{"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":67356,"databundleVersionId":8006601,"sourceType":"competition"},{"sourceId":175804152,"sourceType":"kernelVersion"}],"dockerImageVersionId":30698,"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","execution":{"iopub.status.busy":"2024-05-05T13:35:06.256946Z","iopub.execute_input":"2024-05-05T13:35:06.257363Z","iopub.status.idle":"2024-05-05T13:35:06.876767Z","shell.execute_reply.started":"2024-05-05T13:35:06.257333Z","shell.execute_reply":"2024-05-05T13:35:06.875621Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/complete-test-preprocessed/submission.csv')\ndf['binds'].max() , df['binds'].min()","metadata":{"execution":{"iopub.status.busy":"2024-05-05T13:35:09.752511Z","iopub.execute_input":"2024-05-05T13:35:09.753090Z","iopub.status.idle":"2024-05-05T13:35:10.896840Z","shell.execute_reply.started":"2024-05-05T13:35:09.753054Z","shell.execute_reply":"2024-05-05T13:35:10.895659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df['id']","metadata":{"execution":{"iopub.status.busy":"2024-05-05T13:39:38.388957Z","iopub.execute_input":"2024-05-05T13:39:38.389451Z","iopub.status.idle":"2024-05-05T13:39:38.399732Z","shell.execute_reply.started":"2024-05-05T13:39:38.389406Z","shell.execute_reply":"2024-05-05T13:39:38.398352Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"binds = []\nfor i in df['binds']:\n    if i < 0:\n        binds.append(0)\n    else:\n        binds.append(i)\n\nnew_df = pd.DataFrame({ 'id' : df['id'],\n                      'binds' : binds})\n\nnew_df['binds'].min()","metadata":{"execution":{"iopub.status.busy":"2024-05-05T13:40:14.221148Z","iopub.execute_input":"2024-05-05T13:40:14.221593Z","iopub.status.idle":"2024-05-05T13:40:14.867902Z","shell.execute_reply.started":"2024-05-05T13:40:14.221549Z","shell.execute_reply":"2024-05-05T13:40:14.866640Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"new_df.to_csv('submission.csv' , index=False)","metadata":{"execution":{"iopub.status.busy":"2024-05-05T13:40:42.875441Z","iopub.execute_input":"2024-05-05T13:40:42.876213Z","iopub.status.idle":"2024-05-05T13:40:46.570748Z","shell.execute_reply.started":"2024-05-05T13:40:42.876177Z","shell.execute_reply":"2024-05-05T13:40:46.569722Z"},"trusted":true},"execution_count":null,"outputs":[]}]}