import hiplot as hip import pandas as pd from flask import Flaskapp = fifa_experiment(): fifa = 0') fifa_hiplot = 'Weight', 'Crossing','Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration']]) return fifa_hiplot.
HiPlot has its native HTML rendering function Experience.to_html(), which returns HTML file with the plot embedded with just one line of code.
Pointing the record in interest by mouse immediately highlights the record in the chart.HiPlot Runs Fast To test the run time to show the parallel plot for larger data, I used Kaggle FIFA 19 complete player dataset.
Introduction to Best Parallel Plot Python Library: “HiPlot” Four key characteristics we should all appreciate as a first-choice tool of EDA.
Deployed HiPlot plot through Flask and served by Heroku.Ending Note In this post, I introduced the HiPlot the current best option of parallel plot to start EDA to take a look at the variable interaction overview.
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