{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"a226d79c-1c61-cac5-e6d4-082d5fa984fa"},"source":"This is the next notebook after 'Exploring train_info to get artist characteristics'. This one is intended to look at the image files."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e3038ce4-884a-5766-871b-834520d4b01c"},"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 in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport glob\nfrom IPython.display import Image\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."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"8f19338a-83bb-f2f0-3c83-2422ed0a3837"},"outputs":[],"source":"train_images = glob.glob('../input/train_3/*')\ntrain_images"}],"metadata":{"_change_revision":0,"_is_fork":false,"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}