L07_1_Similarity-Practice.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n# Python용 데이터분석 라이브러리\n# Excel과 같이, 행과 열로 구성된 데이터 객체를 다룸\n \n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-10-21T15:38:10.894593Z","iopub.execute_input":"2022-10-21T15:38:10.895208Z","iopub.status.idle":"2022-10-21T15:38:10.943654Z","shell.execute_reply.started":"2022-10-21T15:38:10.895127Z","shell.execute_reply":"2022-10-21T15:38:10.942580Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/the-movies-dataset/ratings.csv\n/kaggle/input/the-movies-dataset/links_small.csv\n/kaggle/input/the-movies-dataset/credits.csv\n/kaggle/input/the-movies-dataset/keywords.csv\n/kaggle/input/the-movies-dataset/movies_metadata.csv\n/kaggle/input/the-movies-dataset/ratings_small.csv\n/kaggle/input/the-movies-dataset/links.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Metadata 정제하기","metadata":{}},{"cell_type":"code","source":"# pandas로 데이터 읽어오기\nmeta = pd.read_csv(\"/kaggle/input/the-movies-dataset/movies_metadata.csv\")\n\n# 사용하고 싶은 데이터만 뽑기 (metadata에서 필요한 열(column)만 추려내기)\nmeta = meta[['id', 'original_title', 'original_language', 'genres']]\nprint(meta)","metadata":{"execution":{"iopub.status.busy":"2022-10-21T15:38:51.710270Z","iopub.execute_input":"2022-10-21T15:38:51.710621Z","iopub.status.idle":"2022-10-21T15:38:53.333622Z","shell.execute_reply.started":"2022-10-21T15:38:51.710595Z","shell.execute_reply":"2022-10-21T15:38:53.332086Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"           id               original_title original_language  \\\n0         862                    Toy Story                en   \n1        8844                      Jumanji                en   \n2       15602             Grumpier Old Men                en   \n3       31357            Waiting to Exhale                en   \n4       11862  Father of the Bride Part II                en   \n...       ...                          ...               ...   \n45461  439050                      رگ خواب                fa   \n45462  111109          Siglo ng Pagluluwal                tl   \n45463   67758                     Betrayal                en   \n45464  227506          Satana likuyushchiy                en   \n45465  461257                     Queerama                en   \n\n                                                  genres  \n0      [{'id': 16, 'name': 'Animation'}, {'id': 35, '...  \n1      [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...  \n2      [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...  \n3      [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  \n4                         [{'id': 35, 'name': 'Comedy'}]  \n...                                                  ...  \n45461  [{'id': 18, 'name': 'Drama'}, {'id': 10751, 'n...  \n45462                      [{'id': 18, 'name': 'Drama'}]  \n45463  [{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...  \n45464                                                 []  \n45465                                                 []  \n\n[45466 rows x 4 columns]\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3552: DtypeWarning: Columns (10) have mixed types.Specify dtype option on import or set low_memory=False.\n  exec(code_obj, self.user_global_ns, self.user_ns)\n","output_type":"stream"}]},{"cell_type":"code","source":"# rename - column name 바꾸기(바꿀 것만 넣어주기) (열(column) 이름 변경)\n# columns 파라미터를 딕셔너리 형태로 넣어주기\nmeta = meta.rename(columns={'id':'movieId',\n                            'original_title': 'title',\n                            'original_language': 'language'})\n\nprint(meta)","metadata":{"execution":{"iopub.status.busy":"2022-10-21T15:40:29.619310Z","iopub.execute_input":"2022-10-21T15:40:29.619676Z","iopub.status.idle":"2022-10-21T15:40:29.634323Z","shell.execute_reply.started":"2022-10-21T15:40:29.619649Z","shell.execute_reply":"2022-10-21T15:40:29.633217Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"      movieId                        title language  \\\n0         862                    Toy Story       en   \n1        8844                      Jumanji       en   \n2       15602             Grumpier Old Men       en   \n3       31357            Waiting to Exhale       en   \n4       11862  Father of the Bride Part II       en   \n...       ...                          ...      ...   \n45461  439050                      رگ خواب       fa   \n45462  111109          Siglo ng Pagluluwal       tl   \n45463   67758                     Betrayal       en   \n45464  227506          Satana likuyushchiy       en   \n45465  461257                     Queerama       en   \n\n                                                  genres  \n0      [{'id': 16, 'name': 'Animation'}, {'id': 35, '...  \n1      [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...  \n2      [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...  \n3      [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  \n4                         [{'id': 35, 'name': 'Comedy'}]  \n...                                                  ...  \n45461  [{'id': 18, 'name': 'Drama'}, {'id': 10751, 'n...  \n45462                      [{'id': 18, 'name': 'Drama'}]  \n45463  [{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...  \n45464                                                 []  \n45465                                                 []  \n\n[45466 rows x 4 columns]\n","output_type":"stream"}]},{"cell_type":"code","source":"# meta['language'] == 'en' 인 것들만 추출\n# loc - column 조건에 맞는 모든 row 들 출력 \nmeta = meta.loc[meta['language'] == 'en', :]\nmeta","metadata":{"execution":{"iopub.status.busy":"2022-10-21T15:44:42.866063Z","iopub.execute_input":"2022-10-21T15:44:42.866463Z","iopub.status.idle":"2022-10-21T15:44:42.900462Z","shell.execute_reply.started":"2022-10-21T15:44:42.866435Z","shell.execute_reply":"2022-10-21T15:44:42.898940Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"      movieId                        title language  \\\n0         862                    Toy Story       en   \n1        8844                      Jumanji       en   \n2       15602             Grumpier Old Men       en   \n3       31357            Waiting to Exhale       en   \n4       11862  Father of the Bride Part II       en   \n...       ...                          ...      ...   \n45459  222848              Caged Heat 3000       en   \n45460   30840                   Robin Hood       en   \n45463   67758                     Betrayal       en   \n45464  227506          Satana likuyushchiy       en   \n45465  461257                     Queerama       en   \n\n                                                  genres  \n0      [{'id': 16, 'name': 'Animation'}, {'id': 35, '...  \n1      [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...  \n2      [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...  \n3      [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  \n4                         [{'id': 35, 'name': 'Comedy'}]  \n...                                                  ...  \n45459           [{'id': 878, 'name': 'Science Fiction'}]  \n45460  [{'id': 18, 'name': 'Drama'}, {'id': 28, 'name...  \n45463  [{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...  \n45464                                                 []  \n45465                                                 []  \n\n[32269 rows x 4 columns]","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>movieId</th>\n      <th>title</th>\n      <th>language</th>\n      <th>genres</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>862</td>\n      <td>Toy Story</td>\n      <td>en</td>\n      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8844</td>\n      <td>Jumanji</td>\n      <td>en</td>\n      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15602</td>\n      <td>Grumpier Old Men</td>\n      <td>en</td>\n      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31357</td>\n      <td>Waiting to Exhale</td>\n      <td>en</td>\n      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11862</td>\n      <td>Father of the Bride Part II</td>\n      <td>en</td>\n      <td>[{'id': 35, 'name': 'Comedy'}]</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>45459</th>\n      <td>222848</td>\n      <td>Caged Heat 3000</td>\n      <td>en</td>\n      <td>[{'id': 878, 'name': 'Science Fiction'}]</td>\n    </tr>\n    <tr>\n      <th>45460</th>\n      <td>30840</td>\n      <td>Robin Hood</td>\n      <td>en</td>\n      <td>[{'id': 18, 'name': 'Drama'}, {'id': 28, 'name...</td>\n    </tr>\n    <tr>\n      <th>45463</th>\n      <td>67758</td>\n      <td>Betrayal</td>\n      <td>en</td>\n      <td>[{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...</td>\n    </tr>\n    <tr>\n      <th>45464</th>\n      <td>227506</td>\n      <td>Satana likuyushchiy</td>\n      <td>en</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>45465</th>\n      <td>461257</td>\n      <td>Queerama</td>\n      <td>en</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n<p>32269 rows × 4 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# language 가 en 인 row를 출력하는데 그 중 column이 titler과 language 인 것만 출력\nmeta.loc[meta['language'] == 'en', [\"title\", \"language\"]]","metadata":{"execution":{"iopub.status.busy":"2022-10-21T15:47:31.892030Z","iopub.execute_input":"2022-10-21T15:47:31.892455Z","iopub.status.idle":"2022-10-21T15:47:31.917690Z","shell.execute_reply.started":"2022-10-21T15:47:31.892427Z","shell.execute_reply":"2022-10-21T15:47:31.916369Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"                             title language\n0                        Toy Story       en\n1                          Jumanji       en\n2                 Grumpier Old Men       en\n3                Waiting to Exhale       en\n4      Father of the Bride Part II       en\n...                            ...      ...\n45459              Caged Heat 3000       en\n45460                   Robin Hood       en\n45463                     Betrayal       en\n45464          Satana likuyushchiy       en\n45465                     Queerama       en\n\n[32269 rows x 2 columns]","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>title</th>\n      <th>language</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Toy Story</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Jumanji</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Grumpier Old Men</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Waiting to Exhale</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Father of the Bride Part II</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>45459</th>\n      <td>Caged Heat 3000</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>45460</th>\n      <td>Robin Hood</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>45463</th>\n      <td>Betrayal</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>45464</th>\n      <td>Satana likuyushchiy</td>\n      <td>en</td>\n    </tr>\n    <tr>\n      <th>45465</th>\n      <td>Queerama</td>\n      <td>en</td>\n    </tr>\n  </tbody>\n</table>\n<p>32269 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# to_numeric 데이터 타입을 숫자(int)로 변경\nmeta.movieId = pd.to_numeric(meta.movieId)\nmeta.movieId","metadata":{"execution":{"iopub.status.busy":"2022-10-21T15:49:30.268285Z","iopub.execute_input":"2022-10-21T15:49:30.268674Z","iopub.status.idle":"2022-10-21T15:49:30.299596Z","shell.execute_reply.started":"2022-10-21T15:49:30.268648Z","shell.execute_reply":"2022-10-21T15:49:30.297380Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"0           862\n1          8844\n2         15602\n3         31357\n4         11862\n          ...  \n45459    222848\n45460     30840\n45463     67758\n45464    227506\n45465    461257\nName: movieId, Length: 32269, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# genre의 정보가 많아 필요한 것만 사용하기 위해 수정\nprint(meta.genres)","metadata":{"execution":{"iopub.status.busy":"2022-10-21T15:51:17.928392Z","iopub.execute_input":"2022-10-21T15:51:17.928809Z","iopub.status.idle":"2022-10-21T15:51:17.937027Z","shell.execute_reply.started":"2022-10-21T15:51:17.928775Z","shell.execute_reply":"2022-10-21T15:51:17.935694Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"0        [{'id': 16, 'name': 'Animation'}, {'id': 35, '...\n1        [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...\n2        [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...\n3        [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...\n4                           [{'id': 35, 'name': 'Comedy'}]\n                               ...                        \n45459             [{'id': 878, 'name': 'Science Fiction'}]\n45460    [{'id': 18, 'name': 'Drama'}, {'id': 28, 'name...\n45463    [{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...\n45464                                                   []\n45465                                                   []\nName: genres, Length: 32269, dtype: object\n","output_type":"stream"}]},{"cell_type":"code","source":"# eval -> 파이썬에서 사용가능한 데이터타입으로 변환시켜줌\ntxt = meta.genres[0]\n\nmyset = set()\nfor d in eval(txt):\n    myset.add(d['name'])\n    \nprint(myset)","metadata":{"execution":{"iopub.status.busy":"2022-10-21T15:53:23.825612Z","iopub.execute_input":"2022-10-21T15:53:23.825957Z","iopub.status.idle":"2022-10-21T15:53:23.836729Z","shell.execute_reply.started":"2022-10-21T15:53:23.825932Z","shell.execute_reply":"2022-10-21T15:53:23.835150Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"{'Animation', 'Comedy', 'Family'}\n","output_type":"stream"}]},{"cell_type":"code","source":"# apply('함수') -> 함수를 실행하여 리턴되는 값을 넣어줌\n\n# genre 정제하기: json string을 python set으로 변환하는 함수 구현\ndef str_to_set(genre):\n    myset = set()\n    for d in eval(genre):\n        myset.add(d['name'])\n    return myset\n\n# genres를 사용하려는 값으로만 덮어씀 -> meta.genres의 모든 값에 str_to_set 적용\nmeta.genres = meta.genres.apply(str_to_set)\n\nmeta","metadata":{"execution":{"iopub.status.busy":"2022-10-21T15:55:40.756268Z","iopub.execute_input":"2022-10-21T15:55:40.756618Z","iopub.status.idle":"2022-10-21T15:55:41.584939Z","shell.execute_reply.started":"2022-10-21T15:55:40.756592Z","shell.execute_reply":"2022-10-21T15:55:41.583225Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"       movieId                        title language  \\\n0          862                    Toy Story       en   \n1         8844                      Jumanji       en   \n2        15602             Grumpier Old Men       en   \n3        31357            Waiting to Exhale       en   \n4        11862  Father of the Bride Part II       en   \n...        ...                          ...      ...   \n45459   222848              Caged Heat 3000       en   \n45460    30840                   Robin Hood       en   \n45463    67758                     Betrayal       en   \n45464   227506          Satana likuyushchiy       en   \n45465   461257                     Queerama       en   \n\n                             genres  \n0       {Animation, Comedy, Family}  \n1      {Adventure, Fantasy, Family}  \n2                 {Comedy, Romance}  \n3          {Drama, Comedy, Romance}  \n4                          {Comedy}  \n...                             ...  \n45459             {Science Fiction}  \n45460      {Drama, Action, Romance}  \n45463     {Drama, Action, Thriller}  \n45464                            {}  \n45465                            {}  \n\n[32269 rows x 4 columns]","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>movieId</th>\n      <th>title</th>\n      <th>language</th>\n      <th>genres</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>862</td>\n      <td>Toy Story</td>\n      <td>en</td>\n      <td>{Animation, Comedy, Family}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8844</td>\n      <td>Jumanji</td>\n      <td>en</td>\n      <td>{Adventure, Fantasy, Family}</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15602</td>\n      <td>Grumpier Old Men</td>\n      <td>en</td>\n      <td>{Comedy, Romance}</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31357</td>\n      <td>Waiting to Exhale</td>\n      <td>en</td>\n      <td>{Drama, Comedy, Romance}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11862</td>\n      <td>Father of the Bride Part II</td>\n      <td>en</td>\n      <td>{Comedy}</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>45459</th>\n      <td>222848</td>\n      <td>Caged Heat 3000</td>\n      <td>en</td>\n      <td>{Science Fiction}</td>\n    </tr>\n    <tr>\n      <th>45460</th>\n      <td>30840</td>\n      <td>Robin Hood</td>\n      <td>en</td>\n      <td>{Drama, Action, Romance}</td>\n    </tr>\n    <tr>\n      <th>45463</th>\n      <td>67758</td>\n      <td>Betrayal</td>\n      <td>en</td>\n      <td>{Drama, Action, Thriller}</td>\n    </tr>\n    <tr>\n      <th>45464</th>\n      <td>227506</td>\n      <td>Satana likuyushchiy</td>\n      <td>en</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>45465</th>\n      <td>461257</td>\n      <td>Queerama</td>\n      <td>en</td>\n      <td>{}</td>\n    </tr>\n  </tbody>\n</table>\n<p>32269 rows × 4 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Keywords 정제하기","metadata":{}},{"cell_type":"code","source":"keywords = pd.read_csv( '/kaggle/input/the-movies-dataset/keywords.csv' ) \n\n# 아래 둘은 같은 기능\n# keywords['keywords']\n# keywords.keywords\n\n# keywords.keywords 에서도 필요한 정보만 뽑아오기\nkeywords['keywords'] = keywords['keywords'].apply(str_to_set)\nkeywords = keywords.rename(columns={'id':'movieId'})\n\nkeywords","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:01:03.476900Z","iopub.execute_input":"2022-10-21T16:01:03.477298Z","iopub.status.idle":"2022-10-21T16:01:04.889481Z","shell.execute_reply.started":"2022-10-21T16:01:03.477269Z","shell.execute_reply":"2022-10-21T16:01:04.887550Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"       movieId                                           keywords\n0          862  {friends, rivalry, jealousy, boy next door, fr...\n1         8844  {new home, recluse, disappearance, giant insec...\n2        15602  {duringcreditsstinger, old men, fishing, best ...\n3        31357  {single mother, interracial relationship, base...\n4        11862  {daughter, contraception, gynecologist, mother...\n...        ...                                                ...\n46414   439050                                      {tragic love}\n46415   111109                              {artist, pinoy, play}\n46416    67758                                                 {}\n46417   227506                                                 {}\n46418   461257                                                 {}\n\n[46419 rows x 2 columns]","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>movieId</th>\n      <th>keywords</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>862</td>\n      <td>{friends, rivalry, jealousy, boy next door, fr...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8844</td>\n      <td>{new home, recluse, disappearance, giant insec...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15602</td>\n      <td>{duringcreditsstinger, old men, fishing, best ...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31357</td>\n      <td>{single mother, interracial relationship, base...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11862</td>\n      <td>{daughter, contraception, gynecologist, mother...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>46414</th>\n      <td>439050</td>\n      <td>{tragic love}</td>\n    </tr>\n    <tr>\n      <th>46415</th>\n      <td>111109</td>\n      <td>{artist, pinoy, play}</td>\n    </tr>\n    <tr>\n      <th>46416</th>\n      <td>67758</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>46417</th>\n      <td>227506</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>46418</th>\n      <td>461257</td>\n      <td>{}</td>\n    </tr>\n  </tbody>\n</table>\n<p>46419 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Keywords와 Metadata 합치기","metadata":{}},{"cell_type":"code","source":"# merge 하기 ( 조건 : 같은 movieId를 갖는 데이터, 값이 있는것만(inner))\n# on 기준으로 how 방식으로\nmeta = pd.merge(meta, keywords, on='movieId', how='inner')\nmeta","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:03:51.021849Z","iopub.execute_input":"2022-10-21T16:03:51.022199Z","iopub.status.idle":"2022-10-21T16:03:51.073561Z","shell.execute_reply.started":"2022-10-21T16:03:51.022174Z","shell.execute_reply":"2022-10-21T16:03:51.071952Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"       movieId                        title language  \\\n0          862                    Toy Story       en   \n1         8844                      Jumanji       en   \n2        15602             Grumpier Old Men       en   \n3        31357            Waiting to Exhale       en   \n4        11862  Father of the Bride Part II       en   \n...        ...                          ...      ...   \n32847   222848              Caged Heat 3000       en   \n32848    30840                   Robin Hood       en   \n32849    67758                     Betrayal       en   \n32850   227506          Satana likuyushchiy       en   \n32851   461257                     Queerama       en   \n\n                             genres  \\\n0       {Animation, Comedy, Family}   \n1      {Adventure, Fantasy, Family}   \n2                 {Comedy, Romance}   \n3          {Drama, Comedy, Romance}   \n4                          {Comedy}   \n...                             ...   \n32847             {Science Fiction}   \n32848      {Drama, Action, Romance}   \n32849     {Drama, Action, Thriller}   \n32850                            {}   \n32851                            {}   \n\n                                                keywords  \n0      {friends, rivalry, jealousy, boy next door, fr...  \n1      {new home, recluse, disappearance, giant insec...  \n2      {duringcreditsstinger, old men, fishing, best ...  \n3      {single mother, interracial relationship, base...  \n4      {daughter, contraception, gynecologist, mother...  \n...                                                  ...  \n32847                                                 {}  \n32848                                                 {}  \n32849                                                 {}  \n32850                                                 {}  \n32851                                                 {}  \n\n[32852 rows x 5 columns]","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>movieId</th>\n      <th>title</th>\n      <th>language</th>\n      <th>genres</th>\n      <th>keywords</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>862</td>\n      <td>Toy Story</td>\n      <td>en</td>\n      <td>{Animation, Comedy, Family}</td>\n      <td>{friends, rivalry, jealousy, boy next door, fr...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8844</td>\n      <td>Jumanji</td>\n      <td>en</td>\n      <td>{Adventure, Fantasy, Family}</td>\n      <td>{new home, recluse, disappearance, giant insec...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15602</td>\n      <td>Grumpier Old Men</td>\n      <td>en</td>\n      <td>{Comedy, Romance}</td>\n      <td>{duringcreditsstinger, old men, fishing, best ...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31357</td>\n      <td>Waiting to Exhale</td>\n      <td>en</td>\n      <td>{Drama, Comedy, Romance}</td>\n      <td>{single mother, interracial relationship, base...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11862</td>\n      <td>Father of the Bride Part II</td>\n      <td>en</td>\n      <td>{Comedy}</td>\n      <td>{daughter, contraception, gynecologist, mother...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>32847</th>\n      <td>222848</td>\n      <td>Caged Heat 3000</td>\n      <td>en</td>\n      <td>{Science Fiction}</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>32848</th>\n      <td>30840</td>\n      <td>Robin Hood</td>\n      <td>en</td>\n      <td>{Drama, Action, Romance}</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>32849</th>\n      <td>67758</td>\n      <td>Betrayal</td>\n      <td>en</td>\n      <td>{Drama, Action, Thriller}</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>32850</th>\n      <td>227506</td>\n      <td>Satana likuyushchiy</td>\n      <td>en</td>\n      <td>{}</td>\n      <td>{}</td>\n    </tr>\n    <tr>\n      <th>32851</th>\n      <td>461257</td>\n      <td>Queerama</td>\n      <td>en</td>\n      <td>{}</td>\n      <td>{}</td>\n    </tr>\n  </tbody>\n</table>\n<p>32852 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Jaccard Similarity ","metadata":{}},{"cell_type":"code","source":"# iloc -> 인덱스 번호로 위치에 해당하는거 출력\ndk = meta.loc[meta.title == 'The Dark Knight'].iloc[0]\ndkr = meta.loc[meta.title == 'The Dark Knight Rises'].iloc[0]\n\nprint(dk)\nprint(dkr)\n\n# concat 합치기 , axis = 1이면 같은 컬럼을 묶어서 테이블 형태로\npd.concat([dk, dkr], axis=1).T","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:09:11.467652Z","iopub.execute_input":"2022-10-21T16:09:11.468025Z","iopub.status.idle":"2022-10-21T16:09:11.505644Z","shell.execute_reply.started":"2022-10-21T16:09:11.467979Z","shell.execute_reply":"2022-10-21T16:09:11.503172Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"movieId                                                   155\ntitle                                         The Dark Knight\nlanguage                                                   en\ngenres                       {Drama, Action, Crime, Thriller}\nkeywords    {batman, chaos, joker, criminal mastermind, or...\nName: 10278, dtype: object\nmovieId                                                 49026\ntitle                                   The Dark Knight Rises\nlanguage                                                   en\ngenres                       {Crime, Action, Drama, Thriller}\nkeywords    {batman, villainess, catwoman, crime fighter, ...\nName: 14315, dtype: object\n","output_type":"stream"},{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"      movieId                  title language  \\\n10278     155        The Dark Knight       en   \n14315   49026  The Dark Knight Rises       en   \n\n                                 genres  \\\n10278  {Drama, Action, Crime, Thriller}   \n14315  {Crime, Action, Drama, Thriller}   \n\n                                                keywords  \n10278  {batman, chaos, joker, criminal mastermind, or...  \n14315  {batman, villainess, catwoman, crime fighter, ...  ","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>movieId</th>\n      <th>title</th>\n      <th>language</th>\n      <th>genres</th>\n      <th>keywords</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>10278</th>\n      <td>155</td>\n      <td>The Dark Knight</td>\n      <td>en</td>\n      <td>{Drama, Action, Crime, Thriller}</td>\n      <td>{batman, chaos, joker, criminal mastermind, or...</td>\n    </tr>\n    <tr>\n      <th>14315</th>\n      <td>49026</td>\n      <td>The Dark Knight Rises</td>\n      <td>en</td>\n      <td>{Crime, Action, Drama, Thriller}</td>\n      <td>{batman, villainess, catwoman, crime fighter, ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Jaccard 유사도 함수 구현 및 실행\n\ndef jaccard_similarity(s1, s2):\n    \n    # 분모가 0이면 안되니까 조건을 걸어 체크\n    # |은 합집합을 의미\n    if len(s1|s2) == 0:\n        return 0\n    \n    return len(s1&s2)/len(s1|s2) # 합집합 크기 분의 교집합 크기 \n","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:13:30.951785Z","iopub.execute_input":"2022-10-21T16:13:30.952176Z","iopub.status.idle":"2022-10-21T16:13:30.959312Z","shell.execute_reply.started":"2022-10-21T16:13:30.952149Z","shell.execute_reply":"2022-10-21T16:13:30.957669Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"print(dk.genres)\nprint(dk.keywords)\nprint(dk.genres | dk.keywords) # 장르와 키워드를 합침","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:13:51.883659Z","iopub.execute_input":"2022-10-21T16:13:51.884083Z","iopub.status.idle":"2022-10-21T16:13:51.891958Z","shell.execute_reply.started":"2022-10-21T16:13:51.884055Z","shell.execute_reply":"2022-10-21T16:13:51.890882Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"{'Drama', 'Action', 'Crime', 'Thriller'}\n{'batman', 'chaos', 'joker', 'criminal mastermind', 'organized crime', 'sadism', 'crime fighter', 'dc comics', 'vigilante', 'based on comic', 'gotham city', 'district attorney', 'superhero', 'tragic hero', 'super powers', 'scarecrow', 'secret identity', 'super villain', 'imax'}\n{'batman', 'Action', 'chaos', 'joker', 'district attorney', 'criminal mastermind', 'superhero', 'tragic hero', 'Thriller', 'organized crime', 'super powers', 'sadism', 'crime fighter', 'scarecrow', 'dc comics', 'vigilante', 'secret identity', 'super villain', 'Drama', 'Crime', 'imax', 'based on comic', 'gotham city'}\n","output_type":"stream"}]},{"cell_type":"code","source":"# 두 영화의 장르와 키워드를 합친 후 자카드 유사도를 구해보자\njaccard_similarity(dk.genres | dk.keywords, dkr.genres | dkr.keywords)","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:14:22.200669Z","iopub.execute_input":"2022-10-21T16:14:22.201104Z","iopub.status.idle":"2022-10-21T16:14:22.211296Z","shell.execute_reply.started":"2022-10-21T16:14:22.201074Z","shell.execute_reply":"2022-10-21T16:14:22.209561Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"0.37142857142857144"},"metadata":{}}]},{"cell_type":"markdown","source":"# Rating 데이터 읽고 정제하기","metadata":{}},{"cell_type":"code","source":"# 평점 데이터 가져오기\nratings = pd.read_csv('/kaggle/input/the-movies-dataset/ratings_small.csv' ) \nratings","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:15:05.074649Z","iopub.execute_input":"2022-10-21T16:15:05.075025Z","iopub.status.idle":"2022-10-21T16:15:05.176627Z","shell.execute_reply.started":"2022-10-21T16:15:05.074978Z","shell.execute_reply":"2022-10-21T16:15:05.175539Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"        userId  movieId  rating   timestamp\n0            1       31     2.5  1260759144\n1            1     1029     3.0  1260759179\n2            1     1061     3.0  1260759182\n3            1     1129     2.0  1260759185\n4            1     1172     4.0  1260759205\n...        ...      ...     ...         ...\n99999      671     6268     2.5  1065579370\n100000     671     6269     4.0  1065149201\n100001     671     6365     4.0  1070940363\n100002     671     6385     2.5  1070979663\n100003     671     6565     3.5  1074784724\n\n[100004 rows x 4 columns]","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>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>31</td>\n      <td>2.5</td>\n      <td>1260759144</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1029</td>\n      <td>3.0</td>\n      <td>1260759179</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1061</td>\n      <td>3.0</td>\n      <td>1260759182</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1129</td>\n      <td>2.0</td>\n      <td>1260759185</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>1172</td>\n      <td>4.0</td>\n      <td>1260759205</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>99999</th>\n      <td>671</td>\n      <td>6268</td>\n      <td>2.5</td>\n      <td>1065579370</td>\n    </tr>\n    <tr>\n      <th>100000</th>\n      <td>671</td>\n      <td>6269</td>\n      <td>4.0</td>\n      <td>1065149201</td>\n    </tr>\n    <tr>\n      <th>100001</th>\n      <td>671</td>\n      <td>6365</td>\n      <td>4.0</td>\n      <td>1070940363</td>\n    </tr>\n    <tr>\n      <th>100002</th>\n      <td>671</td>\n      <td>6385</td>\n      <td>2.5</td>\n      <td>1070979663</td>\n    </tr>\n    <tr>\n      <th>100003</th>\n      <td>671</td>\n      <td>6565</td>\n      <td>3.5</td>\n      <td>1074784724</td>\n    </tr>\n  </tbody>\n</table>\n<p>100004 rows × 4 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# 테이블 합치기 위해 일단 데이터 타입 확인\nratings.movieId","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:15:38.195495Z","iopub.execute_input":"2022-10-21T16:15:38.195814Z","iopub.status.idle":"2022-10-21T16:15:38.205630Z","shell.execute_reply.started":"2022-10-21T16:15:38.195790Z","shell.execute_reply":"2022-10-21T16:15:38.204233Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"0           31\n1         1029\n2         1061\n3         1129\n4         1172\n          ... \n99999     6268\n100000    6269\n100001    6365\n100002    6385\n100003    6565\nName: movieId, Length: 100004, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# metadata로부터 movieId, title 정보 가져와 movieId 기준으로 inner 조건으로 합치기\nratings = pd.merge(ratings, meta[['movieId', 'title']], on='movieId', how='inner')\nratings","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:17:12.245942Z","iopub.execute_input":"2022-10-21T16:17:12.246306Z","iopub.status.idle":"2022-10-21T16:17:12.286453Z","shell.execute_reply.started":"2022-10-21T16:17:12.246280Z","shell.execute_reply":"2022-10-21T16:17:12.285147Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"       userId  movieId  rating   timestamp              title\n0           1     1371     2.5  1260759135          Rocky III\n1           4     1371     4.0   949810302          Rocky III\n2           7     1371     3.0   851869160          Rocky III\n3          19     1371     4.0   855193404          Rocky III\n4          21     1371     3.0   853852263          Rocky III\n...       ...      ...     ...         ...                ...\n33295     652   127728     5.0  1439586990               8:46\n33296     652   129009     4.0  1442690827     Love Is a Ball\n33297     653     2103     3.0   948161066            Solaris\n33298     659      167     4.0   836137550              K-PAX\n33299     659      563     3.0   834694187  Starship Troopers\n\n[33300 rows x 5 columns]","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>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>timestamp</th>\n      <th>title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1371</td>\n      <td>2.5</td>\n      <td>1260759135</td>\n      <td>Rocky III</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>1371</td>\n      <td>4.0</td>\n      <td>949810302</td>\n      <td>Rocky III</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>1371</td>\n      <td>3.0</td>\n      <td>851869160</td>\n      <td>Rocky III</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>19</td>\n      <td>1371</td>\n      <td>4.0</td>\n      <td>855193404</td>\n      <td>Rocky III</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>21</td>\n      <td>1371</td>\n      <td>3.0</td>\n      <td>853852263</td>\n      <td>Rocky III</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>33295</th>\n      <td>652</td>\n      <td>127728</td>\n      <td>5.0</td>\n      <td>1439586990</td>\n      <td>8:46</td>\n    </tr>\n    <tr>\n      <th>33296</th>\n      <td>652</td>\n      <td>129009</td>\n      <td>4.0</td>\n      <td>1442690827</td>\n      <td>Love Is a Ball</td>\n    </tr>\n    <tr>\n      <th>33297</th>\n      <td>653</td>\n      <td>2103</td>\n      <td>3.0</td>\n      <td>948161066</td>\n      <td>Solaris</td>\n    </tr>\n    <tr>\n      <th>33298</th>\n      <td>659</td>\n      <td>167</td>\n      <td>4.0</td>\n      <td>836137550</td>\n      <td>K-PAX</td>\n    </tr>\n    <tr>\n      <th>33299</th>\n      <td>659</td>\n      <td>563</td>\n      <td>3.0</td>\n      <td>834694187</td>\n      <td>Starship Troopers</td>\n    </tr>\n  </tbody>\n</table>\n<p>33300 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# pivot table 만들기\n# 어떤거 기준으로 할지 괄호 안에 적어주기\nmatrix = ratings.pivot_table(index= 'userId', columns='title', values='rating')\nmatrix","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:19:54.707914Z","iopub.execute_input":"2022-10-21T16:19:54.709393Z","iopub.status.idle":"2022-10-21T16:19:54.801513Z","shell.execute_reply.started":"2022-10-21T16:19:54.709338Z","shell.execute_reply":"2022-10-21T16:19:54.800470Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"title   !Women Art Revolution  'Gator Bait  'Twas the Night Before Christmas  \\\nuserId                                                                         \n1                         NaN          NaN                               NaN   \n2                         NaN          NaN                               NaN   \n3                         NaN          NaN                               NaN   \n4                         NaN          NaN                               NaN   \n5                         NaN          NaN                               NaN   \n...                       ...          ...                               ...   \n667                       NaN          NaN                               NaN   \n668                       NaN          NaN                               NaN   \n669                       NaN          NaN                               NaN   \n670                       NaN          NaN                               NaN   \n671                       NaN          NaN                               NaN   \n\ntitle   10 Items or Less  10 Things I Hate About You  10,000 BC  \\\nuserId                                                            \n1                    NaN                         NaN        NaN   \n2                    NaN                         NaN        NaN   \n3                    NaN                         NaN        NaN   \n4                    NaN                         NaN        NaN   \n5                    NaN                         NaN        NaN   \n...                  ...                         ...        ...   \n667                  NaN                         NaN        NaN   \n668                  NaN                         NaN        NaN   \n669                  NaN                         NaN        NaN   \n670                  NaN                         NaN        NaN   \n671                  NaN                         NaN        NaN   \n\ntitle   11'09''01 - September 11  12 + 1  12 Angry Men  1408  ...  \\\nuserId                                                        ...   \n1                            NaN     NaN           NaN   NaN  ...   \n2                            NaN     NaN           NaN   NaN  ...   \n3                            NaN     NaN           NaN   NaN  ...   \n4                            NaN     NaN           NaN   NaN  ...   \n5                            NaN     NaN           NaN   NaN  ...   \n...                          ...     ...           ...   ...  ...   \n667                          NaN     NaN           NaN   NaN  ...   \n668                          NaN     NaN           NaN   NaN  ...   \n669                          NaN     NaN           NaN   NaN  ...   \n670                          NaN     NaN           NaN   NaN  ...   \n671                          NaN     NaN           NaN   NaN  ...   \n\ntitle   Young and Innocent  Zaat  Zabriskie Point  Zapped Again!  Zardoz  \\\nuserId                                                                     \n1                      NaN   NaN              NaN            NaN     NaN   \n2                      NaN   NaN              NaN            NaN     NaN   \n3                      3.5   NaN              NaN            NaN     NaN   \n4                      NaN   NaN              NaN            NaN     NaN   \n5                      3.5   NaN              NaN            NaN     NaN   \n...                    ...   ...              ...            ...     ...   \n667                    NaN   NaN              NaN            NaN     NaN   \n668                    NaN   NaN              NaN            NaN     NaN   \n669                    NaN   NaN              NaN            NaN     NaN   \n670                    NaN   NaN              NaN            NaN     NaN   \n671                    4.0   NaN              NaN            NaN     NaN   \n\ntitle   Zodiac  eXistenZ  xXx  ¡Three Amigos!  Мой сводный брат Франкенштейн  \nuserId                                                                        \n1          NaN       NaN  NaN             NaN                            NaN  \n2          NaN       NaN  NaN             NaN                            NaN  \n3          NaN       NaN  NaN             NaN                            NaN  \n4          NaN       NaN  NaN             NaN                            NaN  \n5          NaN       NaN  NaN             NaN                            NaN  \n...        ...       ...  ...             ...                            ...  \n667        NaN       NaN  NaN             NaN                            NaN  \n668        NaN       NaN  NaN             NaN                            NaN  \n669        NaN       NaN  NaN             NaN                            NaN  \n670        NaN       NaN  NaN             NaN                            NaN  \n671        NaN       NaN  NaN             NaN                            NaN  \n\n[671 rows x 2034 columns]","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>title</th>\n      <th>!Women Art Revolution</th>\n      <th>'Gator Bait</th>\n      <th>'Twas the Night Before Christmas</th>\n      <th>10 Items or Less</th>\n      <th>10 Things I Hate About You</th>\n      <th>10,000 BC</th>\n      <th>11'09''01 - September 11</th>\n      <th>12 + 1</th>\n      <th>12 Angry Men</th>\n      <th>1408</th>\n      <th>...</th>\n      <th>Young and Innocent</th>\n      <th>Zaat</th>\n      <th>Zabriskie Point</th>\n      <th>Zapped Again!</th>\n      <th>Zardoz</th>\n      <th>Zodiac</th>\n      <th>eXistenZ</th>\n      <th>xXx</th>\n      <th>¡Three Amigos!</th>\n      <th>Мой сводный брат Франкенштейн</th>\n    </tr>\n    <tr>\n      <th>userId</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>3.5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>3.5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </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>667</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>668</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>669</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>670</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>671</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>671 rows × 2034 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Pearson Correlation Coefficient","metadata":{}},{"cell_type":"code","source":"def pearson_similarity(u1, u2):\n    # mean()->비어있지 않은 값들의 평균을 구해줌\n    # 평균값을 빼서 축을 옮겨주는 역할\n    u1_c = u1 - u1.mean() \n    u2_c = u2 - u2.mean()\n    \n    \n    denom = np.sqrt(np.sum(u1_c ** 2) * np.sum(u2_c ** 2))\n    \n    if denom != 0:\n        return np.sum(u1_c * u2_c)/denom\n    else:\n        return 0\n    \n\n# 두 영화의 유사도 계산하기\ndk_rating = matrix['The Dark Knight']\npn_rating = matrix['Prom Night']\n\npearson_similarity(dk_rating, pn_rating)","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:24:49.566829Z","iopub.execute_input":"2022-10-21T16:24:49.567161Z","iopub.status.idle":"2022-10-21T16:24:49.579388Z","shell.execute_reply.started":"2022-10-21T16:24:49.567136Z","shell.execute_reply":"2022-10-21T16:24:49.578255Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"0.773565934694095"},"metadata":{}}]},{"cell_type":"markdown","source":"# 비슷한 영화 추천 기능 구현","metadata":{}},{"cell_type":"code","source":"def find_similar_movies(input_title, matrix, n, alpha): \n    # input_meta: 입력된 영화의 metadata\n    input_meta = meta.loc[meta['title'] == input_title].iloc[0] \n    # input_set: 입력된 영화의 genres와 keyword의 합집합\n    input_set = input_meta.genres | input_meta.keywords\n    \n    # result: 모든 영화마다 유사도를 계산하여 저장하기 위함\n    result = []\n    \n    for this_title in matrix.columns: \n        \n        # 입력된 영화는 유사도 계산을 하지 않고 패스\n        if this_title == input_title:\n            continue\n            \n        # this_meta: 유사도를 계산하려는 영화의 metadata\n        this_meta = meta.loc[ meta[ 'title'] == this_title].iloc[0] \n        # this_set: 이 영화의 genres와 keywords의 합집합\n        this_set = this_meta.genres | this_meta.keywords\n        \n        # pearson: 입력 영화와 이번 영화의 pearson 유사도 결과\n        pearson = pearson_similarity(matrix[this_title], matrix[input_title])\n        # jaccard: 입력 영화와 이번 영화의 jaccard 유사도 결과\n        jaccard = jaccard_similarity(this_set, input_set)\n        \n        # score: pearson 점수와 jaccard 점수의 가중치 합\n        score = alpha * pearson + (1 - alpha) * jaccard \n        \n        # result에 계산 결과 추가\n        result.append((this_title, pearson, jaccard, score))\n        \n    # 모든 영화에 대해 유사도 계산이 끝나면 result를 정렬\n    result.sort(key=lambda r: r[3], reverse=True)\n    \n    # 상위 n개를 return\n    return result[:n]","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:30:50.441484Z","iopub.execute_input":"2022-10-21T16:30:50.441861Z","iopub.status.idle":"2022-10-21T16:30:50.451468Z","shell.execute_reply.started":"2022-10-21T16:30:50.441834Z","shell.execute_reply":"2022-10-21T16:30:50.450113Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"# The Dark Knight 와 비슷한 영화 10개 추천 \n# 가중치 0.3 -> pearson보다 jaccard에 좀 더 가중치있게\nresult = find_similar_movies('The Dark Knight', matrix, 10, 0.3)\npd.DataFrame(result, columns = ['title', 'pearson', 'jaccard', 'score'])","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:31:50.002075Z","iopub.execute_input":"2022-10-21T16:31:50.002431Z","iopub.status.idle":"2022-10-21T16:31:58.128202Z","shell.execute_reply.started":"2022-10-21T16:31:50.002405Z","shell.execute_reply":"2022-10-21T16:31:58.126788Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"                                         title   pearson   jaccard     score\n0                               Wild Wild West  0.773566  0.032258  0.254650\n1                                   Prom Night  0.773566  0.022222  0.247625\n2                                Batman Begins  0.005015  0.292683  0.206383\n3  Yamakasi - Les samouraïs des temps modernes  0.377145  0.125000  0.200643\n4                                 Blue Thunder  0.326617  0.133333  0.191318\n5      Midnight in the Garden of Good and Evil  0.373841  0.111111  0.189930\n6                                        Topaz  0.377145  0.103448  0.185557\n7                                 Big Bad Mama  0.344649  0.107143  0.178395\n8                                     Sneakers  0.415830  0.068966  0.173025\n9                                 The Enforcer  0.326617  0.103448  0.170399","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>title</th>\n      <th>pearson</th>\n      <th>jaccard</th>\n      <th>score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Wild Wild West</td>\n      <td>0.773566</td>\n      <td>0.032258</td>\n      <td>0.254650</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Prom Night</td>\n      <td>0.773566</td>\n      <td>0.022222</td>\n      <td>0.247625</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Batman Begins</td>\n      <td>0.005015</td>\n      <td>0.292683</td>\n      <td>0.206383</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Yamakasi - Les samouraïs des temps modernes</td>\n      <td>0.377145</td>\n      <td>0.125000</td>\n      <td>0.200643</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Blue Thunder</td>\n      <td>0.326617</td>\n      <td>0.133333</td>\n      <td>0.191318</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Midnight in the Garden of Good and Evil</td>\n      <td>0.373841</td>\n      <td>0.111111</td>\n      <td>0.189930</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Topaz</td>\n      <td>0.377145</td>\n      <td>0.103448</td>\n      <td>0.185557</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Big Bad Mama</td>\n      <td>0.344649</td>\n      <td>0.107143</td>\n      <td>0.178395</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Sneakers</td>\n      <td>0.415830</td>\n      <td>0.068966</td>\n      <td>0.173025</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>The Enforcer</td>\n      <td>0.326617</td>\n      <td>0.103448</td>\n      <td>0.170399</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}