如何连接Numpy的ndarray来存储对象? [英] How to interface Numpy's ndarrays to store objects in?

问题描述

我想将一系列不同大小的数组存储到一个父"数组中.大批.像这样:

 将numpy导入为npa1 = np.array([[1,2]，[3,4]，[5,6]])a2 = np.array([7,3])a3 = np.array([1])# 我想做的事a_parent = np.ndarray(a1，a2，a3)#期望的输出打印(a_parent [0])>>>[[1 2][3 4][5 6]打印(a_parent [1])>>>[7 3]打印(a_parent [2])>>>[1] 

我知道这是可能的，因为当我从 scipy.io 库中使用 loadmat 导入Matlab单元数据时，数据将转换为numpy的 ndarray ，并且其行为与上述代码完全相同.我已经浏览了 numpy文档，我可以找不到一个可行的例子来说明我自己该怎么做.

解决方案

 在[5]中:a1 = np.array([[1,2,3，[3,4]，[5,6]])...:a2 = np.array([7,3])...:a3 = np.array([1]) 

最好的方法是制作具有所需dtype和形状的'空白'数组:

 在[6]中:a_parent = np.empty(3，object)在[7]中:a_parentOut [7]:array([None，None，None]，dtype = object) 

并从所需数组(或其他对象)的列表中填充"它:

 在[13]中:a_parent [:] = [a1，a2，a3]在[14]中:a_parent出[14]:array([array([[1，2]，[3，4]，[5，6]])，数组([7，3])，array([1])]，dtype = object) 

我确定 loadmat 使用此方法.

将列表直接传递到 np.array 也许可行，但是v1.19希望我们包括 object dtype:

 在[10]中:np.array([a1，a2，a3])/usr/local/bin/ipython3:1:VisibleDeprecationWarning:不推荐使用粗糙的嵌套序列(它是具有不同长度或形状的list-or-tuples或ndarray的列表或元组)创建ndarray.如果您打算这样做，则在创建ndarray时必须指定"dtype = object"#！/usr/bin/python3出[10]:array([array([[1，2]，[3，4]，[5，6]])，数组([7，3])，array([1])]，dtype = object) 

如果数组的形状都相同，则此方法不起作用:

 在[11]中:np.array([a1，a1])出[11]:数组([[[[1，2]，[3，4]，[5，6]，[[1，2]，[3，4]，[5，6]]] 

对于某些形状组合，我们会出错.

 在[15]中:a_parent [:] = [a3，a3，a3]在[16]中:a_parentOut [16]:array([array([1])，array([1])，array([1])]，dtype = object) 

I want to store a series of differently sized arrays into one "parent" array. Like this:

import numpy as np
    
a1 = np.array([[1,2], [3,4], [5,6]]) 
a2 = np.array([7,3]) 
a3 = np.array([1])

# What I want to do
a_parent = np.ndarray(a1, a2, a3)
    
# Desired output
print(a_parent[0])
>>> [[1 2]
    [3 4]
    [5 6]]
    
print(a_parent[1])
>>> [7 3]
    
print(a_parent[2])
>>> [1]

I know this is possible because when I import Matlab cell data using loadmat from the scipy.io library the data gets converted to a numpy ndarray and it behaves exactly like above. I've looked through the numpy docs and I can't find a working example to show how I could do this myself.

解决方案

In [5]: a1 = np.array([[1,2], [3,4], [5,6]])  
   ...: a2 = np.array([7,3])  
   ...: a3 = np.array([1])       

The best way is to make a 'blank' array of the desired dtype and shape:

In [6]: a_parent = np.empty(3, object)                                                               
In [7]: a_parent                                                                                     
Out[7]: array([None, None, None], dtype=object)

and 'fill' it from a list of the desired arrays (or other objects):

In [13]: a_parent[:] = [a1,a2,a3]                                                                    
In [14]: a_parent                                                                                    
Out[14]: 
array([array([[1, 2],
       [3, 4],
       [5, 6]]), array([7, 3]),
       array([1])], dtype=object)

I'm sure loadmat uses this method.

Passing the list directly to np.array may work, but v1.19 wants us to include the object dtype:

In [10]: np.array([a1,a2,a3])                                                                        
/usr/local/bin/ipython3:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  #!/usr/bin/python3
Out[10]: 
array([array([[1, 2],
       [3, 4],
       [5, 6]]), array([7, 3]),
       array([1])], dtype=object)

This does not work if the arrays are all the same shape:

In [11]: np.array([a1,a1])                                                                           
Out[11]: 
array([[[1, 2],
        [3, 4],
        [5, 6]],

       [[1, 2],
        [3, 4],
        [5, 6]]])

And for some shape combinations we get an error.

In [15]: a_parent[:] = [a3,a3,a3]                                                                    
In [16]: a_parent                                                                                    
Out[16]: array([array([1]), array([1]), array([1])], dtype=object)

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