Find mode using polars within group

My solution to find unique mode within group in up and running . But as you can see I am joining dataframe to achieve desired result . Is there way to implement same solution without using join.

import polars as pl

# Sample data (replace with your actual data)
data = pl.DataFrame({
  "group": ["A", "A", "B", "B", "C", "C", "C","C"],
  "value": [1, 1, 2, 2, 3, 3, 4,4]
})

# Group by the "group" column
#############working soltuiom##################
grouped_data = data.group_by("group")


# Find the mode (most frequent value) for each group
mode_values = grouped_data.agg(pl.when(pl.col("value").mode().len()==1)
                                .then(pl.col("value")\
                                .mode().first())\
                               .otherwise(-999)
                               )
print(mode_values)
#############working soltuiom##################


data=data.join(mode_values,"group","left")
print(data)

C:\Dev\Python3.11\python.exe

C:\Python_Projects\Python_extra_code\code_for_stackoverflow.py 
shape: (3, 2)
┌───────┬───────┐
│ group ┆ value │
│ ---   ┆ ---   │
│ str   ┆ i64   │
╞═══════╪═══════╡
│ C     ┆ -999  │
│ A     ┆ 1     │
│ B     ┆ 2     │
└───────┴───────┘
shape: (8, 3)
┌───────┬───────┬─────────────┐
│ group ┆ value ┆ value_right │
│ ---   ┆ ---   ┆ ---         │
│ str   ┆ i64   ┆ i64         │
╞═══════╪═══════╪═════════════╡
│ A     ┆ 1     ┆ 1           │
│ A     ┆ 1     ┆ 1           │
│ B     ┆ 2     ┆ 2           │
│ B     ┆ 2     ┆ 2           │
│ C     ┆ 3     ┆ -999        │
│ C     ┆ 3     ┆ -999        │
│ C     ┆ 4     ┆ -999        │
│ C     ┆ 4     ┆ -999        │
└───────┴───────┴─────────────┘

Process finished with exit code 0

THis question in continuation of find mode in multiple group and return null if no unique mode
Let me know if same can be answered in old solution . I will delete this one .

>Solution :

You could use over:

data.with_columns(pl.when(pl.col('value').mode().len()==1)
                    .then(pl.col('value').mode().first())
                    .otherwise(-999)
                    .over('group')
                    .alias('value_right')
                  )

Output:

shape: (8, 3)
┌───────┬───────┬─────────────┐
│ group ┆ value ┆ value_right │
│ ---   ┆ ---   ┆ ---         │
│ str   ┆ i64   ┆ i64         │
╞═══════╪═══════╪═════════════╡
│ A     ┆ 1     ┆ 1           │
│ A     ┆ 1     ┆ 1           │
│ B     ┆ 2     ┆ 2           │
│ B     ┆ 2     ┆ 2           │
│ C     ┆ 3     ┆ -999        │
│ C     ┆ 3     ┆ -999        │
│ C     ┆ 4     ┆ -999        │
│ C     ┆ 4     ┆ -999        │
└───────┴───────┴─────────────┘