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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Challenge: Mean-Variance-Standard Deviation Calculator

Link to the challenge:

First, you are supposed to return these things, not print them.
Second… ehm you are already using the axis-argument for the Numpy functions? Maybe look at other possible values for it :wink:

Finally, please look at the brackets of the example output - those indicate specific data-structures.

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