Q:

Sort lists in a Pandas Dataframe column

I have a Dataframe column which is a collection of lists
    a
['a', 'b']
['b', 'a']
['a', 'c']
['c', 'a']

I would like to use this list to group by its unique values (['a', 'b'] & ['a', 'c']). However, this generates an error 
TypeError: unhashable type: 'list'

Is there any way around this. Ideally I would like to sort the values in place and create an additional column of a concatenated string.

A:

You can also sort values by column.
Example:
x = [['a', 'b'], ['b', 'a'], ['a', 'c'], ['c', 'a']]
df = pandas.DataFrame({'a': Series(x)})
df.a.sort_values()

     a
0   [a, b]
2   [a, c]
1   [b, a]
3   [c, a]

However, for what I understand, you want to sort [b, a] to [a, b], and [c, a] to [a, c] and then set values in order to get only [a, b][a, c].
i'd recommend use lambda
Try:
result = df.a.sort_values().apply(lambda x: sorted(x))
result = DataFrame(result).reset_index(drop=True)

It returns:
0    [a, b]
1    [a, c]
2    [a, b]
3    [a, c]

Then get unique values:
newdf = pandas.DataFrame({'a': Series(list(set(result['a'].apply(tuple))))})
newdf.sort_values(by='a')

     a
0   (a, b)
1   (a, c)

A:

list are unhashable.  however, tuples are hashable
use
df.groupby([df.a.apply(tuple)])

setup
df = pd.DataFrame(dict(a=[list('ab'), list('ba'), list('ac'), list('ca')]))
results
df.groupby([df.a.apply(tuple)]).size()
a
(a, b)    1
(a, c)    1
(b, a)    1
(c, a)    1
dtype: int64

