{"nbformat": 4, "nbformat_minor": 1, "cells": [{"cell_type": "code", "outputs": [], "execution_count": null, "metadata": {"_cell_guid": "e3f12d38-bc82-41eb-8d89-51ddcdbbb31b", "_uuid": "25c4b829d3d195501dc7db2284b45a2a5f74e422"}, "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", "outputs": [], "execution_count": null, "metadata": {"collapsed": true, "_cell_guid": "1cbd9999-7a57-4db7-aab9-2ec621f8ad31", "_uuid": "2bc3b7c01a15010e92bfc07081f203c5b84533bd"}, "source": []}], "metadata": {"language_info": {"pygments_lexer": "ipython3", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "name": "python", "file_extension": ".py", "version": "3.6.3", "nbconvert_exporter": "python"}, "kernelspec": {"name": "python3", "display_name": "Python 3", "language": "python"}}}