How to get a list of dtypes from a numpy structured array?

Question

How can I get a list of dtypes from a numpy structured array?

Create example structured array:

arr = np.array([[1.0, 2.0],[3.0, 4.0]])

dt = {'names':['ID', 'Ring'], 'formats':[np.double, np.double]}
arr.dtype = dt

>>> arr
array([[(1., 2.)],
       [(3., 4.)]], dtype=[('ID', '<f8'), ('Ring', '<f8')])

On one hand, it's easy to isolate the column names.

>>> arr.dtype.names
('ID', 'RING')

However, ironically, none of the dtype attributes seem to reveal the individual dtypes.

numpy.org/doc/stable/reference/generated/numpy.dtype.html lists the attributes of a dtype object. — hpaulj
– hpaulj, Commented Dec 9, 2020 at 21:18

LayneSadler · Accepted Answer · 2020-12-09 20:30:07Z

2

Discovered that, despite not having dictionary methods like .items(), you can still call dtype['<column_name>'].

column_names = list(arr.dtype.names)
dtypes = [str(arr.dtype[n]) for n in column_names]

>>> dtypes

['float64', 'float64']

answered Dec 9, 2020 at 20:30

LayneSadler

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1 Comment

dtype.fields is a dict, with name as key, and (dtype, offset) as value.

LayneSadler · Accepted Answer · 2020-12-10 16:14:17Z

0

Or, as @hpaulj hinted, in one step:

>>> [str(v[0]) for v in arr.dtype.fields.values()]

['float64', 'float64']

answered Dec 10, 2020 at 16:14

LayneSadler

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