Problems computing a new column value from an existing dask dataframe column

Dask column assignment snippet.py

# Here's what I do to get my background data (sorry, not public)
df = dd.read_csv('r1c5va879uaex_r1c639xp952g4.csv', assume_missing=True)

# Now, in order to add a column, I need to be able to add metadata -- 
# if I don't, I get mysterious errors about failing to infer types
newmeta = df._meta.copy() # get the original metadata
# add new column to metadata
newmeta.insert(len(newmeta.columns), 'well', 'foo') 
# specify the dtype of the new column
newmeta = newmeta.astype({'well': str})

# the function we use to compute the new column values --
# note that efm suggests we use a field splitter, instead of
# regular expression matching, which is less efficient
def find_well(x):
    assert isinstance(x, str), f"Trying to find well in non-string value {x}"
    match = re.match(id_re, x)
    if match is None:
        raise ValueError(f"Couldn't find well ID in {x}")
    return match.group(1)
find_wellv = np.vectorize(find_well)

# now actually compute and add the new column
df4 = df.map_partitions(lambda df: df.assign(well=find_wellv(df['id'])), meta=newmeta)

Dask column assignment.ipynb