##Challenge
The purpose of this assignment was to create a summary DataFrame of the key metrics for the ride-sharing data by city type; creating a multiple-line graph that shows the total fares for each week by each city type. The challenge required a specific summary and charts, which helped in guiding how i collected my data. As you manipulate data, you may find you have the exact data you need, but more likely, you might need to revise your original question or collect more data. BY looking at the summary DataFrame, you can conclude that more people in Urban cities do not own a car. More people travel in Urban areas therefore the average fare is chaper because there are more drivers available. Also, people who live in rural or suburban areas may only use a driver during their leisure time. When compared to Urban, they use a driver for work, school, and extra curricular activities. You can also conclude that having more drivers in Urban areas will generate more money for the company compared to sending drivers to Rural or Suburban areas.
I faced many challenges during this analysis and I am still working through them.