{"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\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":"2023-03-03T03:08:55.118771Z","iopub.execute_input":"2023-03-03T03:08:55.119140Z","iopub.status.idle":"2023-03-03T03:08:59.856155Z","shell.execute_reply.started":"2023-03-03T03:08:55.119104Z","shell.execute_reply":"2023-03-03T03:08:59.854592Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# grab your predictions and the sample submission\n\nmy_predictions = pd.read_csv('/kaggle/input/customized200-2/submission200_2.csv')\nsample_submission = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')\n\n# add your predictions and replace nans for the private data set\n\nsample_submission.drop(columns=['diagnosis'], inplace=True)\nsample_submission = pd.merge(sample_submission, my_predictions, how='left', left_on='id_code', right_on='id_code')\nsample_submission.fillna(0,inplace=True)\n\n# you must convert to int or it will fail @ submission\n\nsample_submission['diagnosis'] = sample_submission['diagnosis'].astype('int64') \n\nsample_submission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-03-03T03:09:28.352666Z","iopub.execute_input":"2023-03-03T03:09:28.353059Z","iopub.status.idle":"2023-03-03T03:09:28.378506Z","shell.execute_reply.started":"2023-03-03T03:09:28.353023Z","shell.execute_reply":"2023-03-03T03:09:28.377377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submission.head(20)","metadata":{"execution":{"iopub.status.busy":"2023-03-03T03:09:31.083691Z","iopub.execute_input":"2023-03-03T03:09:31.084092Z","iopub.status.idle":"2023-03-03T03:09:31.102130Z","shell.execute_reply.started":"2023-03-03T03:09:31.084058Z","shell.execute_reply":"2023-03-03T03:09:31.100776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}