{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"0c126705-eff3-1ba2-fcdb-ab7e8a2686b0"},"source":"# Start up"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"d8cd3feb-fe34-9314-ec8d-6850cc32aabd"},"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\", \"..\"]).decode(\"utf8\"))\nprint(check_output([\"ls\", \"../config\"]).decode(\"utf8\"))\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\nprint(check_output([\"ls\", \"../working\"]).decode(\"utf8\"))\n\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"b6962443-12b8-b802-5443-63b6e545e716"},"outputs":[],"source":"df_town_state = pd.read_csv('../input/town_state.csv')\ndf_town_state[:100].to_csv('../working/bytheway.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"996bae76-78f4-8fa0-be2b-6b0cc918faaa"},"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}