{"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":13874099,"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":"2025-10-05T07:22:23.575095Z","iopub.execute_input":"2025-10-05T07:22:23.575428Z","iopub.status.idle":"2025-10-05T07:22:30.126756Z","shell.execute_reply.started":"2025-10-05T07:22:23.575402Z","shell.execute_reply":"2025-10-05T07:22:30.125732Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/MABe-mouse-behavior-detection/sample_submission.csv\n/kaggle/input/MABe-mouse-behavior-detection/train.csv\n/kaggle/input/MABe-mouse-behavior-detection/test.csv\n/kaggle/input/MABe-mouse-behavior-detection/test_tracking/AdaptableSnail/438887472.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1763467574.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1976539996.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/363958890.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1592386054.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1213233769.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1733903958.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/840324395.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1057221056.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1149348188.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1490000283.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/793202924.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/670907179.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1319309143.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1846302042.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1836572955.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1695807459.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/2134094751.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/415181540.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1313797424.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1009459450.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/CRIM13/1381910096.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/DeliriousFly/1894091332.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/DeliriousFly/1549344783.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/DeliriousFly/1378394926.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/DeliriousFly/342269939.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/DeliriousFly/1649549863.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/DeliriousFly/246051591.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/2045808129.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/86824777.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1197493895.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/414099215.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/50183736.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1085105007.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/2104375029.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1960237444.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/403002745.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1610683375.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1170955420.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/532567355.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1131517877.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/815797699.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1725214092.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/602691466.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/659002593.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1266852458.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1241990995.parquet\n/kaggle/input/MABe-mouse-behavior-detection/train_annotation/UppityFerret/1335286655.parquet\n/ka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pandas as pd\nimport cv2\nimport matplotlib.pyplot as plt\nfrom IPython.display import Video\nimport os\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-27T11:53:10.403474Z","iopub.execute_input":"2025-09-27T11:53:10.403816Z","iopub.status.idle":"2025-09-27T11:53:10.408049Z","shell.execute_reply.started":"2025-09-27T11:53:10.403788Z","shell.execute_reply":"2025-09-27T11:53:10.406921Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"# Path to the CSV file (adjust based on dataset structure)\nlabels_path = \"/kaggle/input/MABe-mouse-behavior-detection/train.csv\"\n\n# Load CSV\nlabels_df = pd.read_csv(labels_path)\n\n# Show first 10 rows\nprint(labels_df.head(10))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-27T11:54:52.250262Z","iopub.execute_input":"2025-09-27T11:54:52.250559Z","iopub.status.idle":"2025-09-27T11:54:52.38457Z","shell.execute_reply.started":"2025-09-27T11:54:52.250533Z","shell.execute_reply":"2025-09-27T11:54:52.383765Z"}},"outputs":[{"name":"stdout","text":"           lab_id    video_id mouse1_strain mouse1_color mouse1_sex  \\\n0  AdaptableSnail    44566106    CD-1 (ICR)        white       male   \n1  AdaptableSnail   143861384    CD-1 (ICR)        white       male   \n2  AdaptableSnail   209576908    CD-1 (ICR)        white       male   \n3  AdaptableSnail   278643799    CD-1 (ICR)        white       male   \n4  AdaptableSnail   351967631    CD-1 (ICR)        white       male   \n5  AdaptableSnail   355542626    CD-1 (ICR)        white       male   \n6  AdaptableSnail   678426900    CD-1 (ICR)        white       male   \n7  AdaptableSnail   705948978    CD-1 (ICR)        white       male   \n8  AdaptableSnail   878123481    CD-1 (ICR)        white       male   \n9  AdaptableSnail  1212811043    CD-1 (ICR)        white       male   \n\n   mouse1_id  mouse1_age mouse1_condition mouse2_strain mouse2_color  ...  \\\n0       10.0  8-12 weeks  wireless device    CD-1 (ICR)        white  ...   \n1        3.0  8-12 weeks              NaN    CD-1 (ICR)        white  ...   \n2        7.0  8-12 weeks              NaN    CD-1 (ICR)        white  ...   \n3       11.0  8-12 weeks  wireless device    CD-1 (ICR)        white  ...   \n4       14.0  8-12 weeks              NaN    CD-1 (ICR)        white  ...   \n5       12.0  8-12 weeks  wireless device    CD-1 (ICR)        white  ...   \n6       12.0  8-12 weeks              NaN    CD-1 (ICR)        white  ...   \n7       10.0  8-12 weeks              NaN    CD-1 (ICR)        white  ...   \n8        6.0  8-12 weeks              NaN    CD-1 (ICR)        white  ...   \n9        5.0  8-12 weeks              NaN    CD-1 (ICR)        white  ...   \n\n  pix_per_cm_approx  video_width_pix video_height_pix arena_width_cm  \\\n0              16.0             1228             1068           60.0   \n1               9.7              968              608           60.0   \n2              16.0             1266             1100           60.0   \n3              16.0             1224             1100           60.0   \n4              16.0             1204             1068           60.0   \n5              16.0             1230             1058           60.0   \n6              16.0             1230             1058           60.0   \n7              16.0             1228             1068           60.0   \n8               9.7              968              608           60.0   \n9               9.7              968              608           60.0   \n\n  arena_height_cm arena_shape arena_type  \\\n0            60.0      square   familiar   \n1            60.0      square   familiar   \n2            60.0      square   familiar   \n3            60.0      square   familiar   \n4            60.0      square   familiar   \n5            60.0      square   familiar   \n6            60.0      square   familiar   \n7            60.0      square   familiar   \n8            60.0      square   familiar   \n9            60.0      square   familiar   \n\n                                  body_parts_tracked  \\\n0  [\"body_center\", \"ear_left\", \"ear_right\", \"head...   \n1  [\"body_center\", \"ear_left\", \"ear_right\", \"late...   \n2  [\"body_center\", \"ear_left\", \"ear_right\", \"late...   \n3  [\"body_center\", \"ear_left\", \"ear_right\", \"head...   \n4  [\"body_center\", \"ear_left\", \"ear_right\", \"late...   \n5  [\"body_center\", \"ear_left\", \"ear_right\", \"head...   \n6  [\"body_center\", \"ear_left\", \"ear_right\", \"late...   \n7  [\"body_center\", \"ear_left\", \"ear_right\", \"late...   \n8  [\"body_center\", \"ear_left\", \"ear_right\", \"late...   \n9  [\"body_center\", \"ear_left\", \"ear_right\", \"late...   \n\n                                   behaviors_labeled tracking_method  \n0  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n1  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n2  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n3  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n4  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n5  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n6  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n7  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n8  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n9  [\"mouse1,mouse2,approach\", \"mouse1,mouse2,atta...      DeepLabCut  \n\n[10 rows x 38 columns]\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"import pandas as pd\n\n# set the dataset path\ndata_path = \"/kaggle/input/MABe-mouse-behavior-detection/sample_submission.csv\"\n\n# load a parquet file\ndf = pd.read_parquet(f\"{data_path}\")\n\nprint(df.head())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-05T07:23:21.964537Z","iopub.execute_input":"2025-10-05T07:23:21.964962Z","iopub.status.idle":"2025-10-05T07:23:22.118236Z","shell.execute_reply.started":"2025-10-05T07:23:21.964931Z","shell.execute_reply":"2025-10-05T07:23:22.116724Z"}},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mArrowInvalid\u001b[0m                              Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_36/176369253.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# load a parquet file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_parquet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{data_path}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parquet.py\u001b[0m in \u001b[0;36mread_parquet\u001b[0;34m(path, engine, columns, storage_options, use_nullable_dtypes, dtype_backend, filesystem, filters, **kwargs)\u001b[0m\n\u001b[1;32m    665\u001b[0m     \u001b[0mcheck_dtype_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m     return impl.read(\n\u001b[0m\u001b[1;32m    668\u001b[0m         \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parquet.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, path, columns, filters, use_nullable_dtypes, dtype_backend, storage_options, filesystem, **kwargs)\u001b[0m\n\u001b[1;32m    272\u001b[0m         )\n\u001b[1;32m    273\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m             pa_table = self.api.parquet.read_table(\n\u001b[0m\u001b[1;32m    275\u001b[0m                 \u001b[0mpath_or_handle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m                 \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pyarrow/parquet/core.py\u001b[0m in \u001b[0;36mread_table\u001b[0;34m(source, columns, use_threads, schema, use_pandas_metadata, read_dictionary, memory_map, buffer_size, partitioning, filesystem, filters, use_legacy_dataset, ignore_prefixes, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit, page_checksum_verification)\u001b[0m\n\u001b[1;32m   1791\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1792\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1793\u001b[0;31m         dataset = ParquetDataset(\n\u001b[0m\u001b[1;32m   1794\u001b[0m             \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1795\u001b[0m             \u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pyarrow/parquet/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path_or_paths, filesystem, schema, filters, read_dictionary, memory_map, buffer_size, partitioning, ignore_prefixes, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit, page_checksum_verification, use_legacy_dataset)\u001b[0m\n\u001b[1;32m   1358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1359\u001b[0m             self._dataset = ds.FileSystemDataset(\n\u001b[0;32m-> 1360\u001b[0;31m                 \u001b[0;34m[\u001b[0m\u001b[0mfragment\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mschema\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfragment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mphysical_schema\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1361\u001b[0m                 \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparquet_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1362\u001b[0m                 \u001b[0mfilesystem\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfragment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilesystem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pyarrow/_dataset.pyx\u001b[0m in \u001b[0;36mpyarrow._dataset.Fragment.physical_schema.__get__\u001b[0;34m()\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[0;34m()\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n","\u001b[0;31mArrowInvalid\u001b[0m: Could not open Parquet input source '<Buffer>': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file."],"ename":"ArrowInvalid","evalue":"Could not open Parquet input source '<Buffer>': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.","output_type":"error"}],"execution_count":8},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}