{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":22962,"databundleVersionId":3171193,"sourceType":"competition"}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mapper = pd.read_csv('/kaggle/input/happy-whale-and-dolphin/train.csv')\n\n# mapper = mapper.to_dict('records')\nformatted_mapper = {entry['image']: (entry['species'], entry['individual_id']) for entry in mapper.to_dict('records')}\n\n\nfor image, details in formatted_mapper.items():\n    print(f\"{image}: {details}\")\n\n    \n# Пример: {\n# 00021adfb725ed.jpg: ('melon_headed_whale', 'cadddb1636b9')\n# 000562241d384d.jpg: ('humpback_whale', '1a71fbb72250')\n# }","metadata":{"execution":{"iopub.status.busy":"2024-10-04T10:11:40.183056Z","iopub.execute_input":"2024-10-04T10:11:40.183470Z","iopub.status.idle":"2024-10-04T10:11:40.512130Z","shell.execute_reply.started":"2024-10-04T10:11:40.183427Z","shell.execute_reply":"2024-10-04T10:11:40.510887Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Путь к папке с изображениями\nimage_folder = '/kaggle/input/happy-whale-and-dolphin/train_images'\n","metadata":{"execution":{"iopub.status.busy":"2024-10-04T10:11:08.332374Z","iopub.execute_input":"2024-10-04T10:11:08.332798Z","iopub.status.idle":"2024-10-04T10:11:08.337363Z","shell.execute_reply.started":"2024-10-04T10:11:08.332756Z","shell.execute_reply":"2024-10-04T10:11:08.336282Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nfor filename in os.listdir(image_folder):\n    if filename in formatted_mapper:\n        species, individual_id = formatted_mapper[filename]\n        print(f\"{filename}: (species: {species}, individual_id: {individual_id})\")\n    else:\n        print(f\"{filename}: No data found in mapper\")","metadata":{"execution":{"iopub.status.busy":"2024-10-04T10:11:26.823050Z","iopub.execute_input":"2024-10-04T10:11:26.823450Z","iopub.status.idle":"2024-10-04T10:11:26.848552Z","shell.execute_reply.started":"2024-10-04T10:11:26.823410Z","shell.execute_reply":"2024-10-04T10:11:26.847448Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}