we encourage you to try and plot the four seasons across different subplots as an exercise to employ plotting concepts and see the trends for each season separately moving up the aggregation levellet' look at the distribution at year level our dataset contains year value of representing and representing we use violin plot to understand multiple facets of this distribution in crisp format ##note violin plots are similar to boxplots like boxplotsviolin plots also visualize inter-quartile range and other summary statistics like mean/median yet these plots are more powerful than standard boxplots due to their ability to visualize probability density of data this is particularly helpful if data is multimodal the following snippet plots yearly distribution on violin plots in [ ]sn violinplot(data=hour_df[['year''total_count']] ="year", ="total_count"figure - clearly helps us understand the multimodal distribution in both and ridership counts with having peaks at lower values as compared to the spread of counts is also much more for although the max density for both the years is between - rides figure - violin plot showcasing year-wise ridership distribution outliers while exploring and learning about any datasetit is imperative that we check for extreme and unlikely values though we handle missing and incorrect information while preprocessing the datasetoutliers are usually caught during eda outliers can severely and adversely impact the downstream steps like modeling and the results we usually utilize boxplots to check for outliers in the data in the following snippetwe analyze outliers for numeric attributes like total_counttemperatureand wind_speed