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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def filter(self, func, dropna=True, *args, **kwargs):
 # noqa """ Return a copy of a DataFrame excluding elements from groups that do not satisfy the boolean criterion specified by func. Parameters f : function Function to apply to each subframe. Should return True or False. dropna : Drop groups that do not pass the filter. True by default; if False, groups that evaluate False are filled with NaNs. Returns ------- filtered : DataFrame Notes ----- Each subframe is endowed the attribute 'name' in case you need to know which group you are working on. Examples -------- A B C 1 bar 2 5.0 3 bar 4 1.0 5 bar 6 9.0 """


        indices = []

        obj = self._selected_obj
        gen = self.grouper.get_iterator(obj, axis=self.axis)

        for name, group in gen:
            object.__setattr__(group, 'name', name)

            res = func(group, *args, **kwargs)

            try:
                res = res.squeeze()
            except AttributeError:  # allow e.g., scalars and frames to pass
                pass

            # interpret the result of the filter
            if is_bool(res) or (is_scalar(res) and isna(res)):
                if res and notna(res):
                    indices.append(self._get_index(name))
            else:
                # non scalars aren't allowed
                raise TypeError("filter function returned a %s, "
                                "but expected a scalar bool" %
                                type(res).__name__)

        return self._apply_filter(indices, dropna)