{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"3288b5d9-29d2-cda0-e845-a817efd20303"},"source":"# This is my python program"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"0b223810-a7b8-7d02-8562-bf819cd78f42"},"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":"01d6cbb7-f4f1-15ec-c206-77fd04a959ec"},"outputs":[],"source":"events = pd.read_csv('../input/clicks_train.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"33b2b412-73be-9f5f-6128-8eb622cd4d4e"},"outputs":[],"source":"events.head()"}],"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}