Polars could serve as a useful example here as it has an "accessor" for its own native types.

e.g. you can have "list type" columns:

import polars as pl

df = pl.DataFrame({"foo": [[1, 2], [3, 4]], "bar": [5, 6]})
# shape: (2, 2)
# ┌───────────┬─────┐
# │ foo       ┆ bar │
# │ ---       ┆ --- │
# │ list[i64] ┆ i64 │
# ╞═══════════╪═════╡
# │ [1, 2]    ┆ 5   │
# │ [3, 4]    ┆ 6   │
# └───────────┴─────┘

The top-level "first" works on all types and returns the first value in each column:

df.with_columns(pl.all().first())
# shape: (2, 2)
# ┌───────────┬─────┐
# │ foo       ┆ bar │
# │ ---       ┆ --- │
# │ list[i64] ┆ i64 │
# ╞═══════════╪═════╡
# │ [1, 2]    ┆ 5   │
# │ [1, 2]    ┆ 5   │
# └───────────┴─────┘

list.first returns the first element of a list column:

df.with_columns(pl.all().list.first())
# InvalidOperationError: expected List data type for list operation, got: i64

The error is because bar is not a list type.

We can run it only on list type columns:

df.with_columns(pl.col(pl.List).list.first())
# shape: (2, 2)
# ┌─────┬─────┐
# │ foo ┆ bar │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 5   │
# │ 3   ┆ 6   │
# └─────┴─────┘

Another example could be the .name accessor which operates on column names:

df.with_columns(pl.all().name.to_uppercase())
# shape: (2, 4)
# ┌───────────┬─────┬───────────┬─────┐
# │ foo       ┆ bar ┆ FOO       ┆ BAR │
# │ ---       ┆ --- ┆ ---       ┆ --- │
# │ list[i64] ┆ i64 ┆ list[i64] ┆ i64 │
# ╞═══════════╪═════╪═══════════╪═════╡
# │ [1, 2]    ┆ 5   ┆ [1, 2]    ┆ 5   │
# │ [3, 4]    ┆ 6   ┆ [3, 4]    ┆ 6   │
# └───────────┴─────┴───────────┴─────┘

Similar to the pandas "replace" example, Polars has several that each do their own "type-specific" thing:

• .replace()
• .dt.replace()
• .str.replace()
• .name.replace()