{"nbformat":4,"metadata":{"_is_fork":false,"language_info":{"version":"3.6.4","file_extension":".py","mimetype":"text/x-python","codemirror_mode":{"version":3,"name":"ipython"},"nbconvert_exporter":"python","name":"python","pygments_lexer":"ipython3"},"_change_revision":0,"kernelspec":{"name":"python3","language":"python","display_name":"Python 3"}},"nbformat_minor":1,"cells":[{"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 the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"cell_type":"code","metadata":{"_cell_guid":"76faf286-ce2a-2c64-0c0e-c22ef88d733e","_uuid":"b27b274871d1b2b695f78d587287a25d439f3492","collapsed":true},"outputs":[]},{"source":"print(__version__)","execution_count":null,"cell_type":"code","metadata":{"_cell_guid":"f9c051f0-7b89-21a3-187c-057702e5d228","_uuid":"90883c88b8ec0c4f983b16b2510930d30f3c47a3","collapsed":true},"outputs":[]}]}