Structured numpy array within a multidimensional array

Question

Imagine a numpy array of N x M dimension. In each cell, it contains a structured array with X elements, each containing an x_label.

I would like to access a specific x_label so it returns a N x M array only containing the value of the label of interest.

Is there a way to so so without having to use a for loop (or a np.map()) function and creating a new array?

Example:

import numpy as np
arr = np.array([[[],[]],
                [[],[]]])

# Each cell contains:
np.array([('par1', 'par2', 'par3')], dtype=[('label_1', 'U10'), ('label_2', 'U10'), ('label3', 'U10')])

How can I get a 2x2 np.array returned with the par1 values only? I have tried unsuccessfully:

arr['label_1']
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

Thank you!

What dtype is your outer array?

Paul Panzer
– Paul Panzer

2020-04-08 10:52:46 +00:00
Commented Apr 8, 2020 at 10:52 — Paul Panzer
– Paul Panzer, Commented Apr 8, 2020 at 10:52

Paul Panzer · Accepted Answer · 2020-04-08 11:06:56Z

1

I'm assuming your outer array is of Object dtype, otherwise there should be no problems:

>>> x = np.array([('par1', 'par2', 'par3')], dtype=[('label_1', 'U10'), ('label_2', 'U10'), ('label3', 'U10')])
>>> Y = np.array(4*[x]+[None])[:-1].reshape(2,2)
>>> Y
array([[array([('par1', 'par2', 'par3')],
      dtype=[('label_1', '<U10'), ('label_2', '<U10'), ('label3', '<U10')]),
        array([('par1', 'par2', 'par3')],
      dtype=[('label_1', '<U10'), ('label_2', '<U10'), ('label3', '<U10')])],
       [array([('par1', 'par2', 'par3')],
      dtype=[('label_1', '<U10'), ('label_2', '<U10'), ('label3', '<U10')]),
        array([('par1', 'par2', 'par3')],
      dtype=[('label_1', '<U10'), ('label_2', '<U10'), ('label3', '<U10')])]],
      dtype=object)

(Note how I have to jump through hoops to even create such a thing.)

Make your life easy by converting to a proper structured array:

>>> Z = np.concatenate(Y.ravel()).reshape(Y.shape)
>>> Z
array([[('par1', 'par2', 'par3'), ('par1', 'par2', 'par3')],
       [('par1', 'par2', 'par3'), ('par1', 'par2', 'par3')]],
      dtype=[('label_1', '<U10'), ('label_2', '<U10'), ('label3', '<U10')])

Now, you can simply index by label:

>>> Z['label_1']
array([['par1', 'par1'],
       ['par1', 'par1']], dtype='<U10')

answered Apr 8, 2020 at 11:06

Paul Panzer

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5 Comments

Jordi Ferrer Over a year ago

Thank you @paul! I did not create the array myself, it is the way a Python package returns a function... I will convert the array to a proper structured array :-)

Jordi Ferrer Over a year ago

Another question on this: Imagine one of the values in the array (the parameters called 'parX') is empty. Then, the np.concatenate() will change the size of the initial array and the .np.reshape() will give the error ValueError: cannot reshape array of size ### into shape (####). Any easy fix for that @paul ?

Paul Panzer Over a year ago

numpy.lib.recfunctions.stack_arrays(arr.ravel().tolist(),usemask=False).reshape(arr.shape) might work.

Jordi Ferrer Over a year ago

Unfortunately it is not working. It gives the same ValueError...

Paul Panzer Over a year ago

Hm, sorry, this is now getting too complex to work it out with comments. Perhaps if the field is empty you could delete it completely (there is a function for that in np.lib.recfunctions and then try again. In any case I suggest you make a new question with an example that shows your problem.

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