Data
We used the data from citi-bike and we selected datasets from July and December 2016 since we assume that the biking pattern would be slight different from summer to winter and looking into 2 seasons would lead us to the reliable conclusion. As the figures show, the counts of July is greatly higher than December. In the data processing, we convert the "starttime" column which is in string format into "date" using the function "pd.to_datetime", thus, we can learn users counts in specific weekdays.
According to my peer Heci's suggestion, he thinks it's better to dig more into weekdays' rush hours, after careful consideration, I didn't select peak hour to analyze this problem, the reason is the working hour is basically same for both workers(more pre-90s) and students(more post-90s). In future projects, if it is necessary, I would definitely take rush hours into consideration.