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

Question

You can groupby on cols 'A' and 'B' and call size and then reset_index and rename the generated column:

In [26]:

df1.groupby(['A','B']).size().reset_index().rename(columns={0:'count'})
Out[26]:
     A    B  count
0   no   no      1
1   no  yes      2
2  yes   no      4
3  yes  yes      3

update

A little explanation, by grouping on the 2 columns, this groups rows where A and B values are the same, we call size which returns the number of unique groups:

In[202]:
df1.groupby(['A','B']).size()

Out[202]: 
A    B  
no   no     1
     yes    2
yes  no     4
     yes    3
dtype: int64

So now to restore the grouped columns, we call reset_index:

In[203]:
df1.groupby(['A','B']).size().reset_index()

Out[203]: 
     A    B  0
0   no   no  1
1   no  yes  2
2  yes   no  4
3  yes  yes  3

This restores the indices but the size aggregation is turned into a generated column 0, so we have to rename this:

In[204]:
df1.groupby(['A','B']).size().reset_index().rename(columns={0:'count'})

Out[204]: 
     A    B  count
0   no   no      1
1   no  yes      2
2  yes   no      4
3  yes  yes      3

groupby does accept the arg as_index which we could have set to False so it doesn't make the grouped columns the index, but this generates a series and you'd still have to restore the indices and so on....:

In[205]:
df1.groupby(['A','B'], as_index=False).size()

Out[205]: 
A    B  
no   no     1
     yes    2
yes  no     4
     yes    3
dtype: int64

In Pandas 1.1.0 you can use the method value_counts with DataFrames:

df.value_counts() # or df[['A', 'B']].value_counts()

Result:

A    B
yes  no     4
     yes    3
no   yes    2
     no     1
dtype: int64

Convert index to columns and sort by value counts:

df.value_counts(ascending=True).reset_index(name='count')

Result:

     A    B  count
0   no   no      1
1   no  yes      2
2  yes  yes      3
3  yes   no      4

Slightly related, I was looking for the unique combinations and I came up with this method:

def unique_columns(df,columns):

    result = pd.Series(index = df.index)

    groups = meta_data_csv.groupby(by = columns)
    for name,group in groups:
       is_unique = len(group) == 1
       result.loc[group.index] = is_unique

    assert not result.isnull().any()

    return result

And if you only want to assert that all combinations are unique:

df1.set_index(['A','B']).index.is_unique