Data Analyst at PyBer Create line, bar, scatter, bubble, pie, and box-and-whisker plots using Matplotlib. And determine mean, median, and mode using Pandas, NumPy, and SciPy statistics.
Data Source: PyBer_Challenge_starter_code.ipynb named later as PyBer_Challenge.ipynb
Data File: file.csv
Software: Matplotli 3.2.2, Python 3.9, Visual Studio Code 1.50.0, Anaconda 4.8.5, Jupyter Notebook 6.1.4, Pandas
Performing an exploratory analysis and developing visualizations using the PbBer data to facilitate the need for access to services and assess affordability to the underserved neighborhoods. The data vidualization will allow for quick understanding of our findings as well as the ability to detect patterns, trends, and correlations. The data analysis time framewas from January to early May of 2019
The PyBer Summary provides an overview of the statistics deomonstrating that the urban demand, 68.4 %,1624 rides is considerably higher than the suburban cities, 26.3%, with 926 rides, and rural cities at, 5.3%, 125 rides. The figure below highlights how rides in Urban cities contributed the most to PyBer's overall rides during this five-month period.
On a similar pattern, there was also 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. Again, the figure below depicts the significance of drivers in urban cities during this time period.
With the number of rides and the average fare for each city type, V. Isualize wants to see how the rides and fare data stack are affected by the average number of drivers for each city type. This will help V. Isualize make key decisions about where resources and support are needed. It is my understanding that V. Isualize came up with the color scheme herself back in the first few days of the company: gold for profitability, sky blue for strategy, and coral because she loves the ocean! So, of course, we implamented this fact.
If we compare the average number of rides between each city type, we'll notice that the average number of rides in the rural cities is about 4 and 3.5 times lower than urban and suburban cities, respectively. But take note, an outlier in the urban ride count data has influenced the result.
By summarizing the statistics for the average fares for each city type, the chart below can help you determine which city types are generating the most money. Given that there is a greater usage of PyBer in urban cities, the total fares are consequently 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 demonstrates where the majority of PyBer's revenue occurred during this time period: urban cities.
The average fare for rides in the rural cities is about $11 and $5 more per ride than the urban and suburban cities, respectively. As we have seen the supply of rural drivers is less so the demand increases along with the price. The average number of drivers in rural cities is nine to four times less per city than in urban and suburban cities, respectively. The average fare for rides in the rural cities is about $11 and $5 more per ride than the urban and suburban cities, respectively. As we have seen the supply of rural drivers is less so the demand increases along with the price. 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.
The "Total Fare by City Type" chart above 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 trend 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 trend shows how fares in rural cities totaled around $300 from beginning to end during the same time period. The orange trend 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.
• In conclusion, PyBer ridersharing services vary greatly among the varying types of cities. Data supports that there is higher usage of PyBer ridesharing services in urban cities.
• There are more drivers in urban cities than rural cities. As a result, the majority of PyBer's revenue occurs in urban cities.
• The costs for using PyBer is greater among riders in rural cities than urban cities. This could discourage potential riders from using PyBer given the high average fare per ride.
• Drivers in rural cities are earning more than drivers in urban cities. This could discourage discourage potential drivers from working with PyBer given the low average fare per driver.
• A recommendation might be to complete and analysis to determine other factors that are contributing to the high ride costs in rural cities and low driver fares in urban cities. Perhaps, travel distance is a key factor.