PyBer_Analysis

Overview of the analysis

As a Data Analyst at PyBer, you've been assigned to a new project to analyse all the ridershare data from January to early May of 2019 (city_data.csv and ride_data.csv), and create a compelling visualization for the CEO, V. Isualize.

Using your Python skills and knowledge of Pandas, you’ll create a summary DataFrame of the ride-sharing data by city type. Then, using Pandas and Matplotlib, you’ll create a multiple-line graph that shows the total weekly fares for each city type. Finally, you’ll submit a written report that summarizes how the data differs by city type and how those differences can be used by decision-makers at PyBer.

Results

• Based on the ride-sharing summary DataFrame by city type:

  1. Urban has the highest values in Total Rides, Total Drivers, and Total Fares.
  2. Rural has the least values in terms of the Total Rides, Total Drivers, and Total Fares.
  3. When comparing with Suburban and Rural, Urban has 2.6 and 13 times more riders in Total Rides, and has about 4.9 and 30.8 times more drivers in Total Drivers.
  4. However, Rural has the highest values in Average Fare per Ride and Average Fare per Driver.
  5. Urban has the least values in Average Fare per Ride and Average Fare per Driver.
  6. When comparing the Average Fare per Ride among the city type, Rural has 1.1 and 1.4 times more than Suburban and Urban.
  7. When comparing the Average Fare per Driver, Rural has 1.4 and 3.3 times more than Suburban and Urban.

• Based on the multi-line chart of total fares for each city type:

  1. During the time interval of Jan 2019 to Apr 2019, Urban has the highest Total Fare, Suburban comes to second, and Rural has the lowest.
  2. The highest Total Fare in Urban was $2470.93 in the week of 2019-03-10, while the lowest Total Fare in Urban was $1661.68 in the week of 2019-01-06.
  3. The highest Total Fare in Suburban was $1412.74 in the week of 2019-02-24, while the lowest Total Fare in Suburban was $721.60 in the week of 2019-01-06.
  4. The highest Total Fare in Rural was $501.24 in the week of 2019-04-07, while the lowest Total Fare in Rural was $67.65 in the week of 2019-01-13.

Summary

Based on the above results, there are some business recommendations for addressing the disparities among the city types:

1. As the Total Rides in the Urban is the highest, this shows there are great demand on the ride-sharing in Urban. However, the Average Fare per Ride and Average Fare per Driver in the Urban area sre the lowest, increasing the fare in Urban area may improve the drivers' income in Urban area.
2. The Total Rides in the Rural is the lowest, it indicates that the demand on the ride-sharing in Rural is not as high as Suburban and Urban. We may consider to launch some promotion campaigns and providing discounts on longer trip in the Rural area to promote the business.
3. The Urban has 2405 drivers (which is 4.9 and 30.8 times more drivers than Suburban and Rural), but can only contribute 2.6 and 13 times more on Total Rides. This shows that there may be too many drivers working in the Urban. We may consider to reduce the numbers of drivers in Urban and encourage them to work in Suburban or Rural.