{"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":"2021-07-02T15:21:12.730844Z","iopub.execute_input":"2021-07-02T15:21:12.731408Z","iopub.status.idle":"2021-07-02T15:21:12.745885Z","shell.execute_reply.started":"2021-07-02T15:21:12.731364Z","shell.execute_reply":"2021-07-02T15:21:12.744908Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dir_targets = '/kaggle/input/playerid-and-targets/results/targets_df_'\ntrain_df = pd.read_csv('/kaggle/input/mlb-player-digital-engagement-forecasting/train.csv')\ndef unpack_json(json_str):\n    return np.nan if pd.isna(json_str) else pd.read_json(json_str)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T15:21:19.984219Z","iopub.execute_input":"2021-07-02T15:21:19.984582Z","iopub.status.idle":"2021-07-02T15:22:32.012052Z","shell.execute_reply.started":"2021-07-02T15:21:19.984552Z","shell.execute_reply":"2021-07-02T15:22:32.011052Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.tail()","metadata":{"execution":{"iopub.status.busy":"2021-07-02T15:22:32.014280Z","iopub.execute_input":"2021-07-02T15:22:32.015055Z","iopub.status.idle":"2021-07-02T15:22:32.205533Z","shell.execute_reply.started":"2021-07-02T15:22:32.015008Z","shell.execute_reply":"2021-07-02T15:22:32.204446Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"targets_1211 = unpack_json(train_df['nextDayPlayerEngagement'].iloc[1211])\ngames_1211 = unpack_json(train_df['games'].iloc[1211])","metadata":{"execution":{"iopub.status.busy":"2021-07-02T15:22:32.207216Z","iopub.execute_input":"2021-07-02T15:22:32.207661Z","iopub.status.idle":"2021-07-02T15:22:32.249162Z","shell.execute_reply.started":"2021-07-02T15:22:32.207617Z","shell.execute_reply":"2021-07-02T15:22:32.248310Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pd.set_option('display.max_rows', None)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T15:22:32.250780Z","iopub.execute_input":"2021-07-02T15:22:32.251159Z","iopub.status.idle":"2021-07-02T15:22:32.265636Z","shell.execute_reply.started":"2021-07-02T15:22:32.251126Z","shell.execute_reply":"2021-07-02T15:22:32.264574Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"targets_1211","metadata":{"execution":{"iopub.status.busy":"2021-07-02T15:22:32.268644Z","iopub.execute_input":"2021-07-02T15:22:32.269160Z","iopub.status.idle":"2021-07-02T15:22:32.835239Z","shell.execute_reply.started":"2021-07-02T15:22:32.269107Z","shell.execute_reply":"2021-07-02T15:22:32.834134Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"games_1211","metadata":{"execution":{"iopub.status.busy":"2021-07-02T15:22:32.836999Z","iopub.execute_input":"2021-07-02T15:22:32.837609Z","iopub.status.idle":"2021-07-02T15:22:32.883715Z","shell.execute_reply.started":"2021-07-02T15:22:32.837561Z","shell.execute_reply":"2021-07-02T15:22:32.882547Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"games는 팀이랑 연관지은 다음 팀에 소속된 선수로 연관 시켜서 corr 알아봐야할듯 함","metadata":{}}]}