Python pandas insert list into a cell

Question

I have a list 'abc' and a dataframe 'df':

abc = ['foo', 'bar']
df =
    A  B
0  12  NaN
1  23  NaN

I want to insert the list into cell 1B, so I want this result:

    A  B
0  12  NaN
1  23  ['foo', 'bar']

Ho can I do that?

1) If I use this:

df.ix[1,'B'] = abc

I get the following error message:

ValueError: Must have equal len keys and value when setting with an iterable

because it tries to insert the list (that has two elements) into a row / column but not into a cell.

2) If I use this:

df.ix[1,'B'] = [abc]

then it inserts a list that has only one element that is the 'abc' list ( [['foo', 'bar']] ).

3) If I use this:

df.ix[1,'B'] = ', '.join(abc)

then it inserts a string: ( foo, bar ) but not a list.

4) If I use this:

df.ix[1,'B'] = [', '.join(abc)]

then it inserts a list but it has only one element ( ['foo, bar'] ) but not two as I want ( ['foo', 'bar'] ).

Thanks for help!

---

EDIT

My new dataframe and the old list:

abc = ['foo', 'bar']
df2 =
    A    B         C
0  12  NaN      'bla'
1  23  NaN  'bla bla'

Another dataframe:

df3 =
    A    B         C                    D
0  12  NaN      'bla'  ['item1', 'item2']
1  23  NaN  'bla bla'        [11, 12, 13]

I want insert the 'abc' list into df2.loc[1,'B'] and/or df3.loc[1,'B'].

If the dataframe has columns only with integer values and/or NaN values and/or list values then inserting a list into a cell works perfectly. If the dataframe has columns only with string values and/or NaN values and/or list values then inserting a list into a cell works perfectly. But if the dataframe has columns with integer and string values and other columns then the error message appears if I use this: df2.loc[1,'B'] = abc or df3.loc[1,'B'] = abc.

Another dataframe:

df4 =
          A     B
0      'bla'  NaN
1  'bla bla'  NaN

These inserts work perfectly: df.loc[1,'B'] = abc or df4.loc[1,'B'] = abc.

Answer 1

Since set_value has been deprecated since version 0.21.0, you should now use at. It can insert a list into a cell without raising a ValueError as loc does. I think this is because at always refers to a single value, while loc can refer to values as well as rows and columns.

df = pd.DataFrame(data={'A': [1, 2, 3], 'B': ['x', 'y', 'z']})

df.at[1, 'B'] = ['m', 'n']

df =
    A   B
0   1   x
1   2   [m, n]
2   3   z

You also need to make sure the column you are inserting into has dtype=object. For example

>>> df = pd.DataFrame(data={'A': [1, 2, 3], 'B': [1,2,3]})
>>> df.dtypes
A    int64
B    int64
dtype: object

>>> df.at[1, 'B'] = [1, 2, 3]
ValueError: setting an array element with a sequence

>>> df['B'] = df['B'].astype('object')
>>> df.at[1, 'B'] = [1, 2, 3]
>>> df
   A          B
0  1          1
1  2  [1, 2, 3]
2  3          3