       "      <td>0.652778</td>\n",
       "      <td>0.739837</td>\n",
       "      <td>0.732759</td>\n",
       "      <td>0.350438</td>\n",
       "      <td>0.324864</td>\n",
       "      <td>0.288355</td>\n",
       "      <td>0.313620</td>\n",
       "      <td>0.292096</td>\n",
       "      <td>0.302245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238908</td>\n",
       "      <td>0.240631</td>\n",
       "      <td>0.248619</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.268908</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.302083</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.307692</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arithmetic</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.528455</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>0.161452</td>\n",
       "      <td>0.087114</td>\n",
       "      <td>0.031423</td>\n",
       "      <td>0.023297</td>\n",
       "      <td>0.022337</td>\n",
       "      <td>0.018998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>implementation</td>\n",
       "      <td>0.569444</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.596983</td>\n",
       "      <td>0.760951</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.528651</td>\n",
       "      <td>0.433692</td>\n",
       "      <td>0.353952</td>\n",
       "      <td>0.281520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058020</td>\n",
       "      <td>0.061144</td>\n",
       "      <td>0.058011</td>\n",
       "      <td>0.108209</td>\n",
       "      <td>0.084034</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arbitrary_precision</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.016260</td>\n",
       "      <td>0.015086</td>\n",
       "      <td>0.016270</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>0.025878</td>\n",
       "      <td>0.021505</td>\n",
       "      <td>0.017182</td>\n",
       "      <td>0.008636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011945</td>\n",
       "      <td>0.009862</td>\n",
       "      <td>0.008287</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.004202</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.010417</td>\n",
       "      <td>0.021277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>combinatorics</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.008130</td>\n",
       "      <td>0.008621</td>\n",
       "      <td>0.008761</td>\n",
       "      <td>0.018149</td>\n",
       "      <td>0.011091</td>\n",
       "      <td>0.025090</td>\n",
       "      <td>0.037801</td>\n",
       "      <td>0.022453</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046075</td>\n",
       "      <td>0.037475</td>\n",
       "      <td>0.066298</td>\n",
       "      <td>0.048507</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>suffix_tree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>directed_mst</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011696</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>a_star</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>rb_tree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>top_tree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tag    level1    level2    level3    level4    level5  \\\n",
       "0                   math  0.652778  0.739837  0.732759  0.350438  0.324864   \n",
       "1             arithmetic  0.625000  0.528455  0.482759  0.161452  0.087114   \n",
       "2         implementation  0.569444  0.536585  0.596983  0.760951  0.758621   \n",
       "3    arbitrary_precision  0.125000  0.016260  0.015086  0.016270  0.007260   \n",
       "4          combinatorics  0.013889  0.008130  0.008621  0.008761  0.018149   \n",
       "..                   ...       ...       ...       ...       ...       ...   \n",
       "179          suffix_tree  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "180         directed_mst  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "181               a_star  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "182              rb_tree  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "183             top_tree  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "       level6    level7    level8    level9  ...   level21   level22  \\\n",
       "0    0.288355  0.313620  0.292096  0.302245  ...  0.238908  0.240631   \n",
       "1    0.031423  0.023297  0.022337  0.018998  ...  0.000000  0.000000   \n",
       "2    0.528651  0.433692  0.353952  0.281520  ...  0.058020  0.061144   \n",
       "3    0.025878  0.021505  0.017182  0.008636  ...  0.011945  0.009862   \n",
       "4    0.011091  0.025090  0.037801  0.022453  ...  0.046075  0.037475   \n",
       "..        ...       ...       ...       ...  ...       ...       ...   \n",
       "179  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "180  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "181  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "182  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "183  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "\n",
       "      level23   level24   level25   level26   level27   level28   level29  \\\n",
       "0    0.248619  0.250000  0.268908  0.263158  0.302083  0.212766  0.352941   \n",
       "1    0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2    0.058011  0.108209  0.084034  0.070175  0.041667  0.127660  0.294118   \n",
       "3    0.008287  0.003731  0.004202  0.017544  0.010417  0.021277  0.000000   \n",
       "4    0.066298  0.048507  0.071429  0.070175  0.083333  0.042553  0.117647   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "179  0.000000  0.003731  0.000000  0.017544  0.000000  0.000000  0.000000   \n",
       "180  0.000000  0.003731  0.000000  0.011696  0.000000  0.000000  0.000000   \n",
       "181  0.000000  0.000000  0.004202  0.000000  0.000000  0.000000  0.000000   \n",
       "182  0.000000  0.000000  0.000000  0.005848  0.000000  0.000000  0.000000   \n",
       "183  0.000000  0.000000  0.000000  0.005848  0.000000  0.000000  0.000000   \n",
       "\n",
       "      level30  \n",
       "0    0.307692  \n",
       "1    0.000000  \n",
       "2    0.153846  \n",
       "3    0.076923  \n",
       "4    0.076923  \n",
       "..        ...  \n",
       "179  0.000000  \n",
       "180  0.000000  \n",
       "181  0.000000  \n",
       "182  0.000000  \n",
       "183  0.076923  \n",
       "\n",
       "[184 rows x 31 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_all_level_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3da34dd9-190e-4448-a56b-d992835aa7c0",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de19553-3932-4060-bdae-c6ee6d1821bf",
   "metadata": {},
   "source": [
    "#### 가장 대표적인 태그인 data_structures를 예시로 상위 레벨에서의 분포를 확인해볼까요? 🤨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c0cd58fb-50ed-4e4d-830f-27a565da0b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag_name = 'data_structures'\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "\n",
    "x = tags_all_level_ratio[tags_all_level_ratio['tag'] == tag_name].T.iloc[1:,:]\n",
    "x.columns = ['ratio']\n",
    "x = x.sort_values('ratio', ascending=False)[:10]\n",
    "\n",
    "ratio = x['ratio'].values\n",
    "labels = x.index\n",
    "\n",
    "#explode = [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]\n",
    "\n",
    "plt.pie(ratio, labels=labels, autopct='%.1f%%')#, explode=explode)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13bb70d",
   "metadata": {},
   "source": [
    "대표적으로 기본이 되는 태그인 data_structures는 난이도별 등장 분포가 골고루 나오기도 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6986dc0c-a148-4c34-a816-9b8fb43e77d7",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b00c2a-433f-4f2d-81df-2cc8b1f98839",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeda9950-af49-422b-8284-66c978696dc0",
   "metadata": {},
   "source": [
    "## 📨 각 문제의 태그 종류 수 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dade5b0-8e18-4f68-9725-a9f2623e04e2",
   "metadata": {},
   "source": [
    "### 각 문제에 태그가 몇 종류 등장하는지를 확인해볼까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a46f6c7d-6aba-485c-a585-ec4fdb89f35d",
   "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>id</th>\n",
       "      <th>problem_id</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "      <th>is_solvable</th>\n",
       "      <th>accepted_user_count</th>\n",
       "      <th>level</th>\n",
       "      <th>average_tries</th>\n",
       "      <th>tag_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>A+B</td>\n",
       "      <td>arithmetic,implementation,math</td>\n",
       "      <td>True</td>\n",
       "      <td>175152</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1001</td>\n",
       "      <td>A-B</td>\n",
       "      <td>arithmetic,implementation,math</td>\n",
       "      <td>True</td>\n",
       "      <td>146736</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1002</td>\n",
       "      <td>터렛</td>\n",
       "      <td>geometry,math</td>\n",
       "      <td>True</td>\n",
       "      <td>25716</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1003</td>\n",
       "      <td>피보나치 함수</td>\n",
       "      <td>dp</td>\n",
       "      <td>True</td>\n",
       "      <td>34653</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1004</td>\n",
       "      <td>어린 왕자</td>\n",
       "      <td>geometry</td>\n",
       "      <td>True</td>\n",
       "      <td>8438</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  problem_id    title                            tags  is_solvable  \\\n",
       "0   1        1000      A+B  arithmetic,implementation,math         True   \n",
       "1   2        1001      A-B  arithmetic,implementation,math         True   \n",
       "2   3        1002       터렛                   geometry,math         True   \n",
       "3   4        1003  피보나치 함수                              dp         True   \n",
       "4   5        1004    어린 왕자                        geometry         True   \n",
       "\n",
       "   accepted_user_count  level  average_tries  tag_num  \n",
       "0               175152      1              2        3  \n",
       "1               146736      1              1        3  \n",
       "2                25716      7              4        2  \n",
       "3                34653      8              3        1  \n",
       "4                 8438      8              2        1  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "problem_tags = df_problems.tags.str.split(',')\n",
    "problem_tags.fillna('0', inplace=True)\n",
    "df_problems['tag_num'] = problem_tags.apply(lambda x: len(x))\n",
    "df_problems.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c15ca91-1890-459a-86ab-7dd141420c4b",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd0ba535-9293-42b1-87b3-48154942c3a6",
   "metadata": {},
   "source": [
    "### 태그가 없는 문제도 tag_num이 1로 집계되는 현상을 없애볼까요? 😀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cc519297-6144-4210-9cb8-14153c4e6315",
   "metadata": {},
   "outputs": [],
   "source": [
    "# null인 인덱스의 태그 값 제거\n",
    "tags_null_idx = df_problems[df_problems.tags.isnull()].index\n",
    "df_problems.loc[tags_null_idx, 'tag_num'] -= 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef05934-1521-45fd-91aa-e6fb18fc1bb7",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c920b1-2580-4473-8c6a-a8b12341f6b6",
   "metadata": {},
   "source": [
    "### 문제별 태그 종류 수 분포를 알아봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bc29f5a3-b3e5-4480-a956-a5ce937cb1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f78363833a0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUM0lEQVR4nO3df/BddX3n8efbb0RMpQZjIAxh5WvDbEvbKbop0uruKKwY3CrYqqXj1LRDk51p3NFp6y50Z4ddlV2d3Yp2m9oBYY2OK1J/FLSCpYC1dSoQ0AWBunzXa4ZkCAkJoi4rNl/e+8f9JFxjvuSSfM995977fMx8557zOZ9z7vvMHV6cfO7nnBuZiSRp9J5VXYAkTSsDWJKKGMCSVMQAlqQiBrAkFVlSXUAX1q5dmzfeeGN1GZK0TxyscSKvgB955JHqEiTpkCYygCVpHBjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFZnIp6Edjebn5+n1egDMzs4yMzNTXJGkal4Bj0iv12P9phtYv+mG/UEsabp5BTxCS5evrC5B0lHEK2BJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKtJ5AEfETER8LSI+39ZnI+K2iJiLiE9GxDGt/Tltfa5tP3XgGJe09m9GxGu6rlmSRmEUV8BvB+4fWH8fcHlmrgYeBS5q7RcBj7b2y1s/IuJ04ELgZ4G1wJ9GxMwI6pakTnUawBGxCvhXwIfbegBnA59qXTYDF7Tl89s6bfs5rf/5wDWZ+URm9oA54Mwu65akUej6CvgDwL8Fnmzry4HvZObetr4NOLktnww8CNC2P9b6728/yD77RcSGiNgSEVt27dq1yKchSYuvswCOiF8BdmbmnV29x6DMvCIz12TmmhUrVoziLSXpiCzp8NgvB14fEa8FjgV+EvggsCwilrSr3FXA9tZ/O3AKsC0ilgDPB3YPtO8zuI8kja3OroAz85LMXJWZp9L/Eu2WzHwLcCvwxtZtHXBdW76+rdO235KZ2dovbLMkZoHTgNu7qluSRqXLK+CF/Dvgmoh4D/A14KrWfhXwsYiYA/bQD20y896IuBa4D9gLbMzM+dGXLUmLayQBnJlfAr7Ulr/FQWYxZOYPgDctsP9lwGXdVShJo+edcJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIkuqC9CPmp+fp9fr7V+fnZ1lZmamsCJJXTGAjzK9Xo/1m25g6fKVPL57B1duPI/Vq1dXlyWpAwbwUWjp8pUcd8Kq6jIkdcwxYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklSkswCOiGMj4vaI+F8RcW9E/KfWPhsRt0XEXER8MiKOae3PaetzbfupA8e6pLV/MyJe01XNkjRKXV4BPwGcnZm/AJwBrI2Is4D3AZdn5mrgUeCi1v8i4NHWfnnrR0ScDlwI/CywFvjTiJjpsG5JGonOAjj7vt9Wn93+Ejgb+FRr3wxc0JbPb+u07edERLT2azLziczsAXPAmV3VLUmj0ukYcETMRMTXgZ3ATcD/Ab6TmXtbl23AyW35ZOBBgLb9MWD5YPtB9hl8rw0RsSUituzatauDs5GkxdVpAGfmfGaeAayif9X60x2+1xWZuSYz16xYsaKrt5GkRTOSWRCZ+R3gVuCXgGURsaRtWgVsb8vbgVMA2vbnA7sH2w+yjySNrS5nQayIiGVt+bnAq4H76QfxG1u3dcB1bfn6tk7bfktmZmu/sM2SmAVOA27vqm5JGpUlh+5y2E4CNrcZC88Crs3Mz0fEfcA1EfEe4GvAVa3/VcDHImIO2EN/5gOZeW9EXAvcB+wFNmbmfId1S9JIdBbAmXk38JKDtH+Lg8xiyMwfAG9a4FiXAZctdo2SVMk74SSpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKtLlj3JOpPn5eXq93v712dlZZmZmCiuSNK4M4Geo1+uxftMNLF2+ksd37+DKjeexevXq6rIkjSED+DAsXb6S405YVV2GpDHnGLAkFTGAJanIUAEcES8fpk2SNLxhr4D/+5BtkqQhPe2XcBHxS8AvAysi4vcGNv0k4NwrSToCh5oFcQzwvNbvuIH27wJv7KooSZoGTxvAmfk3wN9ExEcyc+uIapKkqTDsPODnRMQVwKmD+2Tm2V0UJUnTYNgA/nPgz4APA/PdlSNJ02PYAN6bmR/qtBJJmjLDTkP7XET8bkScFBEv2PfXaWWSNOGGvQJe117fOdCWwIsXtxxJmh5DBXBmznZdiCRNm6ECOCLeerD2zPzo4pYjSdNj2CGIXxxYPhY4B7gLMIAl6TANOwTxbwbXI2IZcE0XBUnStDjcx1H+X8BxYUk6AsOOAX+O/qwH6D+E52eAa7sqSpKmwbBjwP9tYHkvsDUzt3VQjyRNjaGGINpDef6B/hPRjgd+2GVRkjQNhv1FjDcDtwNvAt4M3BYRPo5Sko7AsEMQ/x74xczcCRARK4C/Bj7VVWGSNOmGnQXxrH3h2+x+BvtKkg5i2CvgGyPii8An2vqvA1/opiRJmg6H+k241cCJmfnOiPhV4BVt098DH++6OEmaZIe6Av4AcAlAZn4G+AxARPx82/a6DmuTpIl2qHHcEzPzngMbW9upnVQkSVPiUAG87Gm2PXcR65CkqXOoAN4SEesPbIyI3wHu7KYkSZoOhxoDfgfw2Yh4C08F7hrgGOANHdYlSRPvaQM4Mx8GfjkiXgX8XGv+y8y8pfPKJGnCDfs84FuBWzuuRZKminezSVIRA1iSinQWwBFxSkTcGhH3RcS9EfH21v6CiLgpIh5or8e39oiIP46IuYi4OyJeOnCsda3/AxGxrquaJWmUurwC3gv8fmaeDpwFbIyI04GLgZsz8zTg5rYOcB5wWvvbAHwI+oENXAq8DDgTuHRfaEvSOOssgDPzocy8qy1/D7gfOBk4H9jcum0GLmjL5wMfzb6vAssi4iTgNcBNmbknMx8FbgLWdlW3JI3KSMaAI+JU4CXAbfRvb36obdoBnNiWTwYeHNhtW2tbqP3A99gQEVsiYsuuXbsW9wQkqQOdB3BEPA/4NPCOzPzu4LbMTJ76sc8jkplXZOaazFyzYsWKxTikJHWq0wCOiGfTD9+Pt6epATzchhZor/se9L4dOGVg91WtbaF2SRprXc6CCOAq4P7MfP/ApuuBfTMZ1gHXDbS/tc2GOAt4rA1VfBE4NyKOb1++ndvaJGmsDfuLGIfj5cBvAvdExNdb2x8C7wWujYiLgK30f+QT+r+w8VpgDngc+G2AzNwTEe8G7mj93pWZezqsW5JGorMAzsy/A2KBzeccpH8CGxc41tXA1YtXnSTV8044SSrS5RCERmR+fp5er7d/fXZ2lpmZmcKKJA3DAJ4AvV6P9ZtuYOnylTy+ewdXbjyP1atXV5cl6RAM4AmxdPlKjjthVXUZkp4Bx4AlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSinQWwBFxdUTsjIhvDLS9ICJuiogH2uvxrT0i4o8jYi4i7o6Ilw7ss671fyAi1nVVrySNWpdXwB8B1h7QdjFwc2aeBtzc1gHOA05rfxuAD0E/sIFLgZcBZwKX7gttSRp3nQVwZn4Z2HNA8/nA5ra8GbhgoP2j2fdVYFlEnAS8BrgpM/dk5qPATfx4qEvSWBr1GPCJmflQW94BnNiWTwYeHOi3rbUt1P5jImJDRGyJiC27du1a3KolqQNlX8JlZgK5iMe7IjPXZOaaFStWLNZhJakzS0b8fg9HxEmZ+VAbYtjZ2rcDpwz0W9XatgOvPKD9SyOoc2LNz8/T6/X2r8/OzjIzM1NYkTS9Rn0FfD2wbybDOuC6gfa3ttkQZwGPtaGKLwLnRsTx7cu3c1ubDlOv12P9pht4+zVfY/2mG34kjCWNVmdXwBHxCfpXry+MiG30ZzO8F7g2Ii4CtgJvbt2/ALwWmAMeB34bIDP3RMS7gTtav3dl5oFf7OkZWrp8JcedsKq6DGnqdRbAmfkbC2w65yB9E9i4wHGuBq5exNIk6ajgnXCSVMQAlqQiBrAkFRn1NLSj1uD0LKdmSRoFr4CbfdOznJolaVS8Ah6wdPnK6hIkTRGvgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEn6XXIc3Pz9Pr9favz87OMjMzU1iRNBkMYB1Sr9dj/aYbWLp8JY/v3sGVG89j9erV1WVJY88A1lCWLl/JcSesqi5DmiiOAUtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUV8HrAWlb+eIQ3PANai8tczpOEZwFp0/nqGNBzHgCWpiAEsSUUMYEkqYgBLUhG/hFM5p65pWhnAKufUNU0rA1hHBaeuaRqNzRhwRKyNiG9GxFxEXFxdj2rMz88zNze3/29+fr66JOmwjcUVcETMAJuAVwPbgDsi4vrMvK+2Mo3a4QxXOMaso9VYBDBwJjCXmd8CiIhrgPOBRQ3gx3fvAGDr1q0L9tm6dev+fo/v3vG0fRfab7GP/0z2mYTjP936Qvtc8tFbeO7zX8j/e+wR/stbz+ZFL3rRIfeTDrTY301EZi7qAbsQEW8E1mbm77T13wRelplvG+izAdjQVv8p8M3DeKsXAo8cYbnjxPOdfNN2zkfr+T6SmWsPbByXK+BDyswrgCuO5BgRsSUz1yxSSUc9z3fyTds5j9v5jsuXcNuBUwbWV7U2SRpb4xLAdwCnRcRsRBwDXAhcX1yTJB2RsRiCyMy9EfE24IvADHB1Zt7bwVsd0RDGGPJ8J9+0nfNYne9YfAknSZNoXIYgJGniGMCSVMQAZjpvc46Ib0fEPRHx9YjYUl3PYouIqyNiZ0R8Y6DtBRFxU0Q80F6Pr6xxsS1wzv8xIra3z/nrEfHayhoXU0ScEhG3RsR9EXFvRLy9tY/N5zz1ATxwm/N5wOnAb0TE6bVVjcyrMvOMcZo3+Qx8BDhw4vvFwM2ZeRpwc1ufJB/hx88Z4PL2OZ+RmV8YcU1d2gv8fmaeDpwFbGz/7Y7N5zz1AczAbc6Z+UNg323OGmOZ+WVgzwHN5wOb2/Jm4IJR1tS1Bc55YmXmQ5l5V1v+HnA/cDJj9DkbwP0P7MGB9W2tbdIl8FcRcWe7jXsanJiZD7XlHcCJlcWM0Nsi4u42RHHU/nP8SETEqcBLgNsYo8/ZAJ5er8jMl9IfetkYEf+iuqBRyv78y2mYg/kh4KeAM4CHgD8qraYDEfE84NPAOzLzu4PbjvbP2QCe0tucM3N7e90JfJb+UMykezgiTgJorzuL6+lcZj6cmfOZ+SRwJRP2OUfEs+mH78cz8zOteWw+ZwN4Cm9zjoifiIjj9i0D5wLfePq9JsL1wLq2vA64rrCWkdgXRM0bmKDPOSICuAq4PzPfP7BpbD5n74QD2tScD/DUbc6X1VbUrYh4Mf2rXujfjv4/J+2cI+ITwCvpP57wYeBS4C+Aa4F/AmwF3pyZE/Ol1QLn/Er6ww8JfBv41wPjo2MtIl4B/C1wD/Bka/5D+uPAY/E5G8CSVMQhCEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAmQkQsi4jfra5DeiYMYE2KZYABrLFiAGtSvBf4qfbM28sj4uaIuKs983j/0+0i4j+0Zz//XUR8IiL+YKEDRsSXIuJ9EXF7RPzviPjnrf23IuJPBvp9PiJe2Za/HxH/tT2f9q8j4sx2nG9FxOs7O3uNpbH4UU5pCBcDP5eZZ0TEEmBpZn43Il4IfDUirgfWAL8G/ALwbOAu4M5DHHdJZp7Z7pa8FPiXh+j/E8AtmfnOiPgs8B7g1fSfNb2ZCb/NXc+MAaxJFMB/bk94e5L+40VPBF4OXJeZPwB+EBGfG+JY+x7wcidw6hD9fwjc2JbvAZ7IzH+MiHuG3F9TxADWJHoLsAL4Zy38vg0ce5jHeqK9zvPUfy97+dHhu8Fj/2M+dX//k/v2z8wn25W5tJ9jwJoU3wOOa8vPB3a28H0V8KLW/hXgdRFxbHuG7K8c5nt9GzgjIp4VEacwYY941Oj4f2RNhMzcHRFfaT9IeQfw0+2f/VuAf2h97mhjwXfTf1rYPcBjh/F2XwF6wH30fwbnrkU4BU0hn4amqRIRz8vM70fEUuDLwIZ9vysmjZpXwJo2V7Rfzj0W2Gz4qpJXwJp6EbGJ/gyJQR/MzP9RUY+mhwEsSUWcBSFJRQxgSSpiAEtSEQNYkooYwJJU5P8DbqqGteZfh3MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_problems.tag_num, bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a11ff4e-5fe6-48d8-912c-bbfb2a641f3a",
   "metadata": {},
   "source": [
    "대체로 문제별로 두 개의 태그가 가장 많이 등장하는 것으로 보이네요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9faf6afb-0f49-4900-b928-b2148a5d7b68",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d42dd9c-1c78-417e-886f-33829dbd403b",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a77c41-34cb-4a78-87f1-26b11cebb559",
   "metadata": {},
   "source": [
    "## ⚔️ 라이벌과 역라이벌 수 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ebd9f3-e63f-4983-bf57-4823fb11702a",
   "metadata": {},
   "source": [
    "### 유저별 라이벌과 역라이벌 수에 관한 통계를 각각 살펴봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b3886f5a-0544-4890-b074-7bf8b53e6586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    63876.000000\n",
       "mean         0.299236\n",
       "std          2.096559\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max        146.000000\n",
       "Name: rival_count, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.rival_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ce17c60d-e95c-45a2-8638-11a5c1945c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    63876.000000\n",
       "mean         0.303666\n",
       "std          1.654004\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max         94.000000\n",
       "Name: reverse_rival_count, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.reverse_rival_count.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4fa6ecf-7f8c-4962-a82b-7f6ecb8cfb75",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "763a520a-2a45-48fc-895d-86155f27b3f7",
   "metadata": {},
   "source": [
    "### 얼마나 많은 유저가 solved.ac의 라이벌 기능을 이용하고 있을까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b90f39b8-7e8c-42c0-bdf9-2ff4bf0ad2e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rival 수가 0인 유저 비율: 0.9186548938568476\n",
      "reverse_rival 수가 0인 유저 비율: 0.8868119481495398\n",
      "reverse_rival과 rival 수가 0인 유저 비율: 0.8689335587701171\n"
     ]
    }
   ],
   "source": [
    "print('rival 수가 0인 유저 비율:',sum(df_users.rival_count==0)/len(df_users))\n",
    "print('reverse_rival 수가 0인 유저 비율:',sum(df_users.reverse_rival_count==0)/len(df_users))\n",
    "print('reverse_rival과 rival 수가 0인 유저 비율:',sum((df_users.reverse_rival_count==0) & (df_users.rival_count==0))/len(df_users))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1e7671-8825-455a-9ade-060da9890e58",
   "metadata": {},
   "source": [
    "전체 유저 중 약 87%가 현재 제공되고 있는 라이벌 기능을 사용하지 않고 있네요. 😯 <br/>\n",
    "대다수의 유저가 라이벌 기능을 사용하지 않는 만큼, 라이벌 추천 서비스를 제공하면 조금이나마 라이벌 기능을 활성화시킬 수 있지 않을까요?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f586f1a-6efb-45c6-abe5-c3bd2cc83863",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c35d50-c999-4461-9555-f22dc786d7b6",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5cae222-f923-47d9-9d0f-26f7e8d27b01",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b89dc86-e8bb-4142-ba6e-c83d1cd0b7b9",
   "metadata": {},
   "source": [
    "## 🔖 라이벌 관련 실험 및 결과"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9903e04b-d338-45ca-9573-99fba7b73434",
   "metadata": {},
   "source": [
    "라이벌에 관해 분석한 데이터를 바탕으로 가설을 세우고 검증해볼게요.<br/>\n",
    "이를 통해 어떤 방법으로 추천 모델을 만들어야 할지, 필터링은 어떤 식으로 해야할지 고민해 봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd86790-674e-4474-813e-94368136ce77",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6325593-0ec0-47ef-ac08-23a7a6487066",
   "metadata": {},
   "source": [
    "### 가설 1: 라이벌이 있는 유저들은 그렇지 않은 유저들에 비해 문제를 더 많이 풉니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f649b956-a4f6-45e9-8ab7-d704eb7dfd45",
   "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>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>solved_count</th>\n",
       "      <th>user_class</th>\n",
       "      <th>tier</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_by_problems_sum</th>\n",
       "      <th>rating_by_class</th>\n",
       "      <th>rating_by_solved_count</th>\n",
       "      <th>exp</th>\n",
       "      <th>rival_count</th>\n",
       "      <th>reverse_rival_count</th>\n",
       "      <th>max_streak</th>\n",
       "      <th>rank</th>\n",
       "      <th>organization</th>\n",
       "      <th>has_rival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>11075</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3252</td>\n",
       "      <td>2802</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>14225816820</td>\n",
       "      <td>5</td>\n",
       "      <td>94</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>394,439,753</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>cki86201</td>\n",
       "      <td>6059</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3231</td>\n",
       "      <td>2781</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>10292773001</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>299</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>mitnegativeinfinity</td>\n",
       "      <td>2188</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3180</td>\n",
       "      <td>2730</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>6393679340</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>440</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ainta</td>\n",
       "      <td>4111</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3135</td>\n",
       "      <td>2685</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>6940261423</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>299,915,1566</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>yclock</td>\n",
       "      <td>2670</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3116</td>\n",
       "      <td>2666</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>4132785157</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>299,439</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64712</th>\n",
       "      <td>64728</td>\n",
       "      <td>ohjcms1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64954</th>\n",
       "      <td>64971</td>\n",
       "      <td>steven1010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65099</th>\n",
       "      <td>65116</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65128</th>\n",
       "      <td>65145</td>\n",
       "      <td>yijh061016</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65143</th>\n",
       "      <td>65160</td>\n",
       "      <td>yoonkoo2001</td>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63876 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id               handle  solved_count  user_class  tier  rating  \\\n",
       "0          1              koosaga         11075          10    31    3252   \n",
       "1          2             cki86201          6059          10    31    3231   \n",
       "2          3  mitnegativeinfinity          2188          10    31    3180   \n",
       "3          4                ainta          4111          10    31    3135   \n",
       "4          5               yclock          2670          10    31    3116   \n",
       "...      ...                  ...           ...         ...   ...     ...   \n",
       "64712  64728              ohjcms1             2           0     0       0   \n",
       "64954  64971           steven1010             1           0     0       0   \n",
       "65099  65116              wwowwww            23           0     0       0   \n",
       "65128  65145           yijh061016           127           0     0       0   \n",
       "65143  65160          yoonkoo2001           306           0     0       0   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "0                        2802              250                     175   \n",
       "1                        2781              250                     175   \n",
       "2                        2730              250                     175   \n",
       "3                        2685              250                     175   \n",
       "4                        2666              250                     175   \n",
       "...                       ...              ...                     ...   \n",
       "64712                       0                0                       0   \n",
       "64954                       0                0                       0   \n",
       "65099                       0                0                       0   \n",
       "65128                       0                0                       0   \n",
       "65143                       0                0                       0   \n",
       "\n",
       "               exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "0      14225816820            5                   94          24      1   \n",
       "1      10292773001            0                   26           3      2   \n",
       "2       6393679340            0                   12           8      3   \n",
       "3       6940261423            0                   14           2      4   \n",
       "4       4132785157            0                   14           3      5   \n",
       "...            ...          ...                  ...         ...    ...   \n",
       "64712            0            0                    0           0  63900   \n",
       "64954            0            0                    0           0  63901   \n",
       "65099            0            0                    0           0  63901   \n",
       "65128            0            0                    0           0  63901   \n",
       "65143            0            0                    3           0  63901   \n",
       "\n",
       "       organization has_rival  \n",
       "0       394,439,753       YES  \n",
       "1               299        NO  \n",
       "2               440        NO  \n",
       "3      299,915,1566        NO  \n",
       "4           299,439        NO  \n",
       "...             ...       ...  \n",
       "64712           NaN        NO  \n",
       "64954           NaN        NO  \n",
       "65099           NaN        NO  \n",
       "65128           NaN        NO  \n",
       "65143           NaN        NO  \n",
       "\n",
       "[63876 rows x 16 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_check_rival = df_users.copy()\n",
    "df_check_rival['has_rival'] = 'NO'\n",
    "\n",
    "idx = df_check_rival[df_check_rival.rival_count >= 1].index\n",
    "df_check_rival.loc[idx, 'has_rival'] = 'YES'\n",
    "df_check_rival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8d89ff0e-18ba-425e-b59b-3e008a054ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='has_rival', ylabel='solved_count'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='has_rival', y = 'solved_count', data=df_check_rival)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411b772a-6934-4516-b00a-610601313e2d",
   "metadata": {},
   "source": [
    "rival이 있는 유저들이 rival이 없는 유저들에 비에 2배 이상 문제를 더 많이 푸네요.<br/>\n",
    "역시 라이벌 기능을 이용하고 있는 사람은 알고리즘 문제를 대체로 더 많이 푸는 것으로 보입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8f49c7db-2f40-4698-8af3-3c3d61b37a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.199235382987701"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_check_rival['has_rival'] = np.where(df_check_rival['has_rival']=='YES', 1 ,0)\n",
    "stats.pointbiserialr(df_check_rival['has_rival'], df_check_rival['solved_count']).correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3231ea9-af3a-4a7c-9042-9e721401a249",
   "metadata": {},
   "source": [
    "상관관계가 0.1995로 약한 양의 상관관계를 보여주네요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00243194-cd75-4c49-ae1d-b958eee58a00",
   "metadata": {},
   "source": [
    "#### [정리]\n",
    "- rival의 여부와 문제 푸는 횟수는 약한 양의 상관관계를 보입니다.\n",
    "- rival이 있는 유저들이 rival이 없는 유저들에 비에 2배 이상의 문제를 더 많이 풉니다.<br/>\n",
    "→ rival이 있으면 유저들이 문제를 더 많이 풉니다.<br/>\n",
    "→ rival을 등록할 수 있도록 적절한 라이벌 추천해주면 유저들이 문제를 더 많이 풀어서 개인 실력 향상과 백준 사이트의 활성화에 도움을 줄 것으로 보입니다. 🥳"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af2cbc4-309f-4db7-888b-649de5ed0a2b",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3c9def-61a6-4c5c-83a4-6bc349d073bd",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f988a25-8ef8-4d01-bf22-223047890eae",
   "metadata": {},
   "source": [
    "### 가설 2: 라이벌이 많을수록 문제를 더 많이 풉니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd901bb2-d65a-4758-ad72-b1f5914e1857",
   "metadata": {},
   "source": [
    "#### 가설 배경: 적절한 추천 라이벌 수를 찾기 위해"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "83b10969-7347-43b0-b1a6-b79babd37adc",
   "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>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>solved_count</th>\n",
       "      <th>user_class</th>\n",
       "      <th>tier</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_by_problems_sum</th>\n",
       "      <th>rating_by_class</th>\n",
       "      <th>rating_by_solved_count</th>\n",
       "      <th>exp</th>\n",
       "      <th>rival_count</th>\n",
       "      <th>reverse_rival_count</th>\n",
       "      <th>max_streak</th>\n",
       "      <th>rank</th>\n",
       "      <th>organization</th>\n",
       "      <th>has_rival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>11075</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3252</td>\n",
       "      <td>2802</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>14225816820</td>\n",
       "      <td>5</td>\n",
       "      <td>94</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>394,439,753</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>gs18115</td>\n",
       "      <td>878</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3097</td>\n",
       "      <td>2649</td>\n",
       "      <td>250</td>\n",
       "      <td>173</td>\n",
       "      <td>3216398172</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>65</td>\n",
       "      <td>7</td>\n",
       "      <td>394,439</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>dennisstar</td>\n",
       "      <td>907</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3083</td>\n",
       "      <td>2635</td>\n",
       "      <td>250</td>\n",
       "      <td>173</td>\n",
       "      <td>2948927188</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>150</td>\n",
       "      <td>8</td>\n",
       "      <td>394,439</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>jhnah917</td>\n",
       "      <td>6822</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3071</td>\n",
       "      <td>2621</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>3797137474</td>\n",
       "      <td>47</td>\n",
       "      <td>61</td>\n",
       "      <td>278</td>\n",
       "      <td>9</td>\n",
       "      <td>323,535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>rkm0959</td>\n",
       "      <td>3192</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>3006</td>\n",
       "      <td>2566</td>\n",
       "      <td>240</td>\n",
       "      <td>175</td>\n",
       "      <td>2964503327</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>299,439</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63715</th>\n",
       "      <td>63731</td>\n",
       "      <td>silverbell950</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>63403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63723</th>\n",
       "      <td>63739</td>\n",
       "      <td>skystrike</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>63403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63743</th>\n",
       "      <td>63759</td>\n",
       "      <td>sugosugo222</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>63403</td>\n",
       "      <td>323</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63795</th>\n",
       "      <td>63811</td>\n",
       "      <td>wockd00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63808</th>\n",
       "      <td>63824</td>\n",
       "      <td>yeonk33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>63403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5196 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id         handle  solved_count  user_class  tier  rating  \\\n",
       "0          1        koosaga         11075          10    31    3252   \n",
       "6          7        gs18115           878          10    31    3097   \n",
       "7          8     dennisstar           907          10    31    3083   \n",
       "8          9       jhnah917          6822          10    31    3071   \n",
       "13        14        rkm0959          3192           9    31    3006   \n",
       "...      ...            ...           ...         ...   ...     ...   \n",
       "63715  63731  silverbell950             1           0     0       2   \n",
       "63723  63739      skystrike             1           0     0       2   \n",
       "63743  63759    sugosugo222             1           0     0       2   \n",
       "63795  63811        wockd00             1           0     0       2   \n",
       "63808  63824        yeonk33             1           0     0       2   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "0                        2802              250                     175   \n",
       "6                        2649              250                     173   \n",
       "7                        2635              250                     173   \n",
       "8                        2621              250                     175   \n",
       "13                       2566              240                     175   \n",
       "...                       ...              ...                     ...   \n",
       "63715                       1                0                       1   \n",
       "63723                       1                0                       1   \n",
       "63743                       1                0                       1   \n",
       "63795                       1                0                       1   \n",
       "63808                       1                0                       1   \n",
       "\n",
       "               exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "0      14225816820            5                   94          24      1   \n",
       "6       3216398172           16                   19          65      7   \n",
       "7       2948927188           17                   24         150      8   \n",
       "8       3797137474           47                   61         278      9   \n",
       "13      2964503327           13                   20           2     14   \n",
       "...            ...          ...                  ...         ...    ...   \n",
       "63715          480            1                    0           1  63403   \n",
       "63723          480            4                    0           1  63403   \n",
       "63743          480            2                    2           1  63403   \n",
       "63795          480            1                    0           0  63403   \n",
       "63808          480            2                    3           1  63403   \n",
       "\n",
       "      organization  has_rival  \n",
       "0      394,439,753          1  \n",
       "6          394,439          1  \n",
       "7          394,439          1  \n",
       "8          323,535          1  \n",
       "13         299,439          1  \n",
       "...            ...        ...  \n",
       "63715          NaN          1  \n",
       "63723          NaN          1  \n",
       "63743          323          1  \n",
       "63795          NaN          1  \n",
       "63808          NaN          1  \n",
       "\n",
       "[5196 rows x 16 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_has_rival = df_check_rival[df_check_rival.has_rival == 1]\n",
    "df_has_rival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e99b493a-add2-4f5f-a195-e300ca57a6fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f7820569e20>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rival_count와 solved_count와의 관계성을 시각화.\n",
    "\n",
    "df_for_pairplot = df_has_rival[['solved_count', 'rival_count']]\n",
    "sns.pairplot(df_for_pairplot, kind='reg', height=4)  # 페어플롯 작성"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2094aab2-01f2-4770-ad66-51d612df52e4",
   "metadata": {},
   "source": [
    "라이벌 수와 푼 문제 수 간의 약한 관계성이 있어 보입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b620a051-320c-42f6-919a-b19c924d5581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.38345671401796444, 1.2105032628839198e-181)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 통계적 검정(피어슨 상관계수 검정)을 통해 구체적인 상관관계 파악.\n",
    "stats.pearsonr(df_has_rival.rival_count, df_has_rival.solved_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8134453c-0ceb-4b57-801e-a6e10c1434a9",
   "metadata": {},
   "source": [
    "- 상관관계: 0.385168403093753\n",
    "- p-value: 1.312496185611437e-184"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0476ca-8e66-44a5-908c-577f149554f1",
   "metadata": {},
   "source": [
    "#### [정리]\n",
    "- 라이벌이 많을수록 문제를 푸는 횟수와 약한 상관관계를 보입니다.\n",
    "- 적절히 큰 라이벌 수를 정하면 문제 푸는 횟수가 늘어날 것으로 보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d186188-db5c-49ad-a7bb-02c64cb3ab7b",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3422b8-d45d-409c-b86f-d5d88c6258af",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feddcfcd-77ea-4285-b259-c805e8aa7295",
   "metadata": {},
   "source": [
    "### 실험 1: 라이벌이 있는 유저들의 평균적인 라이벌 수는 몇 명일까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7e04e362-3dd4-42c2-9d61-d38cd86315b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rival_count'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 실제 데이터의 형상을 보기 위해 바이올린 플롯으로 시각화. https://junklee.tistory.com/9\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.violinplot(x='rival_count', data=df_has_rival)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f7e80b37-947b-43ed-9704-1b2f2b668bc4",
   "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>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>solved_count</th>\n",
       "      <th>user_class</th>\n",
       "      <th>tier</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_by_problems_sum</th>\n",
       "      <th>rating_by_class</th>\n",
       "      <th>rating_by_solved_count</th>\n",
       "      <th>exp</th>\n",
       "      <th>rival_count</th>\n",
       "      <th>reverse_rival_count</th>\n",
       "      <th>max_streak</th>\n",
       "      <th>rank</th>\n",
       "      <th>organization</th>\n",
       "      <th>has_rival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>162</td>\n",
       "      <td>tlsdydaud1</td>\n",
       "      <td>2277</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>2586</td>\n",
       "      <td>2156</td>\n",
       "      <td>230</td>\n",
       "      <td>175</td>\n",
       "      <td>442374230</td>\n",
       "      <td>118</td>\n",
       "      <td>62</td>\n",
       "      <td>185</td>\n",
       "      <td>161</td>\n",
       "      <td>425</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>dj035</td>\n",
       "      <td>1380</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>2496</td>\n",
       "      <td>2076</td>\n",
       "      <td>220</td>\n",
       "      <td>175</td>\n",
       "      <td>260456815</td>\n",
       "      <td>146</td>\n",
       "      <td>80</td>\n",
       "      <td>55</td>\n",
       "      <td>213</td>\n",
       "      <td>1026,1222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>481</td>\n",
       "      <td>malkoring</td>\n",
       "      <td>2088</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>2257</td>\n",
       "      <td>1838</td>\n",
       "      <td>220</td>\n",
       "      <td>175</td>\n",
       "      <td>88942642</td>\n",
       "      <td>107</td>\n",
       "      <td>51</td>\n",
       "      <td>114</td>\n",
       "      <td>481</td>\n",
       "      <td>436</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id      handle  solved_count  user_class  tier  rating  \\\n",
       "161  162  tlsdydaud1          2277           8    24    2586   \n",
       "212  213       dj035          1380           7    23    2496   \n",
       "480  481   malkoring          2088           7    21    2257   \n",
       "\n",
       "     rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "161                    2156              230                     175   \n",
       "212                    2076              220                     175   \n",
       "480                    1838              220                     175   \n",
       "\n",
       "           exp  rival_count  reverse_rival_count  max_streak  rank  \\\n",
       "161  442374230          118                   62         185   161   \n",
       "212  260456815          146                   80          55   213   \n",
       "480   88942642          107                   51         114   481   \n",
       "\n",
       "    organization  has_rival  \n",
       "161          425          1  \n",
       "212    1026,1222          1  \n",
       "480          436          1  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 라이벌을 100명 이상으로 정한 유저들 확인\n",
    "df_has_rival[df_has_rival.rival_count >=100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1c6c208a-162d-4888-9b04-67e795b24656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.0, 15.0)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outside point를 제외한 바이올린 플롯의 부분 확대\n",
    "plt.figure(figsize=(10,5))\n",
    "fig = sns.violinplot(x='rival_count', data=df_has_rival)\n",
    "fig.set_xlim([-5, 15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "dcff0d59-ff7b-40a0-94a7-4df982a7c4a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "평균 rival 수: 3.68 명\n"
     ]
    }
   ],
   "source": [
    "# 평균적인 rival 수 확인\n",
    "print(\"평균 rival 수:\", round(np.mean(df_has_rival.rival_count), 2),'명')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2fa90f-d1e4-4a92-bb64-0975f7fa1ad8",
   "metadata": {},
   "source": [
    "#### [정리]\n",
    "\n",
    "- outside point: 20명 이상\n",
    "- upper adjacent values(상위 근접 값: 해당 값보다 크면 이상치로 판별 가능): 8명\n",
    "- median: 약 2.5명\n",
    "- mean 약 3.65명\n",
    "\n",
    "→ rival 수는 2~3명 추천으로 잡아도 될 것으로 보입니다. 😆"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfbaa867-bf1f-4ca5-b860-9f1a31c5db77",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8270ecdf-4638-4a4f-a671-9e421cb820ce",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e36cd6-a879-4001-9d66-365219043022",
   "metadata": {},
   "source": [
    "### 실험 2: 라이벌로 지정할 만한 유저를 찾아볼까요?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388592d6-1840-4d6d-b6c5-25efe967b613",
   "metadata": {},
   "source": [
    "#### 기준: 나와 비슷한 실력의 유저 또는 나보다 실력이 조금 더 높은 유저"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "50045bc5-b284-4565-b2a2-4ddd2e9fcadf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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      "text/plain": [
       "          id    handle  solved_count  user_class  tier  rating  \\\n",
       "26398  26401  1017hana            54           0     8     499   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "26398                     458                0                      41   \n",
       "\n",
       "          exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "26398  563437            0                    1           3  26401   \n",
       "\n",
       "      organization  \n",
       "26398          NaN  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users[df_users.handle == '1017hana']"
   ]
  },
  {
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   "execution_count": 86,
   "id": "8a697e6e-02f0-40d4-9b21-d95ce28ed32e",
   "metadata": {},
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      "text/plain": [
       "          id         handle  solved_count  user_class  tier  rating  \\\n",
       "26398  26401       1017hana            54           0     8     499   \n",
       "26399  26402       arch1229            63           0     8     499   \n",
       "26400  26403  choyeongwoo_s            63           0     8     499   \n",
       "26401  26404    cogksdnf314            86           1     8     499   \n",
       "26402  26405      daeun1284            71           1     8     499   \n",
       "...      ...            ...           ...         ...   ...     ...   \n",
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       "30158  30161        ywj2321            76           1     8     400   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "26398                     458                0                      41   \n",
       "26399                     452                0                      47   \n",
       "26400                     452                0                      47   \n",
       "26401                     413               25                      61   \n",
       "26402                     422               25                      52   \n",
       "...                       ...              ...                     ...   \n",
       "30154                     320               25                      55   \n",
       "30155                     293               50                      57   \n",
       "30156                     315               25                      60   \n",
       "30157                     310               25                      65   \n",
       "30158                     320               25                      55   \n",
       "\n",
       "           exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "26398   563437            0                    1           3  26401   \n",
       "26399  2189586            0                    0           2  26401   \n",
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       "26401   310132            0                    0           1  26401   \n",
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       "...        ...          ...                  ...         ...    ...   \n",
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       "\n",
       "      organization  \n",
       "26398          NaN  \n",
       "26399          NaN  \n",
       "26400          NaN  \n",
       "26401          NaN  \n",
       "26402          NaN  \n",
       "...            ...  \n",
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       "30157          NaN  \n",
       "30158          NaN  \n",
       "\n",
       "[3761 rows x 15 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tier 8(Silver III) 중  1017hana의 라이벌 찾아보기\n",
    "df_tier_8 = df_users[df_users.tier == 8]\n",
    "target_user = '1017hana' # 예시로 쓸 타겟 유저\n",
    "\n",
    "df_tier_8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ba256b-6df5-435b-beee-a8316aae11d3",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced6d113-4e97-4a7e-91fa-3abbda2f625c",
   "metadata": {},
   "source": [
    "#### 1. 타겟 유저와 같거나 한 단계 높은 티어로 실력 범위를 줄입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f2df9d2c-365b-4d99-af7c-6b0ba8556e05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "후보수 전: 63875\n",
      "후보수 후: 8452\n"
     ]
    },
    {
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      "text/plain": [
       "          id         handle  solved_count  user_class  tier  rating  \\\n",
       "26398  26401       1017hana            54           0     8     499   \n",
       "26399  26402       arch1229            63           0     8     499   \n",
       "26400  26403  choyeongwoo_s            63           0     8     499   \n",
       "26401  26404    cogksdnf314            86           1     8     499   \n",
       "26402  26405      daeun1284            71           1     8     499   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "26398                     458                0                      41   \n",
       "26399                     452                0                      47   \n",
       "26400                     452                0                      47   \n",
       "26401                     413               25                      61   \n",
       "26402                     422               25                      52   \n",
       "\n",
       "           exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "26398   563437            0                    1           3  26401   \n",
       "26399  2189586            0                    0           2  26401   \n",
       "26400   492016            0                    0           3  26401   \n",
       "26401   310132            0                    0           1  26401   \n",
       "26402   380033            0                    0           3  26401   \n",
       "\n",
       "      organization  \n",
       "26398          NaN  \n",
       "26399          NaN  \n",
       "26400          NaN  \n",
       "26401          NaN  \n",
       "26402          NaN  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('후보수 전:', len(df_users)-1)\n",
    "\n",
    "df_tier_9 = df_users[df_users.tier == 9]\n",
    "df_candi_by_tier = pd.concat([df_tier_8, df_tier_9], axis=0)\n",
    "\n",
    "print('후보수 후:', len(df_candi_by_tier)-1)\n",
    "df_candi_by_tier.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f046bdb3-2363-4c4b-8c4a-ed275b3db60d",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b4e69e-60d2-4887-a4b7-ca98e65eb05b",
   "metadata": {},
   "source": [
    "#### 2. rating_by_problems_sum(푼 문제의 난이도 합으로 계산한 사용자의 레이팅)이 비슷한 유저들로 범위 줄이기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "790f1f99-1aee-4d50-83ac-2c43f961910b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1017hana의 rating_by_problems_sum: 458\n"
     ]
    }
   ],
   "source": [
    "t_rating_b_problems_sum = df_tier_8[df_tier_8.handle == target_user].rating_by_problems_sum.values[0] \n",
    "print(target_user+'의 rating_by_problems_sum:', t_rating_b_problems_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "34f76942-1d72-416c-a3ec-4feffc076072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f781bdff400>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# target user가 속한 티어와 한 단계 높은 티어와의 rating_by_problems_sum 분포 확인\n",
    "# 초록색 점선: target user의 rating_by_problems_sum\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.violinplot(x=\"tier\", y=\"rating_by_problems_sum\", data=df_candi_by_tier)\n",
    "plt.axhline(y=t_rating_b_problems_sum, color='g', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da327a1-5418-45d1-9d06-ccc538fbf1ca",
   "metadata": {},
   "source": [
    "- 타겟 유저는 현재 속한 티어에서 상위권 유저임을 알 수 있습니다.\n",
    "- 한 단계 높은 레벨에서도 비교적 중위권에 속함을 알 수 있습니다.\n",
    "- 타겟 유저보다 rating_by_problems_sum이 100(임시) 높은 유저로 바운더리 정하는 게 좋아 보입니다. 🙂"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "1a630784-eba7-4950-88b2-09a6eab41d3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "후보수 후: 8452\n",
      "후보수 후: 2641\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>handle</th>\n",
       "      <th>solved_count</th>\n",
       "      <th>user_class</th>\n",
       "      <th>tier</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_by_problems_sum</th>\n",
       "      <th>rating_by_class</th>\n",
       "      <th>rating_by_solved_count</th>\n",
       "      <th>exp</th>\n",
       "      <th>rival_count</th>\n",
       "      <th>reverse_rival_count</th>\n",
       "      <th>max_streak</th>\n",
       "      <th>rank</th>\n",
       "      <th>organization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26398</th>\n",
       "      <td>26401</td>\n",
       "      <td>1017hana</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>499</td>\n",
       "      <td>458</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>563437</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26401</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26404</th>\n",
       "      <td>26407</td>\n",
       "      <td>dhtjs1123</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>499</td>\n",
       "      <td>464</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1840370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26401</td>\n",
       "      <td>202,750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26406</th>\n",
       "      <td>26409</td>\n",
       "      <td>esbr17</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>499</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>739971</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26401</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26417</th>\n",
       "      <td>26420</td>\n",
       "      <td>keumi1004</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>499</td>\n",
       "      <td>464</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>952518</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26401</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26425</th>\n",
       "      <td>26428</td>\n",
       "      <td>sangho_kil</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>499</td>\n",
       "      <td>480</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>176929284</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26401</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id      handle  solved_count  user_class  tier  rating  \\\n",
       "26398  26401    1017hana            54           0     8     499   \n",
       "26404  26407   dhtjs1123            45           0     8     499   \n",
       "26406  26409      esbr17            51           0     8     499   \n",
       "26417  26420   keumi1004            44           0     8     499   \n",
       "26425  26428  sangho_kil            24           0     8     499   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "26398                     458                0                      41   \n",
       "26404                     464                0                      35   \n",
       "26406                     460                0                      39   \n",
       "26417                     464                0                      35   \n",
       "26425                     480                0                      19   \n",
       "\n",
       "             exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "26398     563437            0                    1           3  26401   \n",
       "26404    1840370            0                    0           0  26401   \n",
       "26406     739971            0                    0           2  26401   \n",
       "26417     952518            0                    0           1  26401   \n",
       "26425  176929284            0                    0           0  26401   \n",
       "\n",
       "      organization  \n",
       "26398          NaN  \n",
       "26404      202,750  \n",
       "26406          NaN  \n",
       "26417          NaN  \n",
       "26425          NaN  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 타겟 유저의 rating_by_problems_sum <= 후보군 rating_by_problems_sum <= 타겟 유저의 rating_by_problems_sum + 100\n",
    "threshold = 100\n",
    "\n",
    "print('후보수 후:', len(df_candi_by_tier)-1)\n",
    "df_candi_by_rbps = df_candi_by_tier[(df_candi_by_tier.rating_by_problems_sum >= t_rating_b_problems_sum) & (df_candi_by_tier.rating_by_problems_sum <= t_rating_b_problems_sum+threshold)]\n",
    "print('후보수 후:',len(df_candi_by_rbps)-1)\n",
    "df_candi_by_rbps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "547d9585-f8c1-420d-b120-3152c15a8e1f",
   "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>id_x</th>\n",
       "      <th>handle</th>\n",
       "      <th>solved_count</th>\n",
       "      <th>user_class</th>\n",
       "      <th>tier</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_by_problems_sum</th>\n",
       "      <th>rating_by_class</th>\n",
       "      <th>rating_by_solved_count</th>\n",
       "      <th>exp</th>\n",
       "      <th>rival_count</th>\n",
       "      <th>reverse_rival_count</th>\n",
       "      <th>max_streak</th>\n",
       "      <th>rank</th>\n",
       "      <th>organization</th>\n",
       "      <th>id_y</th>\n",
       "      <th>problems</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>koosaga</td>\n",
       "      <td>11075</td>\n",
       "      <td>10</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>394,439,753</td>\n",
       "      <td>1</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1006,1007,1008,1...</td>\n",
       "      <td>11226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>cki86201</td>\n",
       "      <td>6059</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3231</td>\n",
       "      <td>2781</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>10292773001</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>299</td>\n",
       "      <td>2</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1006,1007,1008,1...</td>\n",
       "      <td>6138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>mitnegativeinfinity</td>\n",
       "      <td>2188</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3180</td>\n",
       "      <td>2730</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
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       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>440</td>\n",
       "      <td>3</td>\n",
       "      <td>1000,1001,1019,1056,1067,1144,1150,1311,1372,1...</td>\n",
       "      <td>2333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ainta</td>\n",
       "      <td>4111</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3135</td>\n",
       "      <td>2685</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>6940261423</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>299,915,1566</td>\n",
       "      <td>4</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1007,1008,1009,1...</td>\n",
       "      <td>4280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>yclock</td>\n",
       "      <td>2670</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3116</td>\n",
       "      <td>2666</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>4132785157</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>299,439</td>\n",
       "      <td>5</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1007,1008,1009,1...</td>\n",
       "      <td>2765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63868</th>\n",
       "      <td>64350</td>\n",
       "      <td>hihimotga</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63869</td>\n",
       "      <td>1237,2555,10943,15643,15802,15913</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63870</th>\n",
       "      <td>64720</td>\n",
       "      <td>number</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63871</td>\n",
       "      <td>1220,1533,1616,1699,1912,2239,2580,3090,5820,5...</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63871</th>\n",
       "      <td>64728</td>\n",
       "      <td>ohjcms1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63872</td>\n",
       "      <td>2557,10718,15596</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63872</th>\n",
       "      <td>64971</td>\n",
       "      <td>steven1010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63873</td>\n",
       "      <td>2557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63873</th>\n",
       "      <td>65116</td>\n",
       "      <td>wwowwww</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63874</td>\n",
       "      <td>1000,1001,1008,1330,2438,2439,2557,2588,2739,2...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63554 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id_x               handle  solved_count  user_class  tier  rating  \\\n",
       "0          1              koosaga         11075          10    31    3252   \n",
       "1          2             cki86201          6059          10    31    3231   \n",
       "2          3  mitnegativeinfinity          2188          10    31    3180   \n",
       "3          4                ainta          4111          10    31    3135   \n",
       "4          5               yclock          2670          10    31    3116   \n",
       "...      ...                  ...           ...         ...   ...     ...   \n",
       "63868  64350            hihimotga             6           0     0       0   \n",
       "63870  64720               number            26           0     0       0   \n",
       "63871  64728              ohjcms1             2           0     0       0   \n",
       "63872  64971           steven1010             1           0     0       0   \n",
       "63873  65116              wwowwww            23           0     0       0   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "0                        2802              250                     175   \n",
       "1                        2781              250                     175   \n",
       "2                        2730              250                     175   \n",
       "3                        2685              250                     175   \n",
       "4                        2666              250                     175   \n",
       "...                       ...              ...                     ...   \n",
       "63868                       0                0                       0   \n",
       "63870                       0                0                       0   \n",
       "63871                       0                0                       0   \n",
       "63872                       0                0                       0   \n",
       "63873                       0                0                       0   \n",
       "\n",
       "               exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "0      14225816820            5                   94          24      1   \n",
       "1      10292773001            0                   26           3      2   \n",
       "2       6393679340            0                   12           8      3   \n",
       "3       6940261423            0                   14           2      4   \n",
       "4       4132785157            0                   14           3      5   \n",
       "...            ...          ...                  ...         ...    ...   \n",
       "63868         1920            0                    0           0  63900   \n",
       "63870            0            0                    0           0  63900   \n",
       "63871            0            0                    0           0  63900   \n",
       "63872            0            0                    0           0  63901   \n",
       "63873            0            0                    0           0  63901   \n",
       "\n",
       "       organization   id_y                                           problems  \\\n",
       "0       394,439,753      1  1000,1001,1002,1003,1004,1005,1006,1007,1008,1...   \n",
       "1               299      2  1000,1001,1002,1003,1004,1005,1006,1007,1008,1...   \n",
       "2               440      3  1000,1001,1019,1056,1067,1144,1150,1311,1372,1...   \n",
       "3      299,915,1566      4  1000,1001,1002,1003,1004,1005,1007,1008,1009,1...   \n",
       "4           299,439      5  1000,1001,1002,1003,1004,1005,1007,1008,1009,1...   \n",
       "...             ...    ...                                                ...   \n",
       "63868           NaN  63869                  1237,2555,10943,15643,15802,15913   \n",
       "63870           NaN  63871  1220,1533,1616,1699,1912,2239,2580,3090,5820,5...   \n",
       "63871           NaN  63872                                   2557,10718,15596   \n",
       "63872           NaN  63873                                               2557   \n",
       "63873           NaN  63874  1000,1001,1008,1330,2438,2439,2557,2588,2739,2...   \n",
       "\n",
       "         num  \n",
       "0      11226  \n",
       "1       6138  \n",
       "2       2333  \n",
       "3       4280  \n",
       "4       2765  \n",
       "...      ...  \n",
       "63868      6  \n",
       "63870     30  \n",
       "63871      3  \n",
       "63872      1  \n",
       "63873     29  \n",
       "\n",
       "[63554 rows x 18 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usp = pd.merge(df_users, df_problems_solved, on='handle')\n",
    "# null이 담긴 사용자 제거\n",
    "df_usp = df_usp[~df_usp.problems.isnull()]\n",
    "df_usp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a88dcfa-16c4-4118-b1f4-f8d543011aca",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45467115-f735-441e-aba9-f6095bea640c",
   "metadata": {},
   "source": [
    "#### 3. 타겟 유저 푼 태그 기반으로 나의 부족한 점을 더 많이 푼 유저 찾기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3ccfa882-bdff-49ed-b984-51b1725c3c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>problem_id</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "      <th>is_solvable</th>\n",
       "      <th>accepted_user_count</th>\n",
       "      <th>level</th>\n",
       "      <th>average_tries</th>\n",
       "      <th>tag_num</th>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>A-B</td>\n",
       "      <td>arithmetic,implementation,math</td>\n",
       "      <td>True</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>True</td>\n",
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       "      <th>3</th>\n",
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       "      <td>피보나치 함수</td>\n",
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       "      <td>34653</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1004</td>\n",
       "      <td>어린 왕자</td>\n",
       "      <td>geometry</td>\n",
       "      <td>True</td>\n",
       "      <td>8438</td>\n",
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      ],
      "text/plain": [
       "   id  problem_id    title                            tags  is_solvable  \\\n",
       "0   1        1000      A+B  arithmetic,implementation,math         True   \n",
       "1   2        1001      A-B  arithmetic,implementation,math         True   \n",
       "2   3        1002       터렛                   geometry,math         True   \n",
       "3   4        1003  피보나치 함수                              dp         True   \n",
       "4   5        1004    어린 왕자                        geometry         True   \n",
       "\n",
       "   accepted_user_count  level  average_tries  tag_num  \n",
       "0               175152      1              2        3  \n",
       "1               146736      1              1        3  \n",
       "2                25716      7              4        2  \n",
       "3                34653      8              3        1  \n",
       "4                 8438      8              2        1  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_problems = df_problems[df_problems.level != 0]\n",
    "# not_solvable == False만\n",
    "df_problems = df_problems[df_problems.is_solvable == True]\n",
    "# tag가 nan인 문제 제거\n",
    "df_problems = df_problems[~df_problems.tags.isnull()]\n",
    "df_problems.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b27f6f04-ff46-4174-bc9a-0f31778c9e58",
   "metadata": {},
   "outputs": [
    {
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       "<p>2633 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "        id_x        handle  solved_count  user_class  tier  rating  \\\n",
       "21706  21708      chb09876           111           2     9     649   \n",
       "21707  21709         dhdgn            75           2     9     649   \n",
       "21708  21710    dnrgus1127           107           2     9     649   \n",
       "21709  21711    ehdrms3825           102           2     9     649   \n",
       "21710  21712   eunchae9999           105           2     9     649   \n",
       "...      ...           ...           ...         ...   ...     ...   \n",
       "26633  26636      l9960204            43           0     8     493   \n",
       "26636  26639       mye0412            43           0     8     493   \n",
       "26662  26665    edward0831            39           0     8     492   \n",
       "26706  26709      nebula64            38           0     8     491   \n",
       "26803  26806  nonickname04            35           0     8     488   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "21706                     524               50                      75   \n",
       "21707                     544               50                      55   \n",
       "21708                     526               50                      73   \n",
       "21709                     529               50                      70   \n",
       "21710                     527               50                      72   \n",
       "...                       ...              ...                     ...   \n",
       "26633                     459                0                      34   \n",
       "26636                     459                0                      34   \n",
       "26662                     461                0                      31   \n",
       "26706                     461                0                      30   \n",
       "26803                     460                0                      28   \n",
       "\n",
       "           exp  rival_count  reverse_rival_count  max_streak   rank  \\\n",
       "21706   452740            6                    5           2  21708   \n",
       "21707   691698            0                    1           6  21708   \n",
       "21708   446712            0                    0           2  21708   \n",
       "21709   421785            0                    0           2  21708   \n",
       "21710  1184691            1                    2           1  21708   \n",
       "...        ...          ...                  ...         ...    ...   \n",
       "26633  1595207            0                    0           2  26618   \n",
       "26636  1427111            0                    0           2  26618   \n",
       "26662  1715797            1                    1           1  26658   \n",
       "26706  3356758            0                    0           0  26693   \n",
       "26803  7849863            0                    0           1  26787   \n",
       "\n",
       "      organization   id_y                                           problems  \\\n",
       "21706          222  21707  1000,1001,1002,1008,1011,1018,1065,1085,1110,1...   \n",
       "21707          229  21708  1003,1012,1018,1026,1058,1065,1149,1152,1157,1...   \n",
       "21708          245  21709  1000,1001,1003,1008,1009,1011,1012,1018,1037,1...   \n",
       "21709          NaN  21710  1000,1001,1002,1003,1004,1008,1010,1011,1012,1...   \n",
       "21710          NaN  21711  1000,1001,1003,1008,1018,1062,1065,1085,1094,1...   \n",
       "...            ...    ...                                                ...   \n",
       "26633          NaN  26634  1000,1003,1012,1085,1120,1149,1152,1157,1158,1...   \n",
       "26636          NaN  26637  1074,1080,1107,1158,1167,1182,1197,1201,1208,1...   \n",
       "26662          NaN  26663  2557,3190,5373,12100,13458,13460,14499,14500,1...   \n",
       "26706      331,500  26707  1014,1039,1062,1068,1072,1103,1697,1713,1753,1...   \n",
       "26803          NaN  26804  1000,1002,1012,1764,1865,1912,1932,1938,1967,2...   \n",
       "\n",
       "       num  \n",
       "21706  113  \n",
       "21707   83  \n",
       "21708  109  \n",
       "21709  109  \n",
       "21710  107  \n",
       "...    ...  \n",
       "26633  147  \n",
       "26636  152  \n",
       "26662   42  \n",
       "26706   40  \n",
       "26803   53  \n",
       "\n",
       "[2633 rows x 18 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 후보 유저들만 추리기\n",
    "candi_users = df_candi_by_rbps.handle.values\n",
    "df_usp_candi = df_usp[df_usp.handle.isin(candi_users)]\n",
    "df_usp_candi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "bef5be4d-7a28-4878-82f5-24455e28a38f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>dp</th>\n",
       "      <th>dfs</th>\n",
       "      <th>bfs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21706</th>\n",
       "      <td>21708</td>\n",
       "      <td>chb09876</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>649</td>\n",
       "      <td>524</td>\n",
       "      <td>50</td>\n",
       "      <td>75</td>\n",
       "      <td>452740</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21707</th>\n",
       "      <td>21709</td>\n",
       "      <td>dhdgn</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>649</td>\n",
       "      <td>544</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "      <td>691698</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21708</th>\n",
       "      <td>21710</td>\n",
       "      <td>dnrgus1127</td>\n",
       "      <td>107</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>649</td>\n",
       "      <td>526</td>\n",
       "      <td>50</td>\n",
       "      <td>73</td>\n",
       "      <td>446712</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21709</th>\n",
       "      <td>21711</td>\n",
       "      <td>ehdrms3825</td>\n",
       "      <td>102</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>649</td>\n",
       "      <td>529</td>\n",
       "      <td>50</td>\n",
       "      <td>70</td>\n",
       "      <td>421785</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21710</th>\n",
       "      <td>21712</td>\n",
       "      <td>eunchae9999</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>649</td>\n",
       "      <td>527</td>\n",
       "      <td>50</td>\n",
       "      <td>72</td>\n",
       "      <td>1184691</td>\n",
       "      <td>...</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26633</th>\n",
       "      <td>26636</td>\n",
       "      <td>l9960204</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>493</td>\n",
       "      <td>459</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1595207</td>\n",
       "      <td>...</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26636</th>\n",
       "      <td>26639</td>\n",
       "      <td>mye0412</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>493</td>\n",
       "      <td>459</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1427111</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26662</th>\n",
       "      <td>26665</td>\n",
       "      <td>edward0831</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>492</td>\n",
       "      <td>461</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>1715797</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26706</th>\n",
       "      <td>26709</td>\n",
       "      <td>nebula64</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>491</td>\n",
       "      <td>461</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>3356758</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26803</th>\n",
       "      <td>26806</td>\n",
       "      <td>nonickname04</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>488</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>7849863</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2633 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id_x        handle  solved_count  user_class  tier  rating  \\\n",
       "21706  21708      chb09876           111           2     9     649   \n",
       "21707  21709         dhdgn            75           2     9     649   \n",
       "21708  21710    dnrgus1127           107           2     9     649   \n",
       "21709  21711    ehdrms3825           102           2     9     649   \n",
       "21710  21712   eunchae9999           105           2     9     649   \n",
       "...      ...           ...           ...         ...   ...     ...   \n",
       "26633  26636      l9960204            43           0     8     493   \n",
       "26636  26639       mye0412            43           0     8     493   \n",
       "26662  26665    edward0831            39           0     8     492   \n",
       "26706  26709      nebula64            38           0     8     491   \n",
       "26803  26806  nonickname04            35           0     8     488   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "21706                     524               50                      75   \n",
       "21707                     544               50                      55   \n",
       "21708                     526               50                      73   \n",
       "21709                     529               50                      70   \n",
       "21710                     527               50                      72   \n",
       "...                       ...              ...                     ...   \n",
       "26633                     459                0                      34   \n",
       "26636                     459                0                      34   \n",
       "26662                     461                0                      31   \n",
       "26706                     461                0                      30   \n",
       "26803                     460                0                      28   \n",
       "\n",
       "           exp  ...  num  math  implementation  greedy string  \\\n",
       "21706   452740  ...  113     0               0       0      0   \n",
       "21707   691698  ...   83     0               0       0      0   \n",
       "21708   446712  ...  109     0               0       0      0   \n",
       "21709   421785  ...  109     0               0       0      0   \n",
       "21710  1184691  ...  107     0               0       0      0   \n",
       "...        ...  ...  ...   ...             ...     ...    ...   \n",
       "26633  1595207  ...  147     0               0       0      0   \n",
       "26636  1427111  ...  152     0               0       0      0   \n",
       "26662  1715797  ...   42     0               0       0      0   \n",
       "26706  3356758  ...   40     0               0       0      0   \n",
       "26803  7849863  ...   53     0               0       0      0   \n",
       "\n",
       "       data-structures geometry  dp  dfs  bfs  \n",
       "21706                0        0   0    0    0  \n",
       "21707                0        0   0    0    0  \n",
       "21708                0        0   0    0    0  \n",
       "21709                0        0   0    0    0  \n",
       "21710                0        0   0    0    0  \n",
       "...                ...      ...  ..  ...  ...  \n",
       "26633                0        0   0    0    0  \n",
       "26636                0        0   0    0    0  \n",
       "26662                0        0   0    0    0  \n",
       "26706                0        0   0    0    0  \n",
       "26803                0        0   0    0    0  \n",
       "\n",
       "[2633 rows x 27 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 필요한 태그 칼럼 정리하기\n",
    "tags = ['math', 'implementation', 'greedy', 'string', 'data-structures', 'geometry', 'dp', 'dfs', 'bfs']\n",
    "for x in tags:\n",
    "    df_usp_candi[x] = 0\n",
    "\n",
    "df_usp_candi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "90acc1cd-f46d-4968-af7c-b21cc7ac4122",
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_user_tag(df):    \n",
    "    # 특정 유저가 푼 problems 구하기\n",
    "    for user in tqdm(candi_users):\n",
    "        if (len(df[df.handle == user].problems.values) == 0):\n",
    "            continue;\n",
    "        df_one_user_problems = df[df.handle == user].problems.values[0]\n",
    "        one_user_problems = df_one_user_problems.split(',')\n",
    "\n",
    "        # 특정 유저가 푼 problems의 태그 값들 구하기\n",
    "        user_tag = []\n",
    "        for i in range(len(one_user_problems)):\n",
    "            one_problem = df_problems[df_problems.problem_id == int(one_user_problems[i])]\n",
    "            one_problem_tags = ','.join(one_problem.tags.values).split(',')\n",
    "            user_tag.extend(one_problem_tags)\n",
    "\n",
    "        # 특정 유저가 푼 태그별 개수 구하기: e.g. counts = {'arithmetic': 1, 'implementation': 3, 'math': 10, ... }\n",
    "        counts = dict()\n",
    "        for i in user_tag:\n",
    "            counts[i] = counts.get(i, 0) + 1\n",
    "\n",
    "        # 특정 유저의 태그 칼럼에 각 태그 값 넣기\n",
    "        idx = df[df['handle'] == user].index\n",
    "        for k, v in counts.items():\n",
    "            if k in df.columns:\n",
    "                df.loc[idx, k] = v\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "05726296-6aff-42b3-a492-a5a1cb508b6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2642/2642 [02:04<00:00, 21.21it/s]\n"
     ]
    }
   ],
   "source": [
    "df_candi_with_tags = class_user_tag(df_usp_candi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "39c5caac-3977-4e52-9533-33e66a13523b",
   "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>tags</th>\n",
       "      <th>num</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>math</td>\n",
       "      <td>43.627421</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>implementation</td>\n",
       "      <td>51.781998</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>greedy</td>\n",
       "      <td>5.479301</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>string</td>\n",
       "      <td>12.44436</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data-structures</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>geometry</td>\n",
       "      <td>2.731105</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dp</td>\n",
       "      <td>9.339537</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dfs</td>\n",
       "      <td>3.495632</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bfs</td>\n",
       "      <td>5.723889</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tags        num  target\n",
       "0             math  43.627421       9\n",
       "1   implementation  51.781998       3\n",
       "2           greedy   5.479301       8\n",
       "3           string   12.44436       7\n",
       "4  data-structures        0.0       0\n",
       "5         geometry   2.731105       1\n",
       "6               dp   9.339537      11\n",
       "7              dfs   3.495632       5\n",
       "8              bfs   5.723889       9"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 라이벌 유저군들의 푼 태그 수와 타겟 유저가 푼 태그 수 확인\n",
    "df_target_candi_tags = pd.DataFrame(df_candi_with_tags.sum(axis=0)[tags]).reset_index()\n",
    "df_target_candi_tags.columns = ['tags', 'num']\n",
    "df_target_candi_tags.num = df_target_candi_tags.num/len(df_candi_with_tags)\n",
    "\n",
    "target_tags_values = df_candi_with_tags[df_candi_with_tags.handle == target_user][tags].values\n",
    "df_target_candi_tags['target'] = target_tags_values[0]\n",
    "df_target_candi_tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b5d28f62-9a4f-4d86-83f8-163d1ae0bd08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 라이벌 유저군 보다 내가 부족한 태그 부분 확인\n",
    "bar_width = 0.35\n",
    "alpha = 0.5\n",
    "index = np.arange(9)\n",
    "label = df_target_candi_tags.tags\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "p1 = plt.bar(index, df_target_candi_tags.target, bar_width, \n",
    "             color='b', alpha=alpha,label=target_user)\n",
    "\n",
    "p2 = plt.bar(index + bar_width, df_target_candi_tags.num, bar_width, \n",
    "            color='r', alpha=alpha,label='mean_users')\n",
    "\n",
    "plt.title('Compare solved tags of '+target_user+' and mean of users in same user_class', fontsize=15)\n",
    "plt.ylabel('tags #', fontsize=10)\n",
    "#plt.xlabel('tags', fontsize=18)\n",
    "plt.xticks(index, label, fontsize=15, rotation=45)\n",
    "plt.legend((p1[0], p2[0]), (target_user, 'rival group users'), fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6758497-ce26-42ec-98f6-1a5c18a6e30e",
   "metadata": {},
   "source": [
    "- 타겟 유저는 라이벌 유저군보다 Implementation, math를 적게 풀었습니다.\n",
    "- greedy, dp, dfs, bfs는 더 많이 풀었음을 알 수 있네요. 😎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "fff6047f-e1e3-4d16-898a-0a3458c66a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['math', 'implementation', 'string', 'geometry'], dtype=object)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "need_more_tags = df_target_candi_tags[df_target_candi_tags.num > df_target_candi_tags.target].tags.values\n",
    "need_more_tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "c3d4110d-f943-4e5c-9c65-3d88a392e915",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "후보수 후: 2641\n",
      "math 태그 필터링 후: 2416\n",
      "implementation 태그 필터링 후: 2407\n",
      "string 태그 필터링 후: 1953\n",
      "geometry 태그 필터링 후: 1698\n"
     ]
    }
   ],
   "source": [
    "# 라이벌 유저군들 중에서 내가 부족한 부분을 더 공부한 유저 확인\n",
    "tag_candi = df_candi_with_tags.copy()\n",
    "\n",
    "print('후보수 후:',len(df_candi_by_rbps)-1)\n",
    "for x in need_more_tags:\n",
    "    tag_candi = tag_candi[tag_candi[x] >= tag_candi[tag_candi.handle==target_user][x].values[0]]\n",
    "    print(x+' 태그 필터링 후: ', end='')\n",
    "    print(len(tag_candi)-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac9f203-aae5-4b23-9510-1e5d44df58c5",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327ed30e-7213-48d4-8869-dcb25879a8f1",
   "metadata": {},
   "source": [
    "#### 4. 후보군들 중 랜덤으로 3명을 추출해봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dadb85b8-20e4-44a5-8aa5-b546f3b5982e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1309, 228, 51]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 42\n",
    "\n",
    "fin_candi = tag_candi[tag_candi.handle != target_user].reset_index()\n",
    "\n",
    "seq = list(np.arange(len(fin_candi)))\n",
    "random.seed(seed)\n",
    "target_rivals_3 = random.sample(seq, 3)\n",
    "target_rivals_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c0c1c252-0928-4f71-bff2-f0f51c71e3ae",
   "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>index</th>\n",
       "      <th>id_x</th>\n",
       "      <th>handle</th>\n",
       "      <th>solved_count</th>\n",
       "      <th>user_class</th>\n",
       "      <th>tier</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_by_problems_sum</th>\n",
       "      <th>rating_by_class</th>\n",
       "      <th>rating_by_solved_count</th>\n",
       "      <th>...</th>\n",
       "      <th>num</th>\n",
       "      <th>math</th>\n",
       "      <th>implementation</th>\n",
       "      <th>greedy</th>\n",
       "      <th>string</th>\n",
       "      <th>data-structures</th>\n",
       "      <th>geometry</th>\n",
       "      <th>dp</th>\n",
       "      <th>dfs</th>\n",
       "      <th>bfs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>23726</td>\n",
       "      <td>23729</td>\n",
       "      <td>ioolvv</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>580</td>\n",
       "      <td>461</td>\n",
       "      <td>50</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>102</td>\n",
       "      <td>53</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>22047</td>\n",
       "      <td>22049</td>\n",
       "      <td>qazxcv0691</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>636</td>\n",
       "      <td>512</td>\n",
       "      <td>50</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>21780</td>\n",
       "      <td>21782</td>\n",
       "      <td>dhs632</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>646</td>\n",
       "      <td>529</td>\n",
       "      <td>50</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index   id_x      handle  solved_count  user_class  tier  rating  \\\n",
       "1309  23726  23729      ioolvv           100           2     9     580   \n",
       "228   22047  22049  qazxcv0691           110           2     9     636   \n",
       "51    21780  21782      dhs632            97           2     9     646   \n",
       "\n",
       "      rating_by_problems_sum  rating_by_class  rating_by_solved_count  ...  \\\n",
       "1309                     461               50                      69  ...   \n",
       "228                      512               50                      74  ...   \n",
       "51                       529               50                      67  ...   \n",
       "\n",
       "      num  math  implementation  greedy  string data-structures  geometry dp  \\\n",
       "1309  102    53              52       1      10               0         6  9   \n",
       "228   113    60              58       3      11               0         6  5   \n",
       "51     99    44              43       2      18               0         5  1   \n",
       "\n",
       "      dfs  bfs  \n",
       "1309    0    0  \n",
       "228     2    4  \n",
       "51      0    0  \n",
       "\n",
       "[3 rows x 28 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin_candi.iloc[target_rivals_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1766f832-cef7-4d0a-b738-d48c431b1e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 라이벌 유저군 보다 내가 부족한 태그 부분 확인\n",
    "bar_width = 0.35\n",
    "alpha = 0.5\n",
    "index = np.arange(9)\n",
    "label = df_target_candi_tags.tags\n",
    "\n",
    "for i in range(3):\n",
    "    rival_data = fin_candi.iloc[target_rivals_3[i]]\n",
    "    plt.figure(figsize=(15,5))\n",
    "    p1 = plt.bar(index, tag_candi[tag_candi.handle == target_user][tags].values[0], bar_width, \n",
    "                color='b', alpha=alpha,label=target_user)\n",
    "\n",
    "    p2 = plt.bar(index + bar_width, rival_data[tags].values, bar_width, \n",
    "                color='r', alpha=alpha,label='mean_users')\n",
    "\n",
    "    plt.title('Compare solved tags of '+target_user+' and their rival', fontsize=15)\n",
    "    plt.ylabel('tags #', fontsize=10)\n",
    "    #plt.xlabel('tags', fontsize=18)\n",
    "    plt.xticks(index, label, fontsize=15, rotation=45)\n",
    "    plt.legend((p1[0], p2[0]), (target_user, 'rival: '+rival_data.handle), fontsize=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ef4356",
   "metadata": {},
   "source": [
    "비슷한 라이벌과 비교했을 때 타겟 유저가 상대적으로 덜 푼 태그 유형이 보이네요.<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee5615fa-223e-4fc0-b9d3-4f3c555b4064",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35288843-cf90-4c0d-8c31-48c461524572",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b32ada9-cbc9-4411-8067-27bfcfc6d247",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b7440d-d02b-4513-99e0-58a53406fe63",
   "metadata": {},
   "source": [
    "## 💡 유저와 문제풀이 관련 실험 및 결과"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41144631-834c-4706-ab78-0538a30eb1c8",
   "metadata": {},
   "source": [
    "유저와 문제 풀이에 관해 분석한 데이터를 바탕으로 가설을 세우고 검증해볼게요.<br/>\n",
    "이를 통해 어떤 방법으로 추천 모델을 만들어야 할지, 필터링은 어떤 식으로 해야할지 고민해 봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6602d2e7-09ef-474e-a623-3e593cbbf31a",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5300241-24d7-4d0f-b318-90876af6c53a",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6e4c41-8e1f-4611-ba4c-cdb3e3181d36",
   "metadata": {},
   "source": [
    "### 가설. 유저 클래스별 많이 푸는 태그 유형이 있을 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4152f15-35bb-4e68-9eee-698baa439f27",
   "metadata": {},
   "source": [
    "#### 우선 unique한 태그 값을 구하여 새로운 칼럼으로 추가해 봅시다. 😏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ce562f54-275e-431f-a81f-fa49ed265809",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_tags = []\n",
    "all_tags = df_problems.tags.str.split(',').values\n",
    "\n",
    "for x in all_tags:\n",
    "    unique_tags.extend(x)\n",
    "    \n",
    "unique_tags = set(unique_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "9e3cd5ad-b349-49e4-a9e6-0850763a777a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>koosaga</td>\n",
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       "<p>63554 rows × 202 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id_x               handle  solved_count  user_class  tier  rating  \\\n",
       "0          1              koosaga         11075          10    31    3252   \n",
       "1          2             cki86201          6059          10    31    3231   \n",
       "2          3  mitnegativeinfinity          2188          10    31    3180   \n",
       "3          4                ainta          4111          10    31    3135   \n",
       "4          5               yclock          2670          10    31    3116   \n",
       "...      ...                  ...           ...         ...   ...     ...   \n",
       "63868  64350            hihimotga             6           0     0       0   \n",
       "63870  64720               number            26           0     0       0   \n",
       "63871  64728              ohjcms1             2           0     0       0   \n",
       "63872  64971           steven1010             1           0     0       0   \n",
       "63873  65116              wwowwww            23           0     0       0   \n",
       "\n",
       "       rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "0                        2802              250                     175   \n",
       "1                        2781              250                     175   \n",
       "2                        2730              250                     175   \n",
       "3                        2685              250                     175   \n",
       "4                        2666              250                     175   \n",
       "...                       ...              ...                     ...   \n",
       "63868                       0                0                       0   \n",
       "63870                       0                0                       0   \n",
       "63871                       0                0                       0   \n",
       "63872                       0                0                       0   \n",
       "63873                       0                0                       0   \n",
       "\n",
       "               exp  ...  rb_tree  graph_traversal  sliding_window  \\\n",
       "0      14225816820  ...        0                0               0   \n",
       "1      10292773001  ...        0                0               0   \n",
       "2       6393679340  ...        0                0               0   \n",
       "3       6940261423  ...        0                0               0   \n",
       "4       4132785157  ...        0                0               0   \n",
       "...            ...  ...      ...              ...             ...   \n",
       "63868         1920  ...        0                0               0   \n",
       "63870            0  ...        0                0               0   \n",
       "63871            0  ...        0                0               0   \n",
       "63872            0  ...        0                0               0   \n",
       "63873            0  ...        0                0               0   \n",
       "\n",
       "       topological_sorting interpreter  splay_tree pick  hirschberg  bitset  \\\n",
       "0                        0           0           0    0           0       0   \n",
       "1                        0           0           0    0           0       0   \n",
       "2                        0           0           0    0           0       0   \n",
       "3                        0           0           0    0           0       0   \n",
       "4                        0           0           0    0           0       0   \n",
       "...                    ...         ...         ...  ...         ...     ...   \n",
       "63868                    0           0           0    0           0       0   \n",
       "63870                    0           0           0    0           0       0   \n",
       "63871                    0           0           0    0           0       0   \n",
       "63872                    0           0           0    0           0       0   \n",
       "63873                    0           0           0    0           0       0   \n",
       "\n",
       "       point_in_non_convex_polygon  \n",
       "0                                0  \n",
       "1                                0  \n",
       "2                                0  \n",
       "3                                0  \n",
       "4                                0  \n",
       "...                            ...  \n",
       "63868                            0  \n",
       "63870                            0  \n",
       "63871                            0  \n",
       "63872                            0  \n",
       "63873                            0  \n",
       "\n",
       "[63554 rows x 202 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 새로운 칼럼으로 추가하기\n",
    "tmp = df_usp.copy()\n",
    "\n",
    "for x in unique_tags:\n",
    "    tmp[x] = 0\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "fb805e1e-5f6f-47a9-b51e-980f54445ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(list(tmp.user_class.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d1acfea1-fec7-4d53-9e74-03d28c74ca7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_user_tag(user_class):\n",
    "\n",
    "    users = tmp[tmp.user_class == user_class].handle.values\n",
    "    \n",
    "    # 특정 유저가 푼 problems 구하기\n",
    "    for user in tqdm(users):\n",
    "        df_one_user_problems = df_usp[df_usp.handle == user].problems.values[0]\n",
    "        one_user_problems = df_one_user_problems.split(',')\n",
    "\n",
    "        # 특정 유저가 푼 problems의 태그 값들 구하기\n",
    "        user_tag = []\n",
    "        for i in range(len(one_user_problems)):\n",
    "            one_problem = df_problems[df_problems.problem_id == int(one_user_problems[i])]\n",
    "            one_problem_tags = ','.join(one_problem.tags.values).split(',')\n",
    "            user_tag.extend(one_problem_tags)\n",
    "\n",
    "        # 특정 유저가 푼 태그별 개수 구하기: e.g. counts = {'arithmetic': 1, 'implementation': 3, 'math': 10, ... }\n",
    "        counts = dict()\n",
    "        for i in user_tag:\n",
    "            counts[i] = counts.get(i, 0) + 1\n",
    "\n",
    "        # 특정 유저의 태그 칼럼에 각 태그 값 넣기\n",
    "        idx = tmp[tmp['handle'] == user].index\n",
    "        for k, v in counts.items():\n",
    "            tmp.loc[idx, k] = v\n",
    "\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "de4aa154-cbc5-4156-b5c4-a28a1ac5353f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 19/19 [00:22<00:00,  1.20s/it]\n"
     ]
    },
    {
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tag    num\n",
       "0                   math  15201\n",
       "1         implementation  12845\n",
       "2        data_structures   9368\n",
       "3                     dp   9159\n",
       "4                 graphs   8114\n",
       "..                   ...    ...\n",
       "179        discrete_sqrt      5\n",
       "180              rb_tree      3\n",
       "181                 pick      3\n",
       "182  generating_function      2\n",
       "183               a_star      0\n",
       "\n",
       "[184 rows x 2 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 클래스 10인 유저가 많이 푼 태그 목록\n",
    "class_num = 10\n",
    "\n",
    "tmp_10 = class_user_tag(class_num)\n",
    "tags_10_sum = pd.DataFrame(tmp_10[tmp_10.user_class == class_num].sum(axis=0)[unique_tags]).reset_index()\n",
    "tags_10_sum.columns = ['tag', 'num']\n",
    "tags_10_sum = tags_10_sum.sort_values('num', ascending=False).reset_index(drop=True)\n",
    "tags_10_sum#.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5926fd16-e746-490d-97ba-33fd26a46a0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 493/493 [02:30<00:00,  3.28it/s]\n"
     ]
    },
    {
     "data": {
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       "      <td>dp</td>\n",
       "      <td>56555</td>\n",
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       "      <td>data_structures</td>\n",
       "      <td>48240</td>\n",
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       "      <th>5</th>\n",
       "      <td>graph_traversal</td>\n",
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       "      <td>string</td>\n",
       "      <td>34259</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bruteforcing</td>\n",
       "      <td>28813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sorting</td>\n",
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       "</table>\n",
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      "text/plain": [
       "               tag     num\n",
       "0             math  102784\n",
       "1   implementation  100013\n",
       "2           graphs   57938\n",
       "3               dp   56555\n",
       "4  data_structures   48240\n",
       "5  graph_traversal   36531\n",
       "6       arithmetic   35454\n",
       "7           string   34259\n",
       "8     bruteforcing   28813\n",
       "9          sorting   26934"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 클래스 6인 유저가 많이 푼 태그 목록\n",
    "class_num = 6\n",
    "\n",
    "tmp_6 = class_user_tag(class_num)\n",
    "tags_6_sum = pd.DataFrame(tmp_6[tmp_6.user_class == class_num].sum(axis=0)[unique_tags]).reset_index()\n",
    "tags_6_sum.columns = ['tag', 'num']\n",
    "tags_6_sum = tags_6_sum.sort_values('num', ascending=False).reset_index(drop=True)\n",
    "tags_6_sum.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e9a380ec-7723-4ee0-aff2-76f9fc77268f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5326/5326 [10:01<00:00,  8.86it/s]\n"
     ]
    },
    {
     "data": {
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       "      <td>123421</td>\n",
       "    </tr>\n",
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       "      <th>8</th>\n",
       "      <td>bruteforcing</td>\n",
       "      <td>107981</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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      "text/plain": [
       "               tag     num\n",
       "0   implementation  475529\n",
       "1             math  414880\n",
       "2       arithmetic  179009\n",
       "3               dp  150415\n",
       "4           graphs  149483\n",
       "5           string  138131\n",
       "6  data_structures  129951\n",
       "7  graph_traversal  123421\n",
       "8     bruteforcing  107981\n",
       "9          sorting  107517"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 클래스 3인 유저가 많이 푼 태그 목록\n",
    "class_num = 3\n",
    "\n",
    "tmp_3 = class_user_tag(class_num)\n",
    "tags_3_sum = pd.DataFrame(tmp_3[tmp_3.user_class == class_num].sum(axis=0)[unique_tags]).reset_index()\n",
    "tags_3_sum.columns = ['tag', 'num']\n",
    "tags_3_sum = tags_3_sum.sort_values('num', ascending=False).reset_index(drop=True)\n",
    "tags_3_sum.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "0280405f-f44b-4547-b4d0-7efc7a96058f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 24763/24763 [12:29<00:00, 33.04it/s]\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bfs</td>\n",
       "      <td>99638</td>\n",
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       "      <th>9</th>\n",
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       "      <td>95406</td>\n",
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      "text/plain": [
       "               tag     num\n",
       "0   implementation  468440\n",
       "1             math  360208\n",
       "2       arithmetic  182230\n",
       "3           graphs  154133\n",
       "4               dp  138431\n",
       "5  graph_traversal  121764\n",
       "6     bruteforcing  104355\n",
       "7  data_structures  103389\n",
       "8              bfs   99638\n",
       "9           string   95406"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 클래스 0인 유저가 많이 푼 태그 목록\n",
    "class_num = 0\n",
    "\n",
    "tmp_0 = class_user_tag(class_num)\n",
    "tags_0_sum = pd.DataFrame(tmp_0[tmp_0.user_class == class_num].sum(axis=0)[unique_tags]).reset_index()\n",
    "tags_0_sum.columns = ['tag', 'num']\n",
    "tags_0_sum = tags_0_sum.sort_values('num', ascending=False).reset_index(drop=True)\n",
    "tags_0_sum.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd6af32",
   "metadata": {},
   "source": [
    "유저별 수준과 tag는 상관관계가 있다고 보았고, tag 정보를 유저가 푸는 문제를 파악하는데 사용할 수 있을 것으로 판단했습니다. 🙂"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe2ee69-b72e-4313-ba0d-8f480a95ff0f",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42622a0c-e015-49bc-8713-225aa7e486e9",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8feb56ed-0119-41a7-a71c-d02e349c04ad",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf23d4d6-1944-4fbc-becc-8912263dbeec",
   "metadata": {},
   "source": [
    "### 실험: 같은 클래스 내 태그별 평균적으로 푼 횟수와 한 유저가 푼 횟수를 비교해봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85901b8-3537-4b21-b7f5-e38d9a2a487e",
   "metadata": {},
   "source": [
    "#### 유저 클래스가 10인 유저를 선정해서 분석해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2b6d6153-61e0-479c-b01d-87baa47c684d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_user_tag(user):\n",
    "    df_one_user_problems = df_usp[df_usp.handle == user].problems.values[0]\n",
    "    one_user_problems = df_one_user_problems.split(',')\n",
    "\n",
    "    # 특정 유저가 푼 problems의 태그 값들 구하기\n",
    "    user_tag = []\n",
    "    for i in range(len(one_user_problems)):\n",
    "        one_problem = df_problems[df_problems.problem_id == int(one_user_problems[i])]\n",
    "        one_problem_tags = ','.join(one_problem.tags.values).split(',')\n",
    "        user_tag.extend(one_problem_tags)\n",
    "\n",
    "    # 특정 유저가 푼 태그별 개수 구하기: e.g. counts = {'arithmetic': 1, 'implementation': 3, 'math': 10, ... }\n",
    "    counts = dict()\n",
    "    for i in user_tag:\n",
    "        counts[i] = counts.get(i, 0) + 1\n",
    "    # 특정 유저의 태그 칼럼에 각 태그 값 넣기\n",
    "    #idx = tmp[tmp['handle'] == user].index\n",
    "    #for k, v in counts.items():\n",
    "    #    tmp.loc[idx, k] = v\n",
    "\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "fa141e42-d5be-4265-a169-d57e23d84a0f",
   "metadata": {},
   "outputs": [
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2333</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ainta</td>\n",
       "      <td>4111</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3135</td>\n",
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       "      <td>299,915,1566</td>\n",
       "      <td>4</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1007,1008,1009,1...</td>\n",
       "      <td>4280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>yclock</td>\n",
       "      <td>2670</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3116</td>\n",
       "      <td>2666</td>\n",
       "      <td>250</td>\n",
       "      <td>175</td>\n",
       "      <td>4132785157</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>299,439</td>\n",
       "      <td>5</td>\n",
       "      <td>1000,1001,1002,1003,1004,1005,1007,1008,1009,1...</td>\n",
       "      <td>2765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_x               handle  solved_count  user_class  tier  rating  \\\n",
       "0     1              koosaga         11075          10    31    3252   \n",
       "1     2             cki86201          6059          10    31    3231   \n",
       "2     3  mitnegativeinfinity          2188          10    31    3180   \n",
       "3     4                ainta          4111          10    31    3135   \n",
       "4     5               yclock          2670          10    31    3116   \n",
       "\n",
       "   rating_by_problems_sum  rating_by_class  rating_by_solved_count  \\\n",
       "0                    2802              250                     175   \n",
       "1                    2781              250                     175   \n",
       "2                    2730              250                     175   \n",
       "3                    2685              250                     175   \n",
       "4                    2666              250                     175   \n",
       "\n",
       "           exp  rival_count  reverse_rival_count  max_streak  rank  \\\n",
       "0  14225816820            5                   94          24     1   \n",
       "1  10292773001            0                   26           3     2   \n",
       "2   6393679340            0                   12           8     3   \n",
       "3   6940261423            0                   14           2     4   \n",
       "4   4132785157            0                   14           3     5   \n",
       "\n",
       "   organization  id_y                                           problems  \\\n",
       "0   394,439,753     1  1000,1001,1002,1003,1004,1005,1006,1007,1008,1...   \n",
       "1           299     2  1000,1001,1002,1003,1004,1005,1006,1007,1008,1...   \n",
       "2           440     3  1000,1001,1019,1056,1067,1144,1150,1311,1372,1...   \n",
       "3  299,915,1566     4  1000,1001,1002,1003,1004,1005,1007,1008,1009,1...   \n",
       "4       299,439     5  1000,1001,1002,1003,1004,1005,1007,1008,1009,1...   \n",
       "\n",
       "     num  \n",
       "0  11226  \n",
       "1   6138  \n",
       "2   2333  \n",
       "3   4280  \n",
       "4   2765  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usp[df_usp.user_class == 10].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58087cff-862f-444e-9f4b-b80ce1d2dea1",
   "metadata": {},
   "source": [
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0052285-dc01-452a-8ed9-48beb9e0ccbb",
   "metadata": {},
   "source": [
    "#### 랜덤으로 뽑은 arnold518 핸들을 지닌 유저를 분석해볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8c10a7dc-85c2-431f-bca8-5494c774ffea",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_name = 'arnold518'\n",
    "user_counts = one_user_tag(user_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "85ebd7bc-d48a-4a4b-aca3-565ed36ad972",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_10_tag = tags_10_sum.copy()\n",
    "user_10_tag['user'] = 0\n",
    "\n",
    "for k, v in user_counts.items():\n",
    "    idx = user_10_tag[user_10_tag.tag == k].index\n",
    "    user_10_tag.loc[idx, 'user'] = v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "93ecfaab-2e99-4c5c-aebe-5bbe5d777d43",
   "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>tag</th>\n",
       "      <th>num</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>math</td>\n",
       "      <td>800.052632</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>implementation</td>\n",
       "      <td>676.052632</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data_structures</td>\n",
       "      <td>493.052632</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dp</td>\n",
       "      <td>482.052632</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>graphs</td>\n",
       "      <td>427.052632</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>discrete_sqrt</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>rb_tree</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>pick</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>generating_function</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>a_star</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tag         num  user\n",
       "0                   math  800.052632   116\n",
       "1         implementation  676.052632    56\n",
       "2        data_structures  493.052632   339\n",
       "3                     dp  482.052632   346\n",
       "4                 graphs  427.052632   196\n",
       "..                   ...         ...   ...\n",
       "179        discrete_sqrt    0.263158     0\n",
       "180              rb_tree    0.157895     0\n",
       "181                 pick    0.157895     0\n",
       "182  generating_function    0.105263     0\n",
       "183               a_star         0.0     0\n",
       "\n",
       "[184 rows x 3 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_10_tag.num = user_10_tag.num / len(df_usp[df_usp.user_class == 10])\n",
    "user_10_tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5fb7c2f4-3d95-4420-974a-4a4b0ada9e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmFDZlStzLlZ0vlTzsST1lHrjlDrB3Web2ctEHfrBFaw+gWivlK8LkStZXfI/ozORszbFzFoQbQmOcPdrcytYphOFPPkllj8SuYc3lrLulFLmxZu4jydykxsRudlDgAfTTWEKcVPKT3eXzGeW5U6i7ckGRHuMRpQs5fiRCDIPK2Xb39Lf74FFbP7eRst7eC9PL6KKaV93n9uW0+Zv7/I9kH/jmu9G5u6PEO3X2hFthS4l2qKU1aZwAlE1NV9VjrN1iRzpLd39k9xMK70NVGVKt0szAWhjZq3yAr4uwAx3/62M7b6n9GO9MnJVMBfEMsSDyTt5898m7klLUcqDexXOufJ8n/5W9XtXRe6YOYT4rvlyQd+uwBXuPrf9oJn1q8Z0PE88pO5u0SnNOpRsM7gN0dZqQCodI5VuVBQ0zWL+DLNF86Z/JK6h6xMlfPkmAXj0+LpNOte3INpZjUhpLU113xMq/C7pGncqcKqZLUtU+77UzD5N16sfiaDoTOb3c970fOdQFa5X1SZdo3a2aEu/IZEB8YhF+9PKXperKvd7bUfpwdSn5Wz7GxX/blOJmgRHmdmqRJX/283sPXf/iAW7Ny/ote8HImOisip1LlZ0vhTyWJK6QSV7UpdcSnQesH/+Aose3LZJk6OAzma2UWZ5K+Ii9lL+tgshv7ONHYE3U7XQ5sT5M/cBOpXe9K/kez9DBBJvuvvovNfXFW3s7nPSBf504mawVErXm+R1tkJ0DDCHaGNV1vt9SLRl2D29nk5V37Lp7QV8W0p630/r5Hr5G5DbyKKjlzJ7GssoLbc59/CQ3cdLEQ+KWW8A2+dVoyrzd3D3ae4+grihr1hOmkYR1XbndmZhMTzAeiz4cVbad1mPaLNTXd4gHj52yXyGpeny0vsG0D9v/+1U1sqZ925JnHNvLmA6v0l/80sE10h/vy5ju4U957LGEgHfgLz5FX7vhfApUe21Zynn0OjM+daSkt+xMdX4UJauE3cT5/luRClLtmOslsT14o/MvN2oOHN4HNAzBeU5+T3BPkuU7LUrYx+UGGLE3We6+0NECVNF52p13hPGEW1gs8rs1TZVrz6O+N1y6cxd4z8s5XuWF7Dkv3dlr1fVzt1nu/uzRPDQjej0o7LX5ap6lWjDungZx0h+oJxV4ndLGUGbl7Wyu79HVClvxLweiBfq3lyBZ4Dd8s6R8izQuVjR+VLIY0kKSyV7Ume4+0NmdjFwk0WvXg8QddGXJ3JNvwYed/cnzOwVoifCE4ncsuOIC+N8QwgshL5mdjaRE74TEbQMSGmdZtGF+almNp24IJ9IVE+qsPt2okTudSK37WYix7B7+oxh7v5c/gYpV+4JojODz4iH32OJh9aP02qnAU+Y2S1Eda1ViJzlG1J7s/LcSXTC0Y7o9SvrVuI3eM6iS/IviSopawHfu/sl7v6hmT1I9FralshtP55oY1CRT4ABZrYDccP+Ls0bB1xkZqcQvcGdTjwwZ51PPOzdkb53rvdMSKUHZnYoUbr2eHrvZYmg+NZy0jSMqM7zmJmdSrSzO434ra6rxHfKeo04lm8wswuIUr4hpXyXKnP3j83sv8CVKQj6gtgPy1N6iWxObv/dZWY3EW3HDs6ukI69h4H/I9rYdCR6s1uc+Xty7Uu0uVktTeeCzzfc/Rt3n2hm/wPOTw8976V1hwB3u3t+ldzc91vYcy77Xn+m3+FCi27ZXyR6sst/wK827j7HzI4Fbkvnx2NEJscyRGc7u6QS2aeAIyza7P0IHEGmx91qcidRnW0Q8L+8ICsXkN2SjoeViOvr1Are839EJxo3Wgyn8jei85m53P1TM7uWOFcvIGoLtEifsZy7/z2VYh6U3u9b4rp4KCXbeZVQA/eE+4ErzOwkIjNkZ/JK+c3sfiKj420iONmFeKZ6Ia1yMdEu9Vkzu4I417sQbVRfcvf/lvXhVbxeVYtU2nUhcYx8SZSM/Rt4191/NLNfqdx1uUo8xuocAlyWgsgXiGBsOWBTdy+vx+P7iXPn7ZT2v5N3bbAYTuh+InPTiWvkr8T9GKpwb14ApxPH0wtmdhFxnP6N6EBlviGnqMS5WNH5UshjSeoQrwO9xOilV/ZF3FhHEg9xvxOBzYVA18w6nYiL1U/EjfZ5YM289/ma+XtUG8b8PcYdQMle1jZJ01sTD2QziJvb4Xnb9SJy6n4lLrInMH/PXyXeO2/75YleKH9M32EMEUT0KGO/NCe6Wv80pWkK8QC+St56uxONsn9P6T4baFJRmtL3caIKU7tSPr8dcBlRKpJ77/uA9TPrLEoEmb8SVXBOTb/d1xX85h2JG/CPKQ1D0vw1iRvvTKLziQPK+A13S/tvFpGTv0V6nx3S8nWJxvTfpXW+IoKc5hWkaxniJvozEaw9TKZHzLTOc1SiN06iSs4H6bu8R7THKrFtad+tnOO2J5meDtO8VkT1nIlEzvtoYOuK0kvc/LP7b01K9mbaIv3WY9P7TiMeHtYpJa1fU7KXTc++V1qnbTouvmDesX8BFfTAyEKcc+RdD4h2MGcS7Tt/Bm4nhsRwKtcbZ5vMvPl+i3KuQX2J4PJXYDpRnfUs0jlKBAT3p2UT0375B6Vfo1bOe+/5ftsyvoOl/ef5x0davm/mt3kNWLuU/VfacXRA2m4Gca6sl79f0mcfQ7TJ+i3t/+eZ17tgb+K6mDvWxgHXkunVsozvVJl7wjAq1xtnUyJY+z6932VE9dvsb3A8cX5NS8fPKKK6XfZ9FieGo8mdj18TGSYrVXDcLPD1qrRjgkr0TlnK+3QmeqT8Mn3290Qvkktm1qnwulza55SWxnLSuQ8RTM9Mv8Eo4F8V/G5tiCFRfkzpHkwEWNnrw1Di/vgzETSNBDbMe59y781lfY/KvIBVibZzP2eOm83L+Q3LPRep4HypyrGkV/G9LB0MIpKY2SbEDWAVd/+gsKmRqjCzfYgHlmW8ZM+rIiIiIg2GqnGKSL1nZtcQ1d9+ItqCDQYeUaAnIiIiDZmCPREpBosBV6e/PxDtTU4oaIpERKRBSJ3BlNfp4Z+uqnRSIKrGKSIiIiJSRalTmdPKWWVTX7jOXUSqTMGeiIiIiEgVmdniRIc8ZfnUyx82QqTG1Otgr2PHjt6zZ89CJ0NERERERKQg3nzzzSnu3qm0ZfW6zV7Pnj0ZPXp0oZMhIiIiIiJSEGb2TVnLymtMKiIiIiIiIvWUgj0REREREZEipGBPRERERESkCCnYExERERERKUIK9kRERERERIqQgj0REREREZEiVK+HXhARERERyZo+fTqTJk1i9uzZhU6KyEJr2rQpnTt3pm3btlXaXsGeiIiIiBSF6dOnM3HiRLp3707Lli0xs0InSaTK3J2ZM2cyfvx4gCoFfDVajdPMBpnZh2b2gZn918xamNnSZjbKzMaY2Z1m1iyt2zxNj0nLe9Zk2kRERESkuEyaNInu3bvTqlUrBXpS75kZrVq1onv37kyaNKlK71FjwZ6ZdQeOAvq4+8pAY2AP4HzgEnfvBfwEHJw2ORj4Kc2/JK0nIiIiIlIps2fPpmXLloVOhki1atmyZZWrJdd0By1NgJZm1gRoBUwANgPuScuHAzuk/wekadLyzU1ZMiIiIiKyAPT4KMVmYY7pGmuz5+7jzexC4FtgJvAk8CYw1d3/SKuNA7qn/7sDY9O2f5jZNGAxYEr2fc3sEOAQgCWXXLKmkl+/DBlSnJ8lIiIiIiJVVpPVOBclSuuWBhYHWgPbLOz7uvv17t7H3ft06tRpYd9ORERERESkKNVkb5xbAF+5+2QAM7sPWB9ob2ZNUuleD2B8Wn88sAQwLlX7bAf8UIPpExEREZEGoFAVk+pzhaghQ4Zw5ZVXMmXKlHLX69OnDyuvvDLDhg0DYNiwYRx44IHzrXfNNdcwcODAudNXX301jzzyCK+99ho//vgjI0eOZJNNNplvuzvuuIMLLriAzz77jHbt2rH55ptz3nnnsfjiiy/U92soarLN3rfAOmbWKrW92xz4CBgJ7JLW2R94IP3/YJomLX/W3b0G0yciIiIiIjXg2Wef5dVXX5372mmnnUosv/XWW/nxxx/Zeuuty3yPBx98kD333JP11luPBx54gPPPP58XXniBfv36MWfOnJr+CkWhJtvsjTKze4C3gD+At4HrgUeAO8zsrDTvprTJTcBtZjYG+JHouVNERERERCowa9YsWrRoUehkzLXmmmvSpk2bMpe/8sorNGrUiA8++ID//ve/pa4zYsQIVl99da688sq589q2bcuAAQP49NNPWWGFFao93cWmRnvjdPfT3H15d1/Z3fd199/c/Ut3X8vde7n7ru7+W1p3VprulZZ/WZNpExERERGpi1599VX69+9Pt27daN26Nautthq333773OXDhg3DzHj99dfZZJNNaNmyJUOHDp07//3332fLLbekdevWLL/88tx3333zfcaVV17JsssuS/PmzenVqxeXXHJJhen64IMPWH/99WnRogUrrLACDz74YJW/Y6NGFYchs2fPpl27diXmtW/fHogBx6ViNT30goiIiIiILIBvvvmG9ddfn5tuuomHHnqInXfemQMPPHC+ErA999yT7bffnkcffZTttttu7vy99tqL/v37c//997Psssuyxx57MG7cuLnLb7jhBv75z3/Sv39/HnroIXbddVeOPfZYzjvvvDLTNHPmTLbeemt++eUXRowYweDBgznmmGP49ttvS13/L3/5C02aNKF3795cd911VdoPBx10EC+++CK33nor06dP57PPPmPw4MFsttlmrLjiilV6z4amJjtoERERERGRBbTHHvNaM7k7G220EePGjeOGG25gzz33nLvsqKOO4uijj547/e677wIwaNAgDjroIADWWGMNunTpwsMPP8zAgQOZM2cOQ4YM4YADDuCiiy4CYKuttmLatGmce+65HHPMMaVWB73llluYNGkSo0aNokePHgD07NmTDTbYoMR63bp148wzz2Sttdbizz//5I477mDgwIHMmDGDQYMGLdB+6NevH8OGDePggw9m//2ja4/11ltvoUoUGxqV7ImIiIiI1CE//fQTRx11FEsttRRNmzaladOmXH/99Xz22Wcl1uvXr1+p22+11VZz/19sscXo3Lnz3JK9cePG8d1337HrrruW2Gb33Xdn+vTpvP/++6W+5+uvv84aa6wxN9ADWH/99encuXOJ9bbeemsGDx7MVlttRd++fRk+fDi77bYbZ5111gJ3qjJy5EgGDhzI0UcfzciRI7njjjv48ccf2XHHHfnzzz8X6L0aKpXsiYiIiIjUIQcccACvvfYap5xyCiuuuCJt27blmmuu4YEHHiixXpcuXUrdPteuLadZs2bMmjULgAkTJpS6bW76xx9/LPU9v//++/kCO6DUefl22WUX7rrrLr7++muWWWaZCtfPOfbYY+nfvz/nn3/+3HmrrbYayy+/PA888MB8PXzK/BTsiYiIiIjUEbNmzeLhhx/mqquuKjEuXWmlYjG62YLp1q0bAJMmTSoxf+LEiQB06NCh1O26du3KJ598Mt/8/PcpTS6dC5reTz75pES1VYDevXvTsmVLvvjiiwV6r4ZK1ThFREREROqI3377jTlz5tC8efO5837++edqa6fWo0cPFl98ce6+++4S8++66y7atm3LKqusUup2a665Jm+++WaJjl5efvnlSgV799xzDx07dmSppZZaoLQutdRSvPXWWyXmffzxx8ycOZOePXsu0Hs1VCrZExERERGpI9q1a8eaa67JGWecQdu2bWnUqBHnnXce7dq1Y/r06Qv9/o0aNWLIkCEceuihLLbYYmy55ZY8//zzXHPNNZxzzjlljtV34IEHctZZZ9GvXz+GDBnCzJkzOeWUU+jYsWOJ9XbeeWfWWmstVl11Vf7880/uvPNO7rzzTi6//PISwy2MHj2ar7/+mrFjxwLw/PPPM2XKFHr27EmfPn0AGDhwIIMGDWLxxRenb9++TJw4kTPOOIOePXuy7bbbLvS+aAgU7ImIiIhIURsypNApWDAjRozg0EMPZb/99mOxxRbjyCOPZMaMGSUGF18Y//jHP5g1axaXXXYZl112GT169OCiiy4qt7fMVq1a8cQTTzBw4ED22GMPevbsyUUXXcRZZ51VYr3evXtz8803M3bsWNydFVdckVtvvZV99923xHpXXnklw4cPnzs9JP1I+++/P8OGDQOit9FmzZpxzTXXcO2119K+fXs22GADzj33XFq3bl0t+6LYWX0ekLBPnz4+evToQiej8GrzClbfrpYiIiLSYHz88cessMIKhU6GSLUr79g2szfdvU9py9RmT0REREREpAgp2BMRERERESlCCvZERERERESKkII9ERERERGRIqRgT0REREREpAgp2BMRERERESlCCvZERERERESKkII9ERERERGRIqRgT0REREREpAgp2BMRERERkRp1wAEH0KdPn0Ino8FpUugEiIiIiIjUqCFDGtbniiQq2RMREREREalhM2fOrPXPVLAnIiIiIlJHfPjhh2yzzTZ06NCB1q1bs8IKK3DVVVfNXf7II4+w5ZZb0rlzZ9q2bcs666zDk08+WeI9hgwZQseOHRk1ahR9+vShZcuWbLDBBnz11VdMmjSJHXbYgTZt2rDCCivw7LPPzpeGG2+8kZVWWonmzZuz1FJLccEFF1Qq7TfccAOrrLIKLVq0oEuXLuyyyy5Mmzat1HUnTJjAQQcdxDLLLEPLli1ZbrnlGDx4ML///nuJ9c4991x69eo19z232WYbvv/+ewBmz57Ncccdx5JLLknz5s1ZfPHF2XHHHed7j6xNNtmEXXbZpcS85557DjPjgw8+qNTnAvz4448ccsghdOnShRYtWrDeeusxatSoEu9rZlx88cUcc8wxdOrUiVVWWQWAl156iQ033JC2bdvStm1bVlttNe6+++5K7OEFp2qcIiIiIiJ1xPbbb88KK6zA//3f/9G8eXM+/fRTpk+fPnf5V199xfbbb89xxx1Ho0aNeOyxx+jbty8vvPAC66+//tz1ZsyYwSGHHMIJJ5xA69atOeqoo9h3331p3rw5ffv25fDDD+eCCy5g1113ZezYsbRq1QqAoUOHctJJJ3HCCSewySab8Oabb3LKKafQqlUrjjzyyDLTfdZZZ3Hqqady+OGHM3ToUGbMmMEjjzzCL7/8Qrt27eZbf8qUKXTo0IGLL76YRRddlM8++4whQ4YwefJkrrvuOgBuvfVWzjnnHM4//3xWWmklfvjhB5599ll+/fVXIAKy22+/nfPOO4+ll16a77//nkcffZQ///xzoX6Dij73t99+Y4sttmDq1KkMHTqUzp07c80117DFFlvw+eef07Vr17nvNXToUDbaaCNuu+025syZw/Tp09luu+0YMGAAp556Ku7O+++/z9SpUxcqzWVRsCciIiIiUgdMmTKFr776igceeGBuKdDmm29eYp1swDVnzhw23XRTPvzwQ2666aYSwd7MmTO5/PLL2XjjjQH47rvvOOKIIzj99NM57rjjAOjRowcrrbQSzz//PH379mX69OmcfvrpDB48mNNOOw2ALbfckhkzZnDWWWdx2GGH0bhx4/nSPXXqVM455xyOOeYYLr744rnzd9pppzK/6yqrrMKFF144d3r99dendevWHHTQQVxxxRU0a9aM119/na222orDDz+81Pd8/fXX2Wuvvdh///3nztttt93K/MzKquhz/+///o8PPviADz/8kGWXXRaALbbYgt69e3PRRRcxdOjQuet269aNO++8c+706NGjmTZtGldeeSWLLLIIAFtttdVCp7ksNVaN08x6m9k7mdd0MzvGzDqY2VNm9nn6u2ha38zscjMbY2bvmdnqNZU2EREREZG6pkOHDiyxxBIMHDiQO++8k0mTJs23zrhx49h///3p3r07TZo0oWnTpjz55JN89tlnJdZr1qwZG2644dzpXr16AbDZZpvNN2/8+PEAvPrqq/z666/suuuu/PHHH3Nfm222GRMnTmTcuHGlpvvVV19l5syZHHjggZX+ru7OpZdeyoorrkjLli1p2rQpe++9N7/99hvffvstAKutthqPPvoop512Gq+//vp8JXarrbYaw4YN44ILLuC9997D3Sv9+eWp6HOffvpp1lhjDZZeeum5+whg4403ZvTo0SXW3XbbbUtM/+Uvf6FNmzbstddePPDAAzVWopdTY8Geu3/q7qu5+2rAGsAM4H7gROAZd18WeCZNA/QFlk2vQ4BraiptIiIiIiJ1TaNGjXjyySfp2rUrBx10EF27dmXDDTfk7bffBqIkr3///rzyyiucccYZjBw5kjfeeIO+ffsya9asEu+1yCKL0KjRvEf9Zs2aAdC+ffv55uW2nTJlCgArrbQSTZs2nfvadNNNARg7dmyp6f7hhx+AKMWqrEsvvZTjjjuOHXfckQceeIDXX399btvEXHoOOuggzjnnHO666y7WXnttunTpwuDBg+cGX4MHD+aII47g6quv5q9//StLLLEEl112WaXTUJaKPnfKlCm89tprJfZR06ZNueWWW+bbR126dCkxveiii/LUU08xe/ZsdtttNzp16kS/fv348ssvFzrdpamtapybA1+4+zdmNgDYJM0fDjwH/BsYANzqEZK/Zmbtzaybu0+opTSKiIiIiBTU8ssvz7333svs2bN58cUX+fe//02/fv0YN24cY8aM4e233+axxx5jm222mbtNdfXy2KFDBwAefvjh+YIUgN69e5e63WKLLQZEpysdO3as1Gfdfffd7LLLLpx99tlz53300Ucl1mnUqBGDBg1i0KBBjB07lttvv52TTz6ZHj16MHDgQFq0aMEZZ5zBGWecweeff861117LMcccQ+/evUvsn6wWLVrM14HLTz/9tECf26FDB/r06cM118xfNtW8efMS02Y23zrrrLMOjz/+ODNnzuTpp5/mX//6F3vttRevvfZa+TutCmqrN849gP+m/7tkArjvgdyR1B3IhsLj0rwSzOwQMxttZqMnT55cU+kVERERESmYpk2bstlmm/Gvf/2LCRMmMHXq1LlBXTag+Oabb3j55Zer5TPXXXddWrZsyXfffUefPn3me+XamJW13fDhwyv9WTNnzpwvMLr99tvLXH+JJZbgxBNPpFevXvMFhQDLLrssF154Ic2bNy91eU6PHj345JNPSszL7820os/dfPPNGTNmDEsuueR8+yjX1rIyWrZsyfbbb89BBx1UbpoXRo2X7JlZM6A/8J/8Ze7uZrZAlWvd/XrgeoA+ffpUT8VcEREREZECe++99zjuuOPYfffdWWaZZfjpp584//zz+etf/zp3KIYePXpw7LHHcuaZZ/Lzzz9z2mmn0b37fOUjVdK+fXuGDBnC0UcfzTfffMNGG23EnDlz+Oyzzxg5ciT3339/mdudcsopnHzyyfz+++9su+22/PbbbzzyyCNlpm/LLbfk8ssvZ+211+Yvf/kLt99+O2PGjCmxzqGHHkqHDh1YZ511aNeuHSNHjuTzzz/n/PPPB2DHHXdkjTXW4G9/+xstW7bknnvu4Y8//mCjjTYq8zvuuOOO3HTTTQwaNIh+/foxcuRIHn/88QX63P32249rr72WTTbZhOOOO45lllmGH374gddff52uXbsyaNCgMj//kUce4eabb2aHHXZgySWXZPz48Vx33XUl2lJWp9qoxtkXeMvdJ6bpibnqmWbWDci1PB0PLJHZrkeaJyIiIiJSdUOGFDoFldK1a1e6dOnC2WefzXfffUf79u3ZdNNN5wYZzZs357777uOII45gl112oUePHpx88sk899xzJcaIWxgnnHACiy++OJdccgkXXXQRLVq0YLnllmP33Xcvd7v//Oc/dOjQgcsuu4zrrruORRddlI022qjM0sBTTz2VyZMnM3jwYCB6u7z88svZfvvt566z7rrrcsMNN3Ddddcxa9YsevXqxQ033MAOO+wAwHrrrcedd97J0KFDmTNnDiuuuCL33nsvffr0KTOd/fr145xzzuHqq6/mxhtvZMCAAVx22WUMGDCg0p/bokULRo4cyamnnsppp53GxIkT6dy5M2uttRb9+/cvdz/16tULM+Okk05i0qRJdOrUie22245zzjmn3O2qyqqr15oyP8DsDuAJd78lTQ8FfnD388zsRKCDu59gZv2AI4FtgbWBy919rfLeu0+fPp7f402DVJsXsHpysRQREZGG5+OPP2aFFVYodDJEql15x7aZvenupUa4NVqyZ2atgS2BQzOzzwPuMrODgW+A3GAYjxKB3hii587K990qIiIiIiIiJdRosOfuvwKL5c37geidM39dB46oyfSIiIiIiIg0FLXVG6eIiIiIiIjUotoaZ0+k7lFbRxEREREpYirZExEREZGiUdOdD4rUtoU5phXsiYiIiEhRaNq06dyBx0WKxcyZM2natGmVtlWwJyIiIiJFoXPnzowfP54ZM2aohE/qPXdnxowZjB8/ns6dO1fpPdRmT0RERESKQtu2bQH47rvvmD17doFTI7LwmjZtSpcuXeYe2wtKwZ6IiIiIFI22bdtW+cFYpNioGqeIiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkWoRoM9M2tvZveY2Sdm9rGZrWtmHczsKTP7PP1dNK1rZna5mY0xs/fMbPWaTJuIiIiIiEgxq+mSvcuAx919eeCvwMfAicAz7r4s8EyaBugLLJtehwDX1HDaREREREREilaNBXtm1g7YCLgJwN1/d/epwABgeFptOLBD+n8AcKuH14D2ZtatptInIiIiIiJSzGqyZG9pYDJwi5m9bWY3mllroIu7T0jrfA90Sf93B8Zmth+X5pVgZoeY2WgzGz158uQaTL6IiIiIiEj9VZPBXhNgdeAad/8b8CvzqmwC4O4O+IK8qbtf7+593L1Pp06dqi2xIiIiIiIixaQmg71xwDh3H5Wm7yGCv4m56pnp76S0fDywRGb7HmmeiIiIiIiILKAaC/bc/XtgrJn1TrM2Bz4CHgT2T/P2Bx5I/z8I7Jd65VwHmJap7ikiIiIiIiILoEkNv/8/gdvNrBnwJXAgEWDeZWYHA98Au6V1HwW2BcYAM9K6IiIiIiIiUgU1Guy5+ztAn1IWbV7Kug4cUZPpERERERERaShqepw9ERERERERKQAFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRqtFgz8y+NrP3zewdMxud5nUws6fM7PP0d9E038zscjMbY2bvmdnqNZk2ERERERGRYlYbJXubuvtq7t4nTZ8IPOPuywLPpGmAvsCy6XUIcE0tpE1ERERERKQoFaIa5wBgePp/OLBDZv6tHl4D2ptZtwKkT0REREREpN6r6WDPgSfN7E0zOyTN6+LuE9L/3wNd0v/dgbGZbceleSWY2SFmNtrMRk+ePLmm0i0iIiIiIlKvNanh99/A3cebWWfgKTP7JLvQ3d3MfEHe0N2vB64H6NOnzwJtKyIiIiIi0lDUaMmeu49PfycB9wNrARNz1TPT30lp9fHAEpnNe6R5IiIiIiIisoBqrGTPzFoDjdz95/T/VsAZwIPA/sB56e8DaZMHgSPN7A5gbWBaprqniNQVQ4YU9+eJiIiIFImarMbZBbjfzHKfM8LdHzezN4C7zOxg4Btgt7T+o8C2wBhgBnBgDaZNRERERESkqNVYsOfuXwJ/LWX+D8Dmpcx34IiaSo+IiIiIiEhDUtMdtIiISE5tVklV9VcREZEGrxDj7ImIiIiIiEgNU7AnIiIiIiJShBTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRqjDYM7NtaiMhIiIiIiIiUn3KDPbMbG0zawyck5l3W62kSkRERERERBZKeSV7ewDPA8uY2flmtjeweu0kS0RERERERBZGecHev919A+Bb4GFgUaCrmb1mZnfWSupERERERESkSpqUs+xxM/sT6AR0BB4DDnL3dcysR62kTkRERERERKqkzJI9d98M6A/8AiwDnAn0MrP/EVU8RUREREREpI4qr2QPd59pZmPd/SIAM3sb+AewUW0kTkRERERERKqm3GAP5pbw5Vzp7pOBe2suSSIiIiIiIrKwFmhQdXe/qaYSIiIiIiIiItVngYI9ERERERERqR8U7ImIiIiIiBShBQr2zKyRmbWtqcSIiIiIiIhI9aiwgxYzGwEMBP4E3gDamtll7j60phMnIiLCkCHF/XkiIiI1pDIleyu6+3RgB2Jg9aWBfWsyUSIiIiIiIrJwKhPsNTWzpkSw96C7zwa8RlMlIiIiIiIiC6Uywd51wNdAa+AFM1sKmF6TiRIREREREZGFU5lB1S8HLs/M+sbMNq25JImIiIiIiMjCqkwHLf8qZfY0M3vT3d+p/iSJiIiIiIjIwqpMNc4+RG+c3dPrUGAb4AYzO6Gijc2ssZm9bWYPp+mlzWyUmY0xszvNrFma3zxNj0nLe1b1S4mIiIiIiDR0lQn2egCru/ux7n4ssAbQGdgIOKAS2x8NfJyZPh+4xN17AT8BB6f5BwM/pfmXpPVERERERESkCioT7HUGfstMzwa6uPvMvPnzMbMeQD/gxjRtwGbAPWmV4UQvnwAD0jRp+eZpfREREREREVlAFbbZA24HRpnZA2l6e2CEmbUGPqpg20uBE4BF0vRiwFR3/yNNjyOqhpL+jgVw9z/MbFpaf0r2Dc3sEOAQgCWXXLISyRcREREREWl4KizZc/cziXZ6U9NroLuf4e6/uvveZW1nZtsBk9z9zWpKay4917t7H3fv06lTp+p8axERERERkaJRmZI93P0NM/sGaAFgZku6+7cVbLY+0N/Mtk3btQUuA9qbWZNUutcDGJ/WHw8sAYwzsyZAO+CHBf1CIiIiIiIiUomSPTPrb2afA18Bz6e/j1W0nbv/x917uHtPYA/g2VQSOBLYJa22P5CrHvpgmiYtf9bdfQG+i4iIiIiIiCSV6aDlTGAd4DN3XxrYAnhtIT7z38C/zGwM0SbvpjT/JmCxNP9fwIkL8RkiIiIiIiINWmWqcc529x/MrJGZNXL3kWZ26YJ8iLs/BzyX/v8SWKuUdWYBuy7I+4qIiIiIiEjpKhPsTTWzNsALwO1mNgn4tWaTJSIiIiIiIgujMtU4BwAzgEHA48AXwHY1mSgRERERERFZOJUJ9k519znu/oe7D3f3y4l2dyIiIiIiIlJHVSbY27KUeX2rOyEiIiIiIiJSfcpss2dmhwGHA8uY2XuZRYsAL9d0wkRERERERKTqyuugZQQxnt65lBwG4Wd3/7FGUyUiIiIiIiILpcxgz92nAdOAPWsvOSIiIiIiIlIdKtNmT0REREREROoZBXsiIiIiIiJFSMGeiIiIiIhIEVKwJyIiIiIiUoTK641TRKSoDRlSy59Xux8nIiIiDZxK9kRERERERIqQgj0REREREZEipGqcIkWgNqsj1uJHiYiIiMhCUMmeiIiIiIhIEVKwJyIiIiIiUoQU7ImIiIiIiBQhBXsiIiIiIiJFSMGeiIiIiIhIEVKwJyIiIiIiUoQU7ImIiIiIiBQhBXsiIiIiIiJFqMaCPTNrYWavm9m7ZvahmZ2e5i9tZqPMbIyZ3WlmzdL85ml6TFres6bSJiIiIiIiUuxqsmTvN2Azd/8rsBqwjZmtA5wPXOLuvYCfgIPT+gcDP6X5l6T1REREREREpApqLNjz8EuabJpeDmwG3JPmDwd2SP8PSNOk5ZubmdVU+kRERERERIpZjbbZM7PGZvYOMAl4CvgCmOruf6RVxgHd0//dgbEAafk0YLFS3vMQMxttZqMnT55ck8kXERERERGpt2o02HP3P919NaAHsBawfDW85/Xu3sfd+3Tq1Glh305ERERERKQo1UpvnO4+FRgJrAu0N7MmaVEPYHz6fzywBEBa3g74oTbSJyIiIiIiUmxqsjfOTmbWPv3fEtgS+JgI+nZJq+0PPJD+fzBNk5Y/6+5eU+kTEREREREpZk0qXqXKugHDzawxEVTe5e4Pm9lHwB1mdhbwNnBTWv8m4DYzGwP8COxRg2kTEREREREpajUW7Ln7e8DfSpn/JdF+L3/+LGDXmkqPiIiIiIhIQ1KTJXsiIiJSSEOGFOdniYhIpdRKBy0iIiIiIiJSuxTsiYiIiIiIFCEFeyIiIiIiIkVIwZ6IiIiIiEgRUrAnIiIiIiJShBTsiYiIiIiIFCENvSAiIiKSVdvDSGjYChGpISrZExERERERKUIK9kRERERERIqQgj0REREREZEipGBPRERERESkCCnYExERERERKUIK9kRERERERIqQgj0REREREZEipGBPRERERESkCCnYExERERERKUIK9kRERERERIqQgj0REREREZEi1KTQCRARERGRIjFkSHF+lkg9pWBPRERERKSQajtwVaDcYCjYExERERGR+kmlyeVSsCciIiIiItWi1gspa/fj6p0a66DFzJYws5Fm9pGZfWhmR6f5HczsKTP7PP1dNM03M7vczMaY2XtmtnpNpU1ERERERKTY1WRvnH8Ax7r7isA6wBFmtiJwIvCMuy8LPJOmAfoCy6bXIcA1NZg2ERERERGRolZjwZ67T3D3t9L/PwMfA92BAcDwtNpwYIf0/wDgVg+vAe3NrFtNpU9ERERERKSY1co4e2bWE/gbMAro4u4T0qLvgS7p/+7A2Mxm49K8/Pc6xMxGm9noyZMn11yiRURERERE6rEaD/bMrA1wL3CMu0/PLnN3B3xB3s/dr3f3Pu7ep1OnTtWYUhERERERkeJRo71xmllTItC73d3vS7Mnmlk3d5+QqmlOSvPHA0tkNu+R5omISB1Tqz1d195HiYiIFJWa7I3TgJuAj9394syiB4H90//7Aw9k5u+XeuVcB5iWqe4pIiIiIiIiC6AmS/bWB/YF3jezd9K8k4DzgLvM7GDgG2C3tOxRYFtgDDADOLAG0yYiIiIiIlLUaizYc/eXACtj8ealrO/AETWVHhERERERkYakVnrjFBERERERkdqlYE9ERERERKQIKdgTEREREREpQgr2REREREREipCCPRERERERkSKkYE9ERERERKQIKdgTEREREREpQgr2REREREREipCCPRERERERkSKkYE9ERERERKQIKdgTEREREREpQgr2REREREREipCCPRERERERkSKkYE9ERERERKQIKdgTEREREREpQgr2REREREREipCCPRERERERkSKkYE9ERERERKQIKdgTEREREREpQgr2REREREREipCCPRERERERkSKkYE9ERERERKQINSl0AorRkCG1/Hm1+3EiIiIiIlIPqGRPRERERESkCNVYyZ6Z3QxsB0xy95XTvA7AnUBP4GtgN3f/ycwMuAzYFpgBHODub9VU2kRERKR+qc1aM7X4USIiNaomS/aGAdvkzTsReMbdlwWeSdMAfYFl0+sQ4JoaTJeIiIiIiEjRq7Fgz91fAH7Mmz0AGJ7+Hw7skJl/q4fXgPZm1q2m0iYiIiIiIlLsarvNXhd3n5D+/x7okv7vDozNrDcuzZuPmR1iZqPNbPTkyZNrLqUiIiIiIiL1WME6aHF3B7wK213v7n3cvU+nTp1qIGUiIiIiIiL1X20HexNz1TPT30lp/nhgicx6PdI8ERERERERqYLaDvYeBPZP/+8PPJCZv5+FdYBpmeqeIiIiIiIisoBqcuiF/wKbAB3NbBxwGnAecJeZHQx8A+yWVn+UGHZhDDH0woE1lS4RERERkYpouA8pBjUW7Ln7nmUs2ryUdR04oqbSIiIiIiIi0tAUrIMWERERERERqTkK9kRERERERIqQgj0REREREZEipGBPRERERESkCCnYExERERERKUIK9kRERERERIqQgj0REREREZEipGBPRERERESkCNXYoOoiVTFkSC1+Vu19lIiIiIhIrVPJnoiIiIiISBFSsCciIiIiIlKEFOyJiIiIiIgUIQV7IiIiIiIiRUjBnoiIiIiISBFSsCciIiIiIlKEFOyJiIiIiIgUIQV7IiIiIiIiRUiDqouIiIgUqSFDavnzavfjRKQCKtkTEREREREpQirZExERqSUqZRERkdqkkj0REREREZEipGBPRERERESkCCnYExERERERKUJ1Ktgzs23M7FMzG2NmJxY6PSIiIiIiIvVVnQn2zKwxcBXQF1gR2NPMVixsqkREREREROqnOhPsAWsBY9z9S3f/HbgDGFDgNImIiIiIiNRL5u6FTgMAZrYLsI27/z1N7wus7e5H5q13CHBImuwNfFqrCa1ZHYEphU5EPaT9VnXad1Wj/VZ12ndVo/1Wddp3VaP9VjXab1WnfVd1S7l7p9IW1Ltx9tz9euD6QqejJpjZaHfvU+h01Dfab1WnfVc12m9Vp31XNdpvVad9VzXab1Wj/VZ12nc1oy5V4xwPLJGZ7pHmiYiIiIiIyAKqS8HeG8CyZra0mTUD9gAeLHCaRERERERE6qU6U43T3f8wsyOBJ4DGwM3u/mGBk1XbirJ6ai3Qfqs67buq0X6rOu27qtF+qzrtu6rRfqsa7beq076rAXWmgxYRERERERGpPnWpGqeIiIiIiIhUEwV7IiIiIiIiRUjBnoiIiIiISBFSsCciIiIiIlKEFOyJiIgUATPTPb0aZPejmVkh0yKyMMxsPTNrWeh0SGHpxiAisoCyD4B6GKw5Cl4qz8zM3eeYWXMdk1WX3Y8Ari7La1z+ea7jt3qY2UHAo8A/zKxFodNTG3TslE430jrOzBoXOg1SeaU9nOriU768wGkJM+tUyPRUxMyauLubWRMzaw0sUug0FaO0n3PBS69Cp6cuM7PG6ZhsBDwNjDCzOjOObn2SO7eBJ83s7EKnpyFI53kLM1suTSvArh4jgNeAwynSgC/3jJx7jtCxUzoFe3VQ7qBNDzt/pv+3N7MtzaxPYVMnZcl7OF3FzNYAXXzKk3LRPf2/B3AXsJeZLVbYlJXOzBq5+x9mtghwL/AG8LSZXWxm7QubuuKRgpfcfn4EuM3MVi50uuqi3H0iVdXqC/wI7A4MVclolbUBfgF6m1ljZdjVjLzM7CeBm8xs9cxy7fcqSteFWcAA4EvgaIos4Ev3iT/NrA1wrpndaWaXmFm/QqetrtGNoI4xs1bAYDNb2d3/SPPuAoYBDwAPmdmgAiZRypB5OH0JeBYYZWZ3mdlSBU5anZUJ9PYDbiD23Sh3/6GgCStFCvRy1bueBroCjwOjgIOAR81snUKmsRikDIDcDfwN4E/gUuCrgiasDkr7KnfdGUU80M0A3kv/X6cH5gXn7lOBK4GdgK2UYVf9MpkUrc1sc2AssD5wspmtBnNLWXX8VkG6LjR299+I4/gziijgy7tPjAY2AxYFVgTuN7PTzaxDQRNZh5iuYXWLme0LDCcefC8EVgLOBo4BmgNbAv8Ehrj7GQVKpmRkcpcMuIO44AwjcoeHEA+ph7n7ewVLZB1mZisSQdOVwFXu/mua3w5o5O4/pWmr7YeudCP5NROUNiVuKicAg3K/qZn1JtpGfA/0dffptZnOYpNKpK4Glgf2Bb5L51hXYA7ws7vPLGQa64q0r24E1gB2c/dPzawbsCtwAXAbcKi7zylgMuusFHT8kZluTBxjLYlqcH8CB+qcrn7p+vo68B3wMXHv3Bl4ATjB3d9N69X6tb++y+2zzPNJC+B+YFngMuCGVPJXb6VnrsuBPsD+7v5Zmn8ncf1bB3hDxw6oTn8d4+63mVlH4ERgJuDAY8DIlFMzGvgJGJJO5tMLmFwBMlWotgOmAte6+0gAM3uFqIZ2vZkdooCvVF2AP4Dr3P3XtC8vBlYHGpvZCHe/uACB3nJEEDrQzJ4hHgBfJx7+fs0Eek3SA3Y/onTlFOD42kxrsUklqN2BT9x9rIV9gKOIEtUXzOx8d3+/sCktvLSvlgG+cPdP07wJZnYzYMAlwC9mdlyuWUBDZ9EGdJa7j0v31dbAmcB97v5SWm2Gmb0InAx0AKbnSvcLlOxiNBhoARyRrqEG3EIEJeeb2Ynu/o4e1isnLyhuZ2ZzgF8B3H2Wme1I7Nuj0/r1PeAzYBXg3UygtyewI3CSu7+eMmhnFzCNdYKqcdYh6SEXd78EOA/YC9gD+D6X8+ju3xM53mcAp5nZ4AIlV5J0gzoLuB7YnsihxMyauvsHRDuabkSVqlULltC6awLQGTjRzI4B3gK2BV4mSkUHWmr/WMt+An4gfteN0k10OBGErm9ma8Pc6jJN3P0TomR3AzNro+pHVZPaSDUHxgMdzexconRqGHFsXEfk2u5csETWEWbWKJVEjQdapeqcALj7L8DdwEfEw90lhUll3ZJKk54nOrFZMs3uD2wOPGdmt5nZ3wHc/SLgU+D8NK1Ar5qk6+NSwMRcJgXQ2N2fIc7tjYFTLNOGT8qWDfTMbBei2c/bwOdmtquZdU2B3Y7A58Q14eB6XqWzBRHHzAIws72B24FT3f08i2ZR1xbo+aFOUbBXYOlmvYKZNctVSzKzNVPAdzrQEdjbzFbJbePuk4CrgNOAM8zshEKkXUK6wN7PvHZce6b5s1MVio+AbYgSrPvN7C8FS2wBlRX8pCDpOOAfwG5E72G93f1fRHXmDkRpWq1y98lE0DkGuMvMNnX3S4H9iWvnYbnfMlMN7HegNTBHudEVSyV2JXpTI/bdb8RN24GtgbbAFu4+0N3PJkpclylEmgvJ8npndvc5qbTuOaKKf7+85d8Rwd4dRFudY2onpXVXCoIPAJYDrjGzLu7+XyITZw+gHXCemb1iZkcQpfkdzGwlUKch1SVdH98DuqeS6bntzIBngIeIwOQ/ZrY4aN+XJxPo7UOUjr4B7Efs40uB/cysWybg+wT4N3Bk/nWlLrJSOpty9xlEZswO6VwdDpzi7uelVf5GnOfqN8Hd9Srgi8i9upNo/wPwBPAU0CZNHwlMJkoXeudt24U4WVcs9PdoSC8i97G0+WsCDwLfEm08SqwPrEr04Fjq9sX8IrUPTv/3J9oMPEy0P105ze8MdM2s1wI4GPgQWKmW09s099sR7cbGpnRsmuYfSlTrvBVYN81bHXiX6FHUajO99e0FdMibbkO02byJKCXPXf86EW2nFk3TTYib97vA4EJ/j1reZ03S3xbABkRnFk0yy28mepDcE2ib5q1MdHq0MxHwPUMEzg32+MxcjzcGJgL3AT1z+4RoN9Y7XaufS+f5HOD4Qqe9vr7KuWdun66tZwKd85ZdTQQtM4BzC/0d6sMrPYN8CByXppcnmpZ8TjSVOBFYPC1rma4N+xc63ZX4XrlrXzOgF9EJS+6e0JkIaOcAZ2S2WYGoHfQY0fa/4N+joPuw0Alo6C9gMeKh98P0ADOWqIPcNLPOsemmdAPzB3wN/iCu5d8r+8C1NdF5xFaZ5WsSpXyfUUrAV9Z0Q3kROY2/Af8jGuG/kfbVdnnrrUwEVL/kbly1mMZG6W9b4EUigP+YKLX7lvkDvjnppvl8+k7N0vIG+0Bdwf5dgWjbeHBuPxG5zO+nm/Y4ojSqU952bdM59zLR+1qT2k57oV/EmI5vp/vBHKI9cN+0rA0RLP9J9Ab8EJHrPSotvyrt3+aF/h4F3H+563cronRjeNqPI4Ceees2IoK+49Px+RmwfKG/Q317MS+4bgnsQ/QMuXJm+XlENbyzgL+keSsDr6Tz/VSiqv8SuqZWuK+3JzoKa0Vkiv1AdMQCURX+F6IWTY/sb1OXX5njZxHg1XRNm5Put/9KyzYlqviPJdrYXpvuEW9lzvkG/axc8AQ05BfzchJbpAec2US7itz8Zpl1cwHfNagkr1C/Vy4IWAR4hxi75k+ibdfrwFJp+VpEwPcpcECh011XXkQvYJ8QPVnmcuVWS8f9G8wrzVmdCAa/Ao7NbF9rN3qgKfHA/CoRwHcl2l6+RPS4mQv49k03nieAfpntm9VWWuvbC1g8nRtvpYe/HYiAegkiYOkLfAB8AXRL27QkSq7eIKpw5m7gdf5hpRr2V7b07r/EeGT90usrotrzzpl1/k4EMc8QPdU1S8fzSKLUuWWhv1OB92ebdHyNJDJQHyeCjYeAJcvYZjNgErBDodNfH1+ZfT4uXS/fJJOJB5xLZKR9RzykjwdGp2X/SffatoX+HnXpRcnaMtn/10h/HyQKEnLX0LWJYG8a0USoFfOeNet0EE08I48mStq3T9e+c9OxNCSt052oAfd0+t5nZe4TDS5jcL59WOgENPQXUS1pFeLB8nmihO9fzMvNyAZ8xxDBxWVkSv70qtXfq1l6SHgOWBdYkiitej89nC6Z1lsHuCddWLctdLoLtK8sb3odokry5pl5DxMP/qun6dZENaqdgM0y69VqrhxRx/9L4Kjs9yECkpfTg8kmaf4h6aZzOamKjF5l7tfcdW1xIoPkZaJ64U2ZdZoAGxGlUNmAb22iY5bcexT9DTzzMNYa2IoYhmebzPLl0356gxh2ITc/WzNkRSJQnkoDzyhM5/BFZErpiEBkADEYfYmAj0xmQrrm31Pb16L6+so8aBsxjNQTRBf5GxJVZz8jPain9bYlSlFvI3rpzFWlf4h4PlKwN29fZYO7TYlS6m6ZeYum/XtCZt7aRCB0L3B0ob9DJb9nLoN9u/R91sxc/w9P993/5G3TKm+66DMEK7UvC52Ahvgq7WZBVBlZjOhB6SOiqD13sczeuP8OLFfo79AQXkS1sUZ581YiHvSzD1ZNiBKqD0hVptL89YjcpwZ9sSFy4dqkm9KvwGJp/uNpX/41Ta+ZHsQWydu+1nMd083yK+Cc/LSk7zObqHa9TZp/BJERcwupioxeZe7b3M26B1Gdcw5wT946jYiA712ivckSpb1HMb7SeXBC3rzBaT9NB9ZJ83IPw72JzKZRwC55261A9Mj5ee48a+gvImB7qZT5OxBVzG8nU6UzHYutiBLUG2kAmQzVuK9bE6X315BpG0bU8riJyFAbUsa2axOZFNPIVPvUq8Q+2o+o8fV/ZDJy0vH6FvBgmm4KHJabrusv8go7iLb7E0ntOol2yXNy10miU6UtCp3uuvxSb5y1LPXOOCf9v6yZrZe6y27q7j8QwdwY4CDgqNQD0R9mdqqZbe/uN3oaT0Rqjpn9jXh46lnK4raZ9Rp79MT4IRGo9DKzTQHc/RV3/4/HOHx1vrermpDG9XmIyDl/k6gCeYWZPUA8iO7g7u+mLpK3I0pKW2ffw9PVvJbNIkpMNjez5fPS8i5R4rQMabwid7+K6CxpJzSmT6lyPenlzgd3H0c8YL8CbGZmB+bWTdfIl4gOqhYhxl0ks7wox4tL3aAfRjysZd0DDE3zN0vzckN+fEp0wNIMGGpmm2S2+4KozrSppwGqG6pMb34fE2OQLZm3yjNEG909gVvMrDPMPRb/SpRKXeGZAdhlHjPb0MwuyZu9C9GJ1d+JcYNzQwR8TpRSjwT2yR9CKu37U0klgR5DGElGGl7hOiJD+QyPXr9zZhLBdB8z+4DIwLiIaFOe275O9myajo8/zaw98LTFeLcTiF65G5nZ1sT3OdndL0jPVnsD/dM2UppCR5sN6UWmlIioW/wZUYd6HFF1c4m0rBNRwvcxkSuba0S+QqG/Q0N5EW20Tkz/G/OqU3UlGgFnq53lqhr8Jf1Ouxc6/XXhRfSSdQsxREj7NG8Q0S5jJqmHTeIBdn+iTcxBhU53Jv2rED3B3ZE994jquw8BSzN/ye+ihU53XXyRaSQPtMhb1oOohvgusE/eskZEqXnRluSVsq86pb8ts+cD0QvdVekac0CaZ5l9uxLxYN1g9lUF+7GsHiB3JjJzTiSvaiBRkjScaO+Uf253LPR3qqsvotfiQUQwnL/sH+mYvZ35e9xchmg3+Sup5C9zr+1OpmqiXiX2WwuiX4BLKNmmtz+RgbZGujbsTXTi9CBwSGa9OtlGj0wVfaLTpBeJXsw7EM2cvk3H0pGZbVYkmgMMLXT66/Kr4AloiK90Qx4D7Jqm3yACvvOZF/B1JHoUeim9Vil0uhvKi5L14VsQgfdmzKs2tQ/RjfFxedttRFRL2ay20lpXX0SO7qNEqU2245JORAnF10Q7jP+kh6ufiJy6+X6DAn+PbdKDyBsp3ccTvSE+w7wgvxH1pKF7gfZhtmOj+4m2eqOJktBcu6klKSPgy7xP0QcxeQ9upxOZIidm5i1DVImbQ+rtNz3UNc17n6LfV5XZj0TAvDfRrnbHzPIz0jX8P6Qqm0QPkC8DA7Qfq7TPW6a/rckbpoKoATGHKGVeNG/Zcul3yLaP1HW05D7KPpPkeix9Hbgq/b8a0R5vUrpffQBsndmmdeb/Ot3mNJ2zWxLt+bfN3D/2Sffeb4gArw3xzDUqvea2ES30d6iLr9wDitSSNODlUUQvgy+a2SDgAuLhcSPgCuBKdx+bqvQ0IQ7enwuW6AYiVR/wVMXszzRvMeIB9Gcih/IVIgA8mXhYvZX47Zy4oTmwvhdpNbOy5PZdZnoz4uYDcLi7X5tZ1p5oz3goUVL6IfC0u49Iyxt5qupcF5jZysTD4V+JB5bPiQfC2XUtrXVNbv+YWXOiDclvRFWinkTJ6QSi++zXU7W6u4n2Jde6+/UFSnZBZPZVF+LhbTRxb9iEqElwTlpvGSLT4VAi4BtemBTXTZn9uAjRzs6IMWlnEbUK9nL3z81sCHEN/4q4vnchuqpfq6Fdv6uTme1LZOBd5u6DMvOPJTLMzgEucvefStm2sfZ92cxsP+L58TDiHnoYkWnRjGjPO5AIlq4HHnP3f+VtX+I+XVfkPXPdRWQWf0N0gvZNZr19gQOZ19nbL0Rbvq3T/VjHT1kKHW02tBeRO3FR+v8QIhcmV8J3F5GTew55Y/7oVeO/y2JEl+9Lp+lFmNf4twvx4DUG2CDNa0G07fiK6MXtK6LDkbmDcRf6OxVoPy6a2QdrpeP5TVJ30KWsn19Vqk7mOqbfux1R5TCXSaaOGsrfZ3OHkCF6Mb0X6JVZvidRa+FF0vihRNWtr4DhhU5/AffVp0QGSFOi6uYwosbASZn1lyEGoZ9DA+3tt4L92YQYTPl5osRucaLa9TRiOJVcRw/bA0OIKmPn0ICG9KjGfZ2repc7hhchqnTOJgK+7LrHEh1ZnUHqqEuvSu/nxYkSu+OB5ml6LyKA/kfeuo/mnjPr6ouoEdMpM90yXfNyw8TMAU4hb6iY9Iyxfbp/bMS8kj/dj8vb34VOQDG/yrphAN3SBfFdolrDImn+pkQboR/TDUg3nNr7rZZPD5k3EXXExxJVA9ql5V2JoGUMsEHmAtOZqIayfEO/6BDB8qdErlsu4NuQGIz8ETK9AVKyW+56V+2COhqU1rUX88YrnECU7LXLW74XEcj8KzOvU0O69mWvG0RHRg8RAUruIbqsgG85Mr0261Viny5JBMx7Z+btQZSCnFjBttqfld/Puet4a6Jt9vZExlh7oh+CP5g/4BuUHuT/Udvpra8voh3eoUQ1+L+Us157okBhCpmxN+vaK933DyMyrHoSmVzfAf/MHVfp+WtC+u7lDjWm+3HFryZIjUhVSXLF0kcT9YunuPt17j7BzHoTHXqM8nlVNDsTpUOfASNcxdG1xt0/MbMjiAetHYkHhZ3dfVqq+vC9mfUjgpZhwAFmNsrdJxH15IG5v3tD7a3tUyJ37irgMDN726Oq8pbAUwBm9h93fy+3jzxdqesbV9XNympFVH3OZXDlronN3P13dx9hZnsDuwMXp/NnclqnQVTJ8ahy2Iwo+VwE+N1T74NpH4wxs7OI4RcOMrM57n6eR6/MF6b1mjTg605pWhAlH7keIPckOgg5yd3PS1XJ/+7uF+ZvqP1YObl7Xaou+wqxr78H/nT3qWZ2S1r1AjPD3Y8GcPdLzGwC0cOsVCBVgd+H6On5OyKAzi2bWy3TzDYkAqMDgQvd/d7aT23luLub2ffEmNFdiV5XPwPuzt0bzGx9oh335QBm9oiX0XRC9+OKaeiFGuLzhlcYQTS03x+42szuM7OexEn7XZpPaq+yFfCju5/oGl6hVqULyKNEjlM7YDxpCIB0YWrk7t8TY6xNJcZb2ii/++KGctHJ/97pofRLolpFa6KHtb+ZWVN3f55ocL0JcKGZrVHb6ZXaUcr5MI24oQ8jSltuSfN/z6z2LTGMQPPs+VOsgV4K7LLTjdL+mE7UGljOzDqlxZ4e6MYQtUCeB05JbXfmasgBShldyP9MBB8rmtlOpK7aiU7QIK5T/c1s1dpJZfFJmRRNiQHSfySq1d3k7rPT8p+I8/0EIvPvksy2d6RAUQUOFXD334hS0quIDIx+mWW5QK8DEeitAPzb57XvrbPP+O5+P9HGsD9RLfVsd/8+BXrN0zVtLaId7WVA3/Q80SCesapdoYsWi+1FyeEVliKqMK1NtPXZgihef4boTegfRKnQdOAT4oK5aqG/Q0N+EZ2sHEFUM7kZWCazLNcmoQtRzfPOQqe30C9KtsHKVTtbOh3P7xIX61yVzk3Tfu1f6HTrVSPHQnZ4hebplZvXiegMYxqRo9+O6E57BeAj4L+FTn8t7aO2wL7E+JIQJeHDmddW+GKi2vNQUlf/lOztdXkiaGkw1VwX4Jhrlfs//R1ElILMAY7ObNObaCc6gnpYhbwuvdK1fgxwQGa/W9467ZlXdfOYQqe5Lr9K2XfZXjg7E5kWv5Z2D03PJT0y03WyaiMle109NT0rTAf+S8mmHs3T3yZEnwm/E2MuFvw71MeXeuOsRnk9CjUjeu87C9jDU89TZrYW0Xj2VaLErynRTmMm8cCjEr1aUl41MTPbg3gYGAac5VFqhZm196iisigwvazti1VetZF1iVzd89390jSvsceAqMsQvXFOJnoPe9sjx25xd/+uQMmXGpL53dsQQwMsQ9ykHweudveJqbTqIOK6N4G4wX9DdN6ypkdOf53sLa66pJ42ryc6ormKaOc0DtjFo+YAZnYdsB1ROn6lu09JOfSe3TcNpZprWTLH3CJETYvcIOkvApe6+3dmdgFwDNGr6XNEpuvhxLG5Vjrm1KNuJeWqC6d9bkR70tHAxh5V9nM9Wuf+tkjrNSW60b/HG3ApdHny7q3bE+0fuxNt1+5090/NrB0xkPr2wJ7u/mBF71UXmVlLoKu7f5Wm9wKuJu4X57n7O2l+U4+qm02J6+bfG/I1b2Eo2KsBZnYlsDpRUtcO6Ovuv2RuTmsTbb9GAYPc/bO6fnIWm8xNqyWwHzGu4a/ufmnmRpVr53ETUY3gRyJQH+7ul6T3aRAPXGbWwt1nZS6+7Yl2qFcROeVXuPtVad3cvt0buI1oz3ECkcFhHtV/9IBVJDLnSxviN/6ZqL7VmyjFeo0YfmN8JuA7mMip3dLdJ6T3aRDtzsxsWSIjaVXgY2Jczh9z51Za53qiulY24NM9Io+ZtSaCjWnEPXVpIgBZgqgS+zNxff8PUfo3DviCGMvxj4Zy/a5OKdB7imhL9S7xgP5/RKbor5nrQTPg78DXHk0kcts3iPO8qlIV7euJGmC/EM0fPib6cbjezDoDlxLB8wHu/r/CpHTB5BWGPEWURA4EXk3Hy97E88TjxLH0gZl1I6oHX547ZnTOVlGhixaL4UXJYukrifZetxFVOOcAZ2aW56o6rJmWPUAFPQ3pVTO/F9EZwpvAO0RXvz8QnSSslvmd9ki/06fE+GofNbTfi6hyfCOwQpo+hMioaE70GngHUZXniLzt+hGDoE4B9i3099CrRo+RpsCdxANKpzTvTiKD5Asik6Rbmt8FOJGowj488x5FXTWRkgOmv0K0/X2bqPmRm98s8/91RHvGi8nrxbShvzLX5xOJYC9bnfy8dM3ePzOvG1Ha3AkNnVKV/Z19xjmdGNB7qTR9C/ATUUOpTW59osrxm1TQ+6leJfbzMuk542SgbZrXJR3Pd5AGpCeqdN6T5i9d6HRX4ntle23dnmgiM4cYHmXdzDm5VzqWHiZ6Gn453T/qZJXU+vSqs4036xOfl1uxOlGf+h/uvi+Rq30ucLKZDU7r5ko13iB6IDreU26u1A6P0tWWxIVmOtDP3Tclcil3JHIsV0m/0x1E5yIPE6V8q3qUbDWkhuV/EKU0b6Tj+Fqifv0fHr0Gnk3c1AeZ2TEwtxrz0kQOcA93v60QCZeaY2aNM5MdiVKUc9x9ssXAuBsQ7TRvAbYGrknVeCcSpeUXAVua2X1QvB2yQInS7lZmtjtRCrUtUbp5TKpFgEdV52bp/0OJh51exHWqwcsdcz6vVsCyRBOIcWn5LkQtguPdfbiZLWJmi7r7BHf/0t0nu8+tZqjSpUpK98xWFr3CdgJu8HmDXR9MBNzDgNPMbIs071ai9935ejyVMrUlMqGfdffcOX8lcXyf6+4/mdkiHr2AH0mMsflVgdJaaena14boXfMYIhPwJmJg+KHA2umcHEEMMbE0kan8C7B87rm5IIkvFoWONovlRQx0OZnoYXPlzPwuxACic4DBmfnKqaj932jumG5E98QvkwavJ0r0vgF2S7/hs8DfmFcKmM3ZLOoSiDL2XW4cwtmkkuq8fbIK0dHET2nf/R8xZuQRmXXUGUKRvJhXstKRNPYbEdA1IappfkVUT8yt/yZRkvcG8wa0XpQoJfgcWLzQ36kG91W2JsG7wJNEtVaI6v6j0mvPzDZdSZ1DUUbHFw3tlXfMDUn/DwNeSf/vmu6zJ6bpZkQHEIfpflst+39/IjP7D9IYbkCLzPKbiLEgczVhHmFe51wN7p65gPs2V/K1btp/q6TpR4jS/VXT9IZER1et87av88d3utZ/TQRvuXlrpfvCS5Qs4VuSCPga9NjF1flSpFx9HiIGDe5MVG0DwCMX+0qio5ZTzezcNF/tlWqBmTWz0MjjqpE75p8nGot/bWYXE9Vqd3H3u4jfahPgFOJhDM+UOngRl0Bkpf2W69b8D+A34sJ8pJn19sjtbZZy5N4HhhDVqtoSD2THe2rHB/V3TD0pKdfeMpVAPUh0Yd/N3Z/wKC1ZlcgweSet35x4gHk7zfsB5nbNfimwjhdxpz3pPGlFXHMmElW0bk7L3iJysh042sz+YdG50UNEOzN8Xm2QBnv+lHLM9TWzjkRti1VSSfIdxPUnN7zCakStDNf9tmoy13+IqthDiOrHhwF4tOPOlUQfTAxp0YfY79t5qgXTUO6ZVWFm/Yl7aiOi46oPifE0nyKupdu7+3vpOroVUWOiXfY96snxvQQw1d0/gbm1HV4nqnWuQnRWtR6Au3/r7l9lrn0qhV9YhY42i+lF5ES8TORqb5O3rDNRneEnUpfaetX479EaOJZ5uZBtiJylNdJ0IyK3/Q2i6k8ud209YnDYOUR1lYJ/lwLsu2yXzz2Japy9idy3l9JxnGvD1zRv2+Zk2hhRD3Id9Vqw44IowduKKKVahZKlvLcROfut0nrLAE9QMue2QeX0E13TfwT0zsxbNe3DvxLD9DxPVG8aR5SENqi2weXsu7KOOQMWIzqxmQY8ntZrRmS4vkq0xW5Qx1o17fPGedO5EpZORC2mGcD/ZZY3L+N9dO2veF8/CXycmR6Snj1+ArZK8xYhmgVNBg4udJor+b0s7+8/iWA2W/Mt98w1jHlt+Hpmt9Orel4q2atGHnWn9yOqMlxgZttklk0iGo4v5+5TCpTEhmYWMQjp3Wb2d6Jk4Xui1AGP3LB2RLuPlj4v96grUX1iKaK3qAbHc1fb6CHrQWAX4Cd3f5W42X8EvGJmy/u8HgQ3NbP13P03j8G0cz011odcR6mk1F71DaIEpYm7v+9RepVrx3omUUVzJNGr3H1ESe8o97ntpRpaTn9Hov3SGDPrZmaDgBeIwPhtojOj/kQp32nEsAANrW1wmco45tzdfwAuAe4i2v08SfTmdyspOEzHZuOy3ltKypXEpTZ6R5nZhcDgVJtjMtFx0GnA1mZ2G8TA36Udq7r2ly1Tanos0N7MTgRw9yFEXw+NgCPS/r+G6EvgUne/KW/7OiXTrtazf4lnht+JUsvuaVnumesn4l6xNpHxnt1OqoGGXqgBZtaLuCB2Bo519ycLnKQGzcxuB3Ym2gb1c/dv03wjcsyuIQK+64lA/UxgnLvvntZrkF39Wow1eAvxgPWIR6dCuWXrEiXVyxPda3cgbkZHu/uNBUiu1KBMNbrc32OIXiJnABt5VEfMrrcmMVzJHKJ34r29AY9pZmZ/JXowfJsIQpYnqmmOIgK9Y4i2LBMy2zTI605OZY+5tG5noqR0b+LB8XPg+lwmhKsaWKVk9vUixLHpRAl9YyLD4hiP7v/bERkTxxIlqvsXLNH1RMrkmu+B2+aNnbcY0W53Spr/d2ANomrsc0Tb1NvTsjp5Hc12REX0i9CK6FzqpjT/GOK54QrgZnd/38xWIZ69zidqxw0m2vJ9qYCvGhW6aLFYX0QPak8QDzqbFzo9DflFVA2YkV57MX8Vlc3Sb/UbUfL3KvOqFzTIqgREdZ03iItyy8z8bHW9dYhc9FlE1bOTC51uvWrkWMhV4WpOlNZtk6b3I4K5m0jdsOdvR6Z6Fw28kT1RPfxmomR89cz8o4hqm+0Lnca68qrqMVfK+6gKZ8X7aGWgS3afESWlrwIrENVilyMGvZ5N9Daeu0ccl36PUwr9PerLi2gO0TFv3sZpP+5ZyvrN86brZNVY5hUeLUKMC/ghkXn+HVGqt2laPohoTjMxrTMBGJ2WHQOMBRYr9PcptpeqiNQQdx9jZkcBFxBt+KSWlJLr9Q8iN/1UYrw4M7M7PeX2uvuzZvY50QNUW+BJV45wW6K08wJ3n5nLlfSSHdW8ZmY7E7mPv7n7KKi7uY6y4DI5tc2IKtFrAeeb2W/ufmvKwb0amGFmF3rqjj1TKvVbmm7wXd27+yvE+HoAmFlT4C9EL5KfE+3OGryFOOYaEbW/vLTrlcwv1dB4mWhPletMqwMR5N3r7h+n9T5P6xhwmZk95+6fm9kw4mH+ztpOe31kZisCHwBPmtlj7n5Zul8+n6rEHmtmL7n72Nw27v5b9j3q6r01nXeNiarpU4hemX8kStrHAZea2QB3v8TMRgMrER3gfUbUAoEYqucrokM4qUYK9mqQu39qZru6+++FTktDkXlQaEp00NLM3XNjMB1BlDbckKZHpAvU4kTO5suZ92ncUB5Oy6heMpu44Obq1nt2vVT1oom7v020Pcq9lwK9IpHrBS1V6XqE6BzgSyKX/1Yz28/dr03Voa8CPD18f5v/kF3K8dWgmdlixFhk2xMdR+2TzrEGff4s5DE3d7/peKuYmXUg9uHN7n6VmbVw91lEpoMTGX7A3Ov/HOKhfHtgZzM736PK4Yj0fg262nFluPtHKYN0B+AMi/E1b0lNTYYRQxb9FRhbT/fnokRfB9e6++cAZrYTMQTZZe7+NYC7vwi8mNvIzDY0s4OIXtA38NTmX6qPOmipYQr0ak+u9CDzoDAKeMHM/gHg7jOJLqPvJAYGP9zMNiU6ILktvUejtG59u8hWWSaA62Nm3dLsn4mqFjulNqjZ9doSpaVbpNz37Hs12AfVYuPRdqcp8ACRMXgOsAUwgBiT8r9mtqm7XwMcTgzye7aZdSlUmuuR1YkOWb4G1kzXrSYN/fzRMVernOg0aFIK/F4ws33SM8sYYFszW3ruynH9/4aobtgmP6BuSPfMykgZEvPNc/f7iaqM6xAlX4OIQembEvfdf6f16vz+tPk7PmpG1JDKLd8DuIdo4nG+mbU3sxOz+8bM2hPVN1cDNvQYxkmqmYI9KQopR9gtegR7grjo/I/oEOE6Mzs75ZTNJB4SRhDt0YYTVc1WhYYbrJjZykSviReY2eIeY6AdQ/SOdbaZrZXWW5TI2d0LmKDMjKLXnWg0P8Ld3/QYN/QpYE/gC2B4evi+lhjstxdRGiPle5o4h/ZLgV6DqUlQCTrmasdsovrwDkQbqxlEJinAEUSJ8zXZgI+o2j+TqJYnZcirBbOlmQ0xs1uI8fRau/uPqYrsdkRHN68TzyLdgPWJcX/rNJvXa2tLMzvQzFYnzsMJwMpmtj/xnHUy0ZwJIsDdlaiandtPU4FDiHa579X292go1Bun1Hu5C6vFoKPrEheOs1KViTZEKdRQoheok3M5Zma2JdEY/SlXGz3MbCiwLfAacJq7jzOz7YkL9lRiQPVZRGB8rrufU6i0Su1IOf5vAre7++DMfAO2IUpgPgMOdfeXzaypx5ABDbo64oLQvipJx1zNy9wzlyLaSE0HzgIuydwfNyGqFf5KjAU3jeg51onS6Dpf8lRoKeC5HHiGKLlbhigd3c3df8lbd2ugL/BJysios3JVTFMtqueJ54N73P1qi+GabiXadw7OPSeY2XJEnwkTgd1z52oZzUikminYk6KQSvT+SzT6neXuq2eWtSDGy7uQCPpO9TQ2XGad+lg/vkryL67ZhyQzO5vIeXuB2E/fpVK/nYgg71PgLXe/N39bKT5m1pKo9twZOMDdP8ks6048BLYhbuwruPuvDelckuqnY652pCp4BxGZobkxZy8Chvu8sVO7EsFKr7TZh8CBmdJo7fMyWIyzfAtwobtfZDH8yqvEcfs2MQbkL2bWLFdDJtNuss7fWy06SnqJ6IzlZOB9d5+VmsIcSQyTcjPRm24HYiiGRsQYog12GJ5CUbAnRcPMTiDGfOtO1P3OjsPUgqgucQGRu3RUQ79RmdnGwEfuPjkv4DsL2J0Y2+e0FPDNd2HWxbphsOiMZxTRtvV0n9dD3zrEmEjnEl213+buJxYsoVI0dMzVjLICtFQ9/39EgH0xcEuulksqUW1JdHY2Nc1r0LVgSpNXdbMZcDrQ1t2PSMfzy8DdRPu8s4B3gP4ps6JpfgZ0XWdmBxADoO/o7p+measQQ3K0JKoJX5imvyTagR6caZ+s46cWqTdOqZdKCzTc/QIz+wk4hejp6j+5xr4px+k6Ikd4ayIns0HJuxl1IhpOf2pmO7j7lNw+dffBFgO9HgH8YWZnuvt3+e+nQK9h8Bj4difgXmAZM3uOGI9yP6J9xltEFd/FCpZIKSo65qqfzeupugXR3KEzUYPjN3f/Me3v+4F/pfVzAZ+5e26cWg2jUobMvXU9d3/FzJ4Hplh0QPJ/wMNEidccYEuireQzZraVu08vTKoXSkeig58xFh277QEMIY6TLsBAd1/VovOkGe7+MyijoFBUsif1Tuam1Zzo1a4ZMDFX3cfM/kl0wvIxUTL1fmbbZsDs1F6hQdYVN7NtiYyemcTgxJ8Ae6WAb+6F2Mw+JQY1fg04KN3wpYFK1XnPILoGhzi/dibOvxeBx4H/gLq+l+qhY6565DLyUhurF4lqdR2JB/PbiBLSt1IJ3/1EaczFRJVOPZhXkkXvkyOIDOVnU7u2zYhhFfZ19+fTescABxCZFce7+x0FSfBCSNVSXyeqpDYBlifOxdeJtv9HA6t5Gm4hbdMgn7nqAvXGKfWKlRyH6VWiB6tngf+Z2dUA7n4FcCVx8Tk9PTCQlv3e0AK9VA0n9/+ORNWoRYgg7kCiLd4IM+uUCfT+QjS6HgU8pEBP3P0DogfJNYD13H07otOBy4EewE2eFDCZUkR0zFWPFOg1I679k4hSmM5Eb6a7Er0wd/XohXkHohONC4gOQ6QSLMbr3YKoZvxWprpsZ2LMwly7vCZERy2PApvWx0APwN3fJQZB/4hoY7uRu1/h7qOAH4iec6fmbaPztEBUjVPqlXTTak7k6E4DjiIuorlBd1u7+/4eg8Q6Ma7eVWZ2oLt/mXmfBnPRyVQvaQe0JwbGvc/dZ5rZs8DeRO7uXWZ2JPAT0Ie4YB/u7j+k7RtMgCyl8+g8INeBwJZEu5QlgC08DaIrUp10zFWbpYgx0I4H3vDowfQHYHHgPHf/PnUWMtXMdiX286MFTG+9YdFr9a7AKkQvsj9kFj9HDO80xMweAFoQ1ZEPdPcxaft6eW9191eAV3LTFmNk/oXYF58Tz2hSByjYk/poNaIKymHAKykAXCEt+yi3kkc3wIsQJVdf13YiCy2vjd5GxE3nG+BKj/EGSaWczwK7EO0KXiFyfhcHzlGgJ+V4legB93E9dEst0TFXSaW0jVo6vXKB3r5EzZiT3f3KdK/cwsyeT9f9I9P7qNfNUuTtl78Q40BCVDHOrdMkBdFbEtVjzyICv3M8BlcHiiPz2cwWAw4mxuFtA+yTni/UkVsdoGBP6qP2xOCjk1OgtyfRxfF/3P381CB6a3e/M01bQ7ro5AdmqRrnV8TNZgDRXmPuzSqt+4qZ/Y1onD8b+Nzd7yzt/UQAPMaJuqLQ6ZCGQ8dc+dID99LuPjo1d2hL9JY4nOjUZgKwk5l9TwR6g9393HSP2BvYgGhzNZcCvXnMbFWihPTxFDDvAvzp7pea2c/A1cCxZjbe3T/weUNUvGcx6HgXAHf/LL1fMT2TrA70J541csNzqDOWOkLBntRHU4mqEIuY2VbA7UTu5PkWY7wMAPqZ2SvuPjbTRq9YLqplSjeUrc3sSnf/2cwGApu5+25mNoToPes4Mxvp7k9nGu43cvdpwGl571dMNyMRkWK2ItGcYSjR+dZ7wCdmdg9RFfY5YBBRDfbkFOg1Ikqm9iIGq5+v52WZOy7hFsTYhD3M7E/gWqKjFdz9JosxIk8FjjGzi9z9Y49OWpqk++u0zPsV2zPJ08Q4vLlnrsYK9OoOBXtSZ5WVK+Tuo8zsXuLi0oJoV3ZtWrwcMdbeB8C4zDYNqWTqn8CGZvYo0T7veJjbnfnpRA+bD5lZX3d/rryArshuRiIixewjopbLFUTG3btEcDIjPYCfQlSDNWCOxaDpGwPHEPeFgZnM0YZ0z6xQCtqGEcNWnEyU0g1y91tzzyqpOmwToldKM7Oh7v5JGc8xRbV/0/f5FuZmEqtEuA5Rb5xSJ+VyhcysjZldZmbXmdkxqQEwRG9sI4ncyq/MrKeZ9SWqprQA/pm7aRXoKxTK+8COwIbARcC/3f3i3EJ3/5C4ET0JPGZmG+dK9gqSWhERqRaprd1NabIT0ab9u3QvbOLRSdmewEtE+6pviN4jJwFrZ6odFlUgUh1SAPMj0aNpV6IDsxY2r4fwZgDufilwLrAN8B8zW6lQaS4UZRLXPRpnT+qsVCXiNaAlUf1hVaJ3sKPd/VszW5coxdoRmE7csMYB/VN9+gbZsDxV5RxNNAR/kWiz8Wt2f5jZikRj8R2Ardz96UKlV0REqs5Kjo+6BHFfbAkcARzr7pdk1zOzNkRJ3irAGOC7lOmnNlYVSE1HlgS2I2oS3QJckvZrU3efndYbBAwFttH9VQpNwZ7UKdkqhSloOZW4cf1MNAC+m2hofoi7f5XWW5u4sf0AfNjQblp5vW62Iqpn9yZyH28kBj3dNbXhy+7f5Yhc4Dvc/arCpF5ERBaWmbUmOlz5T5ruRAxsfRKZgC8ta5M6u8lur/bZCyB1BHcLca8dBlyUa58HLObuE81sFXd/v4DJFAFUjVPqkBSgzTGzpqkb6JWIDkUmuPtUd3+WyE1bHbguBSu4+yh3f87d3890NtIQA70dgOuB5dz9DeAxYtD0vwH3pBt8LtBbC/iSKNVToCciUr9tDvzbzO4AcPfJRJvtc4CLzOwoM2tsZt2BB81sn+zGCvQqL9WSmUpUhf2U6KRlkMVYtv2Bx81s+Vygp2YSUmgq2ZM6IRe0pCAvVyd+OjCFuHjOIY7XOan65oPAG8Bx7v5RWe/bUJjZAcDFRDXXW9z9mTS/OfEQMJzYX6cAKwC3Aru7+91pPTXIFxGpp1LVzJ2AK4En3H3XND9bwjcS6Exk9K+Wq3IoCy7XLMLMFgVuANYBfgK6A9e6+0kFTaBIhoI9KbjMRbMR8BQxKOnrRHfQ/YFT3f2stG5uqIB1iAHAL3X3fxUq7XWBmW0C3Ee0wbvF3X8qZZ2+RJWT5oATVU7OrsVkiohINcjeM7Mlcqkq565Eb5yPZwK+tkTb9l2JcdAGucZBW2iZ36EdUYumK/Ceu49Iy5WJKnWCgj2pE1JnLBsBuwBXufs7ZrY4cCRwImlMoLRuLuBbCfikIXbCkmVmJxHVMTfJzDsRWBYYC9zl7h+lbra3JMbBeS6tp3YaIiL1TGqffT5wf2rikJufC/iuAh5w970yy1q4+6z0vwK9alDWPVT3VqlLNM6eFFwq0buUCPSmAccCuPt3ZnYZUYXz7JRJdl4K9CwNI6CbFvwCdDKzzYGmwNnEoLljgO3TsuPd/XvgttxGuhmJiNQfmd40mwJLAYcBS5rZ7+7+EkDqefl/xADrx5nZL+5+SFqWC/Ssgd8z51NWKVxFvXqXdQ/VvVXqEjUalYJLF8UngTeBnsBmmWUTiSop5wBnmdnZab5n1mnoN63XgU+A/wGXABOJ9hjrEVU3tyOCwBJ0MxIRqR9yAVqqMvgSkcm3LdFh2b/NbIPcuqnzkP8B3wN/N7Nzsu+lqoWlagRgZuub2T5mtlMqCf3TzBpXtLFZyTF9K7ONSG1RyZ7UutJyytz9XjP7FWgDXGBmv7r7U2nZRDO7AmgLbKJ68CW5+2upKudlwJ/u/jJAyv2dBXyMMnZEROqlTNuwJkTm5+9AB3d/0swOA64lAr7G7v582qw70QZ+GPBCIdJd15nZMcCvwI1p/x4EXJQWzwHGmNk27v5TeTVh8nrF3tTdRzb05iVSt6jNntSqTDWUlkSuZDPgR3d/Ii3fhmij14EYG+ipzLaLAlNTr50K+Ci36kkPohfOK4AT3f3qWk+ciIhUi0y79iOJ3h8f83kDeG9HXOu/Bu4FPgNOA95x9yPSOg29uUMJqdOat4lg73wiIB5B7L+RxIDzJxPPKBu4+4TSAr68QO+fRKbrhrlMV5G6QMGe1JpMxyqLED1pNiN6r5oJvAMc4O7fp4Dv38wL+J7Oex8FeuUws62BPYBtgMvc/bw0X/tNRKQeMrNbgX2A8cAW7v5pNoAzsy2I++Z6xLBFXwCbuvtsXftLygz11B24B2hMBHlbAn93969TtcyNiCC6NaUEfKUEehcBh7v7jQX4WiJlUrAntSpVLXyMaEN2FPAzsAgwmshN283dp6ahAo4FVga29xgkXCrBzHYkbvij3P2eNE+dsYiI1BNllCI9AvQFzgPOcfdfss0iUvDSBVgUGJkyV1WiV4pM1djuRPvGnsC37r5GZp1GwAbE2IXNieD5u7QsP9C7FDhUgZ7URQr2pMbkdfOcy0nrBTwEDAbuS/N2J6pPnOTu52e23wXYGDhG9d8XjJm1dvdf0//K1RURqSfMrJm7/25mudov7d39vbTsUWB94BDgf+7+Wznd/5fbk2RDlwn4FgfuIjJJjyaGf8qV3jUi9vcNQCeip+uZmUDvKOBiYKACPamr1GmD1Agz+yvwkJmtCiV6/2pNXCx/TYHensB/iXH0zjezRVODc9z9Hnf/Z2V7wyoW2V69zKyJmbXJW16Z83ZGbt1cG8dqTqaIiFQTM1vJzLYESIFeO+B5orfld8zseTPb3t23BV4lxtHbwcyapxK8+a7xCvRKyt9Huf2TSut2B94A/g7slVlnDvAycARwlLvPyAR6fydK9BToSZ2mYE9qihPVHy61GPw852eiJ7GlUnXD24lG0LkSvc2B3c1sxRJv1kBuWnlVQ3Ymchs/MLPhqaSTdGMv89zNK8lbPG2jkj0RkTrIzJoDFwI35wI+og3Zb0QtmB2BFkRP1ce5+zZEO/crgP6pFo2u8RXI3Fv7mtlpZnalmW1pZku5+3hiMPrfgePNbJ/MdnPc/Rl3vz1tn7v/dgD2UaAndZ2qcUqNSaV7jxGDex8GfJRKmc4ATiIyGwa5+2Vp/d7AjcBXwP4N+eaVbjRXA3cQYyoNIto53pDZXxX1DHYk0J/YlxNqM/0iIlJ5ZrYcEbwtTowrO4DoYOvVtLxLmr8e0XHZo2b2IlHFcEt3f6YwKa9fzGx/okrmZ0TbxubAKOA0dx9tZksSgXYj4HJ3H16wxIpUEwV7UqPMbDXgUSLgO9zdP0gNok8CDiU6YXkDWBL4F9Er1toewzM0yE5FzGw94DbgWncfamadiQB4MjFu3tXufnlat7yewS4jeha7uRDfQ0REKi+1ab8W6AF0BlZPPUPm2vB1Idq8/+Lum6VtrgGObCi1XxaGxTiF/yWeOYa5+yQzGwjsTTQx2d/d37cYuuhhoD2wtbt/Wqg0i1QHVeOUaperF58CkXeI8fSWBa4xsxVSdYlziVzKU4AHgeOBscwL9Bo3lECvlCqZvYAngUtSaefnwHBiKIVZwClmdgTMbU9QVs9g/1CgJyJSP7h7rhbMWCLQ2CjN/z0FfBOBC4BNcs0j3P2whtauvSrMbADwAFFy+qq7TwJw92uBS4imJ4enzs3GAdsD/1agJ8VAJXtSbWzegOnz9f5oZn8jSvi+IEqbPknzlyHaIkwDvkvVPBtEV9FmtjQwI93Ac6Wg3xLVSnoTjcKfAKYQObeTzGxTIjj+GrjD3c9WF9AiIsUj3RtuJ4YD+Ie7P5JZdhjRjm/tFJRIBSyGfDoRGAi0BTZy97fzegy/ANgPWMbdZ+Rtrx6tpV5TyZ5Ui1SK90fqOfJGM3vIzB4ws63MrKO7v02U8P0FuMHMVk4X0C/d/SN3H58CvUYNJNBbnOji+Swza5HaETwP9HL3Ce7+HJGzuxQxXtKktGlbopObn4mqndlG5/8mBnU9RIGeiEj95O5fAfsCnxL30wPMrLeZbQgcQLQ3+66ASaxX3H02cB3RtAHgzDR/lsXwFhC1aToQNWvyt1egJ/Wagj1ZaClom2NmLYC3gLUAI9oc3AsMNrPemYBvGaIh+qr579VQqm6mrp4nA1sSN5kbiJzHtzOrtSKqnLSFuT22dSN66Ozn7iPSfDOztsAuRMP9m2rre4iISPVz9y+Ag4lMvZuB14ix9X4AtqqoV+aGqrQhKABShuktwFnAxmY2Is3/PbXlWxP4Cfi1ttIqUluaFDoBUr/lOghJF8sNiRzHgcCE1I7gVOKG5WZ2Vqo60ZcIag4FDi9Y4gskVyUkVcFcDdiZ6A3s6ZQDmduv35jZecDZZrY60Q33TsBgd/8p+17AdDPbzN1/LsiXEhGRauXuX5rZ3sA1RDBym7s/CfOaTRQ0gXVMXpOGLYA1iCYRTwDPuvtkM8u1Yz/DzF4D3gN+IQLps1KQLVJU1GZPFloqcbqJqKI5A9iCOLZynYecBhwH7OjuT6d5vYCvGmoPYqkNgRM9gzUlSjlHAmenG3wuiO5C9BQ2EJgA3OPuVxQq3SIiUrvMbHniHnBsykRVG7JymNl+wOXAC0TNmI7Ax8DR7v6dmXUCDgKOJGog7Qv8kBu+oqH2BC7FS8GeLLR04bwLWAn40t3XSfObufvv6f/XgfHuvmNe7lvjhhLwldFxTYvUbuAU4EDgOSLg+yJ/PaCJu/+SpnUzEhFpYBrSPbMqUs2hW4AL3P3iFCi/R1TRfAs4OAV8XYh77j+Bx9394LR9c3f/rUDJF6kRqu8tCyy/Try7Twb2Ap4FVjezs9L831N7sqbA1Mz6nvm/Qdy08gLcpc1s+dR19p8A7n4mMbzCJsBJZrZUWncHMzuJkoGeKdATEWl4Gso9s7KyzyOpltHmwIgU6K0KvA7cSnTOsjpwrZl1Tb1g30KUAPYzs2EACvSkGCnYkwWS2gm4mTUxs665+e4+gRgU/SFgNzM7Oy1qDCxB9Cr5fa0nuA7IC/T2Ah4DngLeBa4ws/UB3P10IuDbCBieuoK+LxZFoJfWU3G8iIg0eJl763opUHsFuMPMOgD/RwxVdIS7n0N0crMd8H9mtmQK+K4HLgb2M7NDCvIlRGqYOmiRSktByx9mtggRhHQ3sxnAUOCZVDXiaCIH7d9mtjXRhm8W8DtRXaLBydyMdie6fz6faHC/P3Ae0CUF0c+7++lmNp24IW1JtDFQGz0REZFSmNkewAgz6+vu96V5WwDtgBsypXWjgBWJ8QtXB751959Spy0femY8Q5FiojZ7skBSr5sPA4sQg6RvDSxPVIW40d2/txhD7hKiOsVo4Hx3H5nbviH0IGZmg4E3gCdTSWhv4EbgUXc/N1UveZ5oQ7B6WvdMd38xbd+eqLo5JU2rjZ6IiEhGqmF0JfGscV2mp+qDiYznPu7+iZk1JsahnQg87O7vp/VKtKXXvVaKkapxSoXSRTJnDtEr5HHufra7b0RU3TwMOCzVhf+OqNL5ItCJ6DI6u31RM7PWxHAKw4GN0+w5wDdE9ZGlia6g/+fumwNHED2YHmNmmwO4+9RMoKc2eiIiIhlmthtwOtAFeCEX6CUPEH0FDDWzfYCjiQ5ZPior0IOGM9avNCwq2ZNy5UrizKwlsA6wJLA9Ub1wfGa964F+xODg17j7RDPrQeSs9QLud/chtf4FCsTMOgMjgJWBPd19pJn1cPdxZnY5UZq3dxpLrynRfq8bMf7gPilgFhERkVKY2VHApWlya3d/Ks3PPbdsSGS6diaalFzk7ucXJLEiBaRgTyqU2ui9QgR6fwKtgb8Dd3gaBDytdx3QF7iHGD7gBzPrRvSE1QbYzt1/qO3016ZsFZDUtfMdRBB3GJHz+KeZjQQmufvuab0lgJuJktBx7n5z6e8uIiLSsJnZDsAv7v60xaDztxElece6+5d567YClgVmufunaZ6qakqDomBPSmVmTXOBnJmdD6xG5KC1BE4DmhFVIkbl1Xe/Ky3bKRP0dCXan42rze9QSKl6SV9ioPkNgC+AQ1IJ36XAfkRD8RlAf+BEYJO8qps6OUVEpEHL69G6OTCSyHze292fT71oXgtcBVzo7t+kdefrI0CBnjRECvZkLjNbA+jo7k+k6dbAICLA+9DdR6T5PYl2eo2Bg5g/4Gvk7nPMrBE0vDrwZrYjMcj8uUS1zO7AwUS7gr2BmURXz+sAHwLLAaenrqFFREQkT8pE3ZIoqduIaAd/UMpEPYwI9q4Chrr7t4VLqUjdomBPADCzFkQANzIXdJjZLkTQArCru9+bqQvfgxgvrhER8L2RDeoaau6ZmTUjet3sDOySGQh9KaKqSQ9if00BtgE6AG+7+91pPZXoiYiIZGQyUc8iMlG7AYcCizOvXXwu4LsOuMDdvypUekXqEgV7MpeZdXL3yakzlpXcfbSZ/Z0YF+5ld++f1muc2p71IIZh6AFs7O4fFi71dYeZPQR0cPf107Sl4RfWIALkb4ET3P3ZvO0aZIAsIiJSllR18yYic3S3vEzUW4lO4PZJAd+RxFBQm7r784VKs0hdoqEXZK4U6BnRg+ZjZraJu98InARsZWbD03p/poBvHDCACGA+KVjC64i07yDGzOtoZmvBvEHV3f1NIkdydeBhM+ue3V6BnoiISElpUPT2QNtMoGepbd4gwICrzWwjd78SWF2Bnsg8CvakhBSYXAS8A1xhZpu6+3XEGDV7lBLwfePu++amC5fywstUv7yDqMZ5nJktk1ue2kB+D+wF7JwdukJERERKKiUTdU0okYn6FvAW0JsYx7a7u79jQc+4IijYk1Kk7okPAyZTMuA7CtjdzG5J6/2Zt92f871ZA+TunwG7EOMRXmtme5nZqsD+wFbA9+7+GETVzcKlVEREpO7KZKL+l+jk7Pi8TNRWwCSiE7QfgKtz26m2jEhQmz0pk5n1Aq4nSqn+merD57o4PtXdzypoAus4M+sDDAM6AW2B34hG4+p1U0REZAGY2eZEPwEvEPfW94leOU8hMlh3IDJZ13b3aYVJpUjdo2BPypUJ+DoRAd9zaUDTh/PHr5H5mVknYjygxYmB1Eel+eqMRUREZAHkZaIuAswGznf3c8zsEmJc2y0U7InMo2BPKmRmfyFK8/4KbJPqyJc6YKlUTIGeiIhI1aRM1KWInsA/d/cPUxD4EDDC3Y8taAJF6hgFe1IpZtYbOAIYpLZ5IiIiUmhm1oHoQG5v4AN33yHN15i1IomCPVlguXH2Cp0OERERabjMrA0xPFQzdz8uzVPtGZEMBXsiIiIiUi9lgzsFeiLzU7AnIiIiIvWaqm6KlE7BnoiIiIiISBHSgM4iIiIiIiJFSMGeiIiIiIhIEVKwJyIiIiIiUoQU7ImIiIiIiBQhBXsiIiIiIiJF6P8BC8c3Acr/14QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10_user_10_tag = user_10_tag.head(10)\n",
    "\n",
    "bar_width = 0.35\n",
    "alpha = 0.5\n",
    "index = np.arange(10)\n",
    "label = top10_user_10_tag.tag\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "p1 = plt.bar(index, top10_user_10_tag.user, bar_width, \n",
    "             color='b', alpha=alpha,label=user_name)\n",
    "\n",
    "p2 = plt.bar(index + bar_width, top10_user_10_tag.num, bar_width, \n",
    "            color='r', alpha=alpha,label='mean_users')\n",
    "\n",
    "plt.title('Compare solved tags of '+user_name+' and mean values of users in same user_class', fontsize=15)\n",
    "plt.ylabel('tags #', fontsize=10)\n",
    "#plt.xlabel('tags', fontsize=18)\n",
    "plt.xticks(index, label, fontsize=15, rotation=45)\n",
    "plt.legend((p1[0], p2[0]), (user_name, 'same class users'), fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8a6f10-1277-4591-91c5-d689159852f0",
   "metadata": {},
   "source": [
    "arnold518은 같은 클래스 내 유저들에 비해 implementation, string, bruteforcing이 현저히 적음을 알 수 있습니다. <br/>\n",
    "나중에 같은 Class 내 다른 유저들의 풀이 이력과 비교하여 유저가 많이 풀지 않은 tag 문제 위주로 추천하는 방법을 떠올려볼 수 있겠네요. 🤔"
   ]
  }
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