{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n#import os\n#for dirname, _, filenames in os.walk('/kaggle/input'):\n#    for filename in filenames:\n#        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-03-04T23:19:51.422913Z","iopub.execute_input":"2022-03-04T23:19:51.423378Z","iopub.status.idle":"2022-03-04T23:19:51.450843Z","shell.execute_reply.started":"2022-03-04T23:19:51.423257Z","shell.execute_reply":"2022-03-04T23:19:51.450093Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_tt = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')\ndf_tt['t_month'] = df_tt['t_dat'].astype(str).str[:7]\ndf_tt = df_tt[['t_month', 'article_id']]","metadata":{"execution":{"iopub.status.busy":"2022-03-05T00:42:09.746061Z","iopub.execute_input":"2022-03-05T00:42:09.746418Z","iopub.status.idle":"2022-03-05T00:43:33.795303Z","shell.execute_reply.started":"2022-03-05T00:42:09.746370Z","shell.execute_reply":"2022-03-05T00:43:33.794242Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# monthly rank\ndf_mon_rank = pd.DataFrame(index=[list(range(1,11))])\nfor month in sorted(df_tt['t_month'].unique()):\n    df_temp = pd.DataFrame(np.array(df_tt.loc[df_tt['t_month']==month].value_counts('article_id')[:10].reset_index()), index=[list(range(1,11))], columns=[month, 'rank_'+str(month)])\n    df_mon_rank = pd.merge(df_mon_rank, df_temp, right_index=True, left_index=True, how='left')\n\ndf_mon_rank","metadata":{"execution":{"iopub.status.busy":"2022-03-05T00:18:11.210912Z","iopub.execute_input":"2022-03-05T00:18:11.211309Z","iopub.status.idle":"2022-03-05T00:20:21.990664Z","shell.execute_reply.started":"2022-03-05T00:18:11.211274Z","shell.execute_reply":"2022-03-05T00:20:21.989227Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n# #1 of the month\nfor col_name, item in df_mon_rank.iloc[0, ::2].iteritems():\n    print(col_name+'_#1')\n    img = plt.imread('/kaggle/input/h-and-m-personalized-fashion-recommendations/images/0'+str(item)[:2]+'/0'+str(item)+'.jpg')\n    plt.imshow(img)\n\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T00:40:35.263593Z","iopub.execute_input":"2022-03-05T00:40:35.263895Z","iopub.status.idle":"2022-03-05T00:40:46.181951Z","shell.execute_reply.started":"2022-03-05T00:40:35.263861Z","shell.execute_reply":"2022-03-05T00:40:46.180693Z"},"trusted":true},"execution_count":null,"outputs":[]}]}