{"cells":[{"metadata":{"_cell_guid":"3389400c-9217-ef75-7881-3b65a5c4a92e","_uuid":"f4a8a71fcdcea9df07216a17f9ab8bfa78eac8d0","trusted":false},"cell_type":"code","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,"outputs":[]},{"metadata":{"_cell_guid":"c94441b6-9418-9f19-e9d8-4bed55388965","_uuid":"12aacf446a2ab89875834d2af9bb43098a973efe","trusted":false},"cell_type":"code","source":"train_data = pd.read_csv(\"../input/train\")\ntest_data = pd.read_csv(\"../input/test\")","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"b4d2c4c7-78c3-a86a-8c30-e6fc926e1d6c","_uuid":"a31f4ee6421733c88f0f44b6e503eb730d67356f","trusted":false},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}