{"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\nimport os\nfor 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":"2022-05-23T01:39:15.627151Z","iopub.execute_input":"2022-05-23T01:39:15.627524Z","iopub.status.idle":"2022-05-23T01:41:03.309411Z","shell.execute_reply.started":"2022-05-23T01:39:15.627418Z","shell.execute_reply":"2022-05-23T01:41:03.299010Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\ndf_article=pd.read_csv(../input/h-and-m-personalized-fashion-recommendations/customers.csv)\ndf_article","metadata":{"execution":{"iopub.status.busy":"2022-05-23T01:45:14.538711Z","iopub.execute_input":"2022-05-23T01:45:14.539308Z","iopub.status.idle":"2022-05-23T01:45:14.544921Z","shell.execute_reply.started":"2022-05-23T01:45:14.539259Z","shell.execute_reply":"2022-05-23T01:45:14.543805Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"color_group=df_article['color_group_name'].value_counts()\ndf_color_group=pd.DataFrame(color_group)\n","metadata":{},"execution_count":null,"outputs":[]}]}