{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"d914a482-4801-25e9-b66d-75b0cf1d9d7e"},"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":"460d314f-3c9e-e8d0-5d9a-10ee0757c0f3"},"outputs":[],"source":"prom_con = pd.read_csv('../input/promoted_content.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"c1d6d59a-dbff-2e25-0fd8-e0551ff752df"},"outputs":[],"source":"prom_con.head()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"989da4d7-a510-f561-e9ec-5c86dbb1028d"},"outputs":[],"source":"prom_con.shape"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"57319de0-9c19-a7e3-eb0c-f7518eda4c8e"},"outputs":[],"source":"clicks_train = pd.read_csv('../input/clicks_train.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1d2bdeb0-bb8e-a133-cd54-7c76d8690d3b"},"outputs":[],"source":"ads_clicked = clicks_train.loc[clicks_train['clicked'] == 1]['ad_id']"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"968c88a8-5dce-9855-0ce3-d9e7984c5a82"},"outputs":[],"source":"clicks_train.loc[clicks_train['ad_id'] == 97178]"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9ab99444-5a39-898f-9d86-9ac9223278ca"},"outputs":[],"source":"def sort_ads(ad_list):\n    percent_list = [[ad, click_percentage(ad)] for ad in ad_list]\n    percent_list.sort(key=lambda x: x[1], reverse=True)\n    return [ad[0] for ad in percent_list]\n\ndef click_percentage(ad):\n    history = clicks_train.loc[clicks_train['ad_id'] == ad]\n    if len(history) > 0:\n        return len(history.loc[history['clicked'] == 1]) / len(history)\n\nsort_ads([3, 5, 6, 7])"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"4b34f78f-158a-a8c5-de7e-3d22514a8156"},"outputs":[],"source":"click_percentage(504000)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e44466ac-27c4-66f4-94a9-6b461e7847f3"},"outputs":[],"source":"min(clicks_train['ad_id'][:100])"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"3249417e-3e6b-4f13-9ad3-48953caf6a6b"},"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}