Create a summary DataFrame of ride-sharing data by city type and a multiple-line graph showing total weekly fares for each city type to illustrate how the data differs by city type and how those differences can be used by PyBer to make strategic decisions.
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Data Sources: city_data.csv, ride_data.csv
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Software: Anaconda 4.10.3, Python 3.7.1, Jupyter Notebook 6.4.5, Pandas 1.2.4, Matplotlib 3.3.4
Analysis and visualization of the ride-sharing data produced the following insights:
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Total Rides progressively increase from Rural (125) to Suburban (625) to Urban (1,625) cities.
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Total Drivers progressively increase from Rural (78) to Suburban (490) to Urban (2,405) cities.
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Total Fares progressively increase from Rural ($4,328) to Suburban ($19,357) to Urban ($39,854) cities.
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Average Fare per Ride progressively decreases from Rural ($34.62) to Suburban ($30.97) to Urban ($24.53) cities.
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Average Fare per Driver progressively decreases from Rural ($55.49) to Suburban ($39.50) to Urban ($16.57) cities.
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Total Fare by City Type multi-line graph illustrates the progressive increase from Rural to Suburban to Urban cities is maintained over time throughout the year.
Summarize three business recommendations to the PyBer CEO for addressing any disparities among the city types:
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Encourage and incentivize driver mobility between city types to address potential driver shortages in Rural and Suburban cities and potential driver surpluses in Urban cities.
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Lower fares in Rural areas to increase accessibility.
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Raise fares in Urban areas to increase driver wages commensurate with higher cost-of-living.