{"metadata": {"kernelspec": {"language": "python", "name": "python3", "display_name": "Python 3"}, "language_info": {"nbconvert_exporter": "python", "pygments_lexer": "ipython3", "mimetype": "text/x-python", "file_extension": ".py", "version": "3.6.3", "name": "python", "codemirror_mode": {"version": 3, "name": "ipython"}}}, "nbformat_minor": 1, "nbformat": 4, "cells": [{"metadata": {"collapsed": true, "_cell_guid": "e535ee09-c25a-4918-b4c0-7aea5c4668ba", "_uuid": "e9b14ff32c1f5c5c063575df04fb0414f238b039"}, "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", "\n", "import numpy as np # linear algebra\n", "import 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", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output."], "cell_type": "code", "execution_count": null}]}