A simple way to concatenate Dask (horizontal, axis = 1, columns)

Question

Action Read two csv (data.csv and label.csv) into one data frame.

df = dd.read_csv(data_files, delimiter=' ', header=None, names=['x', 'y', 'z', 'intensity', 'r', 'g', 'b'])
df_label = dd.read_csv(label_files, delimiter=' ', header=None, names=['label'])

Problem Concatenating columns requires known sections. However, setting the index will sort the data, which I clearly do not want, because the order of both files matches them.

df = dd.concat([df, df_label], axis=1)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-11-e6c2e1bdde55> in <module>()
----> 1 df = dd.concat([df, df_label], axis=1)

/uhome/hemmest/.local/lib/python3.5/site-packages/dask/dataframe/multi.py in concat(dfs, axis, join, interleave_partitions)
    573             return concat_unindexed_dataframes(dfs)
    574         else:
--> 575             raise ValueError('Unable to concatenate DataFrame with unknown '
    576                              'division specifying axis=1')
    577     else:

ValueError: Unable to concatenate DataFrame with unknown division specifying axis=1

Tried adding a column'id'

df['id'] = pd.Series(range(len(df)))

However, the length of the Dataframe makes the series larger than memory.

Question Apparently, Dask knows that both Dataframes are the same length:

In [15]:
df.index.compute()
Out[15]:
Int64Index([      0,       1,       2,       3,       4,       5,       6,
                  7,       8,       9,
            ...
            1120910, 1120911, 1120912, 1120913, 1120914, 1120915, 1120916,
            1120917, 1120918, 1120919],
           dtype='int64', length=280994776)
In [16]:
df_label.index.compute()
Out[16]:
Int64Index([1, 5, 5, 2, 2, 2, 2, 2, 2, 2,
            ...
            3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
           dtype='int64', length=280994776)

How to use this knowledge to simply concatenate?