=================================== FAILURES ===================================
________________________ test_partition_on[fastparquet] ________________________
[gw0] linux -- Python 3.8.8 /usr/share/miniconda3/envs/test-environment/bin/python

tmpdir = '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_partition_on_fastparquet_0'
engine = 'fastparquet'

    def test_partition_on(tmpdir, engine):
        tmpdir = str(tmpdir)
        df = pd.DataFrame(
            {
                "a1": np.random.choice(["A", "B", "C"], size=100),
                "a2": np.random.choice(["X", "Y", "Z"], size=100),
                "b": np.random.random(size=100),
                "c": np.random.randint(1, 5, size=100),
                "d": np.arange(0, 100),
            }
        )
        d = dd.from_pandas(df, npartitions=2)
        d.to_parquet(tmpdir, partition_on=["a1", "a2"], engine=engine)
        # Note #1: Cross-engine functionality is missing
        # Note #2: The index is not preserved in pyarrow when partition_on is used
        out = dd.read_parquet(
            tmpdir, engine=engine, index=False, gather_statistics=False
        ).compute()
        for val in df.a1.unique():
            assert set(df.b[df.a1 == val]) == set(out.b[out.a1 == val])
    
        # Now specify the columns and allow auto-index detection
>       out = dd.read_parquet(tmpdir, engine=engine, columns=["b", "a2"]).compute()

dask/dataframe/io/tests/test_parquet.py:1257: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
dask/base.py:285: in compute
    (result,) = compute(self, traverse=False, **kwargs)
dask/base.py:567: in compute
    results = schedule(dsk, keys, **kwargs)
dask/threaded.py:79: in get
    results = get_async(
dask/local.py:514: in get_async
    raise_exception(exc, tb)
dask/local.py:325: in reraise
    raise exc
dask/local.py:223: in execute_task
    result = _execute_task(task, data)
dask/core.py:121: in _execute_task
    return func(*(_execute_task(a, cache) for a in args))
dask/optimization.py:963: in __call__
    return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
dask/core.py:151: in get
    result = _execute_task(task, cache)
dask/core.py:121: in _execute_task
    return func(*(_execute_task(a, cache) for a in args))
dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
dask/dataframe/io/parquet/core.py:345: in read_parquet_part
    dfs = [
dask/dataframe/io/parquet/core.py:346: in <listcomp>
    func(fs, rg, columns.copy(), index, **toolz.merge(kwargs, kw))
dask/dataframe/io/parquet/fastparquet.py:801: in read_partition
    parquet_file.read_row_group_file(
/usr/share/miniconda3/envs/test-environment/lib/python3.8/site-packages/fastparquet/api.py:210: in read_row_group_file
    core.read_row_group_file(
/usr/share/miniconda3/envs/test-environment/lib/python3.8/site-packages/fastparquet/core.py:303: in read_row_group_file
    return read_row_group(f, rg, columns, categories, schema_helper, cats,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

file = <_io.BufferedReader name='/tmp/pytest-of-runner/pytest-0/popen-gw0/test_partition_on_fastparquet_0/a1=B/a2=Y/part.0.parquet'>
rg = <class 'fastparquet.parquet_thrift.parquet.ttypes.RowGroup'>
columns: [<class 'fastparquet.parquet_thrift.parquet.ttyp...ompressed_size: 55
  total_uncompressed_size: None
  type: 2

]
num_rows: 4
sorting_columns: None
total_byte_size: 252

columns = ['b', 'a2', '__null_dask_index__'], categories = {}
schema_helper = <Parquet Schema with 5 entries>
cats = OrderedDict([('a1', ['C']), ('a2', ['X'])]), selfmade = True
index = ['__null_dask_index__']
assign = {'__null_dask_index__': array([18, 29, 34, 49]), 'a1': array([0, 0, 0, 0], dtype=int8), 'a1-catdef': ['B', 'B', 'B', 'B']
Categories (3, object): ['B', 'C', 'A'], 'a2': array([0, 0, 0, 0], dtype=int8), ...}
scheme = 'hive', partition_meta = {}

    def read_row_group(file, rg, columns, categories, schema_helper, cats,
                       selfmade=False, index=None, assign=None,
                       scheme='hive', partition_meta=None):
        """
        Access row-group in a file and read some columns into a data-frame.
        """
        partition_meta = partition_meta or {}
        if assign is None:
            raise RuntimeError('Going with pre-allocation!')
        read_row_group_arrays(file, rg, columns, categories, schema_helper,
                              cats, selfmade, assign=assign)
    
        for cat in cats:
            if scheme == 'hive':
                s = ex_from_sep('/')
                partitions = s.findall(rg.columns[0].file_path)
            else:
                partitions = [('dir%i' % i, v) for (i, v) in enumerate(
                    rg.columns[0].file_path.split('/')[:-1])]
            key, val = [p for p in partitions if p[0] == cat][0]
            val = val_to_num(val, meta=partition_meta.get(key))
>           assign[cat][:] = cats[cat].index(val)
E           ValueError: 'B' is not in list

/usr/share/miniconda3/envs/test-environment/lib/python3.8/site-packages/fastparquet/core.py:366: ValueError