@fkiraly The failure in run notebooks is caused by this change, as we no longer return timepoints as name in last level. I tried checking it, the names of index returned is [None, None] and when we do reset_index it turns out to be [level_0, level_1]. The below code demonstrates the failure.

1. load_arrow_head which is nested_univ mtype
2. convert it to pd.multiindex where the index names are [None, None]
3. do reset index, now the index becomes column named as level_0, level_1
4. now when we try to set index through columns. KeyError occurs as we are trying to set timepoints which is not present

from sktime.datasets import load_arrow_head
from sktime.datatypes import convert_to

df_panel = load_arrow_head(split="TRAIN")[0]
df_panel = convert_to(df_panel, "pd-multiindex").reset_index() 
df_panel = df_panel.set_index(["level_0", "timepoints"])

Changing set_index to

df_panel = df_panel.set_index(["level_0", "level_1"])

Fixes it because now the correct column name is being returned.

that seems like a reasonable fix - btw, I think the loader should just use the return_type argument here instead of attempting explicit conversion.

This highlights that this might break user code, although only under very brittle circumstances, so we may consider shipping it only with the full minor version.

that seems like a reasonable fix - btw, I think the loader should just use the return_type argument here instead of attempting explicit conversion.

Do you mean something like this ??

df_panel = load_arrow_head(split="TRAIN", return_type="pd-multiindex")[0]

This highlights that this might break user code, although only under very brittle circumstances, so we may consider shipping it only with the full minor version.

Is this good to go now?? so I will fix the failures in notebook too

yes, that's what I meant - the notebook should of course run with this.

Check out this pull request on 

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see above, the notebook ought to run

made changes in the notebook