{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"52c98dec-d651-4edb-e255-dbf66032095f"},"source":"The descartes package makes correctly visualizing the Polygon objects much simpler."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"229fb855-aef5-f4f3-ca23-5c62d5a7362b"},"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":"c723b763-d13f-5891-f8c3-bdb4537f50e7"},"outputs":[],"source":"# Let's try to import decartes\nfrom descartes.patch import PolygonPatch"},{"cell_type":"markdown","metadata":{"_cell_guid":"2169d950-fde4-a992-dead-ea442f479369"},"source":"So  the decartes package is missing in the Kaggle/python docker image. Fortunately, installation with conda/pip is straight-forward."}],"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}