{"metadata":{"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,"cells":[{"metadata":{"_cell_guid":"17d2115a-f76a-ce33-eb50-b513c43fc7c6","_active":false,"collapsed":false},"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.","execution_count":null,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"e1e8ab28-fe77-7365-882d-25c6ff49f503","_active":false,"collapsed":false},"source":"ad_info = pd.read_csv('../input/promoted_content.csv')\nad_info.info()","execution_count":null,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"d905a4f4-538e-098c-8d07-ac9ac2a3babd","_active":false,"collapsed":false},"source":"ad_info.groupby('document_id')['ad_id']","execution_count":null,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"95d720ff-75e7-0b88-5c69-d1d4ee01f5ce","_active":false,"collapsed":false},"source":null,"execution_count":null,"cell_type":"code","outputs":[],"execution_state":"idle"}]}