{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.5.2"}},"nbformat":4,"nbformat_minor":0,"cells":[{"metadata":{"_cell_guid":"41a8bd71-5674-d0d7-6480-7d13bde061a0","_active":false,"collapsed":false},"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":1,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"e1bd5d30-0d86-11f7-951b-caff0cb1155b","_active":false,"collapsed":false},"source":"\n!ls -alh ../input","execution_count":5,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"aef66493-374b-2f25-1b77-e03cd5a7b74a","_active":false,"collapsed":false},"source":"x=pd.read_csv('../input/train.csv')","execution_count":8,"cell_type":"code","outputs":[],"execution_state":"busy"},{"metadata":{"_cell_guid":"f1a34f2e-ba5d-ace2-3218-564994e47eca","_active":false,"collapsed":false},"source":null,"execution_count":null,"cell_type":"code","outputs":[]}]}