{"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":"2021-08-21T20:13:53.819928Z","iopub.execute_input":"2021-08-21T20:13:53.820295Z","iopub.status.idle":"2021-08-21T20:13:53.831156Z","shell.execute_reply.started":"2021-08-21T20:13:53.820261Z","shell.execute_reply":"2021-08-21T20:13:53.830360Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"path_file_csv = '/kaggle/input/dogs-vs-cats-redux-kernels-edition/sample_submission.csv'\ndf = pd.read_csv(path_file_csv)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-21T20:13:53.835637Z","iopub.execute_input":"2021-08-21T20:13:53.836194Z","iopub.status.idle":"2021-08-21T20:13:53.860827Z","shell.execute_reply.started":"2021-08-21T20:13:53.836146Z","shell.execute_reply":"2021-08-21T20:13:53.859910Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"path_file_save = '/kaggle/working/sample_submission.csv'\ndf.to_csv( path_file_save )","metadata":{"execution":{"iopub.status.busy":"2021-08-21T20:18:48.985386Z","iopub.execute_input":"2021-08-21T20:18:48.985793Z","iopub.status.idle":"2021-08-21T20:18:49.032169Z","shell.execute_reply.started":"2021-08-21T20:18:48.985756Z","shell.execute_reply":"2021-08-21T20:18:49.031306Z"},"trusted":true},"execution_count":null,"outputs":[]}]}