While grouping and aggregating data the TypeError: unhashable numpy.ndarray has been occcured

I have processed data retrieved from Youtube channels statistics and while I have been aggregating data I obtained error message that numpy.ndarray was unhashable type. For creating columns 'body_focus' and ' type_of_workout' I have been used np.where function. I will not post this part of script because I do not think that this was the reason of type error issue.
My lines of code:

workout_df = videos_df[['Year','body_focus','type_of_workout','viewCount','commentCount','likeCount']]
workout_df  

workout_df.groupby(by = ['Year','body_focus'])['viewCount','commentCount','likeCount'].sum()\
                  .sort('Year', ascending = True)  

Then I obtained error message:

----------------------------------------------------------------
TypeError                      Traceback (most recent call last)
Cell In[166], line 1
----> 1 videos_df.groupby(by = ['Year','body_focus'])['viewCount','commentCount','likeCount'].sum()\
      2          .sort('Year', ascending = True)

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\groupby.py:2434, in GroupBy.sum(self, numeric_only, min_count, engine, engine_kwargs)
   2429 else:
   2430     # If we are grouping on categoricals we want unobserved categories to
   2431     # return zero, rather than the default of NaN which the reindexing in
   2432     # _agg_general() returns. GH #31422
   2433     with com.temp_setattr(self, "observed", True):
-> 2434         result = self._agg_general(
   2435             numeric_only=numeric_only,
   2436             min_count=min_count,
   2437             alias="sum",
   2438             npfunc=np.sum,
   2439         )
   2441     return self._reindex_output(result, fill_value=0)

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\groupby.py:1692, in GroupBy._agg_general(self, numeric_only, min_count, alias, npfunc)
   1680 @final
   1681 def _agg_general(
   1682     self,
   (...)
   1687     npfunc: Callable,
   1688 ):
   1690     with self._group_selection_context():
   1691         # try a cython aggregation if we can
-> 1692         result = self._cython_agg_general(
   1693             how=alias,
   1694             alt=npfunc,
   1695             numeric_only=numeric_only,
   1696             min_count=min_count,
   1697         )
   1698         return result.__finalize__(self.obj, method="groupby")

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\groupby.py:1796, in GroupBy._cython_agg_general(self, how, alt, numeric_only, min_count, ignore_failures, **kwargs)
   1793 if not is_ser and len(new_mgr) < orig_len:
   1794     warn_dropping_nuisance_columns_deprecated(type(self), how, numeric_only)
-> 1796 res = self._wrap_agged_manager(new_mgr)
   1797 if is_ser:
   1798     res.index = self.grouper.result_index

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\generic.py:1511, in DataFrameGroupBy._wrap_agged_manager(self, mgr)
   1509     result = result._consolidate()
   1510 else:
-> 1511     index = self.grouper.result_index
   1512     mgr.set_axis(1, index)
   1513     result = self.obj._constructor(mgr)

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\_libs\properties.pyx:36, in pandas._libs.properties.CachedProperty.__get__()

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\ops.py:995, in BaseGrouper.result_index(self)
    992 if len(self.groupings) == 1:
    993     return self.groupings[0].result_index.rename(self.names[0])
--> 995 codes = self.reconstructed_codes
    996 levels = [ping.result_index for ping in self.groupings]
    997 return MultiIndex(
    998     levels=levels, codes=codes, verify_integrity=False, names=self.names
    999 )

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\ops.py:986, in BaseGrouper.reconstructed_codes(self)
    984 @property
    985 def reconstructed_codes(self) -> list[npt.NDArray[np.intp]]:
--> 986     codes = self.codes
    987     ids, obs_ids, _ = self.group_info
    988     return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True)

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\ops.py:897, in BaseGrouper.codes(self)
    894 @final
    895 @property
    896 def codes(self) -> list[npt.NDArray[np.signedinteger]]:
--> 897     return [ping.codes for ping in self.groupings]

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\ops.py:897, in <listcomp>(.0)
    894 @final
    895 @property
    896 def codes(self) -> list[npt.NDArray[np.signedinteger]]:
--> 897     return [ping.codes for ping in self.groupings]

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\grouper.py:621, in Grouping.codes(self)
    617 if self._codes is not None:
    618     # _codes is set in __init__ for MultiIndex cases
    619     return self._codes
--> 621 return self._codes_and_uniques[0]

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\_libs\properties.pyx:36, in pandas._libs.properties.CachedProperty.__get__()

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\groupby\grouper.py:692, in Grouping._codes_and_uniques(self)
    685     uniques = (
    686         self.grouping_vector.result_index._values  # type: ignore[assignment]
    687     )
    688 else:
    689     # GH35667, replace dropna=False with use_na_sentinel=False
    690     # error: Incompatible types in assignment (expression has type "Union[
    691     # ndarray[Any, Any], Index]", variable has type "Categorical")
--> 692     codes, uniques = algorithms.factorize(  # type: ignore[assignment]
    693         self.grouping_vector, sort=self._sort, use_na_sentinel=self._dropna
    694     )
    695 return codes, uniques

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\algorithms.py:818, in factorize(values, sort, na_sentinel, use_na_sentinel, size_hint)
    815             # Don't modify (potentially user-provided) array
    816             values = np.where(null_mask, na_value, values)
--> 818     codes, uniques = factorize_array(
    819         values,
    820         na_sentinel=na_sentinel_arg,
    821         size_hint=size_hint,
    822     )
    824 if sort and len(uniques) > 0:
    825     if na_sentinel is None:
    826         # TODO: Can remove when na_sentinel=na_sentinel as in TODO above

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\algorithms.py:574, in factorize_array(values, na_sentinel, size_hint, na_value, mask)
    571 hash_klass, values = _get_hashtable_algo(values)
    573 table = hash_klass(size_hint or len(values))
--> 574 uniques, codes = table.factorize(
    575     values,
    576     na_sentinel=na_sentinel,
    577     na_value=na_value,
    578     mask=mask,
    579     ignore_na=ignore_na,
    580 )
    582 # re-cast e.g. i8->dt64/td64, uint8->bool
    583 uniques = _reconstruct_data(uniques, original.dtype, original)

File pandas\_libs\hashtable_class_helper.pxi:5943, in pandas._libs.hashtable.PyObjectHashTable.factorize()

File pandas\_libs\hashtable_class_helper.pxi:5857, in pandas._libs.hashtable.PyObjectHashTable._unique()

TypeError: unhashable type: 'numpy.ndarray'

I have checked up the dtype of each variable and needed categories for aggregation were proper data type. Please, help me to find out this code bug and how to perform the aggregation of my results.

Try using this line

videos_grouped = videos_df.groupby(by=['Year', 'body_focus'], as_index=False)['viewCount', 'commentCount', 'likeCount'].sum()

videos_sorted = videos_grouped.sort_values(by='Year', ascending=True)