Accessing Elements in a Numpy Structured Array

Question

I have a situation in which I would like to do the following:

import numpy as np
type1 = np.dtype([('col1', 'i'), ('col2', 'i')])
type2 = np.dtype([('cols', type1), ('info', 'S32')])
data = np.zeros(10, type2)

# The following doesn't work, but I want to do something similar
index = ['cols']['col1']
# Set column ['cols']['col1'] to 5
data[index] = 5

# I can only get this to work if I do the following: 
index = "['cols']['col1']"
eval('data' + index '= 5') # kinda scary

This doesn't work, but I found a workaround using the exec function but that feels pretty hacky. Does anyone have any suggestions how to programmatically create an index for nested structured numpy datatypes?

Thanks

because I need to be able to define the indices in a separate step unfortunately. — user985030
– user985030, Commented Nov 2, 2015 at 21:57
What if you defined them like index1, index2 = 'cols', 'col1' and used data[index1][index2] ? — user3148185
– user3148185, Commented Nov 2, 2015 at 22:00

Mike Müller · Accepted Answer · 2015-11-02 22:46:00Z

1

This would work:

index = ['cols', 'col1']
data[index[0]][index[1]] = 5

UPDATE

This allows setting of values at any depths:

def set_deep(obj, names, value):
    if len(names) > 1:
        obj = obj[names[0]]
        if len(names) > 2:
            for name in names[1:-1]:
                obj = obj[name]
    obj[names[-1]] = value

Usage:

set_deep(data, ['cols', 'col1'], 5)

set_deep(data, ['a', 'b', 'c', 'd'], 5)

edited Nov 2, 2015 at 22:46

answered Nov 2, 2015 at 22:06

Mike Müller

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2 Comments

user985030 Over a year ago

Thanks, this is close to what I might end up having to use, but have structured datatypes that are not just 2 deep. I might have things defined like data['a']['b']['c']['d']. In this case I guess I could do a for loop or something.

user985030 Over a year ago

I think this might be as close as we can get without using the exec statement.

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