The following code might help:

import numpy as np

dt = np.dtype([('name1', '|S10'), ('name2', '<f8')])
tuplelist=[
    ('n1', 1.2),
    ('n2', 3.4),    
     ]
arr = np.array(tuplelist, dtype=dt)

print(arr['name1'])
# ['n1' 'n2']
print(arr['name2'])
# [ 1.2  3.4]

Your immediate problem was that np.dtype expects the format specifiers to be numpy types, such as '|S10' or '<f8' and not Python types, such as str or float. If you type help(np.dtype) you'll see many examples of how np.dtypes can be specified. (I've only mentioned a few.)

Note that np.array expects a list of tuples. It's rather particular about that.

A list of lists raises TypeError: expected a readable buffer object.

A (tuple of tuples) or a (tuple of lists) raises ValueError: setting an array element with a sequence.

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']