Why is pandas.DataFrame.apply printing out junk?

Question

Consider this simple dataframe:

   a  b
0  1  2
1  2  3

I perform a .apply as such:

In [4]: df.apply(lambda x:         


        

Answer 1

I don't have an answer... just a work around

f = lambda x: x.values.reshape(1, -1).tolist()

df.apply(f)

a    [[1, 2]]
b    [[2, 3]]
dtype: object

---

I tracked it down to pd.lib.reduce

pd.lib.reduce(df.values, lambda x: [list(x)])

array([list([[1, 2]]), list([[2, 3]]), list([['a', 'b']])], dtype=object)

Versus

pd.lib.reduce(df.values, lambda x: [x])

array([list([array([None, None], dtype=object)]),
       list([array([None, None], dtype=object)]),
       list([array([None, None], dtype=object)])], dtype=object)

Answer 2

Another work around:

df.apply(lambda x: [list(x)])

Answer 3

It looks like bug, so was opened Issue 17487.

For me working add tolist:

print (df.apply(lambda x: [x.values.tolist()]))
a    [[1, 2]]
b    [[2, 3]]
dtype: object

---

print (df.apply(lambda x: [list(x.values)]))
a    [[1, 2]]
b    [[2, 3]]
dtype: object