{"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":"2022-09-26T05:05:37.619154Z","iopub.execute_input":"2022-09-26T05:05:37.619600Z","iopub.status.idle":"2022-09-26T05:05:37.631006Z","shell.execute_reply.started":"2022-09-26T05:05:37.619545Z","shell.execute_reply":"2022-09-26T05:05:37.629650Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"track = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')\ntrack.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-26T05:23:50.425299Z","iopub.execute_input":"2022-09-26T05:23:50.425880Z","iopub.status.idle":"2022-09-26T05:23:54.575527Z","shell.execute_reply.started":"2022-09-26T05:23:50.425838Z","shell.execute_reply":"2022-09-26T05:23:54.574354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"c = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')\nc.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-26T05:12:04.940765Z","iopub.execute_input":"2022-09-26T05:12:04.941274Z","iopub.status.idle":"2022-09-26T05:12:04.989751Z","shell.execute_reply.started":"2022-09-26T05:12:04.941231Z","shell.execute_reply":"2022-09-26T05:12:04.988340Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')\nrace.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-26T05:24:05.975889Z","iopub.execute_input":"2022-09-26T05:24:05.976288Z","iopub.status.idle":"2022-09-26T05:24:05.997885Z","shell.execute_reply.started":"2022-09-26T05:24:05.976249Z","shell.execute_reply":"2022-09-26T05:24:05.996432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv')\na.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-26T05:06:50.855941Z","iopub.execute_input":"2022-09-26T05:06:50.856370Z","iopub.status.idle":"2022-09-26T05:07:09.866167Z","shell.execute_reply.started":"2022-09-26T05:06:50.856337Z","shell.execute_reply":"2022-09-26T05:07:09.864266Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# In this case, let's just compare with 'latitude' and 'longitude'.\n\n## This can be done by using matplotlib library.\n\n## then, we can see interesting results from this analysis.","metadata":{}},{"cell_type":"code","source":"a = track['latitude']\nb = track['longitude']\n\nimport matplotlib.pyplot as plt\n\nplt.plot(a,b)\nplt.xlabel('latitude')\nplt.ylabel('longitude')\nplt.show()\n\n\n","metadata":{"execution":{"iopub.status.busy":"2022-09-26T05:35:18.418754Z","iopub.execute_input":"2022-09-26T05:35:18.419271Z","iopub.status.idle":"2022-09-26T05:35:20.667157Z","shell.execute_reply.started":"2022-09-26T05:35:18.419234Z","shell.execute_reply":"2022-09-26T05:35:20.665911Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# By this analysis, we can know that track's shape(x = latitude, y = longitude).\n## According to above graph, track's shape is triangle.\n## By using this methods, we can depict shapes of track on 2d graph.\n## In other words, we can see the track's shape without participating directional dubby by this  anlaysis methods.","metadata":{}}]}