[python] Python Pandas : pivot table with aggfunc = count unique distinct

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

aggfunc=pd.Series.nunique provides distinct count.

Full Code:

df2.pivot_table(values='X', rows='Y', cols='Z', 
                         aggfunc=pd.Series.nunique)

Credit to @hume for this solution (see comment under the accepted answer). Adding as an answer here for better discoverability.

You can construct a pivot table for each distinct value of X. In this case,

for xval, xgroup in g:
    ptable = pd.pivot_table(xgroup, rows='Y', cols='Z', 
        margins=False, aggfunc=numpy.size)

will construct a pivot table for each value of X. You may want to index ptable using the xvalue. With this code, I get (for X1)

     X        
Z   Z1  Z2  Z3
Y             
Y1   2   1 NaN
Y2 NaN NaN   1

Since none of the answers are up to date with the last version of Pandas, I am writing another solution for this problem:

In [1]:
import pandas as pd

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

# Pivot
pd.crosstab(index=df2['Y'], columns=df2['Z'], values=df2['X'], aggfunc=pd.Series.nunique)

Out [1]:
Z   Z1  Z2  Z3
Y           
Y1  1.0 1.0 NaN
Y2  NaN NaN 1.0

This is a good way of counting entries within .pivot_table:

df2.pivot_table(values='X', index=['Y','Z'], columns='X', aggfunc='count')


        X1  X2
Y   Z       
Y1  Z1   1   1
    Z2   1  NaN
Y2  Z3   1  NaN

For best performance I recommend doing DataFrame.drop_duplicates followed up aggfunc='count'.

Others are correct that aggfunc=pd.Series.nunique will work. This can be slow, however, if the number of index groups you have is large (>1000).

So instead of (to quote @Javier)

df2.pivot_table('X', 'Y', 'Z', aggfunc=pd.Series.nunique)

I suggest

df2.drop_duplicates(['X', 'Y', 'Z']).pivot_table('X', 'Y', 'Z', aggfunc='count')

This works because it guarantees that every subgroup (each combination of ('Y', 'Z')) will have unique (non-duplicate) values of 'X'.

aggfunc=pd.Series.nunique will only count unique values for a series - in this case count the unique values for a column. But this doesn't quite reflect as an alternative to aggfunc='count'

For simple counting, it better to use aggfunc=pd.Series.count

Since at least version 0.16 of pandas, it does not take the parameter "rows"

As of 0.23, the solution would be:

df2.pivot_table(values='X', index='Y', columns='Z', aggfunc=pd.Series.nunique)

which returns:

Z    Z1   Z2   Z3
Y                
Y1  1.0  1.0  NaN
Y2  NaN  NaN  1.0