{"nbformat_minor": 0, "metadata": {"language_info": {"codemirror_mode": {"version": 3, "name": "ipython"}, "version": "3.6.1", "name": "python", "mimetype": "text/x-python", "pygments_lexer": "ipython3", "file_extension": ".py", "nbconvert_exporter": "python"}, "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}}, "nbformat": 4, "cells": [{"outputs": [], "metadata": {"_execution_state": "idle", "_cell_guid": "8a6a1187-d4fb-42ab-8029-837fa271ab0c", "_uuid": "73a0c19205b2222c2b5c2e91b126468de4ab4148", "trusted": false}, "cell_type": "code", "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."}, {"outputs": [], "metadata": {"_execution_state": "idle", "collapsed": false, "_uuid": "aa34c72bd0ed2d3e5e035a473cd82e624e708f54"}, "cell_type": "markdown", "execution_count": null, "source": "# ???"}, {"outputs": [], "metadata": {"_execution_state": "idle", "collapsed": false, "_uuid": "2d0c326daf9a22006c868c35786f2cf2be71c63a"}, "cell_type": "code", "execution_count": null, "source": "with open('../input/README', 'r') as f:\n    readme = f.read()"}, {"outputs": [], "metadata": {"_execution_state": "idle", "collapsed": false, "_uuid": "fadd6c5e4da30fcea967fb2087260169f592f3f1"}, "cell_type": "code", "execution_count": null, "source": "readme"}, {"outputs": [], "metadata": {"_execution_state": "idle", "collapsed": false, "_uuid": "2e6a7381607a34d0a784710a75225440f8d10faa"}, "cell_type": "markdown", "execution_count": null, "source": "# Oh My God!"}]}