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You have two datasets each containing 10 million integers: one is a Python list, the other a NumPy array. You want to calculate the mean of each dataset efficiently. Which method is best and why?

hard📝 Application Q8 of 15
NumPy - Fundamentals
You have two datasets each containing 10 million integers: one is a Python list, the other a NumPy array. You want to calculate the mean of each dataset efficiently. Which method is best and why?
AUse <code>np.mean()</code> on the NumPy array because it uses optimized C routines and vectorized operations
BUse Python's built-in <code>sum()</code> and <code>len()</code> on the list because it avoids NumPy overhead
CConvert the NumPy array to a list and then use <code>sum()</code> for consistency
DUse a for-loop to iterate over both datasets and compute the mean manually
Step-by-Step Solution
Solution:

  1. Step 1: Consider performance of Python list mean

    Using sum() and len() on a large list is slower due to Python-level iteration.

  2. Step 2: Consider NumPy mean

    np.mean() uses optimized C code and vectorized operations, making it faster on large arrays.

  3. Step 3: Avoid unnecessary conversions

    Converting arrays to lists or manual loops add overhead and reduce performance.

  4. Final Answer:

    Use np.mean() on the NumPy array because it uses optimized C routines and vectorized operations -> Option A

  5. Quick Check:

    NumPy mean is fastest for large numeric arrays [OK]
Quick Trick: np.mean() is fastest for large numeric arrays [OK]
Common Mistakes:
  • Using Python sum/len on large lists for performance
  • Converting arrays to lists unnecessarily
  • Using manual loops instead of vectorized functions

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