{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":28755,"isInternetEnabled":false,"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\n\n# Use the kagglehub client library to attach Kaggle resources like competitions, datasets, and models to your session\n# Learn more about kagglehub: https://github.com/Kaggle/kagglehub/blob/main/README.md\n\nimport kagglehub\n# kagglehub.dataset_download('<owner>/<dataset-slug>')","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os \nimport pandas as pd \nimport numpy as np \n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-08-13T17:41:28.044774Z","iopub.execute_input":"2026-08-13T17:41:28.045006Z","iopub.status.idle":"2026-08-13T17:41:29.478273Z","shell.execute_reply.started":"2026-08-13T17:41:28.044982Z","shell.execute_reply":"2026-08-13T17:41:29.477337Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\nleaderboard = pd.read_csv('/kaggle/input/competitions/rsna-knee-abnormality-detection/train_series.csv')\nleaderboard","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-08-13T17:41:29.48047Z","iopub.execute_input":"2026-08-13T17:41:29.480987Z","iopub.status.idle":"2026-08-13T17:41:29.625682Z","shell.execute_reply.started":"2026-08-13T17:41:29.480945Z","shell.execute_reply":"2026-08-13T17:41:29.624734Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"leaderboard.info","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-08-13T17:41:29.626942Z","iopub.execute_input":"2026-08-13T17:41:29.627316Z","iopub.status.idle":"2026-08-13T17:41:29.63913Z","shell.execute_reply.started":"2026-08-13T17:41:29.627278Z","shell.execute_reply":"2026-08-13T17:41:29.638176Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"leaderboard.count()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-08-13T17:41:29.640405Z","iopub.execute_input":"2026-08-13T17:41:29.640821Z","iopub.status.idle":"2026-08-13T17:41:29.667789Z","shell.execute_reply.started":"2026-08-13T17:41:29.640783Z","shell.execute_reply":"2026-08-13T17:41:29.666999Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"leaderboard.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-08-13T17:41:54.672025Z","iopub.execute_input":"2026-08-13T17:41:54.672453Z","iopub.status.idle":"2026-08-13T17:41:54.680481Z","shell.execute_reply.started":"2026-08-13T17:41:54.672422Z","shell.execute_reply":"2026-08-13T17:41:54.679555Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}