The first step of wrangling was to understand the dataset I was dealing with and the type of columns provided. This helps in kwoning if there is anything out of place. Through data wrangling I added columns where necessary and also changed the data types of some columns to better represent the findings well.
Which gender has more duration time - There are more male users than any other gender.
Which station has more traffic for both start and end station.
What is the average age group for most of the trips - ages 30 - 35 recorded the highest numbers, while generally ages 25 - 40 had a large figure as well.
At what hour do we have highest rides recorded - Peak hours showed more ride than any other hours.
It was noted that there were few rides during the weekend compared to the weekdays.
Type of User and Duration Covered.
I had to change duration from seconds to minutes for better plotting of the figures. My main insight was the duration covered by different users. Customers duration appeared to be greatly the same accros different days.
Day of the where most rides are recorded. At this level it was noted that most rides are recorded during the weekdays which could mean that more people are cycling to work. Weekends have low numbers would mean that not so many people were out on weekends.