Bird
0
0

What does setting axis=0 do when aggregating a 2D NumPy array?

easy📝 Conceptual Q11 of 15
NumPy - Aggregation Functions
What does setting axis=0 do when aggregating a 2D NumPy array?
AAggregates values down each column (per column)
BAggregates values across each row (per row)
CFlattens the array before aggregation
DAggregates only the diagonal elements
Step-by-Step Solution
Solution:

  1. Step 1: Understand axis parameter meaning

    In NumPy, axis=0 means aggregation happens down the rows, combining values in each column.

  2. Step 2: Apply to 2D array

    Aggregating with axis=0 summarizes each column separately.

  3. Final Answer:

    Aggregates values down each column (per column) -> Option A

  4. Quick Check:

    axis=0 means down rows = per column aggregation [OK]
Quick Trick: axis=0 aggregates columns, axis=1 aggregates rows [OK]
Common Mistakes:
  • Confusing axis=0 with axis=1
  • Thinking axis=0 aggregates rows instead of columns
  • Assuming axis=0 flattens the array

Want More Practice?

15+ quiz questions · All difficulty levels · Free

Free Signup - Practice All Questions
More NumPy Quizzes
Aggregation Functions - np.cumsum() for cumulative sum - Quiz 15hard Array Data Types - Float types (float16, float32, float64) - Quiz 1easy Array Data Types - Integer types (int8, int16, int32, int64) - Quiz 3easy Array Manipulation - np.newaxis for adding dimensions - Quiz 12easy Array Operations - In-place operations for memory efficiency - Quiz 11easy Array Operations - In-place operations for memory efficiency - Quiz 2easy Broadcasting - Broadcasting errors and debugging - Quiz 10hard Creating Arrays - np.arange() for range arrays - Quiz 3easy Indexing and Slicing - np.where() for conditional selection - Quiz 14medium Indexing and Slicing - Single element access - Quiz 15hard