PyBer_Analysis

🚙Use SciPy and NumPy to analyze rideshare data from January to early May of 2019 and use Matplotlib to create a compelling visualization.

Overview:

🌇The project goal was to analyze all the rideshare data from January to early May of 2019 and create compelling visualizations comparing the city types, urban, suburban, and rural.

Deliverables:

📊Fig 1. is a bubble chart that showcases the average fare versus the total number of rides with bubble size based on the total number of drivers for each city type, including urban in coral, suburban in light blue, and rural in gold.

Descriptive statistics:

The total number of rides for each city type.

• The mean ride count for urban cities is 24.62, for suburban cities is 17.36, and for rural cities is 6.94.
• The median ride count for urban cities is 24, for suburban cities is 17, and for rural cities is 6.
• The mode for ride counts in urban cities is 22 and occurs 7 times, for suburban cities is 17 and occurs 7 times, and for rural cities is 6 and occurs 5 times.

The average fares for each city type.

• The mean fare price for urban trips is $24.53, for suburban trips is $30.97, and for rural trips is $34.62.
• The median fare price for urban trips is $24.64, for suburban trips is $30.75, and for rural trips is $37.05.
• The mode fare price for urban trips is $22.86 and occurs 5 times, for suburban trips is $17.99 and occurs 3 times, and for rural trips is $37.05 and occurs 2 times.

The total number of drivers for each city type.

• The mean driver count for urban cities is 36.678, for suburban cities is 13.712, and for rural cities is 4.296.
• The median driver count for urban cities is 37, for suburban cities is 16, and for rural cities is 4.
• The mode driver count for urban cities is 39 and occurs 86 times, for suburban cities is 20 and occurs 79 times, and for rural cities is 1 and occurs 32 times.

Box-and-Whisker Plots:

• The number of rides for each city type are seen in Fig 2.
  • 
  • An outlier was identified in the urban city.
• The fares for each city type are seen in Fig 3.
  • 
• The number of drivers for each city type are seen in Fig 4.
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Pie Charts:

• Fig 5 is the percent of total fares.
  • 
• Fig 6 is the percent of total rides.
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• Fig 7 is the percent of total drivers.
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Analysis:

The goal is to analyze the total weekly fare data to summarize how city types differ. These differences will be used to impact the decisions at Pyber.

Results:

Table 1 and Total Fare by City Type are a summary of the ride-share data from January to early May 2019. A general trend is that the total fares increase by week as the seasons change from winter to spring. There is also an increase in total fares for the city types during the spring holidays. The critical difference between the ride-share data for each city type is volume. Urban cities have the most rides, divers, and total fares. Suburban cities have less than half of the total rides and one-fifth of the number of drivers in urban cities. However, suburban cities still manage to earn around half of the total fares that urban cities did, and the average fare earned per driver is double the average fare made per driver in urban cities. Rural cities have the lowest rides, drivers, and total fare, but the average fare per ride and driver is the highest. This suggests that while rural riders are infrequently using the service but spending more per ride.

Summary:

Based on the results of the summary, here are three recommendations to increase earnings.

1. For urban cities, the amount of drivers is greater than the number of rides. Reducing the number of drivers will increase the average fare earned per driver.
2. For suburban cities, the number of rides is greater than the number of drivers. Increasing the number of drivers may increase the number of rides and the number of fares earned.
3. Changes made related to rural cities must be highly targeted. An increase in the total number of drivers does not translate to a rise in total rides or total fares for rural cities. Infrastructure issues related to internet access and widespread use of credit cards in rural America prevent ride-shares expansion. Car ownership is also higher in rural cities compared to suburban and urban cities. Here is one article referencing a Pew Research Center report on the divide among urban-rural ridesharing. Transportation experts see Uber and Lyft as the future. But rural communities still don’t use them. Targeted advertising in high traffic areas like shopping centers or medical complexes may be the best solution to increasing ridership in rural cities.