Python Pandas : pivot table with aggfunc = count unique distinct

Solution 1:

Do you mean something like this?

>>> df2.pivot_table(values='X', index='Y', columns='Z', aggfunc=lambda x: len(x.unique()))

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.

Solution 2:

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

Solution 3:

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

Solution 4:

aggfunc=pd.Series.nunique provides distinct count. Full code is following:

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.

Solution 5:

• The aggfunc parameter in pandas.DataFrame.pivot_table will take 'nunique' as a string, or in a list
  • pandas.Series.nunique or pandas.core.groupby.DataFrameGroupBy.nunique
• Tested in pandas 1.3.1

out = df2.pivot_table(values='X', index='Y', columns='Z', aggfunc=['nunique', 'count', lambda x: len(x.unique()), len])

[out]:
             nunique           count           <lambda>            len          
Z       Z1   Z2   Z3    Z1   Z2   Z3       Z1   Z2   Z3   Z1   Z2   Z3
Y                                                                     
Y1     1.0  1.0  NaN   2.0  1.0  NaN      1.0  1.0  NaN  2.0  1.0  NaN
Y2     NaN  NaN  1.0   NaN  NaN  1.0      NaN  NaN  1.0  NaN  NaN  1.0


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

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

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

[out]:
             nunique          
Z       Z1   Z2   Z3
Y                   
Y1     1.0  1.0  NaN
Y2     NaN  NaN  1.0