dask apply function to return dataframe: AttributeError: 'DataFrame' object has no attribute 'name'

Question

Realize this will appear as a duplicate, but after reviewing dask apply: AttributeError: 'DataFrame' object has no attribute 'name' I still don't have a clear idea of where my code is breaking. Below is a minimum reproducible example that I think reflects the problem I'm running into with my larger code.

import pandas as pd
from dask import dataframe as dd

dummy_df = pd.DataFrame({'a' : [1,2,3,4,5]})

dd_dummy = dd.from_pandas(dummy_df, npartitions = 1)

"""Arbitrary function that returns dataframe, taking keyword argument from apply"""
def test(x, bleh):
    return pd.DataFrame({'test' : 7 * [bleh]})

ok = dd_dummy.apply(test, axis = 1
                , bleh = 'fish'
                , meta = {'test' : 'str'}).compute()

I feel like I'm following the documentation at https://docs.dask.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.apply pretty faithfully, so not sure what's let to try here.

Error Message:

---

AttributeError                            Traceback (most recent call last)
<ipython-input-55-76aefefcffe4> in <module>()
     12 ok = dd_dummy.apply(test, axis = 1
     13                 , bleh = 'fish'
---> 14                 , meta = {'test' : 'str'}).compute()

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/base.py in compute(self, **kwargs)
    164         dask.base.compute
    165         """
--> 166         (result,) = compute(self, traverse=False, **kwargs)
    167         return result
    168 

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/base.py in compute(*args, **kwargs)
    435     keys = [x.__dask_keys__() for x in collections]
    436     postcomputes = [x.__dask_postcompute__() for x in collections]
--> 437     results = schedule(dsk, keys, **kwargs)
    438     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    439 

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs)
     82         get_id=_thread_get_id,
     83         pack_exception=pack_exception,
---> 84         **kwargs
     85     )
     86 

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
    484                         _execute_task(task, data)  # Re-execute locally
    485                     else:
--> 486                         raise_exception(exc, tb)
    487                 res, worker_id = loads(res_info)
    488                 state["cache"][key] = res

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/local.py in reraise(exc, tb)
    314     if exc.__traceback__ is not tb:
    315         raise exc.with_traceback(tb)
--> 316     raise exc
    317 
    318 

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    220     try:
    221         task, data = loads(task_info)
--> 222         result = _execute_task(task, data)
    223         id = get_id()
    224         result = dumps((result, id))

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
    119         # temporaries by their reference count and can execute certain
    120         # operations in-place.
--> 121         return func(*(_execute_task(a, cache) for a in args))
    122     elif not ishashable(arg):
    123         return arg

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/optimization.py in __call__(self, *args)
    989         if not len(args) == len(self.inkeys):
    990             raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args)))
--> 991         return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
    992 
    993     def __reduce__(self):

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/core.py in get(dsk, out, cache)
    149     for key in toposort(dsk):
    150         task = dsk[key]
--> 151         result = _execute_task(task, cache)
    152         cache[key] = result
    153     result = _execute_task(out, cache)

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
    119         # temporaries by their reference count and can execute certain
    120         # operations in-place.
--> 121         return func(*(_execute_task(a, cache) for a in args))
    122     elif not ishashable(arg):
    123         return arg

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/utils.py in apply(func, args, kwargs)
     28 def apply(func, args, kwargs=None):
     29     if kwargs:
---> 30         return func(*args, **kwargs)
     31     else:
     32         return func(*args)

~/anaconda3/envs/python3/lib/python3.6/site-packages/dask/dataframe/core.py in apply_and_enforce(*args, **kwargs)
   5141             c = meta.columns
   5142         else:
-> 5143             c = meta.name
   5144         return _rename(c, df)
   5145     return df

~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/generic.py in __getattr__(self, name)
   5065             if self._info_axis._can_hold_identifiers_and_holds_name(name):
   5066                 return self[name]
-> 5067             return object.__getattribute__(self, name)
   5068 
   5069     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'name'

I am able to get the following code to run, but it winds up returning a pd.Series object with the values of each element being pd.DataFrame objects. Unfortunately this doesn't scale in practice, but posting to offer some potential added context. I got it to work with the following replacement on the meta argument:

df = dd_dummy.apply(test, axis = 1
                , bleh = 'fish'
                , meta = (None, 'object').compute()

Then resolve to final df with:

df= pd.concat(df.values)

Thanks!