With new pandas version, it is easy to get as dataframe

unique_count = pd.groupby(['YEARMONTH'], as_index=False).agg(uniq_CLIENTCODE =('CLIENTCODE',pd.Series.count))

I am also using nunique but it will be very helpful if you have to use an aggregate function like 'min', 'max', 'count' or 'mean' etc.

df.groupby('YEARMONTH')['CLIENTCODE'].transform('nunique') #count(distinct)
df.groupby('YEARMONTH')['CLIENTCODE'].transform('min')     #min
df.groupby('YEARMONTH')['CLIENTCODE'].transform('max')     #max
df.groupby('YEARMONTH')['CLIENTCODE'].transform('mean')    #average
df.groupby('YEARMONTH')['CLIENTCODE'].transform('count')   #count

Using crosstab, this will return more information than groupby nunique

pd.crosstab(df.YEARMONTH,df.CLIENTCODE)
Out[196]: 
CLIENTCODE  1  2  3
YEARMONTH          
201301      2  1  0
201302      1  2  1

After a little bit modify ,yield the result

pd.crosstab(df.YEARMONTH,df.CLIENTCODE).ne(0).sum(1)
Out[197]: 
YEARMONTH
201301    2
201302    3
dtype: int64

Here is another method, much simple, lets say your dataframe name is daat and column name is YEARMONTH

daat.YEARMONTH.value_counts()

Interestingly enough, very often len(unique()) is a few times (3x-15x) faster than nunique().

I believe this is what you want:

table.groupby('YEARMONTH').CLIENTCODE.nunique()

Example:

In [2]: table
Out[2]: 
   CLIENTCODE  YEARMONTH
0           1     201301
1           1     201301
2           2     201301
3           1     201302
4           2     201302
5           2     201302
6           3     201302

In [3]: table.groupby('YEARMONTH').CLIENTCODE.nunique()
Out[3]: 
YEARMONTH
201301       2
201302       3