import numpy as np
list_1=[1,3,3]
a=np.array(list_1)
b=a.reshape(3,3)
#MEAN
c_mean=b.mean(axis=0).tolist()
d_mean=b.mean(axis=1).tolist()
e_mean=b.mean().tolist()
f_mean=[c_mean,d_mean,e_mean]
#VAR
c_var=b.var(axis=0).tolist()
d_var=b.var(axis=1).tolist()
e_var=b.var().tolist()
f_var=[c_var,d_var,e_var]
#STD
c_std=b.std(axis=0).tolist()
d_std=b.std(axis=1).tolist()
e_std=b.std().tolist()
f_std=[c_std,d_std,e_std]
#MAX
c_max=b.max(axis=0).tolist()
d_max=b.max(axis=1).tolist()
e_max=b.max().tolist()
f_max=[c_max,d_max,e_max]
#MIN
c_min=b.min(axis=0).tolist()
d_min=b.min(axis=1).tolist()
e_min=b.min().tolist()
f_min=[c_min,d_min,e_min]
#SUM
c_sum=b.sum(axis=0).tolist()
d_sum=b.sum(axis=1).tolist()
e_sum=b.sum().tolist()
f_sum=[c_sum,d_sum,e_sum]
calculations={ 'mean': f_mean,'variance': f_var,'standard deviation': f_std,'max': f_max,'min': f_min,'sum': f_sum }
print(calculations)
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