PyBer_Analysis

Purpose

The purpose of this project is to create a summary DataFrame of the ride-sharing data by city type. Using Pandas and Matplotlib,a multiple-line graph has been created that shows the total weekly fares for each city type. Average fare per ride and per driver has been calculated using Pandas.

Results

Data was analysed for rural, suburban and urban city types. Total rides were higher in urban while average fare was lowest in urban cities.Total fare in rural type is $4327.93, in suburban is $19,356.33 and in urban is $39854.38. Rural areas had higher average fare rate because of less number of drivers photo.

Attached figures are presenting a clear picture of analysis, average fare per ride for rural type is $35, suburban is $31 and urban is $25 approximately. Total drivers and total rides are higher in urban city type. Suburban is in middle in terms of fare and drivers. Multiline chart has been attached which was created using matplotlib library photo.

Summary

- Average fare is higher in rural areas than urban areas which means company need to invest in plans to expand business in rural areas.

- Total number of drivers is higher in cities which brings competition and hence, lower the average earning of city drivers.

- Company should give discount to people who book in advance so that people going towards same destination can share the ride and save energy.