tajahdaley/pyber_analysis

PyBer_Analysis

Overview

The purpose of this analysis was to give V. Isualize a visual representation of the ride share data that was giving to us. With the data given to us we were able to create different charts and look at some of the data differently. With the new analysis, we needed to find out how to separate the city types and plot the data so we can look at things like getting the fares for the drivers and fares for the ride themselves.

Results

Going through the data we were asked to look for the "Total Riders" in each city type as well as looking at other things like the "Average Fare per Ride" and "Average Fare per Driver".

Looking at the image you can see that there are more riders and drivers in the urban area as opposed to the other communities. Although there are more Riders and Drivers the average fare for the rides are lower than the suburban and rural areas. I wouldn't want to live in these rural areas with these prices.

Looking at this image we can see that in the 1st 3 months the total fare amount is consistent in the rural areas whereas in the suburban and rural areas the total amounts are sporadic.

Summary

As we are looking at the final bits of data along with the graphs/ Dataframes made we can see that majority of the total fares are made from the urban areas. We can suggest that we keep focus in this area and continue to put more drivers here. We can also see which specific cities in this area is bringing in most of the money. Although the total amount of drivers in the rural and suburban area are less than the total Riders, we could add a few more drivers and this can increase the total amount of Fare we would receive per week or per month. But still we should put majority of the focus into the urban areas due to this showing where most of the money is coming from.