{
  "metadata": {
    "kernelspec": {
      "name": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.5.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0,
  "cells": [
    {
      "metadata": {
        "_cell_guid": "67173079-54c2-5958-7b62-bdaffaa30b6d",
        "_active": false,
        "collapsed": false
      },
      "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,
      "cell_type": "code",
      "outputs": []
    }
  ]
}