Is it possible to set dtype to an existing structured array, or add "column name" to an existing array?

Question

this code (snippet_1) is to construct a structured array

>>> dt = np.dtype([('name', np.str_, 16), ('age', np.int)])
>>> x = np.array([('Sarah', 16), ('John', 17)], dtype=dt)
>>> x
array([('Sarah', 16), ('John', 17)],
      dtype=[('name', '<U16'), ('age', '<i8')])

this code is to set dtype to a given simple array

arr = np.array([10, 20, 30, 40, 50]) 
arr = arr.astype('float64')

this code (snippet_3) is trying to set dtype to a structured array,

x = np.array([('Sarah', 16), ('John', 17)])
x = x.astype(dt)

of course, set dtype this way causes ValueError

ValueError                                Traceback (most recent call last)
<ipython-input-18-201b69204e82> in <module>()
      1 x = np.array([('Sarah', 16), ('John', 17)])
----> 2 x = x.astype(dt)

ValueError: invalid literal for int() with base 10: 'Sarah'

Is it possible to set dtype to an existing structured array? something like snippet_3?

Why would I want to do this? Because there is a handy approach to access data in the setting of snippet_1.

x['name']

If I can add "column name" to an existing array, that would be cool.

Why do you want to set the dtype of existing structured array? The dtype is an attribute of the array. You can GET it, but I don't think you can change it, especially if it already has data in it. — kcw78
– kcw78, Commented Oct 8, 2019 at 0:38

Paul Panzer · Accepted Answer · 2019-10-08 01:33:17Z

1

You can use numpy.lib.recfunctions.unstructured_to_structured

x = np.array([('Sarah', 16), ('John', 17)])
x
# array([['Sarah', '16'],
#        ['John', '17']], dtype='<U5')
dt = np.dtype([('name', np.str_, 16), ('age', np.int)])

import numpy.lib.recfunctions as nlr

xs = nlr.unstructured_to_structured(x, dtype=dt)
xs
# array([('Sarah', 16), ('John', 17)],
#       dtype=[('name', '<U16'), ('age', '<i8')])

edited Oct 8, 2019 at 1:33

answered Oct 8, 2019 at 1:22

Paul Panzer

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Massifox · Accepted Answer · 2019-10-08 01:30:11Z

0

Given the following input:

dt = np.dtype([('name', np.str_, 16), ('age', np.int)])
x = np.array([('Sarah', 16), ('John', 17)], dtype=dt)

# array([('Sarah', 16), ('John', 17)],
#      dtype=[('name', '<U16'), ('age', '<i8')])

Let's add a column to the structured array, like this:

new_dt = np.dtype(x.dtype.descr + [('weight', float)])
xx = np.zeros(x.shape, dtype=new_dt)

xx['name'] = x['name']
xx['age'] = x['age']

xx['weight'] = [86.7, 78.9]

and gives:

# array([('Sarah', 16, 86.7), ('John', 17, 78.9)],
#      dtype=[('name', '<U16'), ('age', '<i8'), ('weight', '<f8')])

To change the type of data within the structured array instead look at the answer of @PaulPanzer

edited Oct 8, 2019 at 1:30

answered Oct 8, 2019 at 1:23

Massifox

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