{"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":"c512ce77-af29-f5a3-47cf-91fac58d5a8f","_active":true,"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":1,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"ca655be0-512b-87a1-566c-7485cb1a77bd","_active":false,"collapsed":false},"source":"trainLabels = pd.read_csv(\"../input/trainLabels.csv\")\ntrainLabels.head()","execution_count":2,"cell_type":"code","outputs":[],"execution_state":"idle"}]}