{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"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)\nimport os","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-04-15T12:51:02.118919Z","iopub.execute_input":"2022-04-15T12:51:02.119194Z","iopub.status.idle":"2022-04-15T12:51:02.124830Z","shell.execute_reply.started":"2022-04-15T12:51:02.119167Z","shell.execute_reply":"2022-04-15T12:51:02.123729Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pd.set_option('display.max_columns', 50)\nBASE_PATH = \"../input/h-and-m-personalized-fashion-recommendations/\"","metadata":{"execution":{"iopub.status.busy":"2022-04-15T13:11:43.847175Z","iopub.execute_input":"2022-04-15T13:11:43.847550Z","iopub.status.idle":"2022-04-15T13:11:43.852923Z","shell.execute_reply.started":"2022-04-15T13:11:43.847513Z","shell.execute_reply":"2022-04-15T13:11:43.852113Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"! ls /kaggle/input/h-and-m-personalized-fashion-recommendations\n","metadata":{"execution":{"iopub.status.busy":"2022-04-15T12:51:02.150081Z","iopub.execute_input":"2022-04-15T12:51:02.150401Z","iopub.status.idle":"2022-04-15T12:51:02.965829Z","shell.execute_reply.started":"2022-04-15T12:51:02.150321Z","shell.execute_reply":"2022-04-15T12:51:02.964774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"articles_df = pd.read_csv(BASE_PATH+'articles.csv')\ntransactions_train_df = pd.read_csv(BASE_PATH+'transactions_train.csv')\ncustomers_df = pd.read_csv(BASE_PATH+'customers.csv')\n#sample_submission_df = pd.read_csv(BASE_PATH+'sample_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2022-04-15T13:09:42.772345Z","iopub.execute_input":"2022-04-15T13:09:42.773327Z","iopub.status.idle":"2022-04-15T13:10:31.683255Z","shell.execute_reply.started":"2022-04-15T13:09:42.773283Z","shell.execute_reply":"2022-04-15T13:10:31.680406Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## articles.csv\n1. article_id : 商品画像を識別するための番号\n2. product_code : 商品を識別するためのコード\n3. prod_name : 商品名\n4. product_type_no : 商品種別番号\n5. product_type_name : 商品種別名\n6. product_group_name : 商品グループ名\n7. graphical_appearance_no : 模様番号\n8. graphical_appearance_name : 模様名\n9. colour_group_code : カラーグループ番号\n10. colour_group_name : カラー名\n11. perceived_colour_value_id : 識別カラー値番号\n12. perceived_colour_value_name : 識別カラー値名称\n13. perceived_colour_master_id : 識別カラーマスター番号\n14. perceived_colour_master_name : 識別カラーマスター名\n15. department_no : 部門番号\n16. department_name : 部門名\n17. index_code : インデックスコード\n18. index_name : インデックス名\n19. index_group_no : インデックスグループ番号\n20. index_group_name : インデックスグループ名\n21. section_no : セクション番号\n22. section_name : セクション名\n23. garment_group_no : 衣類グループ番号\n24. garment_group_name : 衣類グループ名\n25. detail_desc : 詳細説明","metadata":{}},{"cell_type":"code","source":"articles_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-04-15T13:20:40.981127Z","iopub.execute_input":"2022-04-15T13:20:40.982905Z","iopub.status.idle":"2022-04-15T13:20:41.012444Z","shell.execute_reply.started":"2022-04-15T13:20:40.982821Z","shell.execute_reply":"2022-04-15T13:20:41.011453Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"articles_df.info()","metadata":{"execution":{"iopub.status.busy":"2022-04-15T12:54:06.809500Z","iopub.execute_input":"2022-04-15T12:54:06.809958Z","iopub.status.idle":"2022-04-15T12:54:06.997634Z","shell.execute_reply.started":"2022-04-15T12:54:06.809926Z","shell.execute_reply":"2022-04-15T12:54:06.996567Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## transactions_train.csv\n1. t_dat : 取引の日付\n2. customer_id : 顧客番号\n3. article_id : 商品画像を識別するための番号\n4. price : 商品価格\n5. sales_channel_id : 販売チャネル","metadata":{}},{"cell_type":"code","source":"transactions_train_df.head(100)","metadata":{"execution":{"iopub.status.busy":"2022-04-15T13:25:55.671747Z","iopub.execute_input":"2022-04-15T13:25:55.673633Z","iopub.status.idle":"2022-04-15T13:25:55.692738Z","shell.execute_reply.started":"2022-04-15T13:25:55.673588Z","shell.execute_reply":"2022-04-15T13:25:55.691504Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"transactions_train_df.info()","metadata":{"execution":{"iopub.status.busy":"2022-04-15T13:21:52.310355Z","iopub.execute_input":"2022-04-15T13:21:52.310879Z","iopub.status.idle":"2022-04-15T13:21:52.329584Z","shell.execute_reply.started":"2022-04-15T13:21:52.310834Z","shell.execute_reply":"2022-04-15T13:21:52.328905Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## customers.csv\n1. customer_id : 顧客番号\n2. FN : ?\n3. Active : ?\n4. club_member_status : メンバー会員のステータス\n5. fashion_news_frequency : ?\n6. age : 年齢\n7. postal_code : 郵便番号(暗号化されている)\n","metadata":{}},{"cell_type":"code","source":"customers_df","metadata":{"execution":{"iopub.status.busy":"2022-04-15T13:32:01.556856Z","iopub.execute_input":"2022-04-15T13:32:01.557687Z","iopub.status.idle":"2022-04-15T13:32:01.581592Z","shell.execute_reply.started":"2022-04-15T13:32:01.557635Z","shell.execute_reply":"2022-04-15T13:32:01.580728Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}