Which visualization will help the data scientist better understand the data trend?
Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.
Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.
Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.
Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot, faceted by year, of average sales for each region. Add a horizontal line in each facet to represent average sales.
Explanations:
This option focuses on individual stores rather than regions, which is not aligned with the goal of comparing regional average sales. Although it provides a bar plot faceted by year, it does not effectively highlight the performance of regions against overall averages.
Similar to option A, this option emphasizes store-level analysis rather than regional comparisons. While it includes a color differentiation by region and a horizontal line for average sales, the faceting by year makes it complex and not directly relevant for regional performance analysis.
This option correctly aggregates sales by region but presents the average sales for each region as a single bar plot without any temporal (yearly) breakdown. It does not allow for the analysis of trends over time, which is essential for understanding how each region performs year over year.
This option aggregates the data by region and year, creating a bar plot faceted by year that shows average sales for each region. The addition of a horizontal line for overall average sales in each facet effectively allows for straightforward comparison of regional performance over time. This visualization aligns perfectly with the data scientist’s goals.
I design that the answer is:
Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot, faceted by year, of average sales for each region. Add a horizontal line in each facet to represent average sales.