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Use Python dataframes and matplotlib library to explore how total rides, total drivers, and average fares collected by the ride-sharing organization vary based on whether the location is in an Urban city, Suburban city, or Rural city.

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Angienoelhaverly/PyBer_Analysis

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PyBer_Analysis

Overview of Analysis

The purpose of this analysis is to explore how total rides, total drivers, and average fares collected by the ride-sharing organization vary based on whether the location is in an Urban city, Suburban city, or Rural city. In this analysis, we will look at visualizations of rideshare data for PyBer to help improve access to ride-sharing services and determine affordability for underserved neighborhoods.

Results

PyBer Summary DataFrame

summary dataframe
Based on the data from the Summary DataFrame above, we can see that there are clearly more drivers in the more populated cities, so much so that the Urban located cities have 13 times as many rides as the Rural Cities and 30 times as many drivers. There are many more fares collected in heavily populated areas and the average fare per ride decreases by a whole $10 from a rural area to an urban area. The average fare per driver also decreases by around $40. Even the suburban areas have two times less rides conducted than the urban area and four times less drivers available.

Total Fare by City Type

pyber_challenge
When viewing the PyBer data by city type over time, we can see that the average total fare has a similar pattern over various months of the year between January and April with total fares peaking around March. Fares in urban cities again clearly lead the way in fares collected and so bring in the most amount of revenue for PyBer, followed by Suburban located cities and then Rural cities. Across the board, fares remain the lowest in January, slowly rise to a peak in beginning of March, back down a little and then slightly back up for Suburban areas in April.

Summary

Based on the above results, I would recommend to the CEO of PyBer three business suggestions in order to address disparity among city types:

  1. Focus recruiting efforts on rural and suburban areas to increase amount of drivers in those areas.
  2. Conduct Target adveritising for both consumers and drivers (contracters) in rural and suburban areas.
  3. Offer marketing incentives for consumers to order app based driving in suburban and rural areas. Incentivize city workforce to go out and work in rural and suburban areas.
  4. Host job fairs and marketing events in rural and suburban areas.

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Use Python dataframes and matplotlib library to explore how total rides, total drivers, and average fares collected by the ride-sharing organization vary based on whether the location is in an Urban city, Suburban city, or Rural city.

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