>>> a = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
>>> a
array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])

>>> a[a[:,0] > 3] # select rows where first column is greater than 3
array([[ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])

>>> a[a[:,0] > 3][:,np.array([True, True, False, True])] # select columns
array([[ 5,  6,  8],
       [ 9, 10, 12]])

# fancier equivalent of the previous
>>> a[np.ix_(a[:,0] > 3, np.array([True, True, False, True]))]
array([[ 5,  6,  8],
       [ 9, 10, 12]])

For an explanation of the obscure np.ix_(), see https://stackoverflow.com/a/13599843/4323

Finally, we can simplify by giving the list of column numbers instead of the tedious boolean mask:

>>> a[np.ix_(a[:,0] > 3, (0,1,3))]
array([[ 5,  6,  8],
       [ 9, 10, 12]])

In Python, the expression -0.4 < x_y_z[2] < 0.1 is roughly equivalent to -0.4 < x_y_z[2] and x_y_z[2] < 0.1. The and operator decides the truth value of each part of the expression by converting it into a bool. Unlike Python lists and tuples, numpy arrays do not support the conversion.

The correct way to specify the condition is with bitwise & (which is unambiguous and non-short-circuiting), rather than the implicit and (which short circuits and is ambiguous in this case):

condition = ((x_y_z[2, :] > - 0.4) & (x_y_z[2, :] < 0.1))

condition is a boolean mask that selects the columns you want. You can select the rows with a simple slice:

selection = x_y_z[:, condition] 

Use a boolean mask:

mask = (z[:, 0] == 6)
z[mask, :]

This is much more efficient than np.where because you can use the boolean mask directly, without having the overhead of converting it to an array of indices first.

One liner:

z[z[:, 0] == 6, :]

To answer your initial question, you want to define both ids and scores as np.array. This will make your code work until you try to define accepted_std:

import numpy as np
N = 5

ids = np.array([1, 2, 3, 4, 5])
scores = np.array([3.75320381, 4.32400937, 2.43537978, 3.73691774,  2.5163266])

ids_col = ids.copy()
scores_col = scores.copy()

students_mat = np.column_stack([ids_col, scores_col])

accepted = scores_col[scores_col[:]>=4.0]

print(accepted)

I think what you actually want is to get all rows where the score is above a certain threshold. For this, you can change your code to:

import numpy as np
N = 5

ids = np.array([1, 2, 3, 4, 5])
scores = np.array([3.75320381, 4.32400937, 2.43537978, 3.73691774,  2.5163266])

students_mat = np.column_stack([ids, scores])

accepted_std = students_mat[np.where(students_mat[1] >= 4.)]

print(accepted_std)
array([[2. , 4.32400937]])

Try setting emailconditions like so:

emailconditions = [
dfFinal['EmailC'].notna(),
dfFinal['EmailC'].isna() & dfFinal['EmailB'].notna(),
dfFinal['EmailC'].isna() & dfFinal['EmailB'].isna()]

Key point is to use notna() instead of is not None and isna() instead of is None.