{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":59156,"databundleVersionId":13719397,"sourceType":"competition"}],"dockerImageVersionId":31089,"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":{"iopub.status.busy":"2025-09-24T09:00:07.948082Z","iopub.execute_input":"2025-09-24T09:00:07.948446Z","iopub.status.idle":"2025-09-24T09:00:18.066225Z","shell.execute_reply.started":"2025-09-24T09:00:07.948421Z","shell.execute_reply":"2025-09-24T09:00:18.065222Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\n\ndf =pd.read_parquet('/kaggle/input/MABe-mouse-behavior-detection/train_tracking/ReflectiveManatee/315178669.parquet')\ndf.head()\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-24T09:21:39.277982Z","iopub.execute_input":"2025-09-24T09:21:39.278265Z","iopub.status.idle":"2025-09-24T09:21:39.352044Z","shell.execute_reply.started":"2025-09-24T09:21:39.278245Z","shell.execute_reply":"2025-09-24T09:21:39.351256Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt \nx = df['x']\ny = df['y']\n\nplt.plot(x[:200],y[:200],marker='o',markersize=2,linestyle='-')\nplt.title(\"Mouse's nose in 200 First frame\")\nplt.xlabel(\"X\")\nplt.ylabel(\"Y\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-24T09:24:54.513796Z","iopub.execute_input":"2025-09-24T09:24:54.514137Z","iopub.status.idle":"2025-09-24T09:24:54.815946Z","shell.execute_reply.started":"2025-09-24T09:24:54.514103Z","shell.execute_reply":"2025-09-24T09:24:54.815056Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\nprint(\"coloumn:\", len(df.columns))\nfor i, col in enumerate(df.columns[:50]):\n    print(i, \":\", repr(col))\n\n\nprint(\"\\ncoloumns List:\")\nprint([str(c) for c in df.columns[:200]])\n\n\ndisplay(df.head(3))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-24T09:27:06.965224Z","iopub.execute_input":"2025-09-24T09:27:06.965652Z","iopub.status.idle":"2025-09-24T09:27:06.978447Z","shell.execute_reply.started":"2025-09-24T09:27:06.965620Z","shell.execute_reply":"2025-09-24T09:27:06.977453Z"}},"outputs":[],"execution_count":null}]}