{"cells":[{"metadata":{"_uuid":"8a1b9754-c63d-4c4b-ad47-3d0d1205a4f8","_cell_guid":"3118615c-7393-47b9-a381-5e7b30be9e07","trusted":true},"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 in \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 \"../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# Any results you write to the current directory are saved as output.\nsub = pd.read_csv('../input/bengaliai-cv19/sample_submission.csv')\n\nsub.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"263efed3-4abf-4aa0-9a21-9089cac91911","_cell_guid":"a962032d-b793-47ef-9814-164c04f7bf3e","trusted":true},"cell_type":"code","source":"","execution_count":0,"outputs":[]}],"metadata":{"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":1}