{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"94e07a6e-152c-13f1-9a66-a44de9729045"},"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 dicom\nimport os\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":"927448dc-d303-5f46-28b0-33de4722dc2d"},"outputs":[],"source":"import dicom # for reading dicom files\nimport os # for doing directory operations \nimport pandas as pd # for some simple data analysis (right now, just to load in the labels data and quickly reference it)\n\n# Change this to wherever you are storing your data:\n# IF YOU ARE FOLLOWING ON KAGGLE, YOU CAN ONLY PLAY WITH THE SAMPLE DATA, WHICH IS MUCH SMALLER\n\ndata_dir = '../input/train/'\npatients = os.listdir(data_dir)\npatients\n#labels_df = pd.read_csv('../input/stage1_labels.csv', index_col=0)\n#labels_df.head()\n\n\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}