{"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":"2022-08-13T14:48:30.909955Z","iopub.execute_input":"2022-08-13T14:48:30.910679Z","iopub.status.idle":"2022-08-13T14:48:30.941372Z","shell.execute_reply.started":"2022-08-13T14:48:30.910585Z","shell.execute_reply":"2022-08-13T14:48:30.940517Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Reading data-files\ntracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')\nstart = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')\nrace = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')\ncomplete_2019 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv')","metadata":{"execution":{"iopub.status.busy":"2022-08-13T14:50:57.114435Z","iopub.execute_input":"2022-08-13T14:50:57.114855Z","iopub.status.idle":"2022-08-13T14:51:15.441335Z","shell.execute_reply.started":"2022-08-13T14:50:57.114821Z","shell.execute_reply":"2022-08-13T14:51:15.440148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tracking.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-13T14:53:44.377859Z","iopub.execute_input":"2022-08-13T14:53:44.378242Z","iopub.status.idle":"2022-08-13T14:53:44.394944Z","shell.execute_reply.started":"2022-08-13T14:53:44.378208Z","shell.execute_reply":"2022-08-13T14:53:44.393766Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tracking['track_id'].value_counts() / tracking.shape[0]","metadata":{"execution":{"iopub.status.busy":"2022-08-13T14:58:26.618296Z","iopub.execute_input":"2022-08-13T14:58:26.618637Z","iopub.status.idle":"2022-08-13T14:58:26.940116Z","shell.execute_reply.started":"2022-08-13T14:58:26.618601Z","shell.execute_reply":"2022-08-13T14:58:26.938893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"start.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-13T14:58:57.459459Z","iopub.execute_input":"2022-08-13T14:58:57.459939Z","iopub.status.idle":"2022-08-13T14:58:57.474307Z","shell.execute_reply.started":"2022-08-13T14:58:57.459892Z","shell.execute_reply":"2022-08-13T14:58:57.473415Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"race.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-13T14:59:00.247937Z","iopub.execute_input":"2022-08-13T14:59:00.248706Z","iopub.status.idle":"2022-08-13T14:59:00.263513Z","shell.execute_reply.started":"2022-08-13T14:59:00.248653Z","shell.execute_reply":"2022-08-13T14:59:00.262420Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"complete_2019.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-13T15:01:44.603123Z","iopub.execute_input":"2022-08-13T15:01:44.603541Z","iopub.status.idle":"2022-08-13T15:01:44.624940Z","shell.execute_reply.started":"2022-08-13T15:01:44.603504Z","shell.execute_reply":"2022-08-13T15:01:44.623511Z"},"trusted":true},"execution_count":null,"outputs":[]}]}