DataFrames do not have that method; columns in DataFrames do:

df['A'].unique()

Or, to get the names with the number of observations (using the DataFrame given by closedloop):

>>> df.groupby('person').person.count()
Out[80]: 
person
0         2
1         3
Name: person, dtype: int64

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.

Reason of error is unique working only for Series, so is only implemented SeriesGroupBy.unique.

For me working Series.unique with convert to list:

df = df.groupby('group')[['param1', 'param2']].agg(lambda x: list(x.unique()))
print (df)
                param1        param2
group                               
1           [1.0, 5.0]        [5, 6]
2                [8.0]           [9]
3      [nan, 2.0, 3.0]  [10, 11, 12]
4                [nan]           [1]

I mean, isn't it groupby(), not group_by()?

On checking I found my polars version :

pl.__version__

0.17.3

https://pola-rs.github.io/polars/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby.html

I need to do:

df.groupby("a").agg(pl.col("b").sum())  # there is no underscore in groupby

#output

shape: (3, 2)
a   b
str i64
"a" 2
"c" 3
"b" 5

and the document says :

Deprecated since version 0.19.0: This method has been renamed to DataFrame.group_by().

This is the new document for polars version 0.19

https://pola-rs.github.io/polars/py-polars/html/reference/dataframe/api/polars.DataFrame.group_by.html#polars-dataframe-group-by