Make a new column with list of unique values grouped by, or over, another column in Polars

Before Polars 0.18.0, I was able to create a column with a list of all unique pokemon by type. I see Expr.list() has been refactored to implode(), but I'm having trouble replicating the following using the new syntax:

df.with_columns(lst_of_pokemon = pl.col('name').unique().list().over('Type 1'))

The mapping_strategy= argument for .over was added.

df = pl.from_repr("""
┌──────┬──────┐
│ name ┆ type │
│ ---  ┆ ---  │
│ str  ┆ i64  │
╞══════╪══════╡
│ a    ┆ 1    │
│ a    ┆ 1    │
│ a    ┆ 2    │
│ b    ┆ 2    │
│ c    ┆ 3    │
│ c    ┆ 3    │
└──────┴──────┘
""")

df.with_columns(lst_of_pokemon = 
   pl.col('name').unique().over('type', mapping_strategy='join')
)

shape: (6, 3)
┌──────┬──────┬────────────────┐
│ name ┆ type ┆ lst_of_pokemon │
│ ---  ┆ ---  ┆ ---            │
│ str  ┆ i64  ┆ list[str]      │
╞══════╪══════╪════════════════╡
│ a    ┆ 1    ┆ ["a"]          │
│ a    ┆ 1    ┆ ["a"]          │
│ a    ┆ 2    ┆ ["a", "b"]     │
│ b    ┆ 2    ┆ ["a", "b"]     │
│ c    ┆ 3    ┆ ["c"]          │
│ c    ┆ 3    ┆ ["c"]          │
└──────┴──────┴────────────────┘

Edit: the answer posted by jqurios is way more elegant

In think the following produces the required result:

df.join(
    df.groupby("Type 1", maintain_order=True).agg(pl.col("Name").alias("combined_names")),
    on="Type 1"
)

With the groupby and agg you can create the lists with all the pokemon names per 'Type 1', which is joined to the original dataframe.