{"nbformat": 4, "nbformat_minor": 0, "metadata": {"language_info": {"version": "3", "name": "python"}, "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}}, "cells": [{"cell_type": "code", "outputs": [], "metadata": {"_cell_guid": "7b375cad-9de7-45e9-872e-0f4555e59581", "_uuid": "209c88e2c7f6a07348ce43801ea63af42645ccac"}, "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": null}]}