{"metadata":{"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":"3.7.12"},"papermill":{"default_parameters":{},"duration":10.36051,"end_time":"2023-01-30T17:58:28.218667","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-01-30T17:58:17.858157","version":"2.3.4"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Below is the leaderboard for the Efficiency Prize track of this competition. See the [**Efficiency Prize Evaluation**](https://www.kaggle.com/competitions/predict-student-performance-from-game-play/overview/evaluation) page for details on how this leaderboard was computed. We show only the top 100 teams here, but you may view the full `leaderboard.csv` file in the output data of this notebook.\n\n**Please Note:** This leaderboard will update once daily. Because of current limitations in our platform, it may take a day or two for submissions marked as `selected` to appear here.","metadata":{"papermill":{"duration":0.002492,"end_time":"2023-01-30T17:58:27.510183","exception":false,"start_time":"2023-01-30T17:58:27.507691","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import pandas as pd\n\npd.options.display.min_rows = 100\npd.options.display.max_rows = 100\n\nleaderboard = pd.read_csv('../input/student-performance-efficiency-data/leaderboard.csv', index_col='EfficiencyRank')\nleaderboard.to_csv('full_leaderboard.csv')\nleaderboard.head(100)","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.083673,"end_time":"2023-01-30T17:58:27.595639","exception":false,"start_time":"2023-01-30T17:58:27.511966","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-01-31T02:25:54.290814Z","iopub.execute_input":"2023-01-31T02:25:54.291306Z","iopub.status.idle":"2023-01-31T02:25:54.37266Z","shell.execute_reply.started":"2023-01-31T02:25:54.291211Z","shell.execute_reply":"2023-01-31T02:25:54.371379Z"},"trusted":true},"execution_count":1,"outputs":[{"execution_count":1,"output_type":"execute_result","data":{"text/plain":"                   TeamName  PublicScore             DateSubmitted\nEfficiencyRank                                                    \n1               Sohier Dane        0.226  Fri Jan 27 20:40:28 2023","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TeamName</th>\n      <th>PublicScore</th>\n      <th>DateSubmitted</th>\n    </tr>\n    <tr>\n      <th>EfficiencyRank</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Sohier Dane</td>\n      <td>0.226</td>\n      <td>Fri Jan 27 20:40:28 2023</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}