{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"0d07a582-606c-215f-4844-cd5795ee279d"},"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":"0d8524de-0782-24c0-4cad-be97aaca2c55"},"outputs":[],"source":"\ntrain_categorical=pd.read_csv(\"../input/train_categorical.csv\")\ntrain_date=pd.read_csv(\"../input/train_date.csv\")\ntrain_numeric=pd.read_csv(\"../input/train_numeric.csv\")"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"12746ef3-f4c5-24b0-e34e-a451ee136aac"},"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}