如何解决如何证明具有不同长度的2d数组的3d矩阵合理？
我有一个时间序列数据帧，据此我生成了以下序列。 我已经为此提供了一个“正当化”功能，但是它根本不起作用，所以：
如何“合理化”，以便样本中的NaN上升（有效值下降）。
print(df)
Time A B C D
2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12504.11 12504.11 12504.11
2019-06-17 08:51:00 12504.11 NaN 12503.11 12503.11
2019-06-17 08:52:00 12504.11 12504.11 12503.11 12503.11
2019-06-17 08:53:00 12503.61 12503.61 12503.61 12503.61
2019-06-17 08:54:00 12503.61 12503.61 12503.11 12503.11
# sequences
print(result)
[
[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12504.11 12504.11 12504.11 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12504.11 12504.11 12504.11
2019-06-17 08:51:00 12504.11 NaN 12503.11 12503.11 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12504.11 12504.11 12504.11
2019-06-17 08:51:00 12504.11 NaN 12503.11 12503.11
2019-06-17 08:52:00 12504.11 12504.11 12503.11 12503.11 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12504.11 12504.11 12504.11
2019-06-17 08:51:00 12504.11 NaN 12503.11 12503.11
2019-06-17 08:52:00 12504.11 12504.11 12503.11 12503.11
2019-06-17 08:53:00 12503.61 12503.61 12503.61 12503.61 ],[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12504.11 12504.11 12504.11
2019-06-17 08:51:00 12504.11 NaN 12503.11 12503.11
2019-06-17 08:52:00 12504.11 12504.11 12503.11 12503.11
2019-06-17 08:53:00 12503.61 12503.61 12503.61 12503.61
2019-06-17 08:54:00 12503.61 12503.61 12503.11 12503.11 ]
# ...
# always a row longer...
]
此代码产生错误：
def justify(a,invalid_val=0,axis=1,side='left'):
""" Justifies a 2D array
Parameters
----------
A : ndarray
Input array to be justified
axis : int
Axis along which justification is to be made
side : str
Direction of justification. It could be 'left','right','up','down'
It should be 'left' or 'right' for axis=1 and 'up' or 'down' for axis=0.
"""
if invalid_val is np.nan:
mask = ~np.isnan(a)
else:
mask = a!=invalid_val
justified_mask = np.sort(mask,axis=axis)
#if (side=='up') | (side=='left'):
# justified_mask = np.flip(justified_mask,axis=axis)
out = np.full(a.shape,invalid_val)
if axis==1:
out[justified_mask] = a[mask]
else:
out.T[justified_mask.T] = a.T[mask.T]
return out
# main:
arr = np.asarray(df)
result = list((map(lambda i: arr[:i],range(1,df.shape[0]+1))))
result = np.asarray(result)#[np.array(x) for x in result])
justify(result,invalid_val=np.nan,side='down')
#this produces the following error:
>>>
File "....py",line ...,in justify
mask = ~np.isnan(a)
TypeError: ufunc 'isnan' not supported for the input types,and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
那我怎么证明呢？ 预期的结果：（时间戳只是临时的）
[
[2019-06-17 08:45:00 12089.89 12089.89 12087.71 12087.71 ],[2019-06-17 08:45:00 12089.89 NaN 12087.71 12087.71
2019-06-17 08:46:00 12087.91 12089.89 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21 ],[2019-06-17 08:45:00 12089.89 NaN 12087.71 12087.71
2019-06-17 08:46:00 12087.91 12089.89 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41 ],[2019-06-17 08:45:00 12089.89 NaN 12087.71 12087.71
2019-06-17 08:46:00 12087.91 12089.89 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21 ],[2019-06-17 08:45:00 12089.89 NaN 12087.71 12087.71
2019-06-17 08:46:00 12087.91 12089.89 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12088.21 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12090.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12504.11 12504.11 12504.11 ],[2019-06-17 08:45:00 12089.89 NaN 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12089.89 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12088.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12090.21 12504.11 12504.11
2019-06-17 08:51:00 12504.11 12504.11 12503.11 12503.11 ],[2019-06-17 08:45:00 12089.89 NaN 12087.71 12087.71
2019-06-17 08:46:00 12087.91 NaN 12087.71 12087.91
2019-06-17 08:47:00 12088.21 12089.89 12084.21 12085.21
2019-06-17 08:48:00 12085.09 12088.21 12084.91 12089.41
2019-06-17 08:49:00 12089.71 12090.21 12087.21 12088.21
2019-06-17 08:50:00 12504.11 12090.21 12504.11 12504.11
2019-06-17 08:51:00 12504.11 12504.11 12503.11 12503.11
2019-06-17 08:52:00 12504.11 12504.11 12503.11 12503.11 ],#...
]
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