Optimizing Numpy Arrays for Efficient Data Manipulation

[ver_mathstats]

Homework Statement

Assign to a variable a NumPy array that has shape (3, 4) and whose entries are all equal to 0.0 (so the values of a are of type float). So, if i= 0,1,2 and j=0,1,2,3 then a[i,j]=0.0.

Relevant Equations

python

I feel like I have over complicated this question but here is the code I wrote out for it, I wrote it as a function but when I printed my values I realized I did not need a function to do so. So would I just use my second code to answer the question.

import numpy as np

def array_zero(n):
    for i in range(n):
        for i in range(n):
            result=print(a1[i,j])
    return result

a1=np.zeros([3,4],dtype=float)
print(a1[0,0])
print(a1[2,3])

Or would the solution be as simple as:

import numpy as np

a1=np.zeros([3,4],dtype=float)
print(a1)
print()
print(a1[0,0])

Thanks.

 

[pbuk]

The function array_zero has quite a few things wrong with it, but as you can see from printing the result, np.zeros([3, 4]) answers the question exactly (you can even omit the second argument as I have done because float is the default).

 

[ver_mathstats]

The function array_zero has quite a few things wrong with it, but as you can see from printing the result, np.zeros([3, 4]) answers the question exactly (you can even omit the second argument as I have done because float is the default).

Okay I understand, and thank you.