{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"d7d41bbe-714a-682f-c3f7-952707cc9d9e"},"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 matplotlib.pyplot as plt\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\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"56cc25af-0ad8-d7ef-d278-42fcb6e93101"},"outputs":[],"source":"train = pd.read_csv('../input/train.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"b20ed19e-b02f-4ea0-e04b-6e234f953c88"},"outputs":[],"source":"y = train['label'].values\nX = train[:1].values"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"98fc00a4-8ba9-4683-eaf8-e13850ceeb2b"},"outputs":[],"source":"X.size()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"57c1d5d7-88fe-cbd7-f0ae-7e72c2c4fcd6"},"outputs":[],"source":""}],"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}