{"metadata":{"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":"3.6.0"}},"nbformat":4,"nbformat_minor":0,"cells":[{"metadata":{"_cell_guid":"792ebe8b-db4c-da87-7d05-8d817753b716","_active":false,"collapsed":false},"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":1,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"22575c07-4bb4-b1c3-a526-c1809f831dee","_active":true,"collapsed":false},"source":"df = pd.read_csv(\"../input/train.csv\")\ndf.head(10)","execution_count":3,"cell_type":"code","outputs":[],"execution_state":"busy"},{"metadata":{"_cell_guid":"5cf826df-5248-abee-bfeb-cc817408d59e","_active":false,"collapsed":false},"source":null,"execution_count":null,"cell_type":"code","outputs":[]},{"metadata":{"_cell_guid":"3d73cbf3-e0e5-46b1-822e-b3f39f33ee96","_active":false,"collapsed":false},"source":null,"execution_count":null,"cell_type":"code","outputs":[]}]}