unique combinations of values in selected columns in pandas data frame and count

Question

I have my data in pandas data frame as follows:

df1 = pd.DataFrame({\'A\':[\'yes\',\'yes\',\'yes\',\'yes\',\'no\',\'no\',\'yes\',\'yes\',\'yes\',\'no\'],
            


        

Answer 1

Placing @EdChum's very nice answer into a function count_unique_index. The unique method only works on pandas series, not on data frames. The function below reproduces the behavior of the unique function in R:

unique returns a vector, data frame or array like x but with duplicate elements/rows removed.

And adds a count of the occurrences as requested by the OP.

df1 = pd.DataFrame({'A':['yes','yes','yes','yes','no','no','yes','yes','yes','no'],                                                                                             
                    'B':['yes','no','no','no','yes','yes','no','yes','yes','no']})                                                                                               
def count_unique_index(df, by):                                                                                                                                                 
    return df.groupby(by).size().reset_index().rename(columns={0:'count'})                                                                                                      

count_unique_index(df1, ['A','B'])                                                                                                                                              
     A    B  count                                                                                                                                                                  
0   no   no      1                                                                                                                                                                  
1   no  yes      2                                                                                                                                                                  
2  yes   no      4                                                                                                                                                                  
3  yes  yes      3