I'm confused why bracket [] and .loc behave differently when creating multiple columns. I've looked into other similar questions but I couldn't get the answer.

For example,

```
>>> dates = pd.date_range('1/1/2000', periods=8)
>>> df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
>>> df
                    A         B         C         D
2000-01-01 -1.011264 -1.751948  0.059012  0.514253
2000-01-02  0.596959  0.348866 -1.011628  0.950259
2000-01-03  0.305281  0.486400 -1.034534 -1.523402
2000-01-04 -0.880457  0.379837  2.023866  1.588379
2000-01-05 -1.142070 -0.168992 -0.391355  0.809820
2000-01-06 -0.335015  0.721563 -0.665120 -1.097811
2000-01-07 -0.160611 -0.601393 -0.257349 -0.830527
2000-01-08  0.197624 -0.082786  1.335873 -0.841006

```

If I create multiple columns with brackets, it works as below.

```
>>> df[['E','F']] = df[['A','B']]
>>> df
                   A         B         C         D         E         F
2000-01-01 -1.011264 -1.751948  0.059012  0.514253 -1.011264 -1.751948
2000-01-02  0.596959  0.348866 -1.011628  0.950259  0.596959  0.348866
2000-01-03  0.305281  0.486400 -1.034534 -1.523402  0.305281  0.486400
2000-01-04 -0.880457  0.379837  2.023866  1.588379 -0.880457  0.379837
2000-01-05 -1.142070 -0.168992 -0.391355  0.809820 -1.142070 -0.168992
2000-01-06 -0.335015  0.721563 -0.665120 -1.097811 -0.335015  0.721563
2000-01-07 -0.160611 -0.601393 -0.257349 -0.830527 -0.160611 -0.601393
2000-01-08  0.197624 -0.082786  1.335873 -0.841006  0.197624 -0.082786

```

However, if I use .loc method for creating multiple columns, it doesn't work.

```
>>> df.loc[:,['H','I']] = df[['A','B']]

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Python\lib\site-packages\pandas\core\indexing.py", line 189, in __setitem__
    indexer = self._get_setitem_indexer(key)
  File "C:\Python\lib\site-packages\pandas\core\indexing.py", line 167, in _get_setitem_indexer
    return self._convert_tuple(key, is_setter=True)
  File "C:\Python\lib\site-packages\pandas\core\indexing.py", line 248, in _convert_tuple
    idx = self._convert_to_indexer(k, axis=i, is_setter=is_setter)
  File "C:\Python\lib\site-packages\pandas\core\indexing.py", line 1354, in _convert_to_indexer
    return self._get_listlike_indexer(obj, axis, **kwargs)[1]
  File "C:\Python\lib\site-packages\pandas\core\indexing.py", line 1161, in _get_listlike_indexer
    raise_missing=raise_missing)
  File "C:\Python\lib\site-packages\pandas\core\indexing.py", line 1246, in _validate_read_indexer
    key=key, axis=self.obj._get_axis_name(axis)))
KeyError: "None of [Index(['H', 'I'], dtype='object')] are in the [columns]"

```

.loc method works well when creating only one column. (square bracket works as well.)

```
>>> df.loc[:,'G'] = df['A']
>>> df
                   A         B         C         D         E         F         G
2000-01-01 -1.011264 -1.751948  0.059012  0.514253 -1.011264 -1.751948 -1.011264
2000-01-02  0.596959  0.348866 -1.011628  0.950259  0.596959  0.348866  0.596959
2000-01-03  0.305281  0.486400 -1.034534 -1.523402  0.305281  0.486400  0.305281
2000-01-04 -0.880457  0.379837  2.023866  1.588379 -0.880457  0.379837 -0.880457
2000-01-05 -1.142070 -0.168992 -0.391355  0.809820 -1.142070 -0.168992 -1.142070
2000-01-06 -0.335015  0.721563 -0.665120 -1.097811 -0.335015  0.721563 -0.335015
2000-01-07 -0.160611 -0.601393 -0.257349 -0.830527 -0.160611 -0.601393 -0.160611
2000-01-08  0.197624 -0.082786  1.335873 -0.841006  0.197624 -0.082786  0.197624

```

I'm confused about why .loc is not functioning as square brackets in creating multiple columns. I prefer explicit way as .loc[] does, and I'm quite bothered by the fact that its functionality is sometimes limited. Am I missing something? Could I ask why they work differently in this case? 

**\* Addition to the original question \***

.loc[] method generates NaN columns when it is assigned to existing columns. For example,

`>>>df[['E','F']] = df[['A','B']]`
`>>> df
 A B ... E F
2000-01-01 0.934380 -0.321112 ... 0.934380 -0.321112
2000-01-02 -0.760045 0.646212 ... -0.760045 0.646212
2000-01-03 0.645231 -0.910008 ... 0.645231 -0.910008
2000-01-04 -1.117132 2.595804 ... -1.117132 2.595804
2000-01-05 -1.273579 0.291202 ... -1.273579 0.291202
2000-01-06 0.142610 -0.368157 ... 0.142610 -0.368157
2000-01-07 0.567490 -1.598343 ... 0.567490 -1.598343
2000-01-08 1.300694 0.498405 ... 1.300694 0.498405`

I generated new columns E,F and tried to assign new values from C,D using .loc[] method. 

`>>>df.loc[:,['E','F']] = df[['C','D']]`

`>>>df
 A B C D E F
2000-01-01 0.934380 -0.321112 0.747195 -0.991180 NaN NaN
2000-01-02 -0.760045 0.646212 -0.121421 2.262384 NaN NaN
2000-01-03 0.645231 -0.910008 0.170989 -1.552823 NaN NaN
2000-01-04 -1.117132 2.595804 0.569809 1.575253 NaN NaN
2000-01-05 -1.273579 0.291202 0.688443 -0.581674 NaN NaN
2000-01-06 0.142610 -0.368157 -0.674774 -1.961087 NaN NaN
2000-01-07 0.567490 -1.598343 -1.346179 -1.139205 NaN NaN
2000-01-08 1.300694 0.498405 -0.358015 -1.637471 NaN NaN`

It seems that using .loc[] still makes problem.