{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"26af79dc-1d58-be29-87ba-39a33de791fa"},"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"6c50a71b-eefd-fe23-b6c1-b2f3032e2eba"},"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":"909f13a7-c42c-d9a3-fb60-70738b00de4f"},"outputs":[],"source":"df_click_train = pd.read_csv('../input/clicks_train.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"81a5ef7e-c375-93aa-86ce-0072b3a83c4b"},"outputs":[],"source":"df_click_train.head()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"c675d7e3-186c-5a4c-a736-bc84107b607b"},"outputs":[],"source":"df_click_train['ad_id'].value_counts()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"465f251c-786f-db13-ddb6-bfb97ba51164"},"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}