[Python] numpy.cov() Giving Incorrect Answer

I'm trying to use Python and NumPy to calculate a covariance matrix.

Here is the matrix:

[[0.69, 0.49],
[-1.31, -1.21],
[0.39, 0.99],
[0.09, 0.29],
[1.29, 1.09],
[0.49, 0.79],
[0.19, -0.31],
[-0.81, -0.81],
[-0.31, -0.31],
[-0.71, -1.01]]


Here is the expected result:
[[0.7322, 0.6189],

[0.6189, 0.5956]]

Here is the equation I was given: covariance matrix = (1 / (n - 1))ZTZ

n is the number of entries (10)

Z is the matrix

I tried np.cov(np_matrix), which didn't return the correct size or values.

I've also tried this:

np.cov(np_matrix, rowvar=False)

>>> array([[0.61655556, 0.61544444],
[0.61544444, 0.71655556]]


I'm also trying to calculate it manually instead of using the cov function, but multiplying by the transpose doesn't even return the correct value.
The correct value is this:
[[6.59, 5.57],
[5.57, 5.36]]

However, this is what my code returns:

np_matrix.T @ np_matrix

>>> array([[5.549, 5.539],
[5.539, 6.449]])


np_matrix.T * np_matrix

>>> ValueError: operands could not be broadcast together with shapes (2,10) (10,2)


I've also tried setting the arrays to floats and doubles.

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