preprocess2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 영화 필터링\n",
    "\n",
    "- 이상한 영화들이 존재하여 제거해야함."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "movie_metadata = pd.read_excel(\"cinemate_data_0511.xlsx\")\n",
    "final_metadata = pd.read_excel(\"final_metadata_with_overview_embeddings_ver5_0511.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# movie_metadata 내에서 title이 영어이거나 한국어가 아닌 것들\n",
    "movie_metadata[~(movie_metadata['title'].str.contains('[a-zA-Z]') | movie_metadata['title'].str.contains('[가-힣]'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# movie_metadata 내에서 title이 영어이거나 한국어인 것들의 인덱스로 final_metadata에서 해당 데이터만 추출\n",
    "final_metadata = final_metadata.iloc[movie_metadata[(movie_metadata['title'].str.contains('[a-zA-Z]') | movie_metadata['title'].str.contains('[가-힣]'))].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_metadata.to_excel(\"final_metadata_with_overview_embeddings_only_eng_kor_0528.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "final_metadata = pd.read_excel(\"final_metadata_with_overview_embeddings_only_eng_kor_0528.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 변경한 데이터의 장르 정보 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tconst             tt6147768\n",
      "primaryTitle           Split\n",
      "originalTitle     Suepeullit\n",
      "startYear               2016\n",
      "runtimeMinutes           123\n",
      "                     ...    \n",
      "embedding_379      -0.002548\n",
      "embedding_380       0.005766\n",
      "embedding_381       0.000557\n",
      "embedding_382       0.029496\n",
      "embedding_383       0.008394\n",
      "Name: 4118, Length: 396, dtype: object\n",
      "['Action', 'Drama', 'Sport']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 각 영화의 인덱스를 키로, 해당 영화의 장르를 값으로 가지는 딕셔너리 생성\n",
    "movie_genres = {}\n",
    "for index, row in final_metadata.iterrows():\n",
    "    if index == 4118:\n",
    "        print(row)\n",
    "        print(row.iloc[5].split(','))\n",
    "    genres = row.iloc[5].split(',')  # ','로 장르 분리\n",
    "    movie_genres[index] = set(genres)  # 장르 집합 생성하여 딕셔너리에 저장\n",
    "# movie_genres\n",
    "# 각 장르별 영화 개수 plot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "genre_count = {}\n",
    "for genres in movie_genres.values():\n",
    "    for genre in genres:\n",
    "        if genre in genre_count:\n",
    "            genre_count[genre] += 1\n",
    "        else:\n",
    "            genre_count[genre] = 1\n",
    "genre_count = dict(sorted(genre_count.items(), key=lambda x: x[1], reverse=True))\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.bar(genre_count.keys(), genre_count.values())\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_metadata 초기화\n",
    "final_metadata.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 이상한 영화 필터링 2차 작업 (24.08.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "movie_metadata = pd.read_excel(\"cinemate_data_only_eng_kor_0528.xlsx\")\n",
    "final_metadata = pd.read_excel(\"final_metadata_with_overview_embeddings_only_eng_kor_0528.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tconst</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>startYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>directors</th>\n",
       "      <th>ordering</th>\n",
       "      <th>...</th>\n",
       "      <th>embedding_374</th>\n",
       "      <th>embedding_375</th>\n",
       "      <th>embedding_376</th>\n",
       "      <th>embedding_377</th>\n",
       "      <th>embedding_378</th>\n",
       "      <th>embedding_379</th>\n",
       "      <th>embedding_380</th>\n",
       "      <th>embedding_381</th>\n",
       "      <th>embedding_382</th>\n",
       "      <th>embedding_383</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt31015344</td>\n",
       "      <td>A Traveler's Needs</td>\n",
       "      <td>Yeohaengjaui Pilyo</td>\n",
       "      <td>2024</td>\n",
       "      <td>90</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.5</td>\n",
       "      <td>75</td>\n",
       "      <td>nm0393254</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016458</td>\n",
       "      <td>-0.023006</td>\n",
       "      <td>-0.050298</td>\n",
       "      <td>0.037739</td>\n",
       "      <td>-0.076090</td>\n",
       "      <td>-0.010432</td>\n",
       "      <td>-0.058701</td>\n",
       "      <td>0.017649</td>\n",
       "      <td>0.035267</td>\n",
       "      <td>-0.023228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt30911335</td>\n",
       "      <td>Dog Days</td>\n",
       "      <td>Dogeudeijeu</td>\n",
       "      <td>2024</td>\n",
       "      <td>120</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.8</td>\n",
       "      <td>31</td>\n",
       "      <td>nm15730187</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034701</td>\n",
       "      <td>-0.008065</td>\n",
       "      <td>-0.024117</td>\n",
       "      <td>-0.003312</td>\n",
       "      <td>0.038821</td>\n",
       "      <td>-0.030186</td>\n",
       "      <td>0.019926</td>\n",
       "      <td>0.074339</td>\n",
       "      <td>-0.018989</td>\n",
       "      <td>0.055851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt9682428</td>\n",
       "      <td>Night Swim</td>\n",
       "      <td>Night Swim</td>\n",
       "      <td>2024</td>\n",
       "      <td>98</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>4.7</td>\n",
       "      <td>12654</td>\n",
       "      <td>nm3241399</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>-0.049932</td>\n",
       "      <td>-0.121888</td>\n",
       "      <td>-0.054388</td>\n",
       "      <td>-0.020564</td>\n",
       "      <td>0.050116</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.052849</td>\n",
       "      <td>0.035944</td>\n",
       "      <td>0.025606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt27802490</td>\n",
       "      <td>Exhuma</td>\n",
       "      <td>Pamyo</td>\n",
       "      <td>2024</td>\n",
       "      <td>134</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>7.4</td>\n",
       "      <td>495</td>\n",
       "      <td>nm6940206</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051392</td>\n",
       "      <td>-0.002096</td>\n",
       "      <td>0.044035</td>\n",
       "      <td>0.059909</td>\n",
       "      <td>-0.044593</td>\n",
       "      <td>-0.032423</td>\n",
       "      <td>0.081002</td>\n",
       "      <td>0.020807</td>\n",
       "      <td>0.021268</td>\n",
       "      <td>0.008938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt27811040</td>\n",
       "      <td>The Roundup: Punishment</td>\n",
       "      <td>Beomjoedosi4</td>\n",
       "      <td>2024</td>\n",
       "      <td>109</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "      <td>8.8</td>\n",
       "      <td>101</td>\n",
       "      <td>nm2441168</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041042</td>\n",
       "      <td>-0.039577</td>\n",
       "      <td>0.034051</td>\n",
       "      <td>0.041895</td>\n",
       "      <td>-0.132000</td>\n",
       "      <td>-0.051895</td>\n",
       "      <td>0.089292</td>\n",
       "      <td>-0.132800</td>\n",
       "      <td>-0.011520</td>\n",
       "      <td>-0.006363</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>9299</th>\n",
       "      <td>tt0017409</td>\n",
       "      <td>So This Is Paris</td>\n",
       "      <td>So This Is Paris</td>\n",
       "      <td>1926</td>\n",
       "      <td>60</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>7.1</td>\n",
       "      <td>746</td>\n",
       "      <td>nm0523932</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052198</td>\n",
       "      <td>-0.056215</td>\n",
       "      <td>-0.065513</td>\n",
       "      <td>0.068392</td>\n",
       "      <td>0.040504</td>\n",
       "      <td>-0.013506</td>\n",
       "      <td>-0.068042</td>\n",
       "      <td>0.001189</td>\n",
       "      <td>-0.021429</td>\n",
       "      <td>-0.103460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9300</th>\n",
       "      <td>tt0015064</td>\n",
       "      <td>The Last Laugh</td>\n",
       "      <td>Der letzte Mann</td>\n",
       "      <td>1924</td>\n",
       "      <td>88</td>\n",
       "      <td>Drama</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15105</td>\n",
       "      <td>nm0003638</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029271</td>\n",
       "      <td>-0.052846</td>\n",
       "      <td>0.032112</td>\n",
       "      <td>0.051352</td>\n",
       "      <td>-0.038684</td>\n",
       "      <td>-0.013877</td>\n",
       "      <td>0.074433</td>\n",
       "      <td>-0.020061</td>\n",
       "      <td>-0.008468</td>\n",
       "      <td>0.026134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9301</th>\n",
       "      <td>tt0014624</td>\n",
       "      <td>A Woman of Paris: A Drama of Fate</td>\n",
       "      <td>A Woman of Paris: A Drama of Fate</td>\n",
       "      <td>1923</td>\n",
       "      <td>82</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6104</td>\n",
       "      <td>nm0000122</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018064</td>\n",
       "      <td>-0.050176</td>\n",
       "      <td>-0.045593</td>\n",
       "      <td>0.036943</td>\n",
       "      <td>-0.046362</td>\n",
       "      <td>-0.033189</td>\n",
       "      <td>-0.061279</td>\n",
       "      <td>-0.036766</td>\n",
       "      <td>0.073398</td>\n",
       "      <td>-0.105024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9302</th>\n",
       "      <td>tt0012806</td>\n",
       "      <td>Four Around the Woman</td>\n",
       "      <td>Vier um die Frau</td>\n",
       "      <td>1921</td>\n",
       "      <td>52</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.9</td>\n",
       "      <td>581</td>\n",
       "      <td>nm0000485</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047176</td>\n",
       "      <td>-0.100745</td>\n",
       "      <td>0.056739</td>\n",
       "      <td>0.063528</td>\n",
       "      <td>-0.031887</td>\n",
       "      <td>-0.028223</td>\n",
       "      <td>-0.002628</td>\n",
       "      <td>-0.041470</td>\n",
       "      <td>0.026425</td>\n",
       "      <td>0.023838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9303</th>\n",
       "      <td>tt0011841</td>\n",
       "      <td>Way Down East</td>\n",
       "      <td>Way Down East</td>\n",
       "      <td>1920</td>\n",
       "      <td>145</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5863</td>\n",
       "      <td>nm0000428</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026308</td>\n",
       "      <td>-0.031079</td>\n",
       "      <td>0.015309</td>\n",
       "      <td>0.108602</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>0.095502</td>\n",
       "      <td>0.000965</td>\n",
       "      <td>-0.005393</td>\n",
       "      <td>-0.005323</td>\n",
       "      <td>-0.025500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9304 rows × 396 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          tconst                       primaryTitle  \\\n",
       "0     tt31015344                 A Traveler's Needs   \n",
       "1     tt30911335                           Dog Days   \n",
       "2      tt9682428                         Night Swim   \n",
       "3     tt27802490                             Exhuma   \n",
       "4     tt27811040            The Roundup: Punishment   \n",
       "...          ...                                ...   \n",
       "9299   tt0017409                   So This Is Paris   \n",
       "9300   tt0015064                     The Last Laugh   \n",
       "9301   tt0014624  A Woman of Paris: A Drama of Fate   \n",
       "9302   tt0012806              Four Around the Woman   \n",
       "9303   tt0011841                      Way Down East   \n",
       "\n",
       "                          originalTitle  startYear  runtimeMinutes  \\\n",
       "0                    Yeohaengjaui Pilyo       2024              90   \n",
       "1                           Dogeudeijeu       2024             120   \n",
       "2                            Night Swim       2024              98   \n",
       "3                                 Pamyo       2024             134   \n",
       "4                          Beomjoedosi4       2024             109   \n",
       "...                                 ...        ...             ...   \n",
       "9299                   So This Is Paris       1926              60   \n",
       "9300                    Der letzte Mann       1924              88   \n",
       "9301  A Woman of Paris: A Drama of Fate       1923              82   \n",
       "9302                   Vier um die Frau       1921              52   \n",
       "9303                      Way Down East       1920             145   \n",
       "\n",
       "                       genres  averageRating  numVotes   directors  ordering  \\\n",
       "0                       Drama            6.5        75   nm0393254         1   \n",
       "1                       Drama            7.8        31  nm15730187         1   \n",
       "2             Horror,Thriller            4.7     12654   nm3241399         1   \n",
       "3     Horror,Mystery,Thriller            7.4       495   nm6940206         1   \n",
       "4       Action,Crime,Thriller            8.8       101   nm2441168        10   \n",
       "...                       ...            ...       ...         ...       ...   \n",
       "9299                   Comedy            7.1       746   nm0523932         1   \n",
       "9300                    Drama            8.0     15105   nm0003638         1   \n",
       "9301            Drama,Romance            6.9      6104   nm0000122        10   \n",
       "9302                    Drama            5.9       581   nm0000485         1   \n",
       "9303            Drama,Romance            7.3      5863   nm0000428         1   \n",
       "\n",
       "      ... embedding_374 embedding_375  embedding_376  embedding_377  \\\n",
       "0     ...      0.016458     -0.023006      -0.050298       0.037739   \n",
       "1     ...     -0.034701     -0.008065      -0.024117      -0.003312   \n",
       "2     ...      0.003738     -0.049932      -0.121888      -0.054388   \n",
       "3     ...      0.051392     -0.002096       0.044035       0.059909   \n",
       "4     ...      0.041042     -0.039577       0.034051       0.041895   \n",
       "...   ...           ...           ...            ...            ...   \n",
       "9299  ...     -0.052198     -0.056215      -0.065513       0.068392   \n",
       "9300  ...      0.029271     -0.052846       0.032112       0.051352   \n",
       "9301  ...      0.018064     -0.050176      -0.045593       0.036943   \n",
       "9302  ...     -0.047176     -0.100745       0.056739       0.063528   \n",
       "9303  ...      0.026308     -0.031079       0.015309       0.108602   \n",
       "\n",
       "      embedding_378  embedding_379  embedding_380  embedding_381  \\\n",
       "0         -0.076090      -0.010432      -0.058701       0.017649   \n",
       "1          0.038821      -0.030186       0.019926       0.074339   \n",
       "2         -0.020564       0.050116       0.021484       0.052849   \n",
       "3         -0.044593      -0.032423       0.081002       0.020807   \n",
       "4         -0.132000      -0.051895       0.089292      -0.132800   \n",
       "...             ...            ...            ...            ...   \n",
       "9299       0.040504      -0.013506      -0.068042       0.001189   \n",
       "9300      -0.038684      -0.013877       0.074433      -0.020061   \n",
       "9301      -0.046362      -0.033189      -0.061279      -0.036766   \n",
       "9302      -0.031887      -0.028223      -0.002628      -0.041470   \n",
       "9303      -0.053923       0.095502       0.000965      -0.005393   \n",
       "\n",
       "      embedding_382  embedding_383  \n",
       "0          0.035267      -0.023228  \n",
       "1         -0.018989       0.055851  \n",
       "2          0.035944       0.025606  \n",
       "3          0.021268       0.008938  \n",
       "4         -0.011520      -0.006363  \n",
       "...             ...            ...  \n",
       "9299      -0.021429      -0.103460  \n",
       "9300      -0.008468       0.026134  \n",
       "9301       0.073398      -0.105024  \n",
       "9302       0.026425       0.023838  \n",
       "9303      -0.005323      -0.025500  \n",
       "\n",
       "[9304 rows x 396 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>backdrop_path</th>\n",
       "      <th>original_title</th>\n",
       "      <th>title</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5812</th>\n",
       "      <td>10882</td>\n",
       "      <td>6.952</td>\n",
       "      <td>/1HWNGfaYB4GPwMg8piXYdQHWNuF.jpg</td>\n",
       "      <td>Sleeping Beauty</td>\n",
       "      <td>잠자는 숲속의 공주</td>\n",
       "      <td>1959-02-17</td>\n",
       "      <td>/t1y45VvDkkrCL3Po9Lk9DoJ8sQt.jpg</td>\n",
       "      <td>오랜 기다림 끝에 스테판 왕과 왕비 사이에 오로라 공주가 태어난다. 왕은 이 날을 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6771</th>\n",
       "      <td>24010</td>\n",
       "      <td>6.100</td>\n",
       "      <td>/5Le00JbfjDA3Gc5wJDKunmZLJZq.jpg</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>버스 정류장</td>\n",
       "      <td>1956-08-31</td>\n",
       "      <td>/oXZiz5zIyynjQMJMBbXijS4434z.jpg</td>\n",
       "      <td>태어나서 한번도 집을 떠나본 적이 없는 시골 출신의 순진한 카우보이 보는 로데오 경...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8907</th>\n",
       "      <td>346</td>\n",
       "      <td>8.500</td>\n",
       "      <td>/qvZ91FwMq6O47VViAr8vZNQz3WI.jpg</td>\n",
       "      <td>七人の侍</td>\n",
       "      <td>7인의 사무라이</td>\n",
       "      <td>1954-04-26</td>\n",
       "      <td>/6Y8Q5t79ybiDA7XubUTneqZhjA3.jpg</td>\n",
       "      <td>일본의 전국시대. 주민들은 황폐한 땅에서 어렵게 수확한 식량으로 한해 한해를 넘기는...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9080</th>\n",
       "      <td>33725</td>\n",
       "      <td>6.000</td>\n",
       "      <td>/37XokaTf9o6nazmKPlDP3LKTYQ6.jpg</td>\n",
       "      <td>A Hole in the Head</td>\n",
       "      <td>머리 속의 구멍</td>\n",
       "      <td>1959-09-15</td>\n",
       "      <td>/BOgeA821uM0G2nrKj8rFSPYyGO.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9103</th>\n",
       "      <td>504532</td>\n",
       "      <td>6.000</td>\n",
       "      <td>/60iTRsldJlRtbyxRQKvhlk4yhqp.jpg</td>\n",
       "      <td>고종황제와 의사 안중근</td>\n",
       "      <td>고종황제와 의사 안중근</td>\n",
       "      <td>1959-04-10</td>\n",
       "      <td>/8EUo28xMwl8dbIS2AENtyHHzWMr.jpg</td>\n",
       "      <td>망국의 기운이 감도는 구한말. 이토오 히로부미와 친일 대신의 압력에 의해 을사조약이...</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>9299</th>\n",
       "      <td>67319</td>\n",
       "      <td>6.500</td>\n",
       "      <td>/3Ztq49qcwnXSC9JFmTuaLNYEGKX.jpg</td>\n",
       "      <td>So This Is Paris</td>\n",
       "      <td>여기는 파리</td>\n",
       "      <td>1926-07-31</td>\n",
       "      <td>/bVcYRrzmA95SIlPVd1RGGrd17XA.jpg</td>\n",
       "      <td>폴과 수자나 부부는 조용한 마을에서 행복하게 살고 있다. 그러던 중 화려한 댄서들이...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9300</th>\n",
       "      <td>5991</td>\n",
       "      <td>7.785</td>\n",
       "      <td>/sTyt7e2PB5nfY02reL1iZUYPbly.jpg</td>\n",
       "      <td>Der letzte Mann</td>\n",
       "      <td>마지막 웃음</td>\n",
       "      <td>1924-12-23</td>\n",
       "      <td>/woR2d4zkWgCPlvwQ9kMHcrdiGPJ.jpg</td>\n",
       "      <td>은발의 멋진 구렛나루 수염을 쓰다듬으며 금빛 단추의 제복을 뽐내는 한 호텔 도어맨이...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9301</th>\n",
       "      <td>28974</td>\n",
       "      <td>6.731</td>\n",
       "      <td>/y8WOKk49VdiA4RZ1UeFCt8xPVTF.jpg</td>\n",
       "      <td>A Woman of Paris: A Drama of Fate</td>\n",
       "      <td>파리의 여인</td>\n",
       "      <td>1923-10-01</td>\n",
       "      <td>/s24VgyvJliT7fUA909ZHJpoBx1D.jpg</td>\n",
       "      <td>마리(Marie St. Clair: 에드나 퍼비안스 분)는 계부의 반대를 피해 애인...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9302</th>\n",
       "      <td>175472</td>\n",
       "      <td>5.800</td>\n",
       "      <td>/bKNfpShviRHQAFwruRpqjj6iqxC.jpg</td>\n",
       "      <td>Vier um die Frau</td>\n",
       "      <td>여자를 둘러싼 넷</td>\n",
       "      <td>1921-02-03</td>\n",
       "      <td>/83nTPJQnzrn78ppVf5L5CEOIkIg.jpg</td>\n",
       "      <td>해리 유켐은 그의 사랑하는 아내에게 가짜와 장물을 거래하는 지하 세계의 중간거래상들...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9303</th>\n",
       "      <td>31509</td>\n",
       "      <td>6.852</td>\n",
       "      <td>/4sPMVIzE4LPA5Buz4yiRW0GcOwW.jpg</td>\n",
       "      <td>Way Down East</td>\n",
       "      <td>동쪽 저 멀리</td>\n",
       "      <td>1920-09-03</td>\n",
       "      <td>/xzrSiQuiSb5hKePkU9AFuCcgRxE.jpg</td>\n",
       "      <td>가난한 시골 처녀 애나는 내키지 않았지만 어머니의 부탁으로 친척에게 돈을 꾸러 도시...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id  rating                     backdrop_path  \\\n",
       "5812     10882   6.952  /1HWNGfaYB4GPwMg8piXYdQHWNuF.jpg   \n",
       "6771     24010   6.100  /5Le00JbfjDA3Gc5wJDKunmZLJZq.jpg   \n",
       "8907       346   8.500  /qvZ91FwMq6O47VViAr8vZNQz3WI.jpg   \n",
       "9080     33725   6.000  /37XokaTf9o6nazmKPlDP3LKTYQ6.jpg   \n",
       "9103    504532   6.000  /60iTRsldJlRtbyxRQKvhlk4yhqp.jpg   \n",
       "...        ...     ...                               ...   \n",
       "9299     67319   6.500  /3Ztq49qcwnXSC9JFmTuaLNYEGKX.jpg   \n",
       "9300      5991   7.785  /sTyt7e2PB5nfY02reL1iZUYPbly.jpg   \n",
       "9301     28974   6.731  /y8WOKk49VdiA4RZ1UeFCt8xPVTF.jpg   \n",
       "9302    175472   5.800  /bKNfpShviRHQAFwruRpqjj6iqxC.jpg   \n",
       "9303     31509   6.852  /4sPMVIzE4LPA5Buz4yiRW0GcOwW.jpg   \n",
       "\n",
       "                         original_title         title release_date  \\\n",
       "5812                    Sleeping Beauty    잠자는 숲속의 공주   1959-02-17   \n",
       "6771                           Bus Stop        버스 정류장   1956-08-31   \n",
       "8907                               七人の侍      7인의 사무라이   1954-04-26   \n",
       "9080                 A Hole in the Head      머리 속의 구멍   1959-09-15   \n",
       "9103                       고종황제와 의사 안중근  고종황제와 의사 안중근   1959-04-10   \n",
       "...                                 ...           ...          ...   \n",
       "9299                   So This Is Paris        여기는 파리   1926-07-31   \n",
       "9300                    Der letzte Mann        마지막 웃음   1924-12-23   \n",
       "9301  A Woman of Paris: A Drama of Fate        파리의 여인   1923-10-01   \n",
       "9302                   Vier um die Frau     여자를 둘러싼 넷   1921-02-03   \n",
       "9303                      Way Down East       동쪽 저 멀리   1920-09-03   \n",
       "\n",
       "                           poster_path  \\\n",
       "5812  /t1y45VvDkkrCL3Po9Lk9DoJ8sQt.jpg   \n",
       "6771  /oXZiz5zIyynjQMJMBbXijS4434z.jpg   \n",
       "8907  /6Y8Q5t79ybiDA7XubUTneqZhjA3.jpg   \n",
       "9080   /BOgeA821uM0G2nrKj8rFSPYyGO.jpg   \n",
       "9103  /8EUo28xMwl8dbIS2AENtyHHzWMr.jpg   \n",
       "...                                ...   \n",
       "9299  /bVcYRrzmA95SIlPVd1RGGrd17XA.jpg   \n",
       "9300  /woR2d4zkWgCPlvwQ9kMHcrdiGPJ.jpg   \n",
       "9301  /s24VgyvJliT7fUA909ZHJpoBx1D.jpg   \n",
       "9302  /83nTPJQnzrn78ppVf5L5CEOIkIg.jpg   \n",
       "9303  /xzrSiQuiSb5hKePkU9AFuCcgRxE.jpg   \n",
       "\n",
       "                                               overview  \n",
       "5812  오랜 기다림 끝에 스테판 왕과 왕비 사이에 오로라 공주가 태어난다. 왕은 이 날을 ...  \n",
       "6771  태어나서 한번도 집을 떠나본 적이 없는 시골 출신의 순진한 카우보이 보는 로데오 경...  \n",
       "8907  일본의 전국시대. 주민들은 황폐한 땅에서 어렵게 수확한 식량으로 한해 한해를 넘기는...  \n",
       "9080                                                NaN  \n",
       "9103  망국의 기운이 감도는 구한말. 이토오 히로부미와 친일 대신의 압력에 의해 을사조약이...  \n",
       "...                                                 ...  \n",
       "9299  폴과 수자나 부부는 조용한 마을에서 행복하게 살고 있다. 그러던 중 화려한 댄서들이...  \n",
       "9300  은발의 멋진 구렛나루 수염을 쓰다듬으며 금빛 단추의 제복을 뽐내는 한 호텔 도어맨이...  \n",
       "9301  마리(Marie St. Clair: 에드나 퍼비안스 분)는 계부의 반대를 피해 애인...  \n",
       "9302  해리 유켐은 그의 사랑하는 아내에게 가짜와 장물을 거래하는 지하 세계의 중간거래상들...  \n",
       "9303  가난한 시골 처녀 애나는 내키지 않았지만 어머니의 부탁으로 친척에게 돈을 꾸러 도시...  \n",
       "\n",
       "[204 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# release_date가 1960년 이전인 영화들의 인덱스\n",
    "movie_metadata[movie_metadata['release_date'] < '1960-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>backdrop_path</th>\n",
       "      <th>original_title</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>poster_path</th>\n",
       "      <th>overview</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>850161</td>\n",
       "      <td>6.700</td>\n",
       "      <td>/jGspqU8fvG35zyeQnOjFytfkuZ.jpg</td>\n",
       "      <td>రామారావు On Duty</td>\n",
       "      <td>రామారావు On Duty</td>\n",
       "      <td>2022-07-29</td>\n",
       "      <td>/59OeAC7ea4kchFbb35ZNHInTpTR.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>957176</td>\n",
       "      <td>7.375</td>\n",
       "      <td>/t90KM83WYtsAt8S4zdorFV7w5qV.jpg</td>\n",
       "      <td>De forbandede år 2</td>\n",
       "      <td>De forbandede år 2</td>\n",
       "      <td>2022-04-21</td>\n",
       "      <td>/rV4R1YsPywSRDPdRcTerKf5LwCz.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>864370</td>\n",
       "      <td>6.807</td>\n",
       "      <td>/vqEX1uF3Spl12rOACQ3gJ6j4MSR.jpg</td>\n",
       "      <td>咒</td>\n",
       "      <td>주(咒)</td>\n",
       "      <td>2022-03-18</td>\n",
       "      <td>/1srUPaXe88qXTrlWQlft5V88x7U.jpg</td>\n",
       "      <td>6년 전 종교적 금기를 깨고서 저주받은 여인. 이제, 자신의 행동이 초래한 결과로부...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>957466</td>\n",
       "      <td>7.200</td>\n",
       "      <td>/jEnjpMAhpDfvjZjH8onONF5tY8A.jpg</td>\n",
       "      <td>Zanox - Kockázatok és mellékhatások</td>\n",
       "      <td>Zanox - Kockázatok és mellékhatások</td>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>/db1ni4k6iW9DMSYonSYy7Ki1wMe.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>741674</td>\n",
       "      <td>6.571</td>\n",
       "      <td>/mShW9snL91la95p60HL8DZmgWfx.jpg</td>\n",
       "      <td>La revanche des Crevettes pailletées</td>\n",
       "      <td>La revanche des Crevettes pailletées</td>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>/9nLB6wq6tow1M6tdadX2tqRuczy.jpg</td>\n",
       "      <td>NaN</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>9046</th>\n",
       "      <td>321699</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>松都末年의 불가사리</td>\n",
       "      <td>松都末年의 불가사리</td>\n",
       "      <td>1962-12-01</td>\n",
       "      <td>/tEnRuYsLerRr794E3HuZlbJXqOE.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9109</th>\n",
       "      <td>98060</td>\n",
       "      <td>7.000</td>\n",
       "      <td>/h3H22pOPXTVCiRP888d5Z194Bcc.jpg</td>\n",
       "      <td>Il tempo si è fermato</td>\n",
       "      <td>Il tempo si è fermato</td>\n",
       "      <td>1960-04-06</td>\n",
       "      <td>/gFJ88hhy2Se6eqyul9zrTs6heu8.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9270</th>\n",
       "      <td>202839</td>\n",
       "      <td>5.900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Schlußakkord</td>\n",
       "      <td>Schlußakkord</td>\n",
       "      <td>1936-06-26</td>\n",
       "      <td>/f8vZi0cCNKYAunHFgYh8B4e1jk5.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9273</th>\n",
       "      <td>130624</td>\n",
       "      <td>7.000</td>\n",
       "      <td>/hXPqtXnWnNajOaPeCg1JpjPomlV.jpg</td>\n",
       "      <td>Das Mädchen vom Moorhof</td>\n",
       "      <td>Das Mädchen vom Moorhof</td>\n",
       "      <td>1935-10-30</td>\n",
       "      <td>/qujAqypdZHrQ8GN34mqd5xsxOjn.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9294</th>\n",
       "      <td>441242</td>\n",
       "      <td>4.800</td>\n",
       "      <td>/2XA4R9iCsawuH38xAoskytlF0YT.jpg</td>\n",
       "      <td>La Divine Croisière</td>\n",
       "      <td>La Divine Croisière</td>\n",
       "      <td>1929-06-13</td>\n",
       "      <td>/4GIvDmE1qb2OdE1FjYSmNhCyVLx.jpg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id  rating                     backdrop_path  \\\n",
       "236     850161   6.700   /jGspqU8fvG35zyeQnOjFytfkuZ.jpg   \n",
       "244     957176   7.375  /t90KM83WYtsAt8S4zdorFV7w5qV.jpg   \n",
       "248     864370   6.807  /vqEX1uF3Spl12rOACQ3gJ6j4MSR.jpg   \n",
       "336     957466   7.200  /jEnjpMAhpDfvjZjH8onONF5tY8A.jpg   \n",
       "414     741674   6.571  /mShW9snL91la95p60HL8DZmgWfx.jpg   \n",
       "...        ...     ...                               ...   \n",
       "9046    321699   0.000                               NaN   \n",
       "9109     98060   7.000  /h3H22pOPXTVCiRP888d5Z194Bcc.jpg   \n",
       "9270    202839   5.900                               NaN   \n",
       "9273    130624   7.000  /hXPqtXnWnNajOaPeCg1JpjPomlV.jpg   \n",
       "9294    441242   4.800  /2XA4R9iCsawuH38xAoskytlF0YT.jpg   \n",
       "\n",
       "                            original_title  \\\n",
       "236                       రామారావు On Duty   \n",
       "244                     De forbandede år 2   \n",
       "248                                      咒   \n",
       "336    Zanox - Kockázatok és mellékhatások   \n",
       "414   La revanche des Crevettes pailletées   \n",
       "...                                    ...   \n",
       "9046                            松都末年의 불가사리   \n",
       "9109                 Il tempo si è fermato   \n",
       "9270                          Schlußakkord   \n",
       "9273               Das Mädchen vom Moorhof   \n",
       "9294                   La Divine Croisière   \n",
       "\n",
       "                                     title release_date  \\\n",
       "236                       రామారావు On Duty   2022-07-29   \n",
       "244                     De forbandede år 2   2022-04-21   \n",
       "248                                   주(咒)   2022-03-18   \n",
       "336    Zanox - Kockázatok és mellékhatások   2022-05-26   \n",
       "414   La revanche des Crevettes pailletées   2022-01-22   \n",
       "...                                    ...          ...   \n",
       "9046                            松都末年의 불가사리   1962-12-01   \n",
       "9109                 Il tempo si è fermato   1960-04-06   \n",
       "9270                          Schlußakkord   1936-06-26   \n",
       "9273               Das Mädchen vom Moorhof   1935-10-30   \n",
       "9294                   La Divine Croisière   1929-06-13   \n",
       "\n",
       "                           poster_path  \\\n",
       "236   /59OeAC7ea4kchFbb35ZNHInTpTR.jpg   \n",
       "244   /rV4R1YsPywSRDPdRcTerKf5LwCz.jpg   \n",
       "248   /1srUPaXe88qXTrlWQlft5V88x7U.jpg   \n",
       "336   /db1ni4k6iW9DMSYonSYy7Ki1wMe.jpg   \n",
       "414   /9nLB6wq6tow1M6tdadX2tqRuczy.jpg   \n",
       "...                                ...   \n",
       "9046  /tEnRuYsLerRr794E3HuZlbJXqOE.jpg   \n",
       "9109  /gFJ88hhy2Se6eqyul9zrTs6heu8.jpg   \n",
       "9270  /f8vZi0cCNKYAunHFgYh8B4e1jk5.jpg   \n",
       "9273  /qujAqypdZHrQ8GN34mqd5xsxOjn.jpg   \n",
       "9294  /4GIvDmE1qb2OdE1FjYSmNhCyVLx.jpg   \n",
       "\n",
       "                                               overview  \n",
       "236                                                 NaN  \n",
       "244                                                 NaN  \n",
       "248   6년 전 종교적 금기를 깨고서 저주받은 여인. 이제, 자신의 행동이 초래한 결과로부...  \n",
       "336                                                 NaN  \n",
       "414                                                 NaN  \n",
       "...                                                 ...  \n",
       "9046                                                NaN  \n",
       "9109                                                NaN  \n",
       "9270                                                NaN  \n",
       "9273                                                NaN  \n",
       "9294                                                NaN  \n",
       "\n",
       "[171 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "# Assuming you've loaded your data into a DataFrame called df\n",
    "# df = pd.read_csv('path_to_your_csv_file.csv')\n",
    "\n",
    "# Regular expression for non-English and non-Korean characters\n",
    "pattern = r'[^a-zA-Z가-힣0-9\\s\\.\\:\\,\\~\\-\\–\\_\\+\\\\/\\(\\)\\[\\]\\!\\?\\&\\'\\\"]'\n",
    "\n",
    "# Filter rows where the title contains other language characters\n",
    "filtered_df = movie_metadata[movie_metadata['title'].str.contains(pattern, na=False)]\n",
    "\n",
    "# Display the filtered rows\n",
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vw/3wnj_h7s68l_wbf3ytj2csd80000gn/T/ipykernel_11338/4032451711.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  filtered_df[:50][filtered_df['release_date'] > '1960-01-01'].count()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "movie_id          50\n",
       "rating            50\n",
       "backdrop_path     49\n",
       "original_title    50\n",
       "title             50\n",
       "release_date      50\n",
       "poster_path       50\n",
       "overview           4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1960년 이후 개봉한 영화 중 영어와 한국어가 아닌 제목을 가진 영화\n",
    "filtered_df[:50][filtered_df['release_date'] > '1960-01-01'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9304, 414)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "feature = np.load(\"./server/data/final_features_0528.npy\")\n",
    "feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vw/3wnj_h7s68l_wbf3ytj2csd80000gn/T/ipykernel_11338/1360746303.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  filtered_indices = list(set(filtered_df[:50][filtered_df['release_date'] > '2000-01-01'].index.tolist() + movie_metadata[movie_metadata['release_date'] < '2000-01-01'].index.tolist()))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2285"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtered_df[:50][filtered_df['release_date'] > '1960-01-01'], movie_metadata[movie_metadata['release_date'] < '1960-01-01']의 인덱스를 저장한 리스트 생성(중복 없이)\n",
    "filtered_indices = list(set(filtered_df[:50][filtered_df['release_date'] > '2000-01-01'].index.tolist() + movie_metadata[movie_metadata['release_date'] < '2000-01-01'].index.tolist()))\n",
    "filtered_indices.__len__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 9179,\n",
       " 9180,\n",
       " 9181,\n",
       " 9182,\n",
       " 9183,\n",
       " ...]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtered_indices 저장\n",
    "np.save(\"./server/data/excluded_movie_indices_0810.npy\", filtered_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2285,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(\"./server/data/excluded_movie_indices_0810.npy\").shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 장르 one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "genres_one_hot_encoded = final_metadata.genres.str.get_dummies(sep=',')\n",
    "genres_one_hot_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 감독, 배우 one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_directors_combinations = final_metadata['directors'].unique()\n",
    "# unique_directors_combinations.__len__()\n",
    "unique_directors = set()\n",
    "\n",
    "for combination in unique_directors_combinations:\n",
    "    directors = combination.split(',')\n",
    "    unique_directors.update(directors)\n",
    "\n",
    "print(sorted(unique_directors))\n",
    "directors_one_hot_encoded = final_metadata.directors.str.get_dummies(sep=',')\n",
    "# final_metadata[directors_one_hot_encoded.sum(axis=1) > 1]\n",
    "directors_one_hot_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_actors_combinations = final_metadata['actors'].unique()\n",
    "unique_actors = set()\n",
    "\n",
    "for combination in unique_actors_combinations:\n",
    "    actors = combination.split(',')\n",
    "    unique_actors.update(actors)\n",
    "\n",
    "print(sorted(unique_actors))\n",
    "actors_one_hot_encoded = final_metadata.actors.str.get_dummies(sep=',')\n",
    "# final_metadata[actors_one_hot_encoded.sum(axis=1) > 1]\n",
    "actors_one_hot_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "directors_pca = pca.fit_transform(directors_one_hot_encoded)\n",
    "directors_pca_df = pd.DataFrame(directors_pca, columns=[f'director_pca_{i+1}' for i in range(10)])\n",
    "\n",
    "actors_pca = pca.fit_transform(actors_one_hot_encoded)\n",
    "actors_pca_df = pd.DataFrame(actors_pca, columns=[f'actor_pca_{i+1}' for i in range(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "genres_pca = pca.fit_transform(genres_one_hot_encoded)\n",
    "genres_pca_df = pd.DataFrame(genres_pca, columns=[f'genre_pca_{i+1}' for i in range(10)])\n",
    "genres_pca_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_features = pd.concat([directors_pca_df, actors_pca_df, genres_pca_df * 2, final_metadata.iloc[:, -384:]], axis=1)\n",
    "final_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.save('final_features_0528.npy', final_features.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_metadata = movie_metadata[(movie_metadata['title'].str.contains('[a-zA-Z]') | movie_metadata['title'].str.contains('[가-힣]'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_metadata.reset_index(drop=True, inplace=True)\n",
    "movie_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_metadata.to_excel(\"cinemate_data_only_eng_kor_0528.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## nan 값 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_metadata.loc[movie_metadata.backdrop_path.isnull(), 'backdrop_path'] = None\n",
    "movie_metadata.loc[movie_metadata.poster_path.isnull(), 'poster_path'] = None\n",
    "movie_metadata.loc[movie_metadata.overview.isnull(), 'overview'] = \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SQL 문 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_mapping = {\n",
    "    \"Action\": 1,\n",
    "    \"Adventure\": 2,\n",
    "    \"Animation\": 3,\n",
    "    \"Biography\": 4,\n",
    "    \"Comedy\": 5,\n",
    "    \"Crime\": 6,\n",
    "    \"Documentary\": 7,\n",
    "    \"Drama\": 8,\n",
    "    \"Family\": 9,\n",
    "    \"Fantasy\": 10,\n",
    "    \"History\": 11,\n",
    "    \"Horror\": 12,\n",
    "    \"Music\": 13,\n",
    "    \"Musical\": 14,\n",
    "    \"Mystery\": 15,\n",
    "    \"Romance\": 16,\n",
    "    \"Sci-Fi\": 17,\n",
    "    \"Sport\": 18,\n",
    "    \"Thriller\": 19,\n",
    "    \"War\": 20,\n",
    "    \"Western\": 21\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 영화 데이터 삽입\n",
    "movie_insert_sql = \"\"\"\n",
    "INSERT INTO movie (id, movie_id, rating, backdrop_path, original_title, movie_title, release_date, poster_path, overview)\n",
    "VALUES ({}, {}, {}, '{}', '{}', '{}', '{}', '{}', '{}');\n",
    "\"\"\"\n",
    "\n",
    "movies_sql_statements = []\n",
    "\n",
    "for index, row in movie_metadata.iterrows():\n",
    "    print(row['backdrop_path'])\n",
    "    backdrop_path = \"https://image.tmdb.org/t/p/original\" + row['backdrop_path'] if row['backdrop_path'] else None\n",
    "    poster_path = \"https://image.tmdb.org/t/p/original\" + row['poster_path'] if row['poster_path'] else None\n",
    "    movie_data = (\n",
    "        index + 1,\n",
    "        row['movie_id'],\n",
    "        row['rating'],\n",
    "        backdrop_path,\n",
    "        row['original_title'].replace(\"'\", \"''\"),\n",
    "        row['title'].replace(\"'\", \"''\"),\n",
    "        row['release_date'],\n",
    "        poster_path,\n",
    "        row['overview'].replace(\"'\", \"''\")\n",
    "    )\n",
    "    movie_sql = movie_insert_sql.format(*movie_data)\n",
    "    movies_sql_statements.append(movie_sql)\n",
    "\n",
    "# # 장르 데이터 삽입\n",
    "# genre_insert_sql = \"\"\"\n",
    "# INSERT INTO genre (genre_name)\n",
    "# VALUES ('{}');\n",
    "# \"\"\"\n",
    "\n",
    "# genres_sql_statements = []\n",
    "\n",
    "# # 장르 데이터를 중복 없이 추출\n",
    "# genres_set = set()\n",
    "# for index, row in cinemate_data.iterrows():\n",
    "#     genres = row['genres'].split(',')\n",
    "#     for genre in genres:\n",
    "#         genres_set.add(genre.strip())\n",
    "\n",
    "# # 중복 없는 장르 데이터를 DB에 삽입\n",
    "# for genre in genres_set:\n",
    "#     genre_sql = genre_insert_sql.format(genre.strip())\n",
    "#     genres_sql_statements.append(genre_sql)\n",
    "\n",
    "# 영화와 장르의 관계 데이터 삽입\n",
    "movie_genre_mapping_insert_sql = \"\"\"\n",
    "INSERT INTO genre_movie (genre_id, movie_id)\n",
    "VALUES ({}, {});\n",
    "\"\"\"\n",
    "\n",
    "movie_genre_sql_statements = []\n",
    "\n",
    "for index, row in final_metadata.iterrows():\n",
    "    genres = row['genres'].split(',')\n",
    "    for genre in genres:\n",
    "        genre_id = genre_mapping[genre]\n",
    "        movie_genre_sql = movie_genre_mapping_insert_sql.format(genre_id, index + 1)\n",
    "        movie_genre_sql_statements.append(movie_genre_sql)\n",
    "\n",
    "# SQL 쿼리를 파일에 쓰기\n",
    "with open('data_ver2.sql', 'w') as f:\n",
    "    for sql_statement in movies_sql_statements + movie_genre_sql_statements:\n",
    "        f.write(sql_statement + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 장르별 index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Action', 'Drama', 'Sport'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_genres.get(4118)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 각 장르별 영화 개수 plot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "genre_count = {}\n",
    "for genres in movie_genres.values():\n",
    "    for genre in genres:\n",
    "        if genre in genre_count:\n",
    "            genre_count[genre] += 1\n",
    "        else:\n",
    "            genre_count[genre] = 1\n",
    "genre_count = dict(sorted(genre_count.items(), key=lambda x: x[1], reverse=True))\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.bar(genre_count.keys(), genre_count.values())\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Drama': 5529,\n",
       " 'Comedy': 2375,\n",
       " 'Action': 1828,\n",
       " 'Thriller': 1583,\n",
       " 'Romance': 1577,\n",
       " 'Crime': 1332,\n",
       " 'Horror': 1189,\n",
       " 'Adventure': 962,\n",
       " 'Mystery': 919,\n",
       " 'Sci-Fi': 541,\n",
       " 'Fantasy': 526,\n",
       " 'Biography': 364,\n",
       " 'Animation': 363,\n",
       " 'Family': 315,\n",
       " 'History': 301,\n",
       " 'War': 221,\n",
       " 'Documentary': 207,\n",
       " 'Music': 199,\n",
       " 'Sport': 105,\n",
       " 'Musical': 92,\n",
       " 'Western': 66}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genre: Drama\n",
      "Index([   0,    1,    6,    8,    9,   11,   12,   14,   15,   17,\n",
      "       ...\n",
      "       9292, 9293, 9294, 9295, 9297, 9298, 9300, 9301, 9302, 9303],\n",
      "      dtype='int64', length=5529)\n",
      "Genre: Comedy\n",
      "Index([   6,   10,   14,   15,   18,   25,   28,   34,   36,   40,\n",
      "       ...\n",
      "       9264, 9268, 9274, 9275, 9278, 9285, 9288, 9291, 9296, 9299],\n",
      "      dtype='int64', length=2375)\n",
      "Genre: Action\n",
      "Index([   4,    6,    7,    9,   17,   23,   32,   34,   36,   55,\n",
      "       ...\n",
      "       9155, 9182, 9187, 9188, 9216, 9218, 9226, 9248, 9296, 9297],\n",
      "      dtype='int64', length=1828)\n",
      "Genre: Thriller\n",
      "Index([   2,    3,    4,    5,   16,   31,   48,   50,   53,   61,\n",
      "       ...\n",
      "       9149, 9152, 9156, 9159, 9167, 9186, 9189, 9233, 9238, 9284],\n",
      "      dtype='int64', length=1583)\n",
      "Genre: Romance\n",
      "Index([   8,   11,   12,   18,   25,   40,   43,   44,   46,   48,\n",
      "       ...\n",
      "       9279, 9280, 9285, 9287, 9288, 9290, 9293, 9297, 9301, 9303],\n",
      "      dtype='int64', length=1577)\n",
      "Genre: Crime\n",
      "Index([   4,    5,   19,   22,   50,   55,   61,   67,   71,   77,\n",
      "       ...\n",
      "       9221, 9224, 9227, 9237, 9238, 9261, 9265, 9274, 9277, 9284],\n",
      "      dtype='int64', length=1332)\n",
      "Genre: Horror\n",
      "Index([   2,    3,   13,   16,   19,   20,   31,   42,   70,   97,\n",
      "       ...\n",
      "       9130, 9155, 9167, 9168, 9184, 9193, 9203, 9276, 9282, 9283],\n",
      "      dtype='int64', length=1189)\n",
      "Genre: Adventure\n",
      "Index([   9,   23,   49,   59,   75,   98,  105,  117,  131,  145,\n",
      "       ...\n",
      "       9253, 9254, 9257, 9265, 9266, 9276, 9283, 9290, 9291, 9296],\n",
      "      dtype='int64', length=962)\n",
      "Genre: Mystery\n",
      "Index([   3,    5,   16,   20,   21,   22,   31,   42,   71,  104,\n",
      "       ...\n",
      "       9189, 9221, 9224, 9237, 9247, 9251, 9252, 9272, 9278, 9284],\n",
      "      dtype='int64', length=919)\n",
      "Genre: Sci-Fi\n",
      "Index([   7,   25,   70,  127,  137,  152,  158,  185,  230,  288,\n",
      "       ...\n",
      "       9130, 9155, 9161, 9168, 9184, 9193, 9215, 9276, 9283, 9298],\n",
      "      dtype='int64', length=541)\n",
      "Genre: Fantasy\n",
      "Index([   7,   19,   58,   85,  115,  133,  169,  221,  227,  255,\n",
      "       ...\n",
      "       9046, 9081, 9087, 9146, 9194, 9196, 9232, 9234, 9254, 9282],\n",
      "      dtype='int64', length=526)\n",
      "Genre: Biography\n",
      "Index([  32,  109,  113,  128,  150,  187,  237,  251,  299,  311,\n",
      "       ...\n",
      "       9092, 9095, 9103, 9150, 9166, 9170, 9177, 9244, 9246, 9269],\n",
      "      dtype='int64', length=364)\n",
      "Genre: Animation\n",
      "Index([  34,   49,   57,   75,  125,  145,  154,  172,  183,  221,\n",
      "       ...\n",
      "       8197, 8216, 8347, 8370, 8431, 8438, 8540, 8662, 9253, 9266],\n",
      "      dtype='int64', length=363)\n",
      "Genre: Family\n",
      "Index([  10,   60,   75,  131,  155,  172,  198,  233,  234,  312,\n",
      "       ...\n",
      "       9094, 9120, 9151, 9203, 9222, 9225, 9231, 9232, 9254, 9266],\n",
      "      dtype='int64', length=315)\n",
      "Genre: History\n",
      "Index([  32,   67,   96,  103,  166,  175,  187,  207,  237,  321,\n",
      "       ...\n",
      "       9020, 9021, 9023, 9060, 9095, 9106, 9136, 9198, 9222, 9269],\n",
      "      dtype='int64', length=301)\n",
      "Genre: War\n",
      "Index([ 206,  244,  254,  307,  315,  380,  426,  442,  552,  643,\n",
      "       ...\n",
      "       9195, 9196, 9239, 9240, 9241, 9242, 9255, 9279, 9289, 9295],\n",
      "      dtype='int64', length=221)\n",
      "Genre: Documentary\n",
      "Index([  27,   39,   54,   56,   72,   76,   78,   84,  123,  128,\n",
      "       ...\n",
      "       5706, 6071, 6320, 6699, 6964, 7648, 7793, 8112, 8723, 8826],\n",
      "      dtype='int64', length=207)\n",
      "Genre: Music\n",
      "Index([  51,   74,   76,   78,   84,   90,   92,  114,  125,  128,\n",
      "       ...\n",
      "       9191, 9197, 9201, 9209, 9235, 9241, 9250, 9270, 9274, 9293],\n",
      "      dtype='int64', length=286)\n",
      "Genre: Sport\n",
      "Index([  14,   41,  109,  113,  214,  410,  717,  727,  856,  923,\n",
      "       ...\n",
      "       7891, 7947, 8034, 8048, 8267, 8335, 8426, 8518, 8576, 9207],\n",
      "      dtype='int64', length=105)\n",
      "Genre: Musical\n",
      "Index([ 568,  629,  651,  696,  697,  702,  775,  956, 1054, 1058, 1179, 1273,\n",
      "       1286, 1443, 1482, 1525, 1528, 1616, 1647, 1816, 1885, 1901, 1944, 1948,\n",
      "       2150, 2331, 2339, 2388, 2735, 3288, 3488, 3492, 3595, 3793, 4186, 4202,\n",
      "       4517, 4617, 4851, 5007, 5026, 5066, 5067, 5848, 5910, 5951, 6060, 6089,\n",
      "       6173, 6233, 6303, 6341, 6525, 6831, 6856, 6865, 6895, 7023, 7062, 7112,\n",
      "       7226, 7380, 7723, 7885, 7903, 7974, 7994, 8161, 8321, 8475, 8566, 8575,\n",
      "       8661, 8698, 8770, 8784, 8845, 8991, 8993, 9058, 9066, 9077, 9140, 9166,\n",
      "       9190, 9191, 9197, 9201, 9209, 9241, 9250, 9293],\n",
      "      dtype='int64')\n",
      "Genre: Western\n",
      "Index([ 515,  733,  735,  830,  992, 1136, 1735, 1896, 1997, 2001, 2020, 2124,\n",
      "       2133, 2135, 2260, 2544, 3192, 3206, 3269, 3667, 4012, 4050, 4441, 4909,\n",
      "       5092, 5528, 7630, 7643, 7760, 7837, 7902, 8384, 8401, 8534, 8655, 8692,\n",
      "       8729, 8737, 8744, 8758, 8786, 8806, 8810, 8811, 8830, 8835, 8844, 8866,\n",
      "       8914, 8922, 8930, 8932, 8946, 8950, 8958, 8985, 9043, 9048, 9069, 9160,\n",
      "       9178, 9198, 9199, 9200, 9205, 9220],\n",
      "      dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "for genre in genre_count.keys():\n",
    "    print(f\"Genre: {genre}\")\n",
    "    print(final_metadata[final_metadata['genres'].str.contains(genre)].index)\n",
    "    # store indexs of final_metadata by genre in npy file\n",
    "    np.save(f\"./server/genre_index/{genre}_indexs.npy\", final_metadata[final_metadata['genres'].str.contains(genre)].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movies for Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "movie_metadata = pd.read_excel(\"cinemate_data_only_eng_kor_0528.xlsx\")\n",
    "final_metadata = pd.read_excel(\"final_metadata_with_overview_embeddings_only_eng_kor_0528.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./server/genre_index/History_indexs.npy',\n",
       " './server/genre_index/Thriller_indexs.npy',\n",
       " './server/genre_index/Adventure_indexs.npy',\n",
       " './server/genre_index/Drama_indexs.npy',\n",
       " './server/genre_index/Sci-Fi_indexs.npy',\n",
       " './server/genre_index/Sport_indexs.npy',\n",
       " './server/genre_index/Crime_indexs.npy',\n",
       " './server/genre_index/Romance_indexs.npy',\n",
       " './server/genre_index/War_indexs.npy',\n",
       " './server/genre_index/Musical_indexs.npy',\n",
       " './server/genre_index/Western_indexs.npy',\n",
       " './server/genre_index/Animation_indexs.npy',\n",
       " './server/genre_index/Fantasy_indexs.npy',\n",
       " './server/genre_index/Horror_indexs.npy',\n",
       " './server/genre_index/Music_indexs.npy',\n",
       " './server/genre_index/Family_indexs.npy',\n",
       " './server/genre_index/Action_indexs.npy',\n",
       " './server/genre_index/Documentary_indexs.npy',\n",
       " './server/genre_index/Comedy_indexs.npy',\n",
       " './server/genre_index/Biography_indexs.npy',\n",
       " './server/genre_index/Mystery_indexs.npy']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "\n",
    "# 모든 npy 파일 읽어오기\n",
    "npy_files = glob.glob(\"./server/genre_index/*.npy\")\n",
    "npy_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for npy_file in npy_files:\n",
    "    # npy_files[0].split(\"/\")[-1].split(\".\")[0]를 변수명으로 불러온 npy 파일 저장\n",
    "    globals()[npy_file.split(\"/\")[-1].split(\".\")[0]] = np.load(npy_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action / Crime\n",
      "-----------------\n",
      "Action,Crime,Drama\n",
      "Action,Crime,Thriller\n",
      "Action,Comedy,Drama\n",
      "Action,Comedy,Crime\n",
      "Action,Crime\n",
      "Action,Comedy,Crime\n",
      "Action,Crime,Drama\n",
      "Action,Crime\n",
      "Action,Crime,Thriller\n",
      "\n",
      "Action / History\n",
      "-----------------\n",
      "Drama,History\n",
      "Drama,History,Thriller\n",
      "Action,Drama,History\n",
      "Drama\n",
      "Action,Drama,Thriller\n",
      "Drama,War\n",
      "Action,Drama,History\n",
      "Mystery,Thriller\n",
      "\n",
      "Action / Disaster\n",
      "-----------------\n",
      "Action,Drama,Sci-Fi\n",
      "Action,Drama,Sci-Fi\n",
      "Action,Adventure,Drama\n",
      "Action,Adventure,Sci-Fi\n",
      "Action,Drama,Sci-Fi\n",
      "\n",
      "Sports\n",
      "-----------------\n",
      "Comedy,Drama,Sport\n",
      "Romance,Sport\n",
      "Drama,Sport\n",
      "Drama,Sport\n",
      "\n",
      "Sci-Fi\n",
      "-----------------\n",
      "Adventure,Drama,Sci-Fi\n",
      "Adventure,Drama,Sci-Fi\n",
      "Action,Adventure,Sci-Fi\n",
      "Action,Adventure,Drama\n",
      "Action,Adventure,Sci-Fi\n",
      "\n",
      "Horror\n",
      "-----------------\n",
      "Horror,Mystery,Thriller\n",
      "Horror,Mystery,Thriller\n",
      "Horror,Mystery,Thriller\n",
      "Horror,Mystery,Thriller\n",
      "Horror,Mystery,Thriller\n",
      "\n",
      "Comedy\n",
      "-----------------\n",
      "Comedy,Drama\n",
      "Comedy,Drama\n",
      "Comedy,Drama,Romance\n",
      "Action,Comedy,Crime\n",
      "\n",
      "Animation\n",
      "-----------------\n",
      "Adventure,Animation,Drama,Family\n",
      "Adventure,Animation,Comedy\n",
      "\n",
      "Musical\n",
      "-----------------\n",
      "Comedy,Drama,Music\n",
      "Drama,Music,Romance\n",
      "Comedy,Drama,Music\n",
      "Biography,Drama,Musical\n",
      "Adventure,Comedy,Family\n",
      "\n",
      "Romance\n",
      "-----------------\n",
      "Comedy,Drama,Fantasy,Romance\n",
      "Comedy,Romance\n",
      "Comedy,Drama,Romance\n",
      "Drama,Fantasy,Romance\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4943, 4856, 6786, 4607, 4433)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Action / Crime\n",
    "print(\"Action / Crime\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[5502].genres) # 아저씨\n",
    "print(final_metadata.iloc[3802].genres) # 범죄도시\n",
    "print(final_metadata.iloc[4788].genres) # 은밀하게 위대하게\n",
    "print(final_metadata.iloc[4165].genres) # 검사외전\n",
    "print(final_metadata.iloc[4108].genres) # 마스터\n",
    "print(final_metadata.iloc[3799].genres) # 청년경찰\n",
    "print(final_metadata.iloc[3843].genres) # 불한당: 나쁜 놈들의 세상\n",
    "print(final_metadata.iloc[3830].genres) # 프리즌\n",
    "print(final_metadata.iloc[5414].genres) # 악마를 보았다\n",
    "print()\n",
    "\n",
    "# Action / History\n",
    "print(\"Action / History\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[5103].genres) # 광해, 왕이 된 남자\n",
    "print(final_metadata.iloc[2724].genres) # 남산의 부장들\n",
    "print(final_metadata.iloc[96].genres) # 서울의 봄\n",
    "print(final_metadata.iloc[4754].genres) # 관상\n",
    "print(final_metadata.iloc[4248].genres) # 암살\n",
    "print(final_metadata.iloc[4716].genres) # 국제시장\n",
    "print(final_metadata.iloc[4588].genres) # 명량\n",
    "print(final_metadata.iloc[1248].genres) # 올빼미\n",
    "print()\n",
    "\n",
    "\n",
    "# Action / Disaster\n",
    "print(\"Action / Disaster\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[5737].genres) # 해운대\n",
    "print(final_metadata.iloc[4815].genres) # 감기\n",
    "print(final_metadata.iloc[105].genres) # 콘크리트 유토피아\n",
    "print(final_metadata.iloc[3008].genres) # 백두산\n",
    "print(final_metadata.iloc[4894].genres) # 설국열차\n",
    "print()\n",
    "\n",
    "# Sports\n",
    "print(\"Sports\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[5778].genres) # 국가대표\n",
    "print(final_metadata.iloc[4769].genres) # 노브레싱\n",
    "print(final_metadata.iloc[5077].genres) # 코리아(탁구영화)\n",
    "print(final_metadata.iloc[5210].genres) # 글러브\n",
    "print()\n",
    "\n",
    "# Sci-Fi\n",
    "print(\"Sci-Fi\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[4548].genres) # 인터스텔라 - Sci-Fi\n",
    "print(final_metadata.iloc[4387].genres) # 마션 - Sci-Fi\n",
    "print(final_metadata.iloc[5543].genres) # 인셉션 - Sci-Fi\n",
    "print(final_metadata.iloc[3201].genres) # 어벤져스: 엔드게임 - Adventure, Action\n",
    "print(final_metadata.iloc[3928].genres) # 트랜스포머: 최후의 기사\n",
    "print()\n",
    "\n",
    "# Horror\n",
    "print(\"Horror\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[4845].genres) # 컨저링\n",
    "print(final_metadata.iloc[4604].genres) # 애나벨\n",
    "print(final_metadata.iloc[300].genres) # 스마일\n",
    "print(final_metadata.iloc[5100].genres) # 미확인 동영상: 절대클릭금지\n",
    "print(final_metadata.iloc[5419].genres) # 인시디어스\n",
    "print()\n",
    "\n",
    "# Comedy\n",
    "print(\"Comedy\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[4763].genres) # 7번방의 선물\n",
    "print(final_metadata.iloc[5701].genres) # 세 얼간이\n",
    "print(final_metadata.iloc[4477].genres) # 스물\n",
    "print(final_metadata.iloc[3095].genres) # 극한직업\n",
    "print()\n",
    "\n",
    "# Animation\n",
    "print(\"Animation\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[3649].genres) # 코코\n",
    "print(final_metadata.iloc[1124].genres) # 미니언즈\n",
    "print()\n",
    "\n",
    "# Musical\n",
    "print(\"Musical\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[3973].genres) # 라라랜드\n",
    "print(final_metadata.iloc[6072].genres) # Once\n",
    "print(final_metadata.iloc[4945].genres) # Begin Again\n",
    "print(final_metadata.iloc[3793].genres) # 위대한 쇼맨\n",
    "print(final_metadata.iloc[6364].genres) # 찰리와 초콜릿 공장\n",
    "print()\n",
    "\n",
    "# Romance\n",
    "print(\"Romance\")\n",
    "print(\"-----------------\")\n",
    "print(final_metadata.iloc[4817].genres) # About Time\n",
    "print(final_metadata.iloc[4732].genres) # Love, Rosie\n",
    "print(final_metadata.iloc[6636].genres) # Love Actually\n",
    "print(final_metadata.iloc[4486].genres) # 뷰티 인사이드\n",
    "print()\n",
    "\n",
    "# IDs\n",
    "# Action / Crime\n",
    "5502, 3802, 4788, 4165, 4108, 3799, 3843, 3830, 5414\n",
    "# Action / History\n",
    "5103, 2724, 96, 4754, 4248, 4716, 4588, 1248\n",
    "# Action / Disaster\n",
    "5737, 4815, 105, 3008, 4894\n",
    "# Sports\n",
    "5778, 4769, 5077, 5210\n",
    "# Sci-Fi\n",
    "4548, 4387, 5543, 3201, 3928\n",
    "# Horror\n",
    "4845, 4604, 300, 5100, 5419\n",
    "# Comedy\n",
    "4763, 5701, 4477, 3095\n",
    "# Animation\n",
    "3649, 1124\n",
    "# Musical\n",
    "3973, 6072, 4945, 3793, 6364\n",
    "# Romance\n",
    "4817, 4732, 6636, 4486"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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