{"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":"2021-06-13T14:32:13.935107Z","iopub.execute_input":"2021-06-13T14:32:13.935396Z","iopub.status.idle":"2021-06-13T14:32:13.947595Z","shell.execute_reply.started":"2021-06-13T14:32:13.935368Z","shell.execute_reply":"2021-06-13T14:32:13.946049Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\ntest = pd.read_csv('../input/mlb-player-digital-engagement-forecasting/example_test.csv')\ntest","metadata":{"execution":{"iopub.status.busy":"2021-06-13T14:32:13.963036Z","iopub.execute_input":"2021-06-13T14:32:13.963355Z","iopub.status.idle":"2021-06-13T14:32:14.545019Z","shell.execute_reply.started":"2021-06-13T14:32:13.963326Z","shell.execute_reply":"2021-06-13T14:32:14.543916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def unpack_json(json_str):\n    return np.nan if pd.isna(json_str) else pd.read_json(json_str)","metadata":{"execution":{"iopub.status.busy":"2021-06-13T14:32:14.547958Z","iopub.execute_input":"2021-06-13T14:32:14.548303Z","iopub.status.idle":"2021-06-13T14:32:14.552607Z","shell.execute_reply.started":"2021-06-13T14:32:14.548271Z","shell.execute_reply":"2021-06-13T14:32:14.551512Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"unpack_json(test[\"games\"].iloc[0])","metadata":{"execution":{"iopub.status.busy":"2021-06-13T14:32:14.553672Z","iopub.execute_input":"2021-06-13T14:32:14.553936Z","iopub.status.idle":"2021-06-13T14:32:14.600680Z","shell.execute_reply.started":"2021-06-13T14:32:14.553906Z","shell.execute_reply":"2021-06-13T14:32:14.599625Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"unpack_json(test[\"rosters\"].iloc[0])","metadata":{"execution":{"iopub.status.busy":"2021-06-13T14:32:14.601703Z","iopub.execute_input":"2021-06-13T14:32:14.601910Z","iopub.status.idle":"2021-06-13T14:32:14.623555Z","shell.execute_reply.started":"2021-06-13T14:32:14.601890Z","shell.execute_reply":"2021-06-13T14:32:14.622645Z"},"trusted":true},"execution_count":null,"outputs":[]}]}