Python 在数据帧中混合三维和二维数据

将这种数据结构存储在数据库中的惯用方法是什么：

### Option 1
df = pd.DataFrame(data = [
    {'kws' : np.array([0,0,0]), 'x' : i, 'y', i} for i in range(10)
])

# df.x and df.y works as expected
# the list and array casting is required because df.kws is
# an array of arrays
np.array(list(df.kws))

# this causes problems when trying to assign as well though:
# for any other data type, this would set all kws in df to the rhs [1,2,3]
# but since the rhs is a list, it tried to do an element-wise assignment and
# errors saying that the length of df and the length of the rhs do not match
df.kws = [1,2,3]

### Option 2
df = pd.DataFrame(data = [
    {'kw_0' : 0, 'kw_1' : 0, 'kw_2' : 0, 'x' : i, 'y', i} for i in range(10)
])

# retrieving 2d array:
df[sorted([c for c in df if c.startswith('kw_')])].values

# batch set :
kws = [1,2,3]
for i, kw in enumerate(kws) :
    df['kw_'+i] = kw

---

我觉得这两种解决方案都不对。首先，它们都不允许在不复制所有数据的情况下检索2d矩阵。有没有更好的方法来处理这种混合维度数据，或者这只是pandas目前没有完成的任务？

只需使用列多索引

选择很容易

In [35]: df['kw']
Out[35]: 
   0  1  2
0  0  0  0
1  0  0  0
2  0  0  0
3  0  0  0
4  0  0  0
5  0  0  0
6  0  0  0
7  0  0  0
8  0  0  0
9  0  0  0

设置太多

In [36]: df.loc[1,'kw'] = [4,5,6]

In [37]: df
Out[37]: 
   kw        value   
    0  1  2      x  y
0   0  0  0      0  0
1   4  5  6      1  1
2   0  0  0      2  2
3   0  0  0      3  3
4   0  0  0      4  4
5   0  0  0      5  5
6   0  0  0      6  6
7   0  0  0      7  7
8   0  0  0      8  8
9   0  0  0      9  9

或者，您可以使用2个数据帧，索引相同，并在需要时合并/合并

In [36]: df.loc[1,'kw'] = [4,5,6]

In [37]: df
Out[37]: 
   kw        value   
    0  1  2      x  y
0   0  0  0      0  0
1   4  5  6      1  1
2   0  0  0      2  2
3   0  0  0      3  3
4   0  0  0      4  4
5   0  0  0      5  5
6   0  0  0      6  6
7   0  0  0      7  7
8   0  0  0      8  8
9   0  0  0      9  9