{"metadata": {"kernelspec": {"language": "python", "display_name": "Python 3", "name": "python3"}, "language_info": {"mimetype": "text/x-python", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "version": "3.6.1", "codemirror_mode": {"name": "ipython", "version": 3}}}, "nbformat": 4, "cells": [{"metadata": {"trusted": false, "_execution_state": "idle", "_cell_guid": "4375a0e5-33d0-4d1e-8c5a-5b29bab4164d", "_uuid": "45c4262fee2f1a07d5a07f30841414aafe9f6663"}, "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": [], "cell_type": "code", "execution_count": 1}], "nbformat_minor": 0}