Running add_column() seems to corrupt existing sequence-type column info
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Nevermind, I was incorrect.
I have a dataset that contains a column ("foo") that is a sequence type of length 4. So when I run .to_pandas() on it, the resulting dataframe correctly contains 4 columns - foo_0, foo_1, foo_2, foo_3. So the 1st row of the dataframe might look like:

ds = load_dataset(...)
df = ds.to_pandas()

df:
foo_0 | foo_1 | foo_2 | foo_3
0.0 | 1.0 | 2.0 | 3.0

If I run .add_column("new_col", data) on the dataset, and then .to_pandas() on the resulting new dataset, the resulting dataframe contains only 2 columns - foo, new_col. The values in column foo are lists of length 4, the 4 elements that should have been split into separate columns. Dataframe 1st row would be:

ds = load_dataset(...)
new_ds = ds.add_column("new_col", data)
df = new_ds.to_pandas()

df:
foo | new_col
[0.0, 1.0, 2.0, 3.0] | new_val

I've explored the 2 datasets in a debugger and haven't noticed any changes to any attributes related to the foo column, but I can't determine why the dataframes are so different.
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