{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"10082dfa-56dd-8227-64c6-34bc3e9c96d6"},"source":"Notebook to do basic exploration of the dataset"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"63261fe4-3f8e-8260-d9ab-bf6375b57a17"},"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)\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":"66fdf61a-0d17-0d3a-d63d-c244f3d4d37a"},"outputs":[],"source":"train_cat = pd.read_csv('../input/train_categorical.csv',)"}],"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}