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

Here is another method and it is much simpler. Let’s say your dataframe name is daat and the column name is YEARMONTH:

daat.YEARMONTH.value_counts()

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 of modification, it yields the result:

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

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

With the new Pandas version, it is easy to get as a data frame:

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

Here is an approach to have count distinct over multiple columns. Let’s have some data:

data = {'CLIENT_CODE':[1,1,2,1,2,2,3],
        'YEAR_MONTH':[201301,201301,201301,201302,201302,201302,201302],
        'PRODUCT_CODE': [100,150,220,400,50,80,100]
       }
table = pd.DataFrame(data)
table

CLIENT_CODE YEAR_MONTH  PRODUCT_CODE
0   1       201301      100
1   1       201301      150
2   2       201301      220
3   1       201302      400
4   2       201302      50
5   2       201302      80
6   3       201302      100

Now, list the columns of interest and use groupby in a slightly modified syntax:

columns = ['YEAR_MONTH', 'PRODUCT_CODE']
table[columns].groupby(table['CLIENT_CODE']).nunique()

We obtain:

YEAR_MONTH  PRODUCT_CODE CLIENT_CODE
1           2            3
2           2            3
3           1            1

Distinct of column along with aggregations on other columns

To get the distinct number of values for any column (CLIENTCODE in your case), we can use nunique. We can pass the input as a dictionary in agg function, along with aggregations on other columns:

grp_df = df.groupby('YEARMONTH').agg({'CLIENTCODE': ['nunique'],
                                      'other_col_1': ['sum', 'count']})

# to flatten the multi-level columns
grp_df.columns = ["_".join(col).strip() for col in grp_df.columns.values]

# if you wish to reset the index
grp_df.reset_index(inplace=True)

Now you are also able to use dplyr syntax in Python to do it:

>>> from datar.all import f, tibble, group_by, summarise, n_distinct
>>>
>>> data = tibble(
...     CLIENT_CODE=[1,1,2,1,2,2,3],
...     YEAR_MONTH=[201301,201301,201301,201302,201302,201302,201302]
... )
>>>
>>> data >> group_by(f.YEAR_MONTH) >> summarise(n=n_distinct(f.CLIENT_CODE))
   YEAR_MONTH       n
      <int64> <int64>
0      201301       2
1      201302       3

Create a pivot table and use the nunique series function:

ID = [ 123, 123, 123, 456, 456, 456, 456, 789, 789]
domain = ['vk.com', 'vk.com', 'twitter.com', 'vk.com', 'facebook.com',
          'vk.com', 'google.com', 'twitter.com', 'vk.com']
df = pd.DataFrame({'id':ID, 'domain':domain})
fp = pd.pivot_table(data=df, index='domain', aggfunc=pd.Series.nunique)
print(fp)

Output:

               id
domain
facebook.com   1
google.com     1
twitter.com    2
vk.com         3

• To obtain the number of different clients and sizes per year (i.e. number of unique values of multiple columns), use a list of columns:

  df.groupby('YEARMONTH')[['CLIENTCODE', 'SIZE']].nunique()
  

• Actually, the result from the above code can be obtained using SQL syntax on df using pandasql (a module built on pandas that lets you query pandas DataFrames using SQL syntax).

  #! pip install pandasql
  from pandasql import sqldf
  sqldf("""
  SELECT COUNT(DISTINCT CLIENTCODE), 
         COUNT(DISTINCT SIZE)
    FROM df 
    GROUP BY YEARMONTH
  """)
  

• If you want to keep YEARMONTH as a column, i.e. the analog of the following SQL query

  SELECT YEARMONTH, 
         COUNT(DISTINCT CLIENTCODE),
         COUNT(DISTINCT SIZE)
    FROM df 
    GROUP BY YEARMONTH
  

  in pandas is the following (set as_index to False):

  df.groupby('YEARMONTH', as_index=False)[['CLIENTCODE', 'SIZE']].nunique()
  

• If you need to set custom names to the aggregated columns, i.e. the analog of the following SQL query:

  SELECT YEARMONTH, 
         COUNT(DISTINCT CLIENTCODE) AS `No. clients`, 
         COUNT(DISTINCT SIZE) AS `No. size`
    FROM df 
    GROUP BY YEARMONTH
  

  use named aggregation in pandas:

  (
      df.groupby('YEARMONTH', as_index=False)
        .agg(**{'No. clients':('CLIENTCODE', 'nunique'), 
                'No. size':('SIZE', 'nunique')})
  )