按组每列的唯一值数

Question

Consider the following dataframe:

      A      B  E
0   bar    one  1
1   bar  three  1
2  flux    six  1
3  flux  three  2
4   foo   five  2
5   foo    one  1
6   foo    two  1
7   foo    two  2

I would like to find, for each value of A , the number of unique values in the other columns.

1. I thought the following would do it:

    df.groupby('A').apply(lambda x: x.nunique())

   but I get an error:

    AttributeError: 'DataFrame' object has no attribute 'nunique'

2. I also tried with:

    df.groupby('A').nunique()

   but I also got the error:

    AttributeError: 'DataFrameGroupBy' object has no attribute 'nunique'

3. Finally I tried with:

    df.groupby('A').apply(lambda x: x.apply(lambda y: y.nunique()))

   which returns:

    ABE A bar 1 2 1 flux 1 2 2 foo 1 3 2

   and seems to be correct. Strangely though, it also returns the column A in the result. Why?

Answer 1

The DataFrame object doesn't have nunique , only Series do. You have to pick out which column you want to apply nunique() on. You can do this with a simple dot operator:

df.groupby('A').apply(lambda x: x.B.nunique())

will print:

A
bar     2
flux    2
foo     3

And doing:

df.groupby('A').apply(lambda x: x.E.nunique())

will print:

A
bar     1
flux    2
foo     2

Alternatively you can do this with one function call using:

df.groupby('A').aggregate({'B': lambda x: x.nunique(), 'E': lambda x: x.nunique()})

which will print:

      B  E
A
bar   2  1
flux  2  2
foo   3  2

To answer your question about why your recursive lambda prints the A column as well, it's because when you do a groupby / apply operation, you're now iterating through three DataFrame objects. Each DataFrame object is a sub- DataFrame of the original. Applying an operation to that will apply it to each Series . There are three Series per DataFrame you're applying the nunique() operator to.

The first Series being evaluated on each DataFrame is the A Series , and since you've done a groupby on A , you know that in each DataFrame , there is only one unique value in the A Series . This explains why you're ultimately given an A result column with all 1 's.

Answer 2

I encountered the same problem. Upgrading pandas to the latest version solved the problem for me.

df.groupby('A').nunique()

The above code did not work for me in Pandas version 0.19.2. I upgraded it to Pandas version 0.21.1 and it worked.

You can check the version using the following code:

print('Pandas version ' + pd.__version__)