{"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":"2022-10-21T16:54:40.917214Z","iopub.execute_input":"2022-10-21T16:54:40.918346Z","iopub.status.idle":"2022-10-21T16:54:40.947085Z","shell.execute_reply.started":"2022-10-21T16:54:40.918236Z","shell.execute_reply":"2022-10-21T16:54:40.946234Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df=pd.read_csv(\"/kaggle/input/tabular-playground-series-oct-2022/train_0.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:55:48.955935Z","iopub.execute_input":"2022-10-21T16:55:48.956352Z","iopub.status.idle":"2022-10-21T16:56:18.925483Z","shell.execute_reply.started":"2022-10-21T16:55:48.956318Z","shell.execute_reply":"2022-10-21T16:56:18.924542Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:56:30.792944Z","iopub.execute_input":"2022-10-21T16:56:30.793328Z","iopub.status.idle":"2022-10-21T16:56:30.835174Z","shell.execute_reply.started":"2022-10-21T16:56:30.793296Z","shell.execute_reply":"2022-10-21T16:56:30.834178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.tail()","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:58:06.933177Z","iopub.execute_input":"2022-10-21T16:58:06.934234Z","iopub.status.idle":"2022-10-21T16:58:06.964604Z","shell.execute_reply.started":"2022-10-21T16:58:06.934197Z","shell.execute_reply":"2022-10-21T16:58:06.963421Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:58:33.692063Z","iopub.execute_input":"2022-10-21T16:58:33.692452Z","iopub.status.idle":"2022-10-21T16:58:33.706917Z","shell.execute_reply.started":"2022-10-21T16:58:33.692420Z","shell.execute_reply":"2022-10-21T16:58:33.705766Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2022-10-21T16:59:51.632009Z","iopub.execute_input":"2022-10-21T16:59:51.632371Z","iopub.status.idle":"2022-10-21T16:59:51.638604Z","shell.execute_reply.started":"2022-10-21T16:59:51.632325Z","shell.execute_reply":"2022-10-21T16:59:51.637820Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}