Getting a shape error when passing in dataframes into python function?

Question

I have this function that tries to take in a dataset and separate it by its labels.

 def separate_by_classes(self, X, y):
            ''' This function separates our dataset in subdatasets by classes '''
            self.classes = np.unique(y)
            classes_index = {}
            subdatasets = {}
            cls, counts = np.unique(y, return_counts=True)
            self.class_freq = dict(zip(cls, counts))
            print(self.class_freq)
            for class_type in self.classes:
                classes_index[class_type] = np.argwhere(y==class_type)
                subdatasets[class_type] = X[classes_index[class_type], :]
                self.class_freq[class_type] = self.class_freq[class_type]/sum(list(self.class_freq.values()))
            return subdatasets

I'm passing in the dataset as two dataframes: one with the features and another with the labels. These are their dimesnions respectively:

(5, 13) , (5, 1)

I am calling this function like so:

nbCLF = gaussClf()  #the function is a part of a class
nbCLF.separate_by_classes(df_1, df_1_label)

But I'm getting this error:

    Traceback (most recent call last):
  File "c:\Users\KASH\Desktop\ML_DATA\classification.py", line 11, in <module>
    nbCLF.separate_by_classes(df_1, df_1_label)
  File "c:\Users\KASH\Desktop\ML_DATA\naivebayes.py", line 20, in separate_by_classes
    classes_index[class_type] = np.argwhere(y==class_type)
  File "<__array_function__ internals>", line 5, in argwhere
  File "C:\Users\KASH\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\numpy\core\numeric.py", line 617, in argwhere        
    return transpose(nonzero(a))
  File "<__array_function__ internals>", line 5, in nonzero
  File "C:\Users\KASH\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\numpy\core\fromnumeric.py", line 1919, in nonzero    
    return _wrapfunc(a, 'nonzero')
  File "C:\Users\KASH\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\numpy\core\fromnumeric.py", line 55, in _wrapfunc    
    return _wrapit(obj, method, *args, **kwds)
  File "C:\Users\KASH\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\numpy\core\fromnumeric.py", line 48, in _wrapit      
    result = wrap(result)
  File "C:\Users\KASH\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\pandas\core\generic.py", line 2095, in __array_wrap__
    return self._constructor(res, **d).__finalize__(self, method="__array_wrap__")
  File "C:\Users\KASH\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\pandas\core\frame.py", line 694, in __init__
    mgr = ndarray_to_mgr(
  File "C:\Users\KASH\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\pandas\core\internals\construction.py", line 351, in ndarray_to_mgr
    _check_values_indices_shape_match(values, index, columns)
  File "C:\Users\KASH\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\pandas\core\internals\construction.py", line 422, in _check_values_indices_shape_match
    raise ValueError(f"Shape of passed values is {passed}, indices imply

 {implied}")
ValueError: Shape of passed values is (2, 2), indices imply (5, 1)

Here's a sample of the features data:

2   1.0 5   4.0 5.0 5.0 3   3.0 0   1.0 1.0 7.0 1.000000e+99
1   1.0 5   5.0 5.0 5.0 3   5.0 2   1.0 1.0 7.0 1.000000e+00
2   1.0 3   5.0 1.0 5.0 2   3.0 1   2.0 3.0 7.0 1.000000e+00
2   5.0 1   2.0 6.0 5.0 1   4.0 2   3.0 1.0 7.0 1.000000e+00
2   5.0 1   2.0 6.0 3.0 1   4.0 2   3.0 1.0 7.0 1.000000e+00

Here's a sample of the label data:

9
9
9
1
1

Any help is appreciated.