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You have two large numpy arrays representing daily sales for two stores over a year. You want to find the total sales per day quickly. Which approach best uses vectorized operations to achieve this?

hard📝 Application Q15 of 15
NumPy - Array Operations
You have two large numpy arrays representing daily sales for two stores over a year. You want to find the total sales per day quickly. Which approach best uses vectorized operations to achieve this?
AUse <code>total_sales = store1_sales + store2_sales</code> to add arrays directly.
BUse a for loop to add each day's sales from both arrays and store in a new list.
CConvert arrays to lists, then use list comprehension to add elements.
DUse nested loops to add each element individually.
Step-by-Step Solution
Solution:

  1. Step 1: Identify vectorized addition for large arrays

    Adding numpy arrays directly uses fast, optimized vectorized operations.

  2. Step 2: Compare with loops and list methods

    Loops and list conversions are slower and less efficient for large data.

  3. Final Answer:

    Use total_sales = store1_sales + store2_sales to add arrays directly. -> Option A

  4. Quick Check:

    Vectorized addition is fastest for big arrays [OK]
Quick Trick: Add arrays directly for fast big data sums [OK]
Common Mistakes:
  • Using loops instead of vectorized addition
  • Converting arrays to lists unnecessarily
  • Using nested loops causing slow code

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