{"nbformat_minor": 0, "cells": [{"outputs": [], "metadata": {"_uuid": "aab9505b6dd9a85b73a733c3aba3525b9e3223a9", "collapsed": false, "_active": false, "_cell_guid": "ac3612d9-9802-8948-1130-824f26e04202"}, "execution_count": null, "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_state": "idle", "cell_type": "code"}, {"outputs": [], "metadata": {"_uuid": "3c27aa8e9712b0584d3841c0fafe7945651563e0", "collapsed": false, "_active": true, "_cell_guid": "764b78fa-5581-5403-24eb-1d14444bee99"}, "execution_count": null, "source": "import os \nimport gc\nimport matplotlib.pylot as plt\nimport seaborn as sns\n\np=sns.", "execution_state": "idle", "cell_type": "code"}], "nbformat": 4, "metadata": {"kernelspec": {"name": "python3", "language": "python", "display_name": "Python 3"}, "language_info": {"name": "python", "mimetype": "text/x-python", "version": "3.5.2", "nbconvert_exporter": "python", "file_extension": ".py", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3"}}}