{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":71549,"databundleVersionId":8561470,"sourceType":"competition"}],"dockerImageVersionId":30698,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np","metadata":{"execution":{"iopub.status.busy":"2024-05-17T08:21:09.996805Z","iopub.execute_input":"2024-05-17T08:21:09.997205Z","iopub.status.idle":"2024-05-17T08:21:11.301974Z","shell.execute_reply.started":"2024-05-17T08:21:09.997172Z","shell.execute_reply":"2024-05-17T08:21:11.300714Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submit = pd.read_csv(\"/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/sample_submission.csv\")\nsample_submit.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-17T08:21:13.541211Z","iopub.execute_input":"2024-05-17T08:21:13.541891Z","iopub.status.idle":"2024-05-17T08:21:13.587684Z","shell.execute_reply.started":"2024-05-17T08:21:13.541848Z","shell.execute_reply":"2024-05-17T08:21:13.586833Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submit.size","metadata":{"execution":{"iopub.status.busy":"2024-05-17T08:21:15.903773Z","iopub.execute_input":"2024-05-17T08:21:15.904188Z","iopub.status.idle":"2024-05-17T08:21:15.913835Z","shell.execute_reply.started":"2024-05-17T08:21:15.904156Z","shell.execute_reply":"2024-05-17T08:21:15.912580Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's submit 0.33 as the prediction for all three columns.\n\nAlso, make sure Internet is turned off before submitting the notebook.","metadata":{}},{"cell_type":"code","source":"# Create a dataframe with all predictions set to 0.33\npredictions = sample_submit.copy()\npredictions.iloc[:, 1:] = 0.33  # Assign 0.33 to all prediction columns\n\n# Save the dataframe to a new csv file\npredictions.to_csv('submission.csv', index=False)\n\nprint(\"Submission file created successfully!\")\n","metadata":{"execution":{"iopub.status.busy":"2024-05-17T08:22:07.677275Z","iopub.execute_input":"2024-05-17T08:22:07.677700Z","iopub.status.idle":"2024-05-17T08:22:07.690318Z","shell.execute_reply.started":"2024-05-17T08:22:07.677668Z","shell.execute_reply":"2024-05-17T08:22:07.689078Z"},"trusted":true},"execution_count":null,"outputs":[]}]}