similarlydistribution of ridership across days of the week also presents interesting trends of higher usage during afternoon hours over weekendswhile weekdays see higher usage during mornings and evenings the code for the same is available in the jupyter notebook bike_sharing_eda ipynb the plot is as shown in figure - figure - day-wise hourly data distribution having observed hourly distribution of data across different categoricalslet' see if there are any aggregated trends the following snippet helps us visualize monthly ridership trends using seaborn' barplot(in [ ]fig,ax plt subplots(sn barplot(data=hour_df[['month''total_count']] ="month", ="total_count"ax set(title="monthly distribution of counts"the generated barplot showcases definite trend in ridership based on month of the year the months june-september see highest ridership looks like fall is good season for bike sharing programs in washingtond the plot is shown in figure - figure - month-wise ridership distribution