Odtnhj

I am facing a very annoying problem. I have a dataset where I have the sales amounts for different regions and years.

I would like to visualize the yearly aggregated sales amounts based on different regions.

Below is my groupby code:

groups = df.groupby(["Region", "Year"])["Sales"].sum()
groups.plot.bar(color="blue")
plt.show()

And the output I get looks like this:

I have two questions:
1. How could I somehow separate the region and year bars from each other as this way my chart looks really confusing? A separator line or actually a highlighter would also work, or even a bigger gap would be a good solution to me.
(Please see below, what I mean:)

or

I have no clue at all, how to solve this problem.

Question no 2. How could I have this image sorted by the regions with most sales, followed by the second most sales yearly, and so on? Kind of sorting in a descending order based on regions.

I tried the code below:

groups = df.groupby(["Region", "Year"])["Sales"].sum()
groups2=groups.sort_values(axis=[0][1],ascending=False)
groups.plot.bar(color="blue")
plt.show()

But I get a list index out of range error. Using axis=[0] does not solve the problem.

Thank you very much for your help in advance!

Following ChrisD's advice you can obtain a working result with seaborn's catplot to display your bars into different facets by region.

sns.catplot(x='Year', y='Sales', col='Region', data=groups, kind='bar')

You may have to format the aspect ratios for your display purposes.