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Visualization of Analysis of a rideshare company using Matplotlib.

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PyBer_Analysis

Visualization of Analysis of a rideshare company using Matplotlib to help improve access to rideshare services and determine affordability for underserved areas.

Overview of the analysis:

The PyBer Summary DataFrame provides an overview comparison of PyBer's ridesharing services in three types of cities: rural, surburban, and urban. The summary demonstrates that there is a larger demand for PyBer among riders in urban cities compared to suburban and rural cities. Between January 2019 and May 2019, there were 1,625 rides in urban cities, 625 rides in suburban cities, and 125 rides in rural cities. The figure below highlights how rides in Urban cities contributed the most to PyBer's overall rides during this five-month period.

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Similar pattern repeats, there was a larger volume of drivers in urban cities compared to suburban and rural cities. There were 2,405 drivers in urban cities, 490 drivers in suburban cities, and 78 drivers in rural cities. The figure below shows it:

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Given that there is a greater usage of PyBer in urban cities, the total of the fares is also higher than suburban and rural cities. PyBer transactions in urban cities totaled nearly $40,000 whereas transactions in urban cities and rural cities totaled at least $19,000 and $4,000, respectively. The figure below shows it:

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In terms of costs, it appears that riders in rural cities pay on average almost $10 more for PyBer than riders in urban cities. The average fare per ride is about $35 in rural cities whereas the average fare per ride is about $25 in urban cities. Suburban cities' average fare per ride falls just in between - at about $31. While it may not be good news for riders in rural cities, it is a better market for drivers in this type of city. The average fare per driver is about $55 in rural cities, whereas the average fare per driver is about $17 in urban cities. Suburban cities' average fare per driver is about $40.

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Results: Following chart shows the differences in ride-sharing data among the different city types. Ride-sharing data include the total rides, total drivers, total fares, average fare per ride and driver, and total fare by city type.

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Summary:

The multiple-line chart "Total Fare by City Type" further supports the PyBer Summary DataFrame by providing trends of total fares in rural, suburban, and urban cities between January 2019 and April 2019. The yellow line shows how fares in urban cities totaled from around $1,600 to $2,300 from beginning to end during this five-month period. In contrast, the blue line shows how fares in rural cities totaled around $300 from beginning to end during the same time period. The red line shows how the total fares in suruban cities fall in between urban and rural cities: around $700 to $1,300 from beginning to end during this time. The chart further demonstrates similar peak times in all these types of cities. One noteworthy peak in total fares among urban, suburban, and rural cities occurred sometime at the end of February 2019.

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Conclusion: There is a larger use of PyBer ridesharing in urban cities,there are more drivers in urban cities than rural cities hence the majority of PyBer's revenue occurs in urban cities. On the other hand, the costs for using PyBer is greater among riders in rural cities than urban cities.This could discourage the customer to use it frequently. Drivers in rural cities are earning more than drivers in urban cities.Overall, PyBer ridersharing services significantly differs in rural, suruban, and urban cities given the number of rides, drivers, and fares. Data and our analysis supports that there is higher usage of PyBer ridesharing services in urban cities.

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