Python Pandas : pivot table with aggfunc = count unique distinct

Question

df2 = pd.DataFrame({'X' : ['X1', 'X1', 'X1', 'X1'], 'Y' : ['Y2','Y1','Y1','Y1'], 'Z' : ['Z3','Z1','Z1','Z2']})

    X   Y   Z
0  X1  Y2  Z3
1  X1  Y1  Z1
2  X1  Y1  Z1
3  X1  Y1  Z2

g=df2.groupby('X')

pd.pivot_table(g, values='X', rows='Y', cols='Z', margins=False, aggfunc='count')

Traceback (most recent call last): ... AttributeError: 'Index' object has no attribute 'index'

How do I get a Pivot Table with counts of unique values of one DataFrame column for two other columns?
Is there aggfunc for count unique? Should I be using np.bincount()?

NB. I am aware of 'Series' values_counts() however I need a pivot table.

---

EDIT: The output should be:

Z   Z1  Z2  Z3
Y             
Y1   1   1 NaN
Y2 NaN NaN   1

Answer 1

Do you mean something like this?

In [39]: df2.pivot_table(values='X', rows='Y', cols='Z', 
                         aggfunc=lambda x: len(x.unique()))
Out[39]: 
Z   Z1  Z2  Z3
Y             
Y1   1   1 NaN
Y2 NaN NaN   1

Note that using len assumes you don't have NAs in your DataFrame. You can do x.value_counts().count() or len(x.dropna().unique()) otherwise.