{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"multisub = pd.read_csv('../input/lyft-prediction-with-multi-mode-confidence/submission.csv', index_col=[0,1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cols = list(multisub.columns)\n\nconf = ['','','']\ncn = cols[0:3]\nconf[0] = cols[3:103]\nconf[1] = cols[103:203]\nconf[2] = cols[203:303]\n\ndef sort_by_conf(x):\n    o = x[cn].argsort()[-3:][::-1]\n    x[cols] = np.array(list(x[o])+\n                       list(x[conf[o[0]]])+\n                       list(x[conf[o[1]]])+\n                       list(x[conf[o[2]]]))\n    return x","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"multisub","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"multisub.apply(sort_by_conf, axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"multisub.to_csv('submission.csv', float_format='%.5g')","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}