{"nbformat_minor": 1, "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"pygments_lexer": "ipython3", "mimetype": "text/x-python", "name": "python", "file_extension": ".py", "nbconvert_exporter": "python", "codemirror_mode": {"name": "ipython", "version": 3}, "version": "3.6.3"}}, "cells": [{"cell_type": "code", "outputs": [], "metadata": {"_uuid": "d6f70fa1ea8c23d3de01669a553d9b025561309a", "_cell_guid": "5c6f267e-a0ae-4aa1-8b5c-a22651c24268", "trusted": false, "collapsed": true}, "execution_count": null, "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."}], "nbformat": 4}