# What kind of plot will be helpful for total count group data ( data grouped by month and year )?

I have a group data frame like this :

group_by_region_year_month=train_data.groupby(['Region Name (GO)','Year','Month'],as_index=False)

region_sum=group_by_region_year_month.agg({"AMV": "sum"})


Here , AMV is my target variable in regression ( this is count of total vehicles).

I have data for each day from 2000-3-17 to 2017-10-11.

I want to show the variation of the count for each year and each month for each Region.

How best can I plot it?

I tried using seaborn.FacetGrid in this way:

# g=sns.FacetGrid(row="Region Name (GO)", col="AMV", data=region_sum)

But it is taking too much time .

Is there any better alternative(s) ?

# Edit 1

I tried this :

sns.set(rc={'figure.figsize':(50,30)})

ax=sns.barplot(x='Region Name (GO)', y='AMV', hue='Year', data=train_data)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)


which gave me a very clumsy plot :

I did not use countplot as countplot would give me count of rows. But , I want sum of all values in a group.

Can I represent it in any better way ?

If you like using Seaborn to construct your plots, sns.countplot is probably the best for plotting data where you want to show the differences in value counts.