PyBer_Analysis

Overview of the analysis.

• Analyse and graph data using the Matplotlib library.

• Use compelling mathematical calculations on series and DataFrames. Use informative data by adding titles, axes labels, legends, and custom colors.

Results:

1- Urban Cities have considerably more rides

• Rural 125

• Suburban 625

• Urban 1625

2- Similarely as ilustrated on data frame 2 and 3 Urban Cities have more total drivers and total amount of fares

• Having more rides as it is the case in Urban Cities it makes sense to have more drivers and fares.

3- As seen on data frame 4 and 5, the average price per city and per driver is lower in Urban cities and highest in rural areas

• With less rides it makes sense tha the prices will be higher in rural areas.

4- The summary of the above data is represented in tables in point 6 and 7

5- The graph on the second deliverable depicts the fare prices from January to April and the prices throughout this period

• Rural areas have consistantly higher prices

• Rural areas have almost twice the price of Suburban areas and the Suburban areas have almost twice the price of Urban Cities

• Rural areas exebit a more dynamic trend especially starting from the end of February to April while Suburban and Urban cities are more stable

Summary:

• Based on the results, the three business recommendations to the CEO for addressing any disparities among the city types are as below:

1- Must focus more on the rural area as the market share is low while the margins are very high.

2- A similar strategy to the above could be pursued with the Suburban areas.

3- Regarding urban cities, despite the lower margins the turnover and dynamism is high. It is the most important part of the business. I would suggest to maintain current track especially in April and end of February in which prices are higher. A shift could be considered towards rural and suburban areas if solid success is achieved.