{"nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 3", "name": "python3", "language": "python"}, "language_info": {"mimetype": "text/x-python", "file_extension": ".py", "name": "python", "version": "3.6.3", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "codemirror_mode": {"name": "ipython", "version": 3}}}, "nbformat_minor": 1, "cells": [{"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\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.", "metadata": {"collapsed": true, "_cell_guid": "e1eafc78-3d8f-4866-85b5-777ea3bf7fac", "trusted": true, "_uuid": "4799b1bee9afcfac82a4548110def73d654cc9b3"}, "execution_count": null, "cell_type": "code"}]}