Given a 2D numpy array representing daily sales for 3 products over 4 days:

hard📝 Application Q15 of 15

NumPy - Aggregation Functions

sales = np.array([[5, 3, 2],
                  [4, 6, 1],
                  [7, 2, 3],
                  [3, 5, 4]])

Which code correctly computes the cumulative sales per product over the days?

Anp.cumsum(sales)

Bnp.cumsum(sales, axis=0)

Cnp.cumsum(sales, axis=1)

Dnp.cumsum(sales, axis=2)

Step-by-Step Solution

Solution:

Step 1: Understand the array shape and axis meaning
The array shape is (4, 3): 4 days (rows), 3 products (columns). Axis 0 moves down rows (days), axis 1 moves across products.
Step 2: Choose axis for cumulative sum per product over days
To get cumulative sales per product over days, sum down each column (axis=0).
Final Answer:
np.cumsum(sales, axis=0) -> Option B
Quick Check:
Cumulative sum down rows = axis 0 [OK]

Quick Trick: Sum down rows (axis=0) for cumulative per product [OK]

Common Mistakes:

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