{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"fe9b7692-969a-34f0-1185-316eea7b3a79"},"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)\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\"))\nprint(\"welcome\")\n# Any results you write to the current directory are saved as output."},{"cell_type":"markdown","metadata":{"_cell_guid":"523cbeb3-878e-a0ec-e36b-e106d73433c0"},"source":"import glob\ntrain= glob.glob(\"../input/train/**/*.jpg\")+glob.glob(\"../input/additional/**/*.jpg\")\ntrain = pd.DataFrame([[p.split('/')[3],p.split('/')[4],p] for p in train], columns = ['type','image','path'])\ntest = glob.glob(\"../input/test/*.jpg\")\ntest = pd.DataFrame([[p.split('/')[3],p] for p in test], columns = ['image','path'])"}],"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":"3.6.0"}},"nbformat":4,"nbformat_minor":0}