{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"84b38c64-4d13-de75-a4f4-24962f0c4438"},"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.\n# Convert data to pandas data.frame\ntrain_date = pd.read_csv('../input/train_date.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"92151944-8837-67bb-2846-5ca0dfba6276"},"outputs":[],"source":"print(train_date.shape)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ccca2700-7f3e-21ae-9a0a-dd9cee62a417"},"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}