{"metadata":{"language_info":{"nbconvert_exporter":"python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","file_extension":".py","mimetype":"text/x-python","name":"python","version":"3.6.0"},"kernelspec":{"language":"python","name":"python3","display_name":"Python 3"}},"nbformat":4,"cells":[{"metadata":{"_execution_state":"idle","_uuid":"2bace4d29dcba174693b7bcb74598ffcd0782d79"},"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,"outputs":[]},{"metadata":{"_execution_state":"idle","collapsed":false,"_uuid":"86c21d712052ff7ddd40cd6be7f0313abca2444b"},"source":"destination = pd.read_csv(\"../input/destinations.csv\")","cell_type":"code","outputs":[],"execution_count":5},{"metadata":{"_execution_state":"idle","collapsed":false,"_uuid":"abaf1d4965ed81f9664c71d44a61d545028d606f"},"source":"destination","cell_type":"code","outputs":[],"execution_count":6}],"nbformat_minor":0}