{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"collapsed":true},"cell_type":"code","source":"# first pass analysis","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"83c74cdf44b27e6b206556b8979bf54eda588e30"},"cell_type":"code","source":"import os\nprint (os.listdir(\"../input\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"bcf5a64aee5c512e5a32f3a9da51a84fcc4c626c"},"cell_type":"code","source":"!printf \"Total unique rows in csv file 'stage_1_train_labels.csv': \"; \\\n         grep -v \"patientId,x,y,width,height,Target\" ../input/stage_1_train_labels.csv | cut -d \",\" -f 1 | sort | uniq | wc -l","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"!printf \"First 10 rows, including header, in stage_1_train_labels.csv: \\n\\n\"; \\\n         head -10 ../input/stage_1_train_labels.csv","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"038b8ce3e963695cdd7001155b3bb878c32bb810"},"cell_type":"code","source":"!printf \"Total unique rows in csv file 'stage_1_detailed_class_info.csv': \"; \\\n         grep -v \"patientId,class\" ../input/stage_1_detailed_class_info.csv | cut -d \",\" -f 1 | sort | uniq | wc -l","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5dc6570775bf2bb0de0af4786e0ee1fd3dd1513b","scrolled":true},"cell_type":"code","source":"!printf \"First 10 rows in stage_1_detailed_class_info.csv:\\n\\n\"; \\\n         head -10 ../input/stage_1_detailed_class_info.csv","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"05141477bcb16f5941332cd9c84acf4179a40a79"},"cell_type":"code","source":"!printf \"High-level totals on unique examples provided: \\n\\n\"\n!printf \"Normal examples: \";grep \",Normal\" ../input/stage_1_detailed_class_info.csv | cut -d \",\" -f 1 | sort | uniq | wc -l\n!printf \"Pneumonia examples: \"; grep \",Lung\\sOpacity\" ../input/stage_1_detailed_class_info.csv | cut -d \",\" -f 1 | sort | uniq | wc -l\n!printf \"Other abnormal examples: \"; grep \",No\\sLung\\sOpacity\\s\\/\\sNot\\sNormal\" ../input/stage_1_detailed_class_info.csv | cut -d \",\" -f 1 | sort | uniq | wc -l\n!printf \"Total examples:: \"; grep -v \"patientId,class\" ../input/stage_1_detailed_class_info.csv | cut -d \",\" -f 1 | sort | uniq | wc -l","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c7730252a679d688db5b00156efaabd15297f7de"},"cell_type":"code","source":"# sanity check from images\n# number of training examples\nprint (\"Training examples provided: {}\".format(len(os.listdir(\"../input/stage_1_train_images\"))))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"41a1e3cc84bb9250e76a5cf9f28d61388bbc793c"},"cell_type":"code","source":"# number of test cases\nprint (\"Test cases to be predicted: {}\".format(len(os.listdir(\"../input/stage_1_test_images\"))))","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}