Given a 2D array arr of shape (4, 5), you want to compute the standard deviation along rows but ignore NaN values. Which NumPy function and axis parameter combination is correct?

hard📝 Application Q9 of 15

NumPy - Aggregation Functions

Given a 2D array arr of shape (4, 5), you want to compute the standard deviation along rows but ignore NaN values. Which NumPy function and axis parameter combination is correct?

Anp.nanstd(arr, axis=1)

Bnp.std(arr, axis=0)

Cnp.nanstd(arr, axis=0)

Dnp.std(arr, axis=1)

Step-by-Step Solution

Solution:

Step 1: Identify function to ignore NaNs
np.nanstd computes standard deviation ignoring NaN values.
Step 2: Choose axis for row-wise std
Axis=1 computes along columns for each row, so std per row.
Final Answer:
np.nanstd(arr, axis=1) correctly computes row-wise std ignoring NaNs -> Option A
Quick Check:
Ignore NaN and std row-wise = np.nanstd + axis=1 [OK]

Quick Trick: Use np.nanstd to ignore NaNs, axis=1 for rows [OK]

Common Mistakes:

Using np.std which includes NaNs
Choosing wrong axis for rows
Confusing axis=0 and axis=1

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Given a 2D array arr of shape (4, 5), you want to compute the standard deviation along rows but ignore NaN values. Which NumPy function and axis parameter combination is correct?

Step 1: Identify function to ignore NaNs

Step 2: Choose axis for row-wise std

Final Answer:

Quick Check:

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