{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"2e6dc24d-1885-50ec-1585-e745bd6515e5"},"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2937fa1d-aea6-1585-e219-aa1540677c7d"},"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/train\"]).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":"6463c898-6119-5eb8-5dc1-e69f3c6c09ff"},"outputs":[],"source":"from os import listdir\nfrom os.path import isfile, join\nonlyfiles = [f for f in listdir(\"../input/train\") if isfile(join(\"../input\", f))]\nonlyfiles\n"}],"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}