{"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":"# 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        pass\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","execution":{"iopub.status.busy":"2023-04-19T13:14:49.040985Z","iopub.execute_input":"2023-04-19T13:14:49.042288Z","iopub.status.idle":"2023-04-19T13:14:49.317969Z","shell.execute_reply.started":"2023-04-19T13:14:49.042230Z","shell.execute_reply":"2023-04-19T13:14:49.316811Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"root_path = \"/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/\"\n\ntdcsfog_train_files = os.listdir(os.path.join(root_path, 'train/tdcsfog'))\nprint(f'a number of files: {len(tdcsfog_train_files)}')","metadata":{"execution":{"iopub.status.busy":"2023-04-19T13:14:49.320956Z","iopub.execute_input":"2023-04-19T13:14:49.321463Z","iopub.status.idle":"2023-04-19T13:14:49.329016Z","shell.execute_reply.started":"2023-04-19T13:14:49.321409Z","shell.execute_reply":"2023-04-19T13:14:49.327794Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"csv_fname = tdcsfog_train_files[0]\ndf_tdcs_train = pd.read_csv(os.path.join(root_path, 'train/tdcsfog', csv_fname))\ndisplay(df_tdcs_train.head(5))","metadata":{"execution":{"iopub.status.busy":"2023-04-19T13:14:49.330638Z","iopub.execute_input":"2023-04-19T13:14:49.331121Z","iopub.status.idle":"2023-04-19T13:14:49.398740Z","shell.execute_reply.started":"2023-04-19T13:14:49.331075Z","shell.execute_reply":"2023-04-19T13:14:49.397448Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(csv_fname)","metadata":{"execution":{"iopub.status.busy":"2023-04-19T13:14:49.402536Z","iopub.execute_input":"2023-04-19T13:14:49.402970Z","iopub.status.idle":"2023-04-19T13:14:49.409669Z","shell.execute_reply.started":"2023-04-19T13:14:49.402930Z","shell.execute_reply":"2023-04-19T13:14:49.408230Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_tdcs_meta = pd.read_csv(os.path.join(root_path, 'tdcsfog_metadata.csv'))\ndisplay(df_tdcs_meta.head(5))","metadata":{"execution":{"iopub.status.busy":"2023-04-19T13:14:49.411090Z","iopub.execute_input":"2023-04-19T13:14:49.412112Z","iopub.status.idle":"2023-04-19T13:14:49.438159Z","shell.execute_reply.started":"2023-04-19T13:14:49.412075Z","shell.execute_reply":"2023-04-19T13:14:49.436848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"PHD_STIPENDS = pd.read_csv(root_path + 'sample_submission.csv') # load from notebook input\nPHD_STIPENDS.to_csv('/kaggle/working/submission.csv',index=False) # save to notebook output\nPHD_STIPENDS.head(10)","metadata":{"execution":{"iopub.status.busy":"2023-04-19T13:14:49.440277Z","iopub.execute_input":"2023-04-19T13:14:49.440789Z","iopub.status.idle":"2023-04-19T13:14:50.239095Z","shell.execute_reply.started":"2023-04-19T13:14:49.440739Z","shell.execute_reply":"2023-04-19T13:14:50.236244Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}