Transform pandas DataFrame from wide to long and count occurrences of a unique value

I have a pretty specific problem that I sadly can not work my mind around. The DataFrame I want to transform currently looks like this:

df_larceny
CATEGORY    INCIDENTYEAR    INCIDENTMONTH       
LARCENY     2009            1
LARCENY     2009            1
LARCENY     2009            1
.............................
.............................
LARCENY     2016           11
LARCENY     2016           12
LARCENY     2016           12
LARCENY     2016           12

after the Transformation it should look like this.

COUNT   INCIDENTYEAR    INCIDENTMONTH       
234     2009            1
453     2009            2
847     2009            3
943     2009            4
958     2009            5
.............................
.............................
324     2016           11
372     2016           12
241     2016           12
412     2016           12

basically I want to count how often Larceny occurred for every month of every year.

I tried this tutorial before, sadly without any luck.

I also tried various methods with value_counts() but sadly with no luck.

Out of pure despair at the end I did it manually for another DataFrame which kind of looked like this

jan09 = df["CATEGORY"].loc['2009-01-01':'2009-02-01'].value_counts().sum()
jan10 = df["CATEGORY"].loc['2010-01-01':'2010-02-01'].value_counts().sum()
jan11 = df["CATEGORY"].loc['2011-01-01':'2011-02-01'].value_counts().sum()
jan12 = df["CATEGORY"].loc['2012-01-01':'2012-02-01'].value_counts().sum()
jan13 = df["CATEGORY"].loc['2013-01-01':'2013-02-01'].value_counts().sum()
jan14 = df["CATEGORY"].loc['2014-01-01':'2014-02-01'].value_counts().sum()
jan15 = df["CATEGORY"].loc['2015-01-01':'2015-02-01'].value_counts().sum()
jan16 = df["CATEGORY"].loc['2016-01-01':'2016-02-01'].value_counts().sum()

jan_df = [jan09,jan10,jan11,jan12,jan13,jan14,jan15,jan16]`

I did this for every month and created a new DataFrame at the end which even for an amateur like me looks like way too inefficient.
I hope anyone can help me out here.