fix DF hashing bug (untested)

[falschparker82]

Should fix

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-15-1e594fb06987> in <module>
      1 dep_var = 'label'
      2 data_bunch = TabularDataBunch.from_df('data/', full_df, valid_df, dep_var, tfms=[FillMissing, Categorify],
----> 3                                       cat_names=[], cont_names=contin_vars, test_df=test_df)

/anaconda3/lib/python3.6/site-packages/fastai/tabular/data.py in from_df(cls, path, df, dep_var, valid_idx, procs, cat_names, cont_names, classes, **kwargs)
    111         "Create a `DataBunch` from train/valid/test dataframes."
    112         cat_names = ifnone(cat_names, [])
--> 113         cont_names = ifnone(cont_names, list(set(df)-set(cat_names)-{dep_var}))
    114         procs = listify(procs)
    115         return (TabularList.from_df(df, path=path, cat_names=cat_names, cont_names=cont_names, procs=procs)

/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in __hash__(self)
   1490     def __hash__(self):
   1491         raise TypeError('{0!r} objects are mutable, thus they cannot be'
-> 1492                         ' hashed'.format(self.__class__.__name__))
   1493 
   1494     def __iter__(self):

TypeError: 'DataFrame' objects are mutable, thus they cannot be hashed

[sgugger]

Not sure what the problem is with that line. set(df) returns the set of the names of the columns.

The problem is that you're not passing the arguments expected by the function: from_df(cls, path, df, dep_var,...) in your error message. So dep_var is here what you called valid_df, and I expect that is a dataframe, so the error is thrown by {dep_var}.

[falschparker82]

Ah, that's it, sorry I did not get that the main function signature silently changed after 1.0.22