When Polars assigns the pl.Object type it essentially means: "I do not understand what this is."

By the time you end up with this type, it is generally too late to do anything useful with it.

In this particular case, numpy.random.choice is creating a numpy array of dtype=object

>>> rng.choice([None, "foo"], 3)
array([None, None, 'foo'], dtype=object)

Polars has native .sample() functionality which you could use to create your data instead.

df = pl.select(date = 
    pl.Series([None, "03.04.1998", "03.05.1834", "05.06.2025"])
      .sample(100, with_replacement=True)
)

# shape: (100, 1)
# ┌────────────┐
# │ date       │
# │ ---        │
# │ str        │
# ╞════════════╡
# │ null       │
# │ 05.06.2025 │
# │ 03.05.1834 │
# │ 03.04.1998 │
# │ …          │
# │ null       │
# │ 03.04.1998 │
# │ 03.05.1834 │
# │ 03.04.1998 │
# └────────────┘

You can use the datatype pl.List(pl.String)

df.with_columns(pl.col("foo").cast(pl.List(pl.String)))

shape: (1, 2)
┌─────────────────┬─────────────┐
│ foo             | bar         │
│ ---             | ---         │
│ list[str]       | str         │
╞═════════════════╪═════════════╡
│ ["1", "2", "3"] | Hello World │
└─────────────────┴─────────────┘

To create an actual string - perhaps:

df.with_columns("[" + 
   pl.col("foo").cast(pl.List(pl.String)).list.join(", ") 
   + "]"
)

There's also pl.format()

df.with_columns(
   pl.format("[{}]",
      pl.col("foo").cast(pl.List(pl.String)).list.join(", ")))

shape: (1, 3)
┌───────────┬─────────────┬───────────┐
│ foo       | bar         | literal   │
│ ---       | ---         | ---       │
│ list[i64] | str         | str       │
╞═══════════╪═════════════╪═══════════╡
│ [1, 2, 3] | Hello World | [1, 2, 3] │
└───────────┴─────────────┴───────────┘