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mean_var_std.py
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mean_var_std.py
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import numpy as np
def calculate(list):
if len(list) < 9:
raise ValueError("List must contain nine numbers.")
x_array = np.array(list)
x_array = x_array.reshape((3,3))
# rows
row1 = x_array[0,:]
row2 = x_array[1,:]
row3 = x_array[2,:]
# columns
col1 = x_array[:,0]
col2 = x_array[:,1]
col3 = x_array[:,2]
calculations = {
'mean': [
[col1.mean(), col2.mean(), col3.mean()],
[row1.mean(), row2.mean(), row3.mean()],
x_array.mean()
],
'variance': [
[col1.var(), col2.var(), col3.var()],
[row1.var(), row2.var(), row3.var()],
x_array.var()
],
'standard deviation': [
[col1.std(), col2.std(), col3.std()],
[row1.std(), row2.std(), row3.std()],
x_array.std()
],
'max': [
[col1.max(), col2.max(), col3.max()],
[row1.max(), row2.max(), row3.max()],
x_array.max()
],
'min': [
[col1.min(), col2.min(), col3.min()],
[row1.min(), row2.min(), row3.min()],
x_array.min()
],
'sum': [
[col1.sum(), col2.sum(), col3.sum()],
[row1.sum(), row2.sum(), row3.sum()],
x_array.sum()
]
}
return calculations
x = [0,1,2,3,4,5,6,7,8]
print(calculate(x))