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What does print(a.sum(axis=1).tolist()) print? NumPy Reductions and the axis Argument
Block 5, Reductions and the axis Argument, question 11 of the NumPy sample
import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
print(a.sum(axis=1).tolist())What does this print?
Show the answer and why each option is right or wrong
Answer, A. [6, 15]
- A. [6, 15]
- Correct. axis=1 collapses the columns and adds across each row, giving 1+2+3 for the first row and 4+5+6 for the second.
- B. 21
- Collapses the whole array into one grand total instead of one sum per row.
- C. [3, 6]
- Keeps only the last entry of each row rather than adding the whole row.
- D. [5, 7, 9]
- Sums down each column instead, the result for axis=0 on the same array.
Reducing along axis=1 keeps one result per row and drops the column axis entirely. It is easy to swap axis=0 and axis=1 in your head, the safest check is to look at how many numbers come out, one per row means axis=1.
The answer above is what NumPy 2.4.4 printed when the snippet was run, not what someone expected it to do.
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