How to use numpy.void type

Question

I loaded a MATLAB .mat file via scipy.io.loadmat and it gave me a list of numpy.void objects.

What are they, how can they be used and where can I get some reference documentation on them?

Answer 1

According to the numpy documentation: http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html , numpy.void types are defined as flexible data types. Basically, these are data types where there is no pre-defined type associated to the variable(s) you're looking at. If you look at numpy , you have data types such as float , uint8 , bool , string , etc.

void is to accommodate for more generic and flexible types and are for those data types that don't necessary fall into any one of these pre-defined data types. This situation is mostly encountered when you're loading in a struct where each element has multiple data types associated with multiple fields. Each structure element could have a combination of different data types, and the amalgamation of all of these data types to represent an instance of this structure element thus leads us to numpy.void .

With the documentation, you can certainly do the same operations like you would with any other data type. Take a look at the generic data type methods here: http://docs.scipy.org/doc/numpy/reference/generated/numpy.generic.html#numpy.generic . In fact, all numpy data types are derived from this generic class, including numpy.void .

In the first link I provided at the beginning of this post, it shows a good example of how to create a custom record type, where a record is a combination of a tuple of numbers and a string. When creating a list of these records, each type in the list is of type numpy.void and it demonstrates that a record is of this data type. However, bear in mind that this record list has a data type that is of this record , but each element of this list will be of type numpy.void .

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However, as a matter of self-containment, let's re-create the example here: Let's create a custom record type where it has two fields associated for each variable you create:

• A 16-bit string with a field named name
• A 2-element tuple of floating point numbers that are 64-bits each, with a field named grades

As such, you'd do something like:

import numpy as np
dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])

As such, let's create an example list of two elements and instantiate their fields:

x = np.array([('Sarah', (8.0, 7.0)), ('John', (6.0, 7.0))], dtype=dt)

Because we made this list into a numpy.array , we expect its data type to be so:

type(x)

We get:

<type 'numpy.ndarray'>

Remember, the list itself is a numpy.array , but not the individual elements.

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To access the second element of this list, which is the second record, we do:

x[1]

We get:

('John', [6.0, 7.0])

To check the type of the second record, we do:

type(x[1])

We get:

<type 'numpy.void'> # As expected

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Some additional bonuses for you

To access the name of the second record, we do:

x[1]['name']

We get:

'John'

To access the grades of the second record, we do:

x[1]['grades']

We get:

array([ 6.,  7.])

To check the type of the name inside the second record, we do:

type(x[1]['name'])

We get:

<type 'numpy.string_'>

To check the type of the grades inside the second record, we do:

type(x[1]['grades'])

We get:

<type 'numpy.ndarray'>

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Take note that each element in this list is of type numpy.void . However, the individual fields for each element in our list is either a tuple of numbers, or a string. The collection of these elements together is of type numpy.void .