in [ ]fig,(ax ,ax )plt subplots(ncols= sn boxplot(data=hour_df[['total_count''casual','registered']],ax=ax sn boxplot(data=hour_df[['temp','windspeed']],ax=ax the generated plot is shown in figure - we can easily mark out that for the three count related attributesall of them seem to have sizable number of outlier values the casual rider distribution has overall lower numbers though for weather attributes of temperature and wind speedwe find outliers only in the case of wind speed figure - outliers in the dataset we can similarly try to check outliers at different granularity levels like hourlymonthlyand so on the visualization in figure - showcases boxplots at hourly level (the code is available in the bike_sharing_ eda ipynb jupyter notebookfigure - outliers in hourly distribution of ridership