Pandas groupby 2 coluns/conditions then value_counts() by another column?

Question

Here's what the dataset I'm working on looks like:

Type	SubType	Municipality
Social Media	Facebook	New Castle
Onground	Campus	Monroe
Onground	Cafe	Kutlski
Social Media	Instagram	New Castle
Social Media	Tiktok	San Andreas
Social Media	Facebook	New Castle
Social Media	Facebook	San Andreas

I want to group it by Type and SubType then further filter it by Municipality and then value_counts() it.

Here's what I've tried:

ab = df.groupby([df['Type'] == 'Social Media', 
                     df['SubType']])
ab['Municipality'].value_counts()

I almost got what I want only that it shows everything, not just the result of the condition (under the Type column, it has 'true' and false' section.

This is the result I'm looking for:

Type	SubType	Municipality
Social Media	Facebook	New Castle	2
		San Andreas	1
	Instagram	New castle	1
	TikTok	San Andreas	1

But instead, this is my result:

Type	SubType	Municipality
True	Facebook	New Castle	2
		San Andreas	1
	Instagram	New Castle	1
	Titkok	San Andreas	1
False	Onground	Cafe	1
		Campus	1
and so on...	....	....	...

Answer 1

Just filter first and group only by subtype. df.query('Type == `Social Media`').groupby('SubType')['Municipality'].value_counts()

Answer 2

I think value_counts is what you are looking for

df.value_counts(['Type','SubType','Municipality'])
Out[169]: 
Type          SubType    Municipality
Social Media  Facebook   New Castle      2
Onground      Cafe       Kutlski         1
              Campus     Monroe          1
Social Media  Facebook   San Andreas     1
              Instagram  New Castle      1