#Use your repository README file to write your analysis of how to address any disparities in the ride-sharing data among the city types. #The analysis should contain the following:
Based on the brand-new assignment Omar was given, we will create a summary DataFrame of the ride-sharing data by city type. Using Pandas and Matplotlib, we will create a graph that should show the total weekly fares for each city type. At the end, we will create a graph that differs by city type that should help PyBer decision-makers.
This new assignment consists of two technical analysis deliverables and a written report to present your results. You will submit the following:
Deliverable 1: A ride-sharing summary DataFrame by city type
Deliverable 2: A multiple-line chart of total fares for each city type
Deliverable 3: A written report for the PyBer analysis (README.md)
Results: Using images from the summary DataFrame and multiple-line chart, describe the differences in ride-sharing data among the different city types.
The image attached show the Total Fare by City Type, we can see how the fare in $US changes from week/month to week/month. We can see that the lower fares happen in rural cities, suburban cities are in the middle, and the fares that are higher are in the urban cities.
Summary: Based on the results, provide three business recommendations to the CEO for addressing any disparities among the city types.
Based on these results, the three business recommendations to the CEO for addressing any disparities among the city types would be:
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I would suggess to look at the time/hour for the rides, this can help the CEO get a better idea about what time of day the rides are more popular.
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I would suggest to look at the consistency of the fares per ride, maybe the numbers went up for a particular week.
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It would be helpful to know how many competitors PyBer has per city type because this could influence the use of its services among consumers.