{"metadata":{"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,"cells":[{"metadata":{"_cell_guid":"ed282643-48b7-4e94-fb8a-b245a60e452e","_active":false,"collapsed":false},"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)\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.","execution_count":null,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"08db5356-c1fc-95ed-78c5-f537c153e789","_active":true,"collapsed":false},"source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\ntrain=pd.read_csv(\"../input/train.csv\", na_values=\"NA\")\ntest = pd.read_csv(\"../input/test.csv\", na_values=\"NA\")\n\na=((train.loc[:,'label']).as_matrix())\ny=[train.shape[0]][10]\nprint(a,y)\nfor i in range(0,train.shape[0]):\n    y[i]","execution_count":null,"cell_type":"code","outputs":[],"execution_state":"idle"}]}