You have a large dataset stored as a NumPy array data. You want to compute the mean of each column efficiently. Which approach is best?

hard📝 Application Q15 of 15

SciPy - Integration with Scientific Ecosystem

You have a large dataset stored as a NumPy array data. You want to compute the mean of each column efficiently. Which approach is best?

AUse a for loop to sum each column and divide by number of rows

BUse <code>np.mean(data)</code> without axis parameter

CConvert array to list and use Python's built-in <code>sum</code> and <code>len</code>

DUse <code>np.mean(data, axis=0)</code> to compute means vectorized

Step-by-Step Solution

Solution:

Step 1: Understand mean calculation per column
Mean per column requires averaging along rows (axis=0).
Step 2: Identify efficient vectorized method
np.mean with axis=0 computes column means efficiently without loops.
Step 3: Evaluate other options
Use a for loop to sum each column and divide by number of rows uses slow loops, C converts to list (slow), D computes overall mean, not per column.
Final Answer:
Use np.mean(data, axis=0) to compute means vectorized -> Option D
Quick Check:
Vectorized mean per column = np.mean(data, axis=0) [OK]

Quick Trick: Use np.mean with axis=0 for column-wise mean [OK]

Common Mistakes:

Master "Integration with Scientific Ecosystem" in SciPy

9 interactive learning modes - each teaches the same concept differently

Want More Practice?

15+ quiz questions · All difficulty levels · Free

More SciPy Quizzes