dask dataframe drop_duplicates support?

edit:

This works

>>> df = pd.DataFrame({"A": [1, 2] * 4, "B": [1] * 4 + [2] * 4})
>>> a = dd.from_pandas(df, 2)
>>> a.drop_duplicates(subset=['A']).compute()

But this fails when subset is specified positionally.

>>> a.drop_duplicates(['A']).compute()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-ad2309a8f442> in <module>
      3 df = dd.from_pandas(pd.DataFrame({'a': [0, 0, 1], 'b': range(3)}), npartitions=1)
      4
----> 5 df.drop_duplicates(['a']).compute()

~/sandbox/dask/dask/dataframe/core.py in drop_duplicates(self, split_every, split_out, **kwargs)
    506             split_out_setup=split_out_setup,
    507             split_out_setup_kwargs=split_out_setup_kwargs,
--> 508             **kwargs
    509         )
    510

~/sandbox/dask/dask/dataframe/core.py in apply_concat_apply(args, chunk, aggregate, combine, meta, token, chunk_kwargs, aggregate_kwargs, combine_kwargs, split_every, split_out, split_out_setup, split_out_setup_kwargs, **kwargs)
   4597     elif split_every is False:
   4598         split_every = npartitions
-> 4599     elif split_every < 2 or not isinstance(split_every, Integral):
   4600         raise ValueError("split_every must be an integer >= 2")
   4601

TypeError: '<' not supported between instances of 'list' and 'int'

We need to make subset a proper keyword in

https://github.com/dask/dask/blob/96bbf636c50a753b76ba4758d03aebb0a42386db/dask/dataframe/core.py#L485

and update lines like

https://github.com/dask/dask/blob/96bbf636c50a753b76ba4758d03aebb0a42386db/dask/dataframe/core.py#L488

to just be

if subset is not None:
    ....

---

The original df is a dask dataframe

This works:

df = df.compute()
columns=['a','b','c']
df = df.drop_duplicates(columns)

This does not, is there a better approach?

columns=['a','b','c']
(Pdb) df = df.drop_duplicates(columns)
*** TypeError: unorderable types: list() < int()

[dsevero]

Actually, I think this works fine and the docs are just outdated.

In [11]: import dask.dataframe as dd                
    ...: import pandas as pd
    ...: df = dd.from_pandas(pd.DataFrame({'a': [0, 0, 1], 'b': range(3)}), npartitions=1) 

In [12]: df.drop_duplicates(subset=['a']).compute() 
Out[12]:                     
   a  b                                                                                    
0  0  0                    
2  1  2 

[WesRoach]

Interested. Reading contribution guidelines (previous contribution was docs).