[Python] Pandas - combine column values into a list in a new column

I have a Python Pandas dataframe df:

d = [['hello', 1, 'GOOD', 'long.kw'],
[1.2, 'chipotle', np.nan, 'bingo'],
['various', np.nan, 3000, 123.456]]
t = pd.DataFrame(data=d, columns=['A','B','C','D'])


which looks like this:

print(t)
A B C D
0 hello 1 GOOD long.kw
1 1.2 chipotle NaN bingo
2 various NaN 3000 123.456


I am trying to create a new column which is a list of the values in A, B, C, and D. So it would look like this:

t['combined']

Out[125]:
0 [hello, 1, GOOD, long.kw]
1 [1.2, chipotle, nan, bingo]
2 [various, nan, 3000, 123.456]
Name: combined, dtype: object


I am trying this code:

t['combined'] = t.apply(lambda x: list([x['A'],
x['B'],
x['C'],
x['D']]),axis=1)


Which returns this error:

ValueError: Wrong number of items passed 4, placement implies 1


What is puzzling to me is if I remove one of the columns that I want to put in the list (or add another column to the dataframe that I DON'T add to the list), my code works.

For instance, run this code:

t['combined'] = t.apply(lambda x: list([x['A'],
x['B'],
x['D']]),axis=1)


Returns this which is perfect if I only wanted the 3 columns:

print(t)
A B C D combined
0 hello 1 GOOD long.kw [hello, 1, long.kw]
1 1.2 chipotle NaN bingo [1.2, chipotle, bingo]
2 various NaN 3000 123.456 [various, nan, 123.456]



I am at a complete loss as to why requesting the 'combined' list be made of all columns in the dataframe would create an error, but selecting all but 1 column to create the 'combined' list works as expected.

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