now that we have attribute names cleaned upwe perform type-casting of attributes using utilities like pd to_datetime(and astype(the following snippet gets the attributes into proper data types in [ ]date time conversion hour_df['datetime'pd to_datetime(hour_df datetimecategorical variables hour_df['season'hour_df season astype('category'hour_df['is_holiday'hour_df is_holiday astype('category'hour_df['weekday'hour_df weekday astype('category'hour_df['weather_condition'hour_df weather_condition astype('category'hour_df['is_workingday'hour_df is_workingday astype('category'hour_df['month'hour_df month astype('category'hour_df['year'hour_df year astype('category'hour_df['hour'hour_df hour astype('category'distribution and trends the dataset after preprocessing (which we performed in the previous stepis ready for some visual inspection we begin with visualizing hourly ridership counts across the seasons the following snippet uses seaborn' pointplot to visualize the same in [ ]fig,ax plt subplots(sn pointplot(data=hour_df[['hour''total_count''season']] ='hour', ='total_count'hue='season',ax=axax set(title="season wise hourly distribution of counts"figure - season wise hourly data distribution the plot in figure - shows similar trends for all seasons with counts peaking in the morning between - am and in the evening between - pmpossibly due to high movement during start and end of office hours the counts are lowest for the spring seasonwhile fall sees highest riders across all hours