{"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":"3.5.2"}},"nbformat":4,"nbformat_minor":0,"cells":[{"metadata":{"_cell_guid":"f69e9e50-0e57-2eff-a5eb-34297fdf5370","_active":true,"collapsed":false},"source":"Looking at the data...","execution_count":null,"cell_type":"markdown","outputs":[]},{"metadata":{"_cell_guid":"3e5324cf-59c2-15d1-59bd-d5b7be118af9","_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":"4a824d55-1c53-3001-7b4d-7ec5c4a30ae5","_active":false,"collapsed":false},"source":"print(check_output([\"ls\",\"../input/sixteen_band\"]).decode(\"utf8\"))","execution_count":3,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"98aef0b0-38e9-9f0c-0833-624e415b0601","_active":false,"collapsed":false},"source":null,"execution_count":null,"cell_type":"code","outputs":[]}]}