How to use sklearn imputation methods on numpy.void (record or structured array, I'm not sure) ndarray

Code:

import numpy as np
import sklearn as skl

data = np.genfromtxt("water_potability.csv", delimiter = ",", names = True)
print(data)
print(data.shape)
print(type(data[0]))
imputer = skl.impute.SimpleImputer()
imputer.fit_transform(data)

(part of) water_potability.csv:

ph,Hardness,Solids,Chloramines,Sulfate,Conductivity,Organic_carbon,Trihalomethanes,Turbidity,Potability
,204.8904554713363,20791.318980747026,7.300211873184757,368.51644134980336,564.3086541722439,10.3797830780847,86.9909704615088,2.9631353806316407,0
3.71608007538699,129.42292051494425,18630.057857970347,6.635245883862,,592.8853591348523,15.180013116357259,56.32907628451764,4.500656274942408,0
8.099124189298397,224.23625939355776,19909.541732292393,9.275883602694089,,418.6062130644815,16.868636929550973,66.42009251176368,3.0559337496641685,0
...
9.41951031641321,175.76264629629543,33155.578218312294,7.350233233214412,,432.04478304536786,11.039069688154314,69.84540029205144,3.298875498646556,1
5.1267629233515315,230.60375750846123,11983.869376336363,6.303356534249105,,402.883113121781,11.168946221056501,77.48821310275477,4.708658467526655,1
7.874671357791283,195.10229858610904,17404.17706105066,7.509305856927908,,327.4597604610721,16.140367626166324,78.69844632549504,2.309149056634923,1

I have a ndarray of item type numpy.void which is actually a numpy.record or structured array (I'm not sure which, I just know that it have field names). When I try to use sklearn's SimpleImputer (or other imputers) on it, it throws an exception:

[(       nan, 204.89045547, 20791.31898075, 7.30021187, 368.51644135, 564.30865417, 10.37978308, 86.99097046, 2.96313538, 0.)
 (3.71608008, 129.42292051, 18630.05785797, 6.63524588,          nan, 592.88535913, 15.18001312, 56.32907628, 4.50065627, 0.)
 (8.09912419, 224.23625939, 19909.54173229, 9.2758836 ,          nan, 418.60621306, 16.86863693, 66.42009251, 3.05593375, 0.)
 ...
 (9.41951032, 175.7626463 , 33155.57821831, 7.35023323,          nan, 432.04478305, 11.03906969, 69.84540029, 3.2988755 , 1.)
 (5.12676292, 230.60375751, 11983.86937634, 6.30335653,          nan, 402.88311312, 11.16894622, 77.4882131 , 4.70865847, 1.)
 (7.87467136, 195.10229859, 17404.17706105, 7.50930586,          nan, 327.45976046, 16.14036763, 78.69844633, 2.30914906, 1.)]
(3276,)
<class 'numpy.void'>
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[4], line 9
      7 print(type(data[0]))
      8 imputer = skl.impute.SimpleImputer()
----> 9 imputer.fit_transform(data)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\_set_output.py:316, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    314 @wraps(f)
    315 def wrapped(self, X, *args, **kwargs):
--> 316     data_to_wrap = f(self, X, *args, **kwargs)
    317     if isinstance(data_to_wrap, tuple):
    318         # only wrap the first output for cross decomposition
    319         return_tuple = (
    320             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    321             *data_to_wrap[1:],
    322         )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\base.py:894, in TransformerMixin.fit_transform(self, X, y, **fit_params)
    879         warnings.warn(
    880             (
    881                 f"This object ({self.__class__.__name__}) has a `transform`"
   (...)    889             UserWarning,
    890         )
    892 if y is None:
    893     # fit method of arity 1 (unsupervised transformation)
--> 894     return self.fit(X, **fit_params).transform(X)
    895 else:
    896     # fit method of arity 2 (supervised transformation)
    897     return self.fit(X, y, **fit_params).transform(X)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\base.py:1365, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1358     estimator._validate_params()
   1360 with config_context(
   1361     skip_parameter_validation=(
   1362         prefer_skip_nested_validation or global_skip_validation
   1363     )
   1364 ):
-> 1365     return fit_method(estimator, *args, **kwargs)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\impute\_base.py:452, in SimpleImputer.fit(self, X, y)
    434 @_fit_context(prefer_skip_nested_validation=True)
    435 def fit(self, X, y=None):
    436     """Fit the imputer on `X`.
    437 
    438     Parameters
   (...)    450         Fitted estimator.
    451     """
--> 452     X = self._validate_input(X, in_fit=True)
    454     # default fill_value is 0 for numerical input and "missing_value"
    455     # otherwise
    456     if self.fill_value is None:

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\impute\_base.py:360, in SimpleImputer._validate_input(self, X, in_fit)
    357     ensure_all_finite = True
    359 try:
--> 360     X = validate_data(
    361         self,
    362         X,
    363         reset=in_fit,
    364         accept_sparse="csc",
    365         dtype=dtype,
    366         force_writeable=True if not in_fit else None,
    367         ensure_all_finite=ensure_all_finite,
    368         copy=self.copy,
    369     )
    370 except ValueError as ve:
    371     if "could not convert" in str(ve):

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py:2954, in validate_data(_estimator, X, y, reset, validate_separately, skip_check_array, **check_params)
   2952         out = X, y
   2953 elif not no_val_X and no_val_y:
-> 2954     out = check_array(X, input_name="X", **check_params)
   2955 elif no_val_X and not no_val_y:
   2956     out = _check_y(y, **check_params)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py:1053, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_all_finite, ensure_non_negative, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
   1051         array = xp.astype(array, dtype, copy=False)
   1052     else:
-> 1053         array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)
   1054 except ComplexWarning as complex_warning:
   1055     raise ValueError(
   1056         "Complex data not supported\n{}\n".format(array)
   1057     ) from complex_warning

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\_array_api.py:757, in _asarray_with_order(array, dtype, order, copy, xp, device)
    755     array = numpy.array(array, order=order, dtype=dtype)
    756 else:
--> 757     array = numpy.asarray(array, order=order, dtype=dtype)
    759 # At this point array is a NumPy ndarray. We convert it to an array
    760 # container that is consistent with the input's namespace.
    761 return xp.asarray(array)

TypeError: Cannot cast array data from dtype([('ph', '<f8'), ('Hardness', '<f8'), ('Solids', '<f8'), ('Chloramines', '<f8'), ('Sulfate', '<f8'), ('Conductivity', '<f8'), ('Organic_carbon', '<f8'), ('Trihalomethanes', '<f8'), ('Turbidity', '<f8'), ('Potability', '<f8')]) to dtype('float64') according to the rule 'unsafe'

After testing, I found out that data.shape is (3276, ), which indicates that data will be interpreted as 1d array, instead of 2d. So my question is: Is there any way to use imputation on the data while preserving the field names, or I have to cast numpy.void to other compatible types, say numpy.ndarray?