{"cells": [{"cell_type": "code", "execution_count": null, "metadata": {"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "trusted": true}, "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\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 read-only \"../input/\" directory\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", "    for filename in filenames:\n", "        print(os.path.join(dirname, filename))\n", "\n", "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["test = 10"]}], "metadata": {"kaggle": {"accelerator": "none", "dataSources": [{"databundleVersionId": 862042, "sourceId": 10338, "sourceType": "competition"}], "isGpuEnabled": false, "isInternetEnabled": true, "language": "python", "sourceType": "notebook"}, "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4"}}, "nbformat": 4, "nbformat_minor": 4}