{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1358104d-32bf-09d0-3b3f-5b9c487e2baa"},"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":"652cd461-94a1-64ae-5f7f-49e987bfedff"},"outputs":[],"source":"date_cols = pd.read_csv(\"../input/train_date.csv\", index_col=0, nrows=1)\nnum_cols = pd.read_csv(\"../input/train_numeric.csv\", index_col=0, nrows=1)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9909ee15-16e0-030e-a402-f1e203da9fc9"},"outputs":[],"source":"print(list(date_cols.iloc[0].items()))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"94e74e22-8262-2e41-b091-f9b2cb6949d2"},"outputs":[],"source":"print(list(num_cols.iloc[0].items()))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e8149cc2-ea59-ef2f-eb91-1d1ae0fd5465"},"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}