{"metadata":{"kernelspec":{"name":"python"},"language_info":{"name":"python","version":"3.5.1"}},"nbformat":4,"nbformat_minor":0,"cells":[{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"_cell_guid":"7d5d3986-802f-4ca7-9bc3-2cba7b8634ea"},"outputs":[],"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\", \"-ltrah\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"_cell_guid":"8b37ad0f-bf06-4e4b-bbf2-1074f0c27809"},"outputs":[],"source":"print(check_output([\"cp\", \"../input/sampleSubmission.csv\", \"sub.csv\"]).decode(\"utf8\"))"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false,"_cell_guid":"2fc7fa1c-e455-46af-810b-fbddabcc2b22"},"outputs":[],"source":"print(check_output([\"ls\", \"-ltrah\", \".\"]).decode(\"utf8\"))"}]}