HomoPythonicus - 1 year ago 96

Python Question

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])`

`<class 'numpy.ndarray'>`

However,

`A.shape`

`(20,)`

`B.shape`

`(8, 5)`

Question 1: Why is

`A.shape`

`B.shape`

Question 2, possibly related to Q1: Why does printing

`A`

`array()`

`B`

`B`

Thanks in advance.

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Answer Source

`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]])
```

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