{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"505d737a-0032-861e-d8d4-88eb9691970b"},"outputs":[],"source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn import scm, svc, preprocessing\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\"))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"8d776f2f-2a3a-80df-8fe4-d77c7fc58a6c"},"outputs":[],"source":"train_df = pd.read_csv('../input/train.csv')\ntest_df = pd.read_csv('../input/test.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"34879063-8850-270a-2f98-ef32bd1627da"},"outputs":[],"source":"# need to place features and labels into separate arrays\n\nX = []\ny = []\n\nnp.append(X,train_df[1],axis=0)\nprint([X])"}],"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":"3.5.2"}},"nbformat":4,"nbformat_minor":0}