Mean-Variance-Standard Deviation Calculator Problem(New)

Tell us what’s happening:
Describe your issue in detail here.

I need to create a function that outputs as the following:

``````{
'mean': [[3.0, 4.0, 5.0], [1.0, 4.0, 7.0], 4.0],
'variance': [[6.0, 6.0, 6.0], [0.6666666666666666, 0.6666666666666666, 0.6666666666666666], 6.666666666666667],
'standard deviation': [[2.449489742783178, 2.449489742783178, 2.449489742783178], [0.816496580927726, 0.816496580927726, 0.816496580927726], 2.581988897471611],
'max': [[6, 7, 8], [2, 5, 8], 8],
'min': [[0, 1, 2], [0, 3, 6], 0],
'sum': [[9, 12, 15], [3, 12, 21], 36]
}
I don't know how to get the function to print out the other axis and the flattened axis, so that my function is identical to the answer.

``````

My code
import numpy as np

def calculate(list):
a = np.arange(9).reshape(3, 3)
b = np.mean(a.copy(), axis=(0))
print(b)
c = np.var(a.copy(), axis=(0))
print(c)
d = np.std(a.copy(), axis=(0))
print(d)
e = np.max(a.copy(), axis=(0))
print(e)
f = np.min(a.copy(), axis=(0))
print(f)
g = np.sum(a.copy(), axis=(0))
print(g)

``````return calculations
``````

my output

[3. 4. 5.]
[6. 6. 6.]
[2.44948974 2.44948974 2.44948974]
[6 7 8]
[0 1 2]
[ 9 12 15]]

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