HomoPythonicus HomoPythonicus - 1 month ago 15
Python Question

Numpy array of numpy arrays has 1D shape

I have two numpy arrays of arrays (A and B). They look something like this when printed:

A:

[array([0, 0, 0]) array([0, 0, 0]) array([1, 0, 0]) array([0, 0, 0])
array([0, 0, 0]) array([0, 0, 0]) array([0, 0, 0]) array([0, 0, 0])
array([0, 0, 0]) array([0, 0, 0]) array([0, 0, 1]) array([0, 0, 0])
array([1, 0, 0]) array([0, 0, 1]) array([0, 0, 0]) array([0, 0, 0])
array([0, 0, 0]) array([1, 0, 0]) array([0, 0, 1]) array([0, 0, 0])]


B:

[[ 4.302135e-01 4.320091e-01 4.302135e-01 4.302135e-01
1.172584e+08]
[ 4.097128e-01 4.097128e-01 4.077675e-01 4.077675e-01
4.397120e+07]
[ 3.796353e-01 3.796353e-01 3.778396e-01 3.778396e-01
2.643200e+07]
[ 3.871173e-01 3.890626e-01 3.871173e-01 3.871173e-01
2.161040e+07]
[ 3.984899e-01 4.002856e-01 3.984899e-01 3.984899e-01
1.836240e+07]
[ 4.227315e-01 4.246768e-01 4.227315e-01 4.227315e-01
1.215760e+07]
[ 4.433817e-01 4.451774e-01 4.433817e-01 4.433817e-01
9.340800e+06]
[ 4.620867e-01 4.638823e-01 4.620867e-01 4.620867e-01
1.173760e+07]]


type(A)
,
type(A[0])
,
type(B)
,
type(B[0])
are all
<class 'numpy.ndarray'>
.

However,
A.shape
is
(20,)
, while
B.shape
is
(8, 5)
.

Question 1: Why is
A.shape
one-dimensional, and how do I make it two-dimensional like
B.shape
? They're both arrays of arrays, right?

Question 2, possibly related to Q1: Why does printing
A
show the calls of
array()
, while printing
B
doesn't, and why do the elements of the subarrays of
B
not have commas in-between them?

Thanks in advance.

Answer

A.dtype is O, object, B.dtype is float.

A is a 1d array that contains objects, which happen to be arrays. They could just as well be lists or None`.

B is a 2d array of floats. Indexing one row of B gives a 1d array.

So A[0] and B[0] can appear to produce the same thing, but the selection process is different.

Try np.concatenate(A), or np.vstack(A). Both of these then treat A as a list of arrays, and join them either in 1 or 2d.

Converting object arrays to regular comes up quite often.

Converting a 3D List to a 3D NumPy array is a little more general that what you need, but gives a lot of useful information.

also

Convert a numpy array of lists to a numpy array

==================

In [28]: A=np.empty((5,),object)
In [31]: A
Out[31]: array([None, None, None, None, None], dtype=object)
In [32]: for i in range(5):A[i]=np.zeros((3,),int)
In [33]: A
Out[33]: 
array([array([0, 0, 0]), array([0, 0, 0]), array([0, 0, 0]),
       array([0, 0, 0]), array([0, 0, 0])], dtype=object)
In [34]: print(A)
[array([0, 0, 0]) array([0, 0, 0]) array([0, 0, 0]) array([0, 0, 0])
 array([0, 0, 0])]
In [35]: np.vstack(A)
Out[35]: 
array([[0, 0, 0],
       [0, 0, 0],
       [0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]])
Comments