{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"a88d9059-7b77-5f5c-c7b5-b70a2ea6f6f0"},"source":"Outbrain"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"8c3c004a-8502-b20a-36e3-ae4418f5b942"},"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)\nimport os\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\nprint('file size')\n\nfor f in os.listdir('../input'):\n \n if 'zip' not in f:\n    print(f.ljust(30,' ') + str(round(os.path.getsize('../input/'+f)/1000000,2)) + 'MB')\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"34f37234-fdbb-b6c9-802e-ab032a8d8099"},"outputs":[],"source":"df_train = pd.read_csv('../input/clicks_train.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"16fcabe6-8397-c505-1231-5653b202aff8"},"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}